"slide","species","tissue","pmid","title","abstract","keywords","involve_cancer","tech","spot_num","gene_num"
"GSE144239_GSM4284316","human","skin","32579974,38037084","Title 1: Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma. Title 2: STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer.","Abstract 1: To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer. Abstract 2: Spatial transcriptomic technologies, such as the Visium platform, measure gene expression in different regions of tissues. Here, we describe new software, STmut, to visualize somatic point mutations, allelic imbalance, and copy number alterations in Visium data. STmut is tested on fresh-frozen Visium data, formalin-fixed paraffin-embedded (FFPE) Visium data, and tumors with and without matching DNA sequencing data. Copy number is inferred on all conditions, but the chemistry of the FFPE platform does not permit analyses of single nucleotide variants. Taken together, we propose solutions to add the genetic dimension to spatial transcriptomic data and describe the limitations of different datatypes.","Keywords 1: CRISPR screen; MIBI; intra-tumoral heterogeneity; multi-omics; scRNA-seq; skin cancer; spatial transcriptomics; squamous cell carcinoma; tumor immunology; tumor microenvironment. Keywords 2:","True","ST","666","17138"
"GSE144239_GSM4284317","human","skin","32579974,38037084","Title 1: Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma. Title 2: STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer.","Abstract 1: To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer. Abstract 2: Spatial transcriptomic technologies, such as the Visium platform, measure gene expression in different regions of tissues. Here, we describe new software, STmut, to visualize somatic point mutations, allelic imbalance, and copy number alterations in Visium data. STmut is tested on fresh-frozen Visium data, formalin-fixed paraffin-embedded (FFPE) Visium data, and tumors with and without matching DNA sequencing data. Copy number is inferred on all conditions, but the chemistry of the FFPE platform does not permit analyses of single nucleotide variants. Taken together, we propose solutions to add the genetic dimension to spatial transcriptomic data and describe the limitations of different datatypes.","Keywords 1: CRISPR screen; MIBI; intra-tumoral heterogeneity; multi-omics; scRNA-seq; skin cancer; spatial transcriptomics; squamous cell carcinoma; tumor immunology; tumor microenvironment. Keywords 2:","True","ST","646","17344"
"GSE144239_GSM4284318","human","skin","32579974,38037084","Title 1: Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma. Title 2: STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer.","Abstract 1: To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer. Abstract 2: Spatial transcriptomic technologies, such as the Visium platform, measure gene expression in different regions of tissues. Here, we describe new software, STmut, to visualize somatic point mutations, allelic imbalance, and copy number alterations in Visium data. STmut is tested on fresh-frozen Visium data, formalin-fixed paraffin-embedded (FFPE) Visium data, and tumors with and without matching DNA sequencing data. Copy number is inferred on all conditions, but the chemistry of the FFPE platform does not permit analyses of single nucleotide variants. Taken together, we propose solutions to add the genetic dimension to spatial transcriptomic data and describe the limitations of different datatypes.","Keywords 1: CRISPR screen; MIBI; intra-tumoral heterogeneity; multi-omics; scRNA-seq; skin cancer; spatial transcriptomics; squamous cell carcinoma; tumor immunology; tumor microenvironment. Keywords 2:","True","ST","638","17883"
"GSE144239_GSM4284319","human","skin","32579974,38037084","Title 1: Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma. Title 2: STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer.","Abstract 1: To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer. Abstract 2: Spatial transcriptomic technologies, such as the Visium platform, measure gene expression in different regions of tissues. Here, we describe new software, STmut, to visualize somatic point mutations, allelic imbalance, and copy number alterations in Visium data. STmut is tested on fresh-frozen Visium data, formalin-fixed paraffin-embedded (FFPE) Visium data, and tumors with and without matching DNA sequencing data. Copy number is inferred on all conditions, but the chemistry of the FFPE platform does not permit analyses of single nucleotide variants. Taken together, we propose solutions to add the genetic dimension to spatial transcriptomic data and describe the limitations of different datatypes.","Keywords 1: CRISPR screen; MIBI; intra-tumoral heterogeneity; multi-omics; scRNA-seq; skin cancer; spatial transcriptomics; squamous cell carcinoma; tumor immunology; tumor microenvironment. Keywords 2:","True","ST","590","16959"
"GSE144239_GSM4284320","human","skin","32579974,38037084","Title 1: Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma. Title 2: STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer.","Abstract 1: To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer. Abstract 2: Spatial transcriptomic technologies, such as the Visium platform, measure gene expression in different regions of tissues. Here, we describe new software, STmut, to visualize somatic point mutations, allelic imbalance, and copy number alterations in Visium data. STmut is tested on fresh-frozen Visium data, formalin-fixed paraffin-embedded (FFPE) Visium data, and tumors with and without matching DNA sequencing data. Copy number is inferred on all conditions, but the chemistry of the FFPE platform does not permit analyses of single nucleotide variants. Taken together, we propose solutions to add the genetic dimension to spatial transcriptomic data and describe the limitations of different datatypes.","Keywords 1: CRISPR screen; MIBI; intra-tumoral heterogeneity; multi-omics; scRNA-seq; skin cancer; spatial transcriptomics; squamous cell carcinoma; tumor immunology; tumor microenvironment. Keywords 2:","True","ST","521","17689"
"GSE144239_GSM4284321","human","skin","32579974,38037084","Title 1: Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma. Title 2: STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer.","Abstract 1: To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer. Abstract 2: Spatial transcriptomic technologies, such as the Visium platform, measure gene expression in different regions of tissues. Here, we describe new software, STmut, to visualize somatic point mutations, allelic imbalance, and copy number alterations in Visium data. STmut is tested on fresh-frozen Visium data, formalin-fixed paraffin-embedded (FFPE) Visium data, and tumors with and without matching DNA sequencing data. Copy number is inferred on all conditions, but the chemistry of the FFPE platform does not permit analyses of single nucleotide variants. Taken together, we propose solutions to add the genetic dimension to spatial transcriptomic data and describe the limitations of different datatypes.","Keywords 1: CRISPR screen; MIBI; intra-tumoral heterogeneity; multi-omics; scRNA-seq; skin cancer; spatial transcriptomics; squamous cell carcinoma; tumor immunology; tumor microenvironment. Keywords 2:","True","ST","521","17399"
"GSE144239_GSM4284322","human","skin","32579974,38037084","Title 1: Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma. Title 2: STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer.","Abstract 1: To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer. Abstract 2: Spatial transcriptomic technologies, such as the Visium platform, measure gene expression in different regions of tissues. Here, we describe new software, STmut, to visualize somatic point mutations, allelic imbalance, and copy number alterations in Visium data. STmut is tested on fresh-frozen Visium data, formalin-fixed paraffin-embedded (FFPE) Visium data, and tumors with and without matching DNA sequencing data. Copy number is inferred on all conditions, but the chemistry of the FFPE platform does not permit analyses of single nucleotide variants. Taken together, we propose solutions to add the genetic dimension to spatial transcriptomic data and describe the limitations of different datatypes.","Keywords 1: CRISPR screen; MIBI; intra-tumoral heterogeneity; multi-omics; scRNA-seq; skin cancer; spatial transcriptomics; squamous cell carcinoma; tumor immunology; tumor microenvironment. Keywords 2:","True","ST","1145","17823"
"GSE144239_GSM4284323","human","skin","32579974,38037084","Title 1: Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma. Title 2: STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer.","Abstract 1: To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer. Abstract 2: Spatial transcriptomic technologies, such as the Visium platform, measure gene expression in different regions of tissues. Here, we describe new software, STmut, to visualize somatic point mutations, allelic imbalance, and copy number alterations in Visium data. STmut is tested on fresh-frozen Visium data, formalin-fixed paraffin-embedded (FFPE) Visium data, and tumors with and without matching DNA sequencing data. Copy number is inferred on all conditions, but the chemistry of the FFPE platform does not permit analyses of single nucleotide variants. Taken together, we propose solutions to add the genetic dimension to spatial transcriptomic data and describe the limitations of different datatypes.","Keywords 1: CRISPR screen; MIBI; intra-tumoral heterogeneity; multi-omics; scRNA-seq; skin cancer; spatial transcriptomics; squamous cell carcinoma; tumor immunology; tumor microenvironment. Keywords 2:","True","ST","1071","19314"
"GSE144239_GSM4284324","human","skin","32579974,38037084","Title 1: Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma. Title 2: STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer.","Abstract 1: To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer. Abstract 2: Spatial transcriptomic technologies, such as the Visium platform, measure gene expression in different regions of tissues. Here, we describe new software, STmut, to visualize somatic point mutations, allelic imbalance, and copy number alterations in Visium data. STmut is tested on fresh-frozen Visium data, formalin-fixed paraffin-embedded (FFPE) Visium data, and tumors with and without matching DNA sequencing data. Copy number is inferred on all conditions, but the chemistry of the FFPE platform does not permit analyses of single nucleotide variants. Taken together, we propose solutions to add the genetic dimension to spatial transcriptomic data and describe the limitations of different datatypes.","Keywords 1: CRISPR screen; MIBI; intra-tumoral heterogeneity; multi-omics; scRNA-seq; skin cancer; spatial transcriptomics; squamous cell carcinoma; tumor immunology; tumor microenvironment. Keywords 2:","True","ST","1182","17976"
"GSE144239_GSM4284325","human","skin","32579974,38037084","Title 1: Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma. Title 2: STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer.","Abstract 1: To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer. Abstract 2: Spatial transcriptomic technologies, such as the Visium platform, measure gene expression in different regions of tissues. Here, we describe new software, STmut, to visualize somatic point mutations, allelic imbalance, and copy number alterations in Visium data. STmut is tested on fresh-frozen Visium data, formalin-fixed paraffin-embedded (FFPE) Visium data, and tumors with and without matching DNA sequencing data. Copy number is inferred on all conditions, but the chemistry of the FFPE platform does not permit analyses of single nucleotide variants. Taken together, we propose solutions to add the genetic dimension to spatial transcriptomic data and describe the limitations of different datatypes.","Keywords 1: CRISPR screen; MIBI; intra-tumoral heterogeneity; multi-omics; scRNA-seq; skin cancer; spatial transcriptomics; squamous cell carcinoma; tumor immunology; tumor microenvironment. Keywords 2:","True","ST","608","15383"
"GSE144239_GSM4284326","human","skin","32579974,38037084","Title 1: Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma. Title 2: STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer.","Abstract 1: To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer. Abstract 2: Spatial transcriptomic technologies, such as the Visium platform, measure gene expression in different regions of tissues. Here, we describe new software, STmut, to visualize somatic point mutations, allelic imbalance, and copy number alterations in Visium data. STmut is tested on fresh-frozen Visium data, formalin-fixed paraffin-embedded (FFPE) Visium data, and tumors with and without matching DNA sequencing data. Copy number is inferred on all conditions, but the chemistry of the FFPE platform does not permit analyses of single nucleotide variants. Taken together, we propose solutions to add the genetic dimension to spatial transcriptomic data and describe the limitations of different datatypes.","Keywords 1: CRISPR screen; MIBI; intra-tumoral heterogeneity; multi-omics; scRNA-seq; skin cancer; spatial transcriptomics; squamous cell carcinoma; tumor immunology; tumor microenvironment. Keywords 2:","True","ST","621","16642"
"GSE144239_GSM4284327","human","skin","32579974,38037084","Title 1: Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma. Title 2: STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer.","Abstract 1: To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer. Abstract 2: Spatial transcriptomic technologies, such as the Visium platform, measure gene expression in different regions of tissues. Here, we describe new software, STmut, to visualize somatic point mutations, allelic imbalance, and copy number alterations in Visium data. STmut is tested on fresh-frozen Visium data, formalin-fixed paraffin-embedded (FFPE) Visium data, and tumors with and without matching DNA sequencing data. Copy number is inferred on all conditions, but the chemistry of the FFPE platform does not permit analyses of single nucleotide variants. Taken together, we propose solutions to add the genetic dimension to spatial transcriptomic data and describe the limitations of different datatypes.","Keywords 1: CRISPR screen; MIBI; intra-tumoral heterogeneity; multi-omics; scRNA-seq; skin cancer; spatial transcriptomics; squamous cell carcinoma; tumor immunology; tumor microenvironment. Keywords 2:","True","ST","462","17047"
"Human_Breast_Andersson_10142021_ST_A1","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","346","15045"
"Human_Breast_Andersson_10142021_ST_A2","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","325","15526"
"Human_Breast_Andersson_10142021_ST_A3","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","359","15517"
"Human_Breast_Andersson_10142021_ST_A4","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","343","15583"
"Human_Breast_Andersson_10142021_ST_A5","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","332","15638"
"Human_Breast_Andersson_10142021_ST_A6","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","360","15645"
"Human_Breast_Andersson_10142021_ST_B1","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","295","15109"
"Human_Breast_Andersson_10142021_ST_B2","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","270","15290"
"Human_Breast_Andersson_10142021_ST_B3","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","298","15215"
"Human_Breast_Andersson_10142021_ST_B4","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","283","15289"
"Human_Breast_Andersson_10142021_ST_B5","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","289","15273"
"Human_Breast_Andersson_10142021_ST_B6","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","277","15387"
"Human_Breast_Andersson_10142021_ST_C1","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","176","15557"
"Human_Breast_Andersson_10142021_ST_C2","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","187","15706"
"Human_Breast_Andersson_10142021_ST_C3","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","180","15821"
"Human_Breast_Andersson_10142021_ST_C4","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","184","15842"
"Human_Breast_Andersson_10142021_ST_C5","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","181","15721"
"Human_Breast_Andersson_10142021_ST_C6","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","178","15772"
"Human_Breast_Andersson_10142021_ST_D1","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","306","15661"
"Human_Breast_Andersson_10142021_ST_D2","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","303","15396"
"Human_Breast_Andersson_10142021_ST_D3","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","301","15529"
"Human_Breast_Andersson_10142021_ST_D4","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","302","15503"
"Human_Breast_Andersson_10142021_ST_D5","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","306","15666"
"Human_Breast_Andersson_10142021_ST_D6","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","315","15409"
"Human_Breast_Andersson_10142021_ST_E1","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","587","15701"
"Human_Breast_Andersson_10142021_ST_E2","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","572","15167"
"Human_Breast_Andersson_10142021_ST_E3","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","570","15097"
"Human_Breast_Andersson_10142021_ST_F1","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","691","14861"
"Human_Breast_Andersson_10142021_ST_F2","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","695","15041"
"Human_Breast_Andersson_10142021_ST_F3","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","712","15067"
"Human_Breast_Andersson_10142021_ST_G1","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","441","14992"
"Human_Breast_Andersson_10142021_ST_G2","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","467","15258"
"Human_Breast_Andersson_10142021_ST_G3","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","463","15035"
"Human_Breast_Andersson_10142021_ST_H1","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","613","15029"
"Human_Breast_Andersson_10142021_ST_H2","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","603","14907"
"Human_Breast_Andersson_10142021_ST_H3","human","breast","34650042","Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.","","True","ST","510","14873"
"Human_Breast_He_06222020_ST_BC23209_C1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","294","15459"
"Human_Breast_He_06222020_ST_BC23209_C2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","318","16458"
"Human_Breast_He_06222020_ST_BC23209_D1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","331","16367"
"Human_Breast_He_06222020_ST_BC23268_C1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","435","15905"
"Human_Breast_He_06222020_ST_BC23268_C2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","498","15824"
"Human_Breast_He_06222020_ST_BC23268_D1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","492","15819"
"Human_Breast_He_06222020_ST_BC23269_C1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","434","16075"
"Human_Breast_He_06222020_ST_BC23269_C2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","435","16382"
"Human_Breast_He_06222020_ST_BC23269_D1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","433","15955"
"Human_Breast_He_06222020_ST_BC23270_D2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","284","16167"
"Human_Breast_He_06222020_ST_BC23270_E1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","283","16271"
"Human_Breast_He_06222020_ST_BC23270_E2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","284","16447"
"Human_Breast_He_06222020_ST_BC23272_D2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","565","16092"
"Human_Breast_He_06222020_ST_BC23272_E1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","578","16003"
"Human_Breast_He_06222020_ST_BC23272_E2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","548","16014"
"Human_Breast_He_06222020_ST_BC23277_D2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","506","16277"
"Human_Breast_He_06222020_ST_BC23277_E1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","513","16465"
"Human_Breast_He_06222020_ST_BC23277_E2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","566","16754"
"Human_Breast_He_06222020_ST_BC23287_C1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","256","15268"
"Human_Breast_He_06222020_ST_BC23287_C2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","334","15333"
"Human_Breast_He_06222020_ST_BC23287_D1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","357","15300"
"Human_Breast_He_06222020_ST_BC23288_D2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","464","15846"
"Human_Breast_He_06222020_ST_BC23288_E1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","480","16011"
"Human_Breast_He_06222020_ST_BC23288_E2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","478","15862"
"Human_Breast_He_06222020_ST_BC23377_C1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","597","16191"
"Human_Breast_He_06222020_ST_BC23377_C2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","687","16437"
"Human_Breast_He_06222020_ST_BC23377_D1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","601","16162"
"Human_Breast_He_06222020_ST_BC23450_D2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","316","17251"
"Human_Breast_He_06222020_ST_BC23450_E1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","301","16689"
"Human_Breast_He_06222020_ST_BC23450_E2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","318","16845"
"Human_Breast_He_06222020_ST_BC23506_C1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","489","16835"
"Human_Breast_He_06222020_ST_BC23506_C2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","502","16921"
"Human_Breast_He_06222020_ST_BC23506_D1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","482","16575"
"Human_Breast_He_06222020_ST_BC23508_D2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","478","16784"
"Human_Breast_He_06222020_ST_BC23508_E1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","473","16668"
"Human_Breast_He_06222020_ST_BC23508_E2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","504","16603"
"Human_Breast_He_06222020_ST_BC23567_D2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","587","15801"
"Human_Breast_He_06222020_ST_BC23567_E1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","572","16301"
"Human_Breast_He_06222020_ST_BC23567_E2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","570","16246"
"Human_Breast_He_06222020_ST_BC23803_D2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","402","17369"
"Human_Breast_He_06222020_ST_BC23803_E1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","340","16748"
"Human_Breast_He_06222020_ST_BC23803_E2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","421","16966"
"Human_Breast_He_06222020_ST_BC23810_D2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","691","16058"
"Human_Breast_He_06222020_ST_BC23810_E1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","695","16246"
"Human_Breast_He_06222020_ST_BC23810_E2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","713","16338"
"Human_Breast_He_06222020_ST_BC23895_C1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","498","15847"
"Human_Breast_He_06222020_ST_BC23895_C2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","454","15832"
"Human_Breast_He_06222020_ST_BC23895_D1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","518","15654"
"Human_Breast_He_06222020_ST_BC23901_C2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","292","16266"
"Human_Breast_He_06222020_ST_BC23901_D1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","297","15962"
"Human_Breast_He_06222020_ST_BC23903_C1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","441","16204"
"Human_Breast_He_06222020_ST_BC23903_C2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","468","16502"
"Human_Breast_He_06222020_ST_BC23903_D1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","464","16287"
"Human_Breast_He_06222020_ST_BC23944_D2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","414","16280"
"Human_Breast_He_06222020_ST_BC23944_E1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","423","16463"
"Human_Breast_He_06222020_ST_BC23944_E2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","403","16610"
"Human_Breast_He_06222020_ST_BC24044_D2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","614","16049"
"Human_Breast_He_06222020_ST_BC24044_E1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","603","15962"
"Human_Breast_He_06222020_ST_BC24044_E2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","511","15949"
"Human_Breast_He_06222020_ST_BC24105_C1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","325","16187"
"Human_Breast_He_06222020_ST_BC24105_C2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","297","16225"
"Human_Breast_He_06222020_ST_BC24105_D1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","338","16100"
"Human_Breast_He_06222020_ST_BC24220_D2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","432","15444"
"Human_Breast_He_06222020_ST_BC24220_E1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","428","15694"
"Human_Breast_He_06222020_ST_BC24220_E2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","437","15693"
"Human_Breast_He_06222020_ST_BC24223_D2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","368","15864"
"Human_Breast_He_06222020_ST_BC24223_E1","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","362","16037"
"Human_Breast_He_06222020_ST_BC24223_E2","human","breast","32572199","Integrating spatial gene expression and breast tumour morphology via deep learning","Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.","","True","ST","363","16107"
"Human_Breast_Stahl_07012016_ST_Layer1","human","breast","27365449","Visualization and analysis of gene expression in tissue sections by spatial transcriptomics","Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call """"spatial transcriptomics,"""" that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.","","True","ST","254","14880"
"Human_Breast_Stahl_07012016_ST_Layer2","human","breast","27365449","Visualization and analysis of gene expression in tissue sections by spatial transcriptomics","Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call """"spatial transcriptomics,"""" that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.","","True","ST","251","14789"
"Human_Breast_Stahl_07012016_ST_Layer3","human","breast","27365449","Visualization and analysis of gene expression in tissue sections by spatial transcriptomics","Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call """"spatial transcriptomics,"""" that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.","","True","ST","264","14929"
"Human_Breast_Stahl_07012016_ST_Layer4","human","breast","27365449","Visualization and analysis of gene expression in tissue sections by spatial transcriptomics","Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call """"spatial transcriptomics,"""" that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.","","True","ST","262","14808"
"Human_Heart_Asp_12122019_ST_ST_Sample_4.5-5PCW_1","human","heart","31835037","A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart","The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.","gene expression; heart development; human development; human developmental cell atlas; in situ sequencing; single-cell RNA-sequencing; spatial transcriptomics; spatially resolved transcriptomics.","False","ST","54","30479"
"Human_Heart_Asp_12122019_ST_ST_Sample_4.5-5PCW_2","human","heart","31835037","A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart","The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.","gene expression; heart development; human development; human developmental cell atlas; in situ sequencing; single-cell RNA-sequencing; spatial transcriptomics; spatially resolved transcriptomics.","False","ST","57","30479"
"Human_Heart_Asp_12122019_ST_ST_Sample_4.5-5PCW_3","human","heart","31835037","A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart","The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.","gene expression; heart development; human development; human developmental cell atlas; in situ sequencing; single-cell RNA-sequencing; spatial transcriptomics; spatially resolved transcriptomics.","False","ST","66","30479"
"Human_Heart_Asp_12122019_ST_ST_Sample_4.5-5PCW_4","human","heart","31835037","A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart","The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.","gene expression; heart development; human development; human developmental cell atlas; in situ sequencing; single-cell RNA-sequencing; spatial transcriptomics; spatially resolved transcriptomics.","False","ST","55","30479"
"Human_Heart_Asp_12122019_ST_ST_Sample_6.5PCW_1","human","heart","31835037","A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart","The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.","gene expression; heart development; human development; human developmental cell atlas; in situ sequencing; single-cell RNA-sequencing; spatial transcriptomics; spatially resolved transcriptomics.","False","ST","100","30479"
"Human_Heart_Asp_12122019_ST_ST_Sample_6.5PCW_2","human","heart","31835037","A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart","The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.","gene expression; heart development; human development; human developmental cell atlas; in situ sequencing; single-cell RNA-sequencing; spatial transcriptomics; spatially resolved transcriptomics.","False","ST","101","30479"
"Human_Heart_Asp_12122019_ST_ST_Sample_6.5PCW_3","human","heart","31835037","A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart","The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.","gene expression; heart development; human development; human developmental cell atlas; in situ sequencing; single-cell RNA-sequencing; spatial transcriptomics; spatially resolved transcriptomics.","False","ST","155","30479"
"Human_Heart_Asp_12122019_ST_ST_Sample_6.5PCW_4","human","heart","31835037","A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart","The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.","gene expression; heart development; human development; human developmental cell atlas; in situ sequencing; single-cell RNA-sequencing; spatial transcriptomics; spatially resolved transcriptomics.","False","ST","182","30479"
"Human_Heart_Asp_12122019_ST_ST_Sample_6.5PCW_5","human","heart","31835037","A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart","The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.","gene expression; heart development; human development; human developmental cell atlas; in situ sequencing; single-cell RNA-sequencing; spatial transcriptomics; spatially resolved transcriptomics.","False","ST","212","30479"
"Human_Heart_Asp_12122019_ST_ST_Sample_6.5PCW_6","human","heart","31835037","A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart","The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.","gene expression; heart development; human development; human developmental cell atlas; in situ sequencing; single-cell RNA-sequencing; spatial transcriptomics; spatially resolved transcriptomics.","False","ST","203","30479"
"Human_Heart_Asp_12122019_ST_ST_Sample_6.5PCW_7","human","heart","31835037","A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart","The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.","gene expression; heart development; human development; human developmental cell atlas; in situ sequencing; single-cell RNA-sequencing; spatial transcriptomics; spatially resolved transcriptomics.","False","ST","173","30479"
"Human_Heart_Asp_12122019_ST_ST_Sample_6.5PCW_8","human","heart","31835037","A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart","The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.","gene expression; heart development; human development; human developmental cell atlas; in situ sequencing; single-cell RNA-sequencing; spatial transcriptomics; spatially resolved transcriptomics.","False","ST","180","30479"
"Human_Heart_Asp_12122019_ST_ST_Sample_6.5PCW_9","human","heart","31835037","A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart","The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.","gene expression; heart development; human development; human developmental cell atlas; in situ sequencing; single-cell RNA-sequencing; spatial transcriptomics; spatially resolved transcriptomics.","False","ST","174","30479"
"Human_Heart_Asp_12122019_ST_ST_Sample_9PCW_1","human","heart","31835037","A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart","The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.","gene expression; heart development; human development; human developmental cell atlas; in situ sequencing; single-cell RNA-sequencing; spatial transcriptomics; spatially resolved transcriptomics.","False","ST","236","30479"
"Human_Heart_Asp_12122019_ST_ST_Sample_9PCW_2","human","heart","31835037","A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart","The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.","gene expression; heart development; human development; human developmental cell atlas; in situ sequencing; single-cell RNA-sequencing; spatial transcriptomics; spatially resolved transcriptomics.","False","ST","240","30479"
"Human_Heart_Asp_12122019_ST_ST_Sample_9PCW_3","human","heart","31835037","A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart","The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.","gene expression; heart development; human development; human developmental cell atlas; in situ sequencing; single-cell RNA-sequencing; spatial transcriptomics; spatially resolved transcriptomics.","False","ST","243","30479"
"Human_Heart_Asp_12122019_ST_ST_Sample_9PCW_4","human","heart","31835037","A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart","The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.","gene expression; heart development; human development; human developmental cell atlas; in situ sequencing; single-cell RNA-sequencing; spatial transcriptomics; spatially resolved transcriptomics.","False","ST","225","30479"
"Human_Heart_Asp_12122019_ST_ST_Sample_9PCW_5","human","heart","31835037","A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart","The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.","gene expression; heart development; human development; human developmental cell atlas; in situ sequencing; single-cell RNA-sequencing; spatial transcriptomics; spatially resolved transcriptomics.","False","ST","196","30479"
"Human_Heart_Asp_12122019_ST_ST_Sample_9PCW_6","human","heart","31835037","A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart","The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.","gene expression; heart development; human development; human developmental cell atlas; in situ sequencing; single-cell RNA-sequencing; spatial transcriptomics; spatially resolved transcriptomics.","False","ST","200","30479"
"Mouse_OlfactoryBulb_Stahl_07012016_ST_Rep1","mouse","olfactory bulb","27365449","Visualization and analysis of gene expression in tissue sections by spatial transcriptomics","Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call """"spatial transcriptomics,"""" that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.","","True","ST","267","16573"
"Mouse_OlfactoryBulb_Stahl_07012016_ST_Rep10","mouse","olfactory bulb","27365449","Visualization and analysis of gene expression in tissue sections by spatial transcriptomics","Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call """"spatial transcriptomics,"""" that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.","","True","ST","281","16416"
"Mouse_OlfactoryBulb_Stahl_07012016_ST_Rep11","mouse","olfactory bulb","27365449","Visualization and analysis of gene expression in tissue sections by spatial transcriptomics","Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call """"spatial transcriptomics,"""" that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.","","True","ST","262","16218"
"Mouse_OlfactoryBulb_Stahl_07012016_ST_Rep12","mouse","olfactory bulb","27365449","Visualization and analysis of gene expression in tissue sections by spatial transcriptomics","Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call """"spatial transcriptomics,"""" that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.","","True","ST","282","16034"
"Mouse_OlfactoryBulb_Stahl_07012016_ST_Rep2","mouse","olfactory bulb","27365449","Visualization and analysis of gene expression in tissue sections by spatial transcriptomics","Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call """"spatial transcriptomics,"""" that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.","","True","ST","280","15981"
"Mouse_OlfactoryBulb_Stahl_07012016_ST_Rep3","mouse","olfactory bulb","27365449","Visualization and analysis of gene expression in tissue sections by spatial transcriptomics","Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call """"spatial transcriptomics,"""" that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.","","True","ST","269","16014"
"Mouse_OlfactoryBulb_Stahl_07012016_ST_Rep4","mouse","olfactory bulb","27365449","Visualization and analysis of gene expression in tissue sections by spatial transcriptomics","Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call """"spatial transcriptomics,"""" that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.","","True","ST","264","15941"
"Mouse_OlfactoryBulb_Stahl_07012016_ST_Rep5","mouse","olfactory bulb","27365449","Visualization and analysis of gene expression in tissue sections by spatial transcriptomics","Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call """"spatial transcriptomics,"""" that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.","","True","ST","267","15290"
"Mouse_OlfactoryBulb_Stahl_07012016_ST_Rep6","mouse","olfactory bulb","27365449","Visualization and analysis of gene expression in tissue sections by spatial transcriptomics","Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call """"spatial transcriptomics,"""" that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.","","True","ST","242","16251"
"Mouse_OlfactoryBulb_Stahl_07012016_ST_Rep7","mouse","olfactory bulb","27365449","Visualization and analysis of gene expression in tissue sections by spatial transcriptomics","Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call """"spatial transcriptomics,"""" that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.","","True","ST","231","16675"
"Mouse_OlfactoryBulb_Stahl_07012016_ST_Rep8","mouse","olfactory bulb","27365449","Visualization and analysis of gene expression in tissue sections by spatial transcriptomics","Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call """"spatial transcriptomics,"""" that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.","","True","ST","234","15288"
"Mouse_OlfactoryBulb_Stahl_07012016_ST_Rep9","mouse","olfactory bulb","27365449","Visualization and analysis of gene expression in tissue sections by spatial transcriptomics","Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call """"spatial transcriptomics,"""" that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.","","True","ST","237","15284"
"GSE144239_GSM4565823","human","skin","32579974,38037084","Title 1: Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma. Title 2: STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer.","Abstract 1: To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer. Abstract 2: Spatial transcriptomic technologies, such as the Visium platform, measure gene expression in different regions of tissues. Here, we describe new software, STmut, to visualize somatic point mutations, allelic imbalance, and copy number alterations in Visium data. STmut is tested on fresh-frozen Visium data, formalin-fixed paraffin-embedded (FFPE) Visium data, and tumors with and without matching DNA sequencing data. Copy number is inferred on all conditions, but the chemistry of the FFPE platform does not permit analyses of single nucleotide variants. Taken together, we propose solutions to add the genetic dimension to spatial transcriptomic data and describe the limitations of different datatypes.","Keywords 1: CRISPR screen; MIBI; intra-tumoral heterogeneity; multi-omics; scRNA-seq; skin cancer; spatial transcriptomics; squamous cell carcinoma; tumor immunology; tumor microenvironment. Keywords 2:","True","Visium","744","33538"
"GSE144239_GSM4565824","human","skin","32579974,38037084","Title 1: Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma. Title 2: STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer.","Abstract 1: To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer. Abstract 2: Spatial transcriptomic technologies, such as the Visium platform, measure gene expression in different regions of tissues. Here, we describe new software, STmut, to visualize somatic point mutations, allelic imbalance, and copy number alterations in Visium data. STmut is tested on fresh-frozen Visium data, formalin-fixed paraffin-embedded (FFPE) Visium data, and tumors with and without matching DNA sequencing data. Copy number is inferred on all conditions, but the chemistry of the FFPE platform does not permit analyses of single nucleotide variants. Taken together, we propose solutions to add the genetic dimension to spatial transcriptomic data and describe the limitations of different datatypes.","Keywords 1: CRISPR screen; MIBI; intra-tumoral heterogeneity; multi-omics; scRNA-seq; skin cancer; spatial transcriptomics; squamous cell carcinoma; tumor immunology; tumor microenvironment. Keywords 2:","True","Visium","696","33538"
"GSE144239_GSM4565825","human","skin","32579974,38037084","Title 1: Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma. Title 2: STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer.","Abstract 1: To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer. Abstract 2: Spatial transcriptomic technologies, such as the Visium platform, measure gene expression in different regions of tissues. Here, we describe new software, STmut, to visualize somatic point mutations, allelic imbalance, and copy number alterations in Visium data. STmut is tested on fresh-frozen Visium data, formalin-fixed paraffin-embedded (FFPE) Visium data, and tumors with and without matching DNA sequencing data. Copy number is inferred on all conditions, but the chemistry of the FFPE platform does not permit analyses of single nucleotide variants. Taken together, we propose solutions to add the genetic dimension to spatial transcriptomic data and describe the limitations of different datatypes.","Keywords 1: CRISPR screen; MIBI; intra-tumoral heterogeneity; multi-omics; scRNA-seq; skin cancer; spatial transcriptomics; squamous cell carcinoma; tumor immunology; tumor microenvironment. Keywords 2:","True","Visium","3650","33538"
"GSE144239_GSM4565826","human","skin","32579974,38037084","Title 1: Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma. Title 2: STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer.","Abstract 1: To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer. Abstract 2: Spatial transcriptomic technologies, such as the Visium platform, measure gene expression in different regions of tissues. Here, we describe new software, STmut, to visualize somatic point mutations, allelic imbalance, and copy number alterations in Visium data. STmut is tested on fresh-frozen Visium data, formalin-fixed paraffin-embedded (FFPE) Visium data, and tumors with and without matching DNA sequencing data. Copy number is inferred on all conditions, but the chemistry of the FFPE platform does not permit analyses of single nucleotide variants. Taken together, we propose solutions to add the genetic dimension to spatial transcriptomic data and describe the limitations of different datatypes.","Keywords 1: CRISPR screen; MIBI; intra-tumoral heterogeneity; multi-omics; scRNA-seq; skin cancer; spatial transcriptomics; squamous cell carcinoma; tumor immunology; tumor microenvironment. Keywords 2:","True","Visium","3838","33538"
"GSE148612_GSM5026144","mouse","brain","34413515","Neuroinflammatory astrocyte subtypes in the mouse brain","Astrocytes undergo an inflammatory transition after infections, acute injuries and chronic neurodegenerative diseases. How this transition is affected by time and sex, its heterogeneity at the single-cell level and how sub-states are spatially distributed in the brain remains unclear. In this study, we investigated transcriptome changes of mouse cortical astrocytes after an acute inflammatory stimulus using the bacterial cell wall endotoxin lipopolysaccharide. We identified fast transcriptomic changes in astrocytes occurring within hours that drastically change over time. By sequencing ~80,000 astrocytes at single-cell resolution, we show that inflammation causes a widespread response with subtypes of astrocytes undergoing distinct inflammatory transitions with defined transcriptomic profiles. We also attribute key sub-states of inflammation-induced reactive astrocytes to specific brain regions using spatial transcriptomics and in situ hybridization. Together, our datasets provide a powerful resource for profiling astrocyte heterogeneity and will be useful for understanding the biological importance of regionally constrained reactive astrocyte sub-states.","","False","Visium","2511","32285"
"GSE148612_GSM5026145","mouse","brain","34413515","Neuroinflammatory astrocyte subtypes in the mouse brain","Astrocytes undergo an inflammatory transition after infections, acute injuries and chronic neurodegenerative diseases. How this transition is affected by time and sex, its heterogeneity at the single-cell level and how sub-states are spatially distributed in the brain remains unclear. In this study, we investigated transcriptome changes of mouse cortical astrocytes after an acute inflammatory stimulus using the bacterial cell wall endotoxin lipopolysaccharide. We identified fast transcriptomic changes in astrocytes occurring within hours that drastically change over time. By sequencing ~80,000 astrocytes at single-cell resolution, we show that inflammation causes a widespread response with subtypes of astrocytes undergoing distinct inflammatory transitions with defined transcriptomic profiles. We also attribute key sub-states of inflammation-induced reactive astrocytes to specific brain regions using spatial transcriptomics and in situ hybridization. Together, our datasets provide a powerful resource for profiling astrocyte heterogeneity and will be useful for understanding the biological importance of regionally constrained reactive astrocyte sub-states.","","False","Visium","3113","32285"
"GSE148612_GSM5026146","mouse","brain","34413515","Neuroinflammatory astrocyte subtypes in the mouse brain","Astrocytes undergo an inflammatory transition after infections, acute injuries and chronic neurodegenerative diseases. How this transition is affected by time and sex, its heterogeneity at the single-cell level and how sub-states are spatially distributed in the brain remains unclear. In this study, we investigated transcriptome changes of mouse cortical astrocytes after an acute inflammatory stimulus using the bacterial cell wall endotoxin lipopolysaccharide. We identified fast transcriptomic changes in astrocytes occurring within hours that drastically change over time. By sequencing ~80,000 astrocytes at single-cell resolution, we show that inflammation causes a widespread response with subtypes of astrocytes undergoing distinct inflammatory transitions with defined transcriptomic profiles. We also attribute key sub-states of inflammation-induced reactive astrocytes to specific brain regions using spatial transcriptomics and in situ hybridization. Together, our datasets provide a powerful resource for profiling astrocyte heterogeneity and will be useful for understanding the biological importance of regionally constrained reactive astrocyte sub-states.","","False","Visium","2728","32285"
"GSE148612_GSM5026147","mouse","brain","34413515","Neuroinflammatory astrocyte subtypes in the mouse brain","Astrocytes undergo an inflammatory transition after infections, acute injuries and chronic neurodegenerative diseases. How this transition is affected by time and sex, its heterogeneity at the single-cell level and how sub-states are spatially distributed in the brain remains unclear. In this study, we investigated transcriptome changes of mouse cortical astrocytes after an acute inflammatory stimulus using the bacterial cell wall endotoxin lipopolysaccharide. We identified fast transcriptomic changes in astrocytes occurring within hours that drastically change over time. By sequencing ~80,000 astrocytes at single-cell resolution, we show that inflammation causes a widespread response with subtypes of astrocytes undergoing distinct inflammatory transitions with defined transcriptomic profiles. We also attribute key sub-states of inflammation-induced reactive astrocytes to specific brain regions using spatial transcriptomics and in situ hybridization. Together, our datasets provide a powerful resource for profiling astrocyte heterogeneity and will be useful for understanding the biological importance of regionally constrained reactive astrocyte sub-states.","","False","Visium","2419","32285"
"GSE148612_GSM5026148","mouse","brain","34413515","Neuroinflammatory astrocyte subtypes in the mouse brain","Astrocytes undergo an inflammatory transition after infections, acute injuries and chronic neurodegenerative diseases. How this transition is affected by time and sex, its heterogeneity at the single-cell level and how sub-states are spatially distributed in the brain remains unclear. In this study, we investigated transcriptome changes of mouse cortical astrocytes after an acute inflammatory stimulus using the bacterial cell wall endotoxin lipopolysaccharide. We identified fast transcriptomic changes in astrocytes occurring within hours that drastically change over time. By sequencing ~80,000 astrocytes at single-cell resolution, we show that inflammation causes a widespread response with subtypes of astrocytes undergoing distinct inflammatory transitions with defined transcriptomic profiles. We also attribute key sub-states of inflammation-induced reactive astrocytes to specific brain regions using spatial transcriptomics and in situ hybridization. Together, our datasets provide a powerful resource for profiling astrocyte heterogeneity and will be useful for understanding the biological importance of regionally constrained reactive astrocyte sub-states.","","False","Visium","2157","32285"
"GSE148612_GSM5026149","mouse","brain","34413515","Neuroinflammatory astrocyte subtypes in the mouse brain","Astrocytes undergo an inflammatory transition after infections, acute injuries and chronic neurodegenerative diseases. How this transition is affected by time and sex, its heterogeneity at the single-cell level and how sub-states are spatially distributed in the brain remains unclear. In this study, we investigated transcriptome changes of mouse cortical astrocytes after an acute inflammatory stimulus using the bacterial cell wall endotoxin lipopolysaccharide. We identified fast transcriptomic changes in astrocytes occurring within hours that drastically change over time. By sequencing ~80,000 astrocytes at single-cell resolution, we show that inflammation causes a widespread response with subtypes of astrocytes undergoing distinct inflammatory transitions with defined transcriptomic profiles. We also attribute key sub-states of inflammation-induced reactive astrocytes to specific brain regions using spatial transcriptomics and in situ hybridization. Together, our datasets provide a powerful resource for profiling astrocyte heterogeneity and will be useful for understanding the biological importance of regionally constrained reactive astrocyte sub-states.","","False","Visium","2730","32285"
"GSE153424_GSM4644079","mouse","brain","35210624","Clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics","The mammalian brain contains many specialized cells that develop from a thin sheet of neuroepithelial progenitor cells. Single-cell transcriptomics revealed hundreds of molecularly diverse cell types in the nervous system, but the lineage relationships between mature cell types and progenitor cells are not well understood. Here we show in vivo barcoding of early progenitors to simultaneously profile cell phenotypes and clonal relations in the mouse brain using single-cell and spatial transcriptomics. By reconstructing thousands of clones, we discovered fate-restricted progenitor cells in the mouse hippocampal neuroepithelium and show that microglia are derived from few primitive myeloid precursors that massively expand to generate widely dispersed progeny. We combined spatial transcriptomics with clonal barcoding and disentangled migration patterns of clonally related cells in densely labeled tissue sections. Our approach enables high-throughput dense reconstruction of cell phenotypes and clonal relations at the single-cell and tissue level in individual animals and provides an integrated approach for understanding tissue architecture.","","False","Visium","3805","27999"
"GSE153424_GSM4644080","mouse","brain","35210624","Clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics","The mammalian brain contains many specialized cells that develop from a thin sheet of neuroepithelial progenitor cells. Single-cell transcriptomics revealed hundreds of molecularly diverse cell types in the nervous system, but the lineage relationships between mature cell types and progenitor cells are not well understood. Here we show in vivo barcoding of early progenitors to simultaneously profile cell phenotypes and clonal relations in the mouse brain using single-cell and spatial transcriptomics. By reconstructing thousands of clones, we discovered fate-restricted progenitor cells in the mouse hippocampal neuroepithelium and show that microglia are derived from few primitive myeloid precursors that massively expand to generate widely dispersed progeny. We combined spatial transcriptomics with clonal barcoding and disentangled migration patterns of clonally related cells in densely labeled tissue sections. Our approach enables high-throughput dense reconstruction of cell phenotypes and clonal relations at the single-cell and tissue level in individual animals and provides an integrated approach for understanding tissue architecture.","","False","Visium","4128","27999"
"GSE153424_GSM4644081","mouse","brain","35210624","Clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics","The mammalian brain contains many specialized cells that develop from a thin sheet of neuroepithelial progenitor cells. Single-cell transcriptomics revealed hundreds of molecularly diverse cell types in the nervous system, but the lineage relationships between mature cell types and progenitor cells are not well understood. Here we show in vivo barcoding of early progenitors to simultaneously profile cell phenotypes and clonal relations in the mouse brain using single-cell and spatial transcriptomics. By reconstructing thousands of clones, we discovered fate-restricted progenitor cells in the mouse hippocampal neuroepithelium and show that microglia are derived from few primitive myeloid precursors that massively expand to generate widely dispersed progeny. We combined spatial transcriptomics with clonal barcoding and disentangled migration patterns of clonally related cells in densely labeled tissue sections. Our approach enables high-throughput dense reconstruction of cell phenotypes and clonal relations at the single-cell and tissue level in individual animals and provides an integrated approach for understanding tissue architecture.","","False","Visium","4169","27999"
"GSE153424_GSM4644082","mouse","brain","35210624","Clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics","The mammalian brain contains many specialized cells that develop from a thin sheet of neuroepithelial progenitor cells. Single-cell transcriptomics revealed hundreds of molecularly diverse cell types in the nervous system, but the lineage relationships between mature cell types and progenitor cells are not well understood. Here we show in vivo barcoding of early progenitors to simultaneously profile cell phenotypes and clonal relations in the mouse brain using single-cell and spatial transcriptomics. By reconstructing thousands of clones, we discovered fate-restricted progenitor cells in the mouse hippocampal neuroepithelium and show that microglia are derived from few primitive myeloid precursors that massively expand to generate widely dispersed progeny. We combined spatial transcriptomics with clonal barcoding and disentangled migration patterns of clonally related cells in densely labeled tissue sections. Our approach enables high-throughput dense reconstruction of cell phenotypes and clonal relations at the single-cell and tissue level in individual animals and provides an integrated approach for understanding tissue architecture.","","False","Visium","3922","27999"
"GSE153424_GSM4644083","mouse","brain","35210624","Clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics","The mammalian brain contains many specialized cells that develop from a thin sheet of neuroepithelial progenitor cells. Single-cell transcriptomics revealed hundreds of molecularly diverse cell types in the nervous system, but the lineage relationships between mature cell types and progenitor cells are not well understood. Here we show in vivo barcoding of early progenitors to simultaneously profile cell phenotypes and clonal relations in the mouse brain using single-cell and spatial transcriptomics. By reconstructing thousands of clones, we discovered fate-restricted progenitor cells in the mouse hippocampal neuroepithelium and show that microglia are derived from few primitive myeloid precursors that massively expand to generate widely dispersed progeny. We combined spatial transcriptomics with clonal barcoding and disentangled migration patterns of clonally related cells in densely labeled tissue sections. Our approach enables high-throughput dense reconstruction of cell phenotypes and clonal relations at the single-cell and tissue level in individual animals and provides an integrated approach for understanding tissue architecture.","","False","Visium","3736","27999"
"GSE153424_GSM4644084","mouse","brain","35210624","Clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics","The mammalian brain contains many specialized cells that develop from a thin sheet of neuroepithelial progenitor cells. Single-cell transcriptomics revealed hundreds of molecularly diverse cell types in the nervous system, but the lineage relationships between mature cell types and progenitor cells are not well understood. Here we show in vivo barcoding of early progenitors to simultaneously profile cell phenotypes and clonal relations in the mouse brain using single-cell and spatial transcriptomics. By reconstructing thousands of clones, we discovered fate-restricted progenitor cells in the mouse hippocampal neuroepithelium and show that microglia are derived from few primitive myeloid precursors that massively expand to generate widely dispersed progeny. We combined spatial transcriptomics with clonal barcoding and disentangled migration patterns of clonally related cells in densely labeled tissue sections. Our approach enables high-throughput dense reconstruction of cell phenotypes and clonal relations at the single-cell and tissue level in individual animals and provides an integrated approach for understanding tissue architecture.","","False","Visium","3563","27999"
"GSE153424_GSM4644085","mouse","brain","35210624","Clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics","The mammalian brain contains many specialized cells that develop from a thin sheet of neuroepithelial progenitor cells. Single-cell transcriptomics revealed hundreds of molecularly diverse cell types in the nervous system, but the lineage relationships between mature cell types and progenitor cells are not well understood. Here we show in vivo barcoding of early progenitors to simultaneously profile cell phenotypes and clonal relations in the mouse brain using single-cell and spatial transcriptomics. By reconstructing thousands of clones, we discovered fate-restricted progenitor cells in the mouse hippocampal neuroepithelium and show that microglia are derived from few primitive myeloid precursors that massively expand to generate widely dispersed progeny. We combined spatial transcriptomics with clonal barcoding and disentangled migration patterns of clonally related cells in densely labeled tissue sections. Our approach enables high-throughput dense reconstruction of cell phenotypes and clonal relations at the single-cell and tissue level in individual animals and provides an integrated approach for understanding tissue architecture.","","False","Visium","3610","27999"
"GSE153424_GSM4644086","mouse","brain","35210624","Clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics","The mammalian brain contains many specialized cells that develop from a thin sheet of neuroepithelial progenitor cells. Single-cell transcriptomics revealed hundreds of molecularly diverse cell types in the nervous system, but the lineage relationships between mature cell types and progenitor cells are not well understood. Here we show in vivo barcoding of early progenitors to simultaneously profile cell phenotypes and clonal relations in the mouse brain using single-cell and spatial transcriptomics. By reconstructing thousands of clones, we discovered fate-restricted progenitor cells in the mouse hippocampal neuroepithelium and show that microglia are derived from few primitive myeloid precursors that massively expand to generate widely dispersed progeny. We combined spatial transcriptomics with clonal barcoding and disentangled migration patterns of clonally related cells in densely labeled tissue sections. Our approach enables high-throughput dense reconstruction of cell phenotypes and clonal relations at the single-cell and tissue level in individual animals and provides an integrated approach for understanding tissue architecture.","","False","Visium","3734","27999"
"GSE153859_GSM4656179","mouse","brain","36928528","The spatial landscape of gene expression isoforms in tissue sections","In situ capturing technologies add tissue context to gene expression data, with the potential of providing a greater understanding of complex biological systems. However, splicing variants and full-length sequence heterogeneity cannot be characterized at spatial resolution with current transcriptome profiling methods. To that end, we introduce spatial isoform transcriptomics (SiT), an explorative method for characterizing spatial isoform variation and sequence heterogeneity using long-read sequencing. We show in mouse brain how SiT can be used to profile isoform expression and sequence heterogeneity in different areas of the tissue. SiT reveals regional isoform switching of Plp1 gene between different layers of the olfactory bulb, and the use of external single-cell data allows the nomination of cell types expressing each isoform. Furthermore, SiT identifies differential isoform usage for several major genes implicated in brain function (Snap25, Bin1, Gnas) that are independently validated by in situ sequencing. SiT also provides for the first time an in-depth A-to-I RNA editing map of the adult mouse brain. Data exploration can be performed through an online resource (https://www.isomics.eu), where isoform expression and RNA editing can be visualized in a spatial context.","","False","Visium","2500","31053"
"GSE153859_GSM4656180","mouse","brain","36928528","The spatial landscape of gene expression isoforms in tissue sections","In situ capturing technologies add tissue context to gene expression data, with the potential of providing a greater understanding of complex biological systems. However, splicing variants and full-length sequence heterogeneity cannot be characterized at spatial resolution with current transcriptome profiling methods. To that end, we introduce spatial isoform transcriptomics (SiT), an explorative method for characterizing spatial isoform variation and sequence heterogeneity using long-read sequencing. We show in mouse brain how SiT can be used to profile isoform expression and sequence heterogeneity in different areas of the tissue. SiT reveals regional isoform switching of Plp1 gene between different layers of the olfactory bulb, and the use of external single-cell data allows the nomination of cell types expressing each isoform. Furthermore, SiT identifies differential isoform usage for several major genes implicated in brain function (Snap25, Bin1, Gnas) that are independently validated by in situ sequencing. SiT also provides for the first time an in-depth A-to-I RNA editing map of the adult mouse brain. Data exploration can be performed through an online resource (https://www.isomics.eu), where isoform expression and RNA editing can be visualized in a spatial context.","","False","Visium","2560","31053"
"GSE153859_GSM4656181","mouse","olfactory bulb","36928528","The spatial landscape of gene expression isoforms in tissue sections","In situ capturing technologies add tissue context to gene expression data, with the potential of providing a greater understanding of complex biological systems. However, splicing variants and full-length sequence heterogeneity cannot be characterized at spatial resolution with current transcriptome profiling methods. To that end, we introduce spatial isoform transcriptomics (SiT), an explorative method for characterizing spatial isoform variation and sequence heterogeneity using long-read sequencing. We show in mouse brain how SiT can be used to profile isoform expression and sequence heterogeneity in different areas of the tissue. SiT reveals regional isoform switching of Plp1 gene between different layers of the olfactory bulb, and the use of external single-cell data allows the nomination of cell types expressing each isoform. Furthermore, SiT identifies differential isoform usage for several major genes implicated in brain function (Snap25, Bin1, Gnas) that are independently validated by in situ sequencing. SiT also provides for the first time an in-depth A-to-I RNA editing map of the adult mouse brain. Data exploration can be performed through an online resource (https://www.isomics.eu), where isoform expression and RNA editing can be visualized in a spatial context.","","False","Visium","918","31053"
"GSE158450_GSM4800808","mouse","brain","33469025,35301264,35604081,36593406","Title 1: A spatially resolved brain region- and cell type-specific isoform atlas of the postnatal mouse brain. Title 2: Sequencing of individual barcoded cDNAs using Pacific Biosciences and Oxford Nanopore Technologies reveals platform-specific error patterns. Title 3: ScisorWiz: visualizing differential isoform expression in single-cell long-read data. Title 4: Accurate isoform discovery with IsoQuant using long reads.","Abstract 1: Splicing varies across brain regions, but the single-cell resolution of regional variation is unclear. We present a single-cell investigation of differential isoform expression (DIE) between brain regions using single-cell long-read sequencing in mouse hippocampus and prefrontal cortex in 45 cell types at postnatal day 7 ( www.isoformAtlas.com ). Isoform tests for DIE show better performance than exon tests. We detect hundreds of DIE events traceable to cell types, often corresponding to functionally distinct protein isoforms. Mostly, one cell type is responsible for brain-region specific DIE. However, for fewer genes, multiple cell types influence DIE. Thus, regional identity can, although rarely, override cell-type specificity. Cell types indigenous to one anatomic structure display distinctive DIE, e.g. the choroid plexus epithelium manifests distinct transcription-start-site usage. Spatial transcriptomics and long-read sequencing yield a spatially resolved splicing map. Our methods quantify isoform expression with cell-type and spatial resolution and it contributes to further our understanding of how the brain integrates molecular and cellular complexity. Abstract 2: Long-read transcriptomics require understanding error sources inherent to technologies. Current approaches cannot compare methods for an individual RNA molecule. Here, we present a novel platform-comparison method that combines barcoding strategies and long-read sequencing to sequence cDNA copies representing an individual RNA molecule on both Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT). We compare these long-read pairs in terms of sequence content and isoform patterns. Although individual read pairs show high similarity, we find differences in (1) aligned length, (2) transcription start site (TSS), (3) polyadenylation site (poly(A)-site) assignment, and (4) exon-intron structures. Overall, 25% of read pairs disagree on either TSS, poly(A)-site, or splice site. Intron-chain disagreement typically arises from alignment errors of microexons and complicated splice sites. Our single-molecule technology comparison reveals that inconsistencies are often caused by sequencing error-induced inaccurate ONT alignments, especially to downstream GUNNGU donor motifs. However, annotation-disagreeing upstream shifts in NAGNAG acceptors in ONT are often confirmed by PacBio and are thus likely real. In both barcoded and nonbarcoded ONT reads, we find that intron number and proximity of GU/AGs better predict inconsistencies with the annotation than read quality alone. We summarize these findings in an annotation-based algorithm for spliced alignment correction that improves subsequent transcript construction with ONT reads. Abstract 3: Summary: RNA isoforms contribute to the diverse functionality of the proteins they encode within the cell. Visualizing how isoform expression differs across cell types and brain regions can inform our understanding of disease and gain or loss of functionality caused by alternative splicing with potential negative impacts. However, the extent to which this occurs in specific cell types and brain regions is largely unknown. This is the kind of information that ScisorWiz plots can provide in an informative and easily communicable manner. ScisorWiz affords its user the opportunity to visualize specific genes across any number of cell types, and provides various sorting options for the user to gain different ways to understand their data. ScisorWiz provides a clear picture of differential isoform expression through various clustering methods and highlights features such as alternative exons and single-nucleotide variants. Tools like ScisorWiz are key for interpreting single-cell isoform sequencing data. This tool applies to any single-cell long-read RNA sequencing data in any cell type, tissue or species.

Availability and implementation: Source code is available at http://github.com/ans4013/ScisorWiz. No new data were generated for this publication. Data used to generate figures was sourced from GEO accession token GSE158450 and available on GitHub as example data. Abstract 4: Annotating newly sequenced genomes and determining alternative isoforms from long-read RNA data are complex and incompletely solved problems. Here we present IsoQuant-a computational tool using intron graphs that accurately reconstructs transcripts both with and without reference genome annotation. For novel transcript discovery, IsoQuant reduces the false-positive rate fivefold and 2.5-fold for Oxford Nanopore reference-based or reference-free mode, respectively. IsoQuant also improves performance for Pacific Biosciences data. ","Keywords 1:  Keywords 2:  Keywords 3:  Keywords 4:","False","Visium","3024","31053"
"GSE158730_GSM7770348","mouse","olfactory epithelium","36993168","Opposing, spatially-determined epigenetic forces impose restrictions on stochastic olfactory receptor choice","Olfactory receptor (OR) choice represents an example of genetically hardwired stochasticity, where every olfactory neuron expresses one out of ~2000 OR alleles in a probabilistic, yet stereotypic fashion. Here, we propose that topographic restrictions in OR expression are established in neuronal progenitors by two opposing forces: polygenic transcription and genomic silencing, both of which are influenced by dorsoventral gradients of transcription factors NFIA, B, and X. Polygenic transcription of OR genes may define spatially constrained OR repertoires, among which one OR allele is selected for singular expression later in development. Heterochromatin assembly and genomic compartmentalization of OR alleles also vary across the axes of the olfactory epithelium and may preferentially eliminate ectopically expressed ORs with more dorsal expression destinations from this """"""""""""""""""""""""""""""""privileged"""""""""""""""""""""""""""""""" repertoire. Our experiments identify early transcription as a potential """"""""""""""""""""""""""""""""epigenetic"""""""""""""""""""""""""""""""" contributor to future developmental patterning and reveal how two spatially responsive probabilistic processes may act in concert to establish deterministic, precise, and reproducible territories of stochastic gene expression.","","False","Visium","1387","32285"
"GSE158730_GSM7770349","mouse","olfactory epithelium","36993168","Opposing, spatially-determined epigenetic forces impose restrictions on stochastic olfactory receptor choice","Olfactory receptor (OR) choice represents an example of genetically hardwired stochasticity, where every olfactory neuron expresses one out of ~2000 OR alleles in a probabilistic, yet stereotypic fashion. Here, we propose that topographic restrictions in OR expression are established in neuronal progenitors by two opposing forces: polygenic transcription and genomic silencing, both of which are influenced by dorsoventral gradients of transcription factors NFIA, B, and X. Polygenic transcription of OR genes may define spatially constrained OR repertoires, among which one OR allele is selected for singular expression later in development. Heterochromatin assembly and genomic compartmentalization of OR alleles also vary across the axes of the olfactory epithelium and may preferentially eliminate ectopically expressed ORs with more dorsal expression destinations from this """"""""""""""""""""""""""""""""privileged"""""""""""""""""""""""""""""""" repertoire. Our experiments identify early transcription as a potential """"""""""""""""""""""""""""""""epigenetic"""""""""""""""""""""""""""""""" contributor to future developmental patterning and reveal how two spatially responsive probabilistic processes may act in concert to establish deterministic, precise, and reproducible territories of stochastic gene expression.","","False","Visium","1535","32285"
"GSE158730_GSM7770350","mouse","olfactory epithelium","36993168","Opposing, spatially-determined epigenetic forces impose restrictions on stochastic olfactory receptor choice","Olfactory receptor (OR) choice represents an example of genetically hardwired stochasticity, where every olfactory neuron expresses one out of ~2000 OR alleles in a probabilistic, yet stereotypic fashion. Here, we propose that topographic restrictions in OR expression are established in neuronal progenitors by two opposing forces: polygenic transcription and genomic silencing, both of which are influenced by dorsoventral gradients of transcription factors NFIA, B, and X. Polygenic transcription of OR genes may define spatially constrained OR repertoires, among which one OR allele is selected for singular expression later in development. Heterochromatin assembly and genomic compartmentalization of OR alleles also vary across the axes of the olfactory epithelium and may preferentially eliminate ectopically expressed ORs with more dorsal expression destinations from this """"""""""""""""""""""""""""""""privileged"""""""""""""""""""""""""""""""" repertoire. Our experiments identify early transcription as a potential """"""""""""""""""""""""""""""""epigenetic"""""""""""""""""""""""""""""""" contributor to future developmental patterning and reveal how two spatially responsive probabilistic processes may act in concert to establish deterministic, precise, and reproducible territories of stochastic gene expression.","","False","Visium","1562","32285"
"GSE158730_GSM7770351","mouse","olfactory epithelium","36993168","Opposing, spatially-determined epigenetic forces impose restrictions on stochastic olfactory receptor choice","Olfactory receptor (OR) choice represents an example of genetically hardwired stochasticity, where every olfactory neuron expresses one out of ~2000 OR alleles in a probabilistic, yet stereotypic fashion. Here, we propose that topographic restrictions in OR expression are established in neuronal progenitors by two opposing forces: polygenic transcription and genomic silencing, both of which are influenced by dorsoventral gradients of transcription factors NFIA, B, and X. Polygenic transcription of OR genes may define spatially constrained OR repertoires, among which one OR allele is selected for singular expression later in development. Heterochromatin assembly and genomic compartmentalization of OR alleles also vary across the axes of the olfactory epithelium and may preferentially eliminate ectopically expressed ORs with more dorsal expression destinations from this """"""""""""""""""""""""""""""""privileged"""""""""""""""""""""""""""""""" repertoire. Our experiments identify early transcription as a potential """"""""""""""""""""""""""""""""epigenetic"""""""""""""""""""""""""""""""" contributor to future developmental patterning and reveal how two spatially responsive probabilistic processes may act in concert to establish deterministic, precise, and reproducible territories of stochastic gene expression.","","False","Visium","1714","32285"
"GSE160137_GSM4861202","mouse","embryo","33976190","A coordinated progression of progenitor cell states initiates urinary tract development","The kidney and upper urinary tract develop through reciprocal interactions between the ureteric bud and the surrounding mesenchyme. Ureteric bud branching forms the arborized collecting duct system of the kidney, while ureteric tips promote nephron formation from dedicated progenitor cells. While nephron progenitor cells are relatively well characterized, the origin of ureteric bud progenitors has received little attention so far. It is well established that the ureteric bud is induced from the nephric duct, an epithelial duct derived from the intermediate mesoderm of the embryo. However, the cell state transitions underlying the progression from intermediate mesoderm to nephric duct and ureteric bud remain unknown. Here we show that nephric duct morphogenesis results from the coordinated organization of four major progenitor cell populations. Using single cell RNA-seq and Cluster RNA-seq, we show that these progenitors emerge in time and space according to a stereotypical pattern. We identify the transcription factors Tfap2a/b and Gata3 as critical coordinators of this progenitor cell progression. This study provides a better understanding of the cellular origin of the renal collecting duct system and associated urinary tract developmental diseases, which may inform guided differentiation of functional kidney tissue.","","False","Visium","198","53574"
"GSE160137_GSM4861203","mouse","embryo","33976190","A coordinated progression of progenitor cell states initiates urinary tract development","The kidney and upper urinary tract develop through reciprocal interactions between the ureteric bud and the surrounding mesenchyme. Ureteric bud branching forms the arborized collecting duct system of the kidney, while ureteric tips promote nephron formation from dedicated progenitor cells. While nephron progenitor cells are relatively well characterized, the origin of ureteric bud progenitors has received little attention so far. It is well established that the ureteric bud is induced from the nephric duct, an epithelial duct derived from the intermediate mesoderm of the embryo. However, the cell state transitions underlying the progression from intermediate mesoderm to nephric duct and ureteric bud remain unknown. Here we show that nephric duct morphogenesis results from the coordinated organization of four major progenitor cell populations. Using single cell RNA-seq and Cluster RNA-seq, we show that these progenitors emerge in time and space according to a stereotypical pattern. We identify the transcription factors Tfap2a/b and Gata3 as critical coordinators of this progenitor cell progression. This study provides a better understanding of the cellular origin of the renal collecting duct system and associated urinary tract developmental diseases, which may inform guided differentiation of functional kidney tissue.","","False","Visium","222","53574"
"GSE160137_GSM4861204","mouse","embryo","33976190","A coordinated progression of progenitor cell states initiates urinary tract development","The kidney and upper urinary tract develop through reciprocal interactions between the ureteric bud and the surrounding mesenchyme. Ureteric bud branching forms the arborized collecting duct system of the kidney, while ureteric tips promote nephron formation from dedicated progenitor cells. While nephron progenitor cells are relatively well characterized, the origin of ureteric bud progenitors has received little attention so far. It is well established that the ureteric bud is induced from the nephric duct, an epithelial duct derived from the intermediate mesoderm of the embryo. However, the cell state transitions underlying the progression from intermediate mesoderm to nephric duct and ureteric bud remain unknown. Here we show that nephric duct morphogenesis results from the coordinated organization of four major progenitor cell populations. Using single cell RNA-seq and Cluster RNA-seq, we show that these progenitors emerge in time and space according to a stereotypical pattern. We identify the transcription factors Tfap2a/b and Gata3 as critical coordinators of this progenitor cell progression. This study provides a better understanding of the cellular origin of the renal collecting duct system and associated urinary tract developmental diseases, which may inform guided differentiation of functional kidney tissue.","","False","Visium","192","53574"
"GSE160137_GSM4861205","mouse","embryo","33976190","A coordinated progression of progenitor cell states initiates urinary tract development","The kidney and upper urinary tract develop through reciprocal interactions between the ureteric bud and the surrounding mesenchyme. Ureteric bud branching forms the arborized collecting duct system of the kidney, while ureteric tips promote nephron formation from dedicated progenitor cells. While nephron progenitor cells are relatively well characterized, the origin of ureteric bud progenitors has received little attention so far. It is well established that the ureteric bud is induced from the nephric duct, an epithelial duct derived from the intermediate mesoderm of the embryo. However, the cell state transitions underlying the progression from intermediate mesoderm to nephric duct and ureteric bud remain unknown. Here we show that nephric duct morphogenesis results from the coordinated organization of four major progenitor cell populations. Using single cell RNA-seq and Cluster RNA-seq, we show that these progenitors emerge in time and space according to a stereotypical pattern. We identify the transcription factors Tfap2a/b and Gata3 as critical coordinators of this progenitor cell progression. This study provides a better understanding of the cellular origin of the renal collecting duct system and associated urinary tract developmental diseases, which may inform guided differentiation of functional kidney tissue.","","False","Visium","178","53574"
"GSE169749_GSM5213483","mouse","colon","35149721","The spatial transcriptomic landscape of the healing mouse intestine following damage","The intestinal barrier is composed of a complex cell network defining highly compartmentalized and specialized structures. Here, we use spatial transcriptomics to define how the transcriptomic landscape is spatially organized in the steady state and healing murine colon. At steady state conditions, we demonstrate a previously unappreciated molecular regionalization of the colon, which dramatically changes during mucosal healing. Here, we identified spatially-organized transcriptional programs defining compartmentalized mucosal healing, and regions with dominant wired pathways. Furthermore, we showed that decreased p53 activation defined areas with increased presence of proliferating epithelial stem cells. Finally, we mapped transcriptomics modules associated with human diseases demonstrating the translational potential of our dataset. Overall, we provide a publicly available resource defining principles of transcriptomic regionalization of the colon during mucosal healing and a framework to develop and progress further hypotheses.","","False","Visium","2604","31053"
"GSE169749_GSM5213484","mouse","colon","35149721","The spatial transcriptomic landscape of the healing mouse intestine following damage","The intestinal barrier is composed of a complex cell network defining highly compartmentalized and specialized structures. Here, we use spatial transcriptomics to define how the transcriptomic landscape is spatially organized in the steady state and healing murine colon. At steady state conditions, we demonstrate a previously unappreciated molecular regionalization of the colon, which dramatically changes during mucosal healing. Here, we identified spatially-organized transcriptional programs defining compartmentalized mucosal healing, and regions with dominant wired pathways. Furthermore, we showed that decreased p53 activation defined areas with increased presence of proliferating epithelial stem cells. Finally, we mapped transcriptomics modules associated with human diseases demonstrating the translational potential of our dataset. Overall, we provide a publicly available resource defining principles of transcriptomic regionalization of the colon during mucosal healing and a framework to develop and progress further hypotheses.","","False","Visium","3630","31053"
"GSE173651_GSM5273010","human","skin","","Single-cell and spatial transcriptomic analysis of homeostatic adult human skin","","","False","Visium","2260","33538"
"GSE173651_GSM5273011","human","skin","","Single-cell and spatial transcriptomic analysis of homeostatic adult human skin","","","False","Visium","2692","33538"
"GSE173651_GSM5273012","human","skin","","Single-cell and spatial transcriptomic analysis of homeostatic adult human skin","","","False","Visium","1869","33538"
"GSE173651_GSM5273013","human","skin","","Single-cell and spatial transcriptomic analysis of homeostatic adult human skin","","","False","Visium","3354","33538"
"GSE173651_GSM5273014","human","skin","","Single-cell and spatial transcriptomic analysis of homeostatic adult human skin","","","False","Visium","2333","33538"
"GSE173651_GSM5273015","human","skin","","Single-cell and spatial transcriptomic analysis of homeostatic adult human skin","","","False","Visium","2516","33538"
"GSE174313_GSM5291683","mouse","brain","34663698","Spatial transcriptomics reveals a role for sensory nerves in preserving cranial suture patency through modulation of BMP/TGF-β signaling","The patterning and ossification of the mammalian skeleton requires the coordinated actions of both intrinsic bone morphogens and extrinsic neurovascular signals, which function in a temporal and spatial fashion to control mesenchymal progenitor cell (MPC) fate. Here, we show the genetic inhibition of tropomyosin receptor kinase A (TrkA) sensory nerve innervation of the developing cranium results in premature calvarial suture closure, associated with a decrease in suture MPC proliferation and increased mineralization. In vitro, axons from peripheral afferent neurons derived from dorsal root ganglions (DRGs) of wild-type mice induce MPC proliferation in a spatially restricted manner via a soluble factor when cocultured in microfluidic chambers. Comparative spatial transcriptomic analysis of the cranial sutures in vivo confirmed a positive association between sensory axons and proliferative MPCs. SpatialTime analysis across the developing suture revealed regional-specific alterations in bone morphogenetic protein (BMP) and TGF-β signaling pathway transcripts in response to TrkA inhibition. RNA sequencing of DRG cell bodies, following direct, axonal coculture with MPCs, confirmed the alterations in BMP/TGF-β signaling pathway transcripts. Among these, the BMP inhibitor follistatin-like 1 (FSTL1) replicated key features of the neural-to-bone influence, including mitogenic and anti-osteogenic effects via the inhibition of BMP/TGF-β signaling. Taken together, our results demonstrate that sensory nerve-derived signals, including FSTL1, function to coordinate cranial bone patterning by regulating MPC proliferation and differentiation in the suture mesenchyme.","TrkA; calvarial bone; cranial suture; skeletal innervation; spatial transcriptomics.","False","Visium","423","31053"
"GSE174313_GSM5291684","mouse","brain","34663698","Spatial transcriptomics reveals a role for sensory nerves in preserving cranial suture patency through modulation of BMP/TGF-β signaling","The patterning and ossification of the mammalian skeleton requires the coordinated actions of both intrinsic bone morphogens and extrinsic neurovascular signals, which function in a temporal and spatial fashion to control mesenchymal progenitor cell (MPC) fate. Here, we show the genetic inhibition of tropomyosin receptor kinase A (TrkA) sensory nerve innervation of the developing cranium results in premature calvarial suture closure, associated with a decrease in suture MPC proliferation and increased mineralization. In vitro, axons from peripheral afferent neurons derived from dorsal root ganglions (DRGs) of wild-type mice induce MPC proliferation in a spatially restricted manner via a soluble factor when cocultured in microfluidic chambers. Comparative spatial transcriptomic analysis of the cranial sutures in vivo confirmed a positive association between sensory axons and proliferative MPCs. SpatialTime analysis across the developing suture revealed regional-specific alterations in bone morphogenetic protein (BMP) and TGF-β signaling pathway transcripts in response to TrkA inhibition. RNA sequencing of DRG cell bodies, following direct, axonal coculture with MPCs, confirmed the alterations in BMP/TGF-β signaling pathway transcripts. Among these, the BMP inhibitor follistatin-like 1 (FSTL1) replicated key features of the neural-to-bone influence, including mitogenic and anti-osteogenic effects via the inhibition of BMP/TGF-β signaling. Taken together, our results demonstrate that sensory nerve-derived signals, including FSTL1, function to coordinate cranial bone patterning by regulating MPC proliferation and differentiation in the suture mesenchyme.","TrkA; calvarial bone; cranial suture; skeletal innervation; spatial transcriptomics.","False","Visium","363","31053"
"GSE174313_GSM5291685","mouse","brain","34663698","Spatial transcriptomics reveals a role for sensory nerves in preserving cranial suture patency through modulation of BMP/TGF-β signaling","The patterning and ossification of the mammalian skeleton requires the coordinated actions of both intrinsic bone morphogens and extrinsic neurovascular signals, which function in a temporal and spatial fashion to control mesenchymal progenitor cell (MPC) fate. Here, we show the genetic inhibition of tropomyosin receptor kinase A (TrkA) sensory nerve innervation of the developing cranium results in premature calvarial suture closure, associated with a decrease in suture MPC proliferation and increased mineralization. In vitro, axons from peripheral afferent neurons derived from dorsal root ganglions (DRGs) of wild-type mice induce MPC proliferation in a spatially restricted manner via a soluble factor when cocultured in microfluidic chambers. Comparative spatial transcriptomic analysis of the cranial sutures in vivo confirmed a positive association between sensory axons and proliferative MPCs. SpatialTime analysis across the developing suture revealed regional-specific alterations in bone morphogenetic protein (BMP) and TGF-β signaling pathway transcripts in response to TrkA inhibition. RNA sequencing of DRG cell bodies, following direct, axonal coculture with MPCs, confirmed the alterations in BMP/TGF-β signaling pathway transcripts. Among these, the BMP inhibitor follistatin-like 1 (FSTL1) replicated key features of the neural-to-bone influence, including mitogenic and anti-osteogenic effects via the inhibition of BMP/TGF-β signaling. Taken together, our results demonstrate that sensory nerve-derived signals, including FSTL1, function to coordinate cranial bone patterning by regulating MPC proliferation and differentiation in the suture mesenchyme.","TrkA; calvarial bone; cranial suture; skeletal innervation; spatial transcriptomics.","False","Visium","560","31053"
"GSE174313_GSM5291686","mouse","brain","34663698","Spatial transcriptomics reveals a role for sensory nerves in preserving cranial suture patency through modulation of BMP/TGF-β signaling","The patterning and ossification of the mammalian skeleton requires the coordinated actions of both intrinsic bone morphogens and extrinsic neurovascular signals, which function in a temporal and spatial fashion to control mesenchymal progenitor cell (MPC) fate. Here, we show the genetic inhibition of tropomyosin receptor kinase A (TrkA) sensory nerve innervation of the developing cranium results in premature calvarial suture closure, associated with a decrease in suture MPC proliferation and increased mineralization. In vitro, axons from peripheral afferent neurons derived from dorsal root ganglions (DRGs) of wild-type mice induce MPC proliferation in a spatially restricted manner via a soluble factor when cocultured in microfluidic chambers. Comparative spatial transcriptomic analysis of the cranial sutures in vivo confirmed a positive association between sensory axons and proliferative MPCs. SpatialTime analysis across the developing suture revealed regional-specific alterations in bone morphogenetic protein (BMP) and TGF-β signaling pathway transcripts in response to TrkA inhibition. RNA sequencing of DRG cell bodies, following direct, axonal coculture with MPCs, confirmed the alterations in BMP/TGF-β signaling pathway transcripts. Among these, the BMP inhibitor follistatin-like 1 (FSTL1) replicated key features of the neural-to-bone influence, including mitogenic and anti-osteogenic effects via the inhibition of BMP/TGF-β signaling. Taken together, our results demonstrate that sensory nerve-derived signals, including FSTL1, function to coordinate cranial bone patterning by regulating MPC proliferation and differentiation in the suture mesenchyme.","TrkA; calvarial bone; cranial suture; skeletal innervation; spatial transcriptomics.","False","Visium","324","31053"
"GSE175540_GSM5924030","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","4510","17943"
"GSE175540_GSM5924031","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","4755","17943"
"GSE175540_GSM5924032","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","3829","17943"
"GSE175540_GSM5924033","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","4975","17943"
"GSE175540_GSM5924034","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","4860","17943"
"GSE175540_GSM5924035","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","4948","17943"
"GSE175540_GSM5924036","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","4915","17943"
"GSE175540_GSM5924037","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","3585","17943"
"GSE175540_GSM5924038","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","3206","17943"
"GSE175540_GSM5924039","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","4940","17943"
"GSE175540_GSM5924040","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","4562","17943"
"GSE175540_GSM5924041","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","4359","17943"
"GSE175540_GSM5924042","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","1451","36601"
"GSE175540_GSM5924043","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","1439","36601"
"GSE175540_GSM5924044","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","1983","36601"
"GSE175540_GSM5924045","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","1696","36601"
"GSE175540_GSM5924046","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","1949","36601"
"GSE175540_GSM5924047","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","2374","36601"
"GSE175540_GSM5924048","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","1678","36601"
"GSE175540_GSM5924049","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","1186","36601"
"GSE175540_GSM5924050","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","1349","36601"
"GSE175540_GSM5924051","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","2007","36601"
"GSE175540_GSM5924052","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","2580","36601"
"GSE175540_GSM5924053","human","kidney","35231421","Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer","The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.","B cell maturation; B cell repertoire; Visium; anti-tumor IgG; fibroblasts; plasma cells; renal cell cancer; response to immune check point inhibition; spatial transcriptomics; tertiary lymphoid structures; tumor microenvironment.","True","Visium","1084","36601"
"GSE178221_GSM5384657","human & mouse","mouse colon and human skin","35624112","SpotClean adjusts for spot swapping in spatial transcriptomics data","Spatial transcriptomics is a powerful and widely used approach for profiling the gene expression landscape across a tissue with emerging applications in molecular medicine and tumor diagnostics. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind RNA. Ideally, unique molecular identifiers (UMIs) at a spot measure spot-specific expression, but this is often not the case in practice due to bleed from nearby spots, an artifact we refer to as spot swapping. To improve the power and precision of downstream analyses in spatial transcriptomics experiments, we propose SpotClean, a probabilistic model that adjusts for spot swapping to provide more accurate estimates of gene-specific UMI counts. SpotClean provides substantial improvements in marker gene analyses and in clustering, especially when tissue regions are not easily separated. As demonstrated in multiple studies of cancer, SpotClean improves tumor versus normal tissue delineation and improves tumor burden estimation thus increasing the potential for clinical and diagnostic applications of spatial transcriptomics technologies.","","True","Visium","1326","68886"
"GSE178221_GSM5384658","human & mouse","mouse duodenum and human skin","35624112","SpotClean adjusts for spot swapping in spatial transcriptomics data","Spatial transcriptomics is a powerful and widely used approach for profiling the gene expression landscape across a tissue with emerging applications in molecular medicine and tumor diagnostics. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind RNA. Ideally, unique molecular identifiers (UMIs) at a spot measure spot-specific expression, but this is often not the case in practice due to bleed from nearby spots, an artifact we refer to as spot swapping. To improve the power and precision of downstream analyses in spatial transcriptomics experiments, we propose SpotClean, a probabilistic model that adjusts for spot swapping to provide more accurate estimates of gene-specific UMI counts. SpotClean provides substantial improvements in marker gene analyses and in clustering, especially when tissue regions are not easily separated. As demonstrated in multiple studies of cancer, SpotClean improves tumor versus normal tissue delineation and improves tumor burden estimation thus increasing the potential for clinical and diagnostic applications of spatial transcriptomics technologies.","","True","Visium","2030","68886"
"GSE178221_GSM5384659","human & mouse","mouse heart and human skin","35624112","SpotClean adjusts for spot swapping in spatial transcriptomics data","Spatial transcriptomics is a powerful and widely used approach for profiling the gene expression landscape across a tissue with emerging applications in molecular medicine and tumor diagnostics. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind RNA. Ideally, unique molecular identifiers (UMIs) at a spot measure spot-specific expression, but this is often not the case in practice due to bleed from nearby spots, an artifact we refer to as spot swapping. To improve the power and precision of downstream analyses in spatial transcriptomics experiments, we propose SpotClean, a probabilistic model that adjusts for spot swapping to provide more accurate estimates of gene-specific UMI counts. SpotClean provides substantial improvements in marker gene analyses and in clustering, especially when tissue regions are not easily separated. As demonstrated in multiple studies of cancer, SpotClean improves tumor versus normal tissue delineation and improves tumor burden estimation thus increasing the potential for clinical and diagnostic applications of spatial transcriptomics technologies.","","True","Visium","1900","68886"
"GSE178361_GSM5388414","human","lung","35355018","Human distal lung maps and lineage hierarchies reveal a bipotent progenitor","Mapping the spatial distribution and molecular identity of constituent cells is essential for understanding tissue dynamics in health and disease. We lack a comprehensive map of human distal airways, including the terminal and respiratory bronchioles (TRBs), which are implicated in respiratory diseases1-4. Here, using spatial transcriptomics and single-cell profiling of microdissected distal airways, we identify molecularly distinct TRB cell types that have not-to our knowledge-been previously characterized. These include airway-associated LGR5+ fibroblasts and TRB-specific alveolar type-0 (AT0) cells and TRB secretory cells (TRB-SCs). Connectome maps and organoid-based co-cultures reveal that LGR5+ fibroblasts form a signalling hub in the airway niche. AT0 cells and TRB-SCs are conserved in primates and emerge dynamically during human lung development. Using a non-human primate model of lung injury, together with human organoids and tissue specimens, we show that alveolar type-2 cells in regenerating lungs transiently acquire an AT0 state from which they can differentiate into either alveolar type-1 cells or TRB-SCs. This differentiation programme is distinct from that identified in the mouse lung5-7. Our study also reveals mechanisms that drive the differentiation of the bipotent AT0 cell state into normal or pathological states. In sum, our findings revise human lung cell maps and lineage trajectories, and implicate an epithelial transitional state in primate lung regeneration and disease.","","False","Visium","1175","36601"
"GSE178361_GSM5388415","human","lung","35355018","Human distal lung maps and lineage hierarchies reveal a bipotent progenitor","Mapping the spatial distribution and molecular identity of constituent cells is essential for understanding tissue dynamics in health and disease. We lack a comprehensive map of human distal airways, including the terminal and respiratory bronchioles (TRBs), which are implicated in respiratory diseases1-4. Here, using spatial transcriptomics and single-cell profiling of microdissected distal airways, we identify molecularly distinct TRB cell types that have not-to our knowledge-been previously characterized. These include airway-associated LGR5+ fibroblasts and TRB-specific alveolar type-0 (AT0) cells and TRB secretory cells (TRB-SCs). Connectome maps and organoid-based co-cultures reveal that LGR5+ fibroblasts form a signalling hub in the airway niche. AT0 cells and TRB-SCs are conserved in primates and emerge dynamically during human lung development. Using a non-human primate model of lung injury, together with human organoids and tissue specimens, we show that alveolar type-2 cells in regenerating lungs transiently acquire an AT0 state from which they can differentiate into either alveolar type-1 cells or TRB-SCs. This differentiation programme is distinct from that identified in the mouse lung5-7. Our study also reveals mechanisms that drive the differentiation of the bipotent AT0 cell state into normal or pathological states. In sum, our findings revise human lung cell maps and lineage trajectories, and implicate an epithelial transitional state in primate lung regeneration and disease.","","False","Visium","1045","36601"
"GSE178758_GSM6443210","mouse","skin","34620713","Integrated spatial multiomics reveals fibroblast fate during tissue repair","In the skin, tissue injury results in fibrosis in the form of scars composed of dense extracellular matrix deposited by fibroblasts. The therapeutic goal of regenerative wound healing has remained elusive, in part because principles of fibroblast programming and adaptive response to injury remain incompletely understood. Here, we present a multimodal -omics platform for the comprehensive study of cell populations in complex tissue, which has allowed us to characterize the cells involved in wound healing across both time and space. We employ a stented wound model that recapitulates human tissue repair kinetics and multiple Rainbow transgenic lines to precisely track fibroblast fate during the physiologic response to skin injury. Through integrated analysis of single cell chromatin landscapes and gene expression states, coupled with spatial transcriptomic profiling, we are able to impute fibroblast epigenomes with temporospatial resolution. This has allowed us to reveal potential mechanisms controlling fibroblast fate during migration, proliferation, and differentiation following skin injury, and thereby reexamine the canonical phases of wound healing. These findings have broad implications for the study of tissue repair in complex organ systems.","chromatin accessibility; fibrosis; multiomics; spatial epigenomics; spatial transcriptomics.","False","Visium","949","32285"
"GSE178758_GSM6443211","mouse","skin","34620713","Integrated spatial multiomics reveals fibroblast fate during tissue repair","In the skin, tissue injury results in fibrosis in the form of scars composed of dense extracellular matrix deposited by fibroblasts. The therapeutic goal of regenerative wound healing has remained elusive, in part because principles of fibroblast programming and adaptive response to injury remain incompletely understood. Here, we present a multimodal -omics platform for the comprehensive study of cell populations in complex tissue, which has allowed us to characterize the cells involved in wound healing across both time and space. We employ a stented wound model that recapitulates human tissue repair kinetics and multiple Rainbow transgenic lines to precisely track fibroblast fate during the physiologic response to skin injury. Through integrated analysis of single cell chromatin landscapes and gene expression states, coupled with spatial transcriptomic profiling, we are able to impute fibroblast epigenomes with temporospatial resolution. This has allowed us to reveal potential mechanisms controlling fibroblast fate during migration, proliferation, and differentiation following skin injury, and thereby reexamine the canonical phases of wound healing. These findings have broad implications for the study of tissue repair in complex organ systems.","chromatin accessibility; fibrosis; multiomics; spatial epigenomics; spatial transcriptomics.","False","Visium","1088","32285"
"GSE178758_GSM6443212","mouse","skin","34620713","Integrated spatial multiomics reveals fibroblast fate during tissue repair","In the skin, tissue injury results in fibrosis in the form of scars composed of dense extracellular matrix deposited by fibroblasts. The therapeutic goal of regenerative wound healing has remained elusive, in part because principles of fibroblast programming and adaptive response to injury remain incompletely understood. Here, we present a multimodal -omics platform for the comprehensive study of cell populations in complex tissue, which has allowed us to characterize the cells involved in wound healing across both time and space. We employ a stented wound model that recapitulates human tissue repair kinetics and multiple Rainbow transgenic lines to precisely track fibroblast fate during the physiologic response to skin injury. Through integrated analysis of single cell chromatin landscapes and gene expression states, coupled with spatial transcriptomic profiling, we are able to impute fibroblast epigenomes with temporospatial resolution. This has allowed us to reveal potential mechanisms controlling fibroblast fate during migration, proliferation, and differentiation following skin injury, and thereby reexamine the canonical phases of wound healing. These findings have broad implications for the study of tissue repair in complex organ systems.","chromatin accessibility; fibrosis; multiomics; spatial epigenomics; spatial transcriptomics.","False","Visium","1671","32285"
"GSE178758_GSM6443213","mouse","skin","34620713","Integrated spatial multiomics reveals fibroblast fate during tissue repair","In the skin, tissue injury results in fibrosis in the form of scars composed of dense extracellular matrix deposited by fibroblasts. The therapeutic goal of regenerative wound healing has remained elusive, in part because principles of fibroblast programming and adaptive response to injury remain incompletely understood. Here, we present a multimodal -omics platform for the comprehensive study of cell populations in complex tissue, which has allowed us to characterize the cells involved in wound healing across both time and space. We employ a stented wound model that recapitulates human tissue repair kinetics and multiple Rainbow transgenic lines to precisely track fibroblast fate during the physiologic response to skin injury. Through integrated analysis of single cell chromatin landscapes and gene expression states, coupled with spatial transcriptomic profiling, we are able to impute fibroblast epigenomes with temporospatial resolution. This has allowed us to reveal potential mechanisms controlling fibroblast fate during migration, proliferation, and differentiation following skin injury, and thereby reexamine the canonical phases of wound healing. These findings have broad implications for the study of tissue repair in complex organ systems.","chromatin accessibility; fibrosis; multiomics; spatial epigenomics; spatial transcriptomics.","False","Visium","720","32285"
"GSE179572_GSM5420749","human","brain","35584630,35707680","Title 1: Distinct phenotypic states and spatial distribution of CD8+ T cell clonotypes in human brain metastases. Title 2: Localization of T cell clonotypes using the Visium spatial transcriptomics platform.","Abstract 1: Metastatic disease in the brain is difficult to control and predicts poor prognosis. Here, we analyze human brain metastases and demonstrate their robust infiltration by CD8+ T cell subsets with distinct antigen specificities, phenotypic states, and spatial localization within the tumor microenvironment. Brain metastases are densely infiltrated by T cells; the majority of infiltrating CD8+ T cells express PD-1. Single-cell RNA sequencing shows significant clonal overlap between proliferating and exhausted CD8+ T cells, but these subsets have minimal clonal overlap with circulating and other tumor-infiltrating CD8+ T cells, including bystander CD8+ T cells specific for microbial antigens. Using spatial transcriptomics and spatial T cell receptor (TCR) sequencing, we show these clonally unrelated, phenotypically distinct CD8+ T cell populations occupy discrete niches within the brain metastasis tumor microenvironment. Together, our work identifies signaling pathways within CD8+ T cells and in their surrounding environment that may be targeted for immunotherapy of brain metastases. Abstract 2: We present a protocol to localize T cell receptor clones using the Visium spatial transcriptomics platform. This approach permits simultaneous localization of both gene expression and T cell clonotypes in situ within tissue sections. T cell receptor sequences identified by this protocol are readily recapitulated by single-cell sequencing. This technique enables detailed studies of the spatial organization of the human T cell repertoire, such as the localization of infiltrating T cell clones within the tumor microenvironment. For complete details on the use and execution of this protocol, please refer to Sudmeier et al. (2022).","Keywords 1: CD8(+) T cells; TCR-sequencing; brain metastases; bystander; exhaustion; spatial transcriptomics. Keywords 2: Immunology; Molecular Biology; Sequence analysis.","True","Visium","2112","36601"
"GSE179572_GSM5420750","human","brain","35584630,35707680","Title 1: Distinct phenotypic states and spatial distribution of CD8+ T cell clonotypes in human brain metastases. Title 2: Localization of T cell clonotypes using the Visium spatial transcriptomics platform.","Abstract 1: Metastatic disease in the brain is difficult to control and predicts poor prognosis. Here, we analyze human brain metastases and demonstrate their robust infiltration by CD8+ T cell subsets with distinct antigen specificities, phenotypic states, and spatial localization within the tumor microenvironment. Brain metastases are densely infiltrated by T cells; the majority of infiltrating CD8+ T cells express PD-1. Single-cell RNA sequencing shows significant clonal overlap between proliferating and exhausted CD8+ T cells, but these subsets have minimal clonal overlap with circulating and other tumor-infiltrating CD8+ T cells, including bystander CD8+ T cells specific for microbial antigens. Using spatial transcriptomics and spatial T cell receptor (TCR) sequencing, we show these clonally unrelated, phenotypically distinct CD8+ T cell populations occupy discrete niches within the brain metastasis tumor microenvironment. Together, our work identifies signaling pathways within CD8+ T cells and in their surrounding environment that may be targeted for immunotherapy of brain metastases. Abstract 2: We present a protocol to localize T cell receptor clones using the Visium spatial transcriptomics platform. This approach permits simultaneous localization of both gene expression and T cell clonotypes in situ within tissue sections. T cell receptor sequences identified by this protocol are readily recapitulated by single-cell sequencing. This technique enables detailed studies of the spatial organization of the human T cell repertoire, such as the localization of infiltrating T cell clones within the tumor microenvironment. For complete details on the use and execution of this protocol, please refer to Sudmeier et al. (2022).","Keywords 1: CD8(+) T cells; TCR-sequencing; brain metastases; bystander; exhaustion; spatial transcriptomics. Keywords 2: Immunology; Molecular Biology; Sequence analysis.","True","Visium","1716","36601"
"GSE179572_GSM5420751","human","brain","35584630,35707680","Title 1: Distinct phenotypic states and spatial distribution of CD8+ T cell clonotypes in human brain metastases. Title 2: Localization of T cell clonotypes using the Visium spatial transcriptomics platform.","Abstract 1: Metastatic disease in the brain is difficult to control and predicts poor prognosis. Here, we analyze human brain metastases and demonstrate their robust infiltration by CD8+ T cell subsets with distinct antigen specificities, phenotypic states, and spatial localization within the tumor microenvironment. Brain metastases are densely infiltrated by T cells; the majority of infiltrating CD8+ T cells express PD-1. Single-cell RNA sequencing shows significant clonal overlap between proliferating and exhausted CD8+ T cells, but these subsets have minimal clonal overlap with circulating and other tumor-infiltrating CD8+ T cells, including bystander CD8+ T cells specific for microbial antigens. Using spatial transcriptomics and spatial T cell receptor (TCR) sequencing, we show these clonally unrelated, phenotypically distinct CD8+ T cell populations occupy discrete niches within the brain metastasis tumor microenvironment. Together, our work identifies signaling pathways within CD8+ T cells and in their surrounding environment that may be targeted for immunotherapy of brain metastases. Abstract 2: We present a protocol to localize T cell receptor clones using the Visium spatial transcriptomics platform. This approach permits simultaneous localization of both gene expression and T cell clonotypes in situ within tissue sections. T cell receptor sequences identified by this protocol are readily recapitulated by single-cell sequencing. This technique enables detailed studies of the spatial organization of the human T cell repertoire, such as the localization of infiltrating T cell clones within the tumor microenvironment. For complete details on the use and execution of this protocol, please refer to Sudmeier et al. (2022).","Keywords 1: CD8(+) T cells; TCR-sequencing; brain metastases; bystander; exhaustion; spatial transcriptomics. Keywords 2: Immunology; Molecular Biology; Sequence analysis.","True","Visium","946","36601"
"GSE179572_GSM5420752","human","brain","35584630,35707680","Title 1: Distinct phenotypic states and spatial distribution of CD8+ T cell clonotypes in human brain metastases. Title 2: Localization of T cell clonotypes using the Visium spatial transcriptomics platform.","Abstract 1: Metastatic disease in the brain is difficult to control and predicts poor prognosis. Here, we analyze human brain metastases and demonstrate their robust infiltration by CD8+ T cell subsets with distinct antigen specificities, phenotypic states, and spatial localization within the tumor microenvironment. Brain metastases are densely infiltrated by T cells; the majority of infiltrating CD8+ T cells express PD-1. Single-cell RNA sequencing shows significant clonal overlap between proliferating and exhausted CD8+ T cells, but these subsets have minimal clonal overlap with circulating and other tumor-infiltrating CD8+ T cells, including bystander CD8+ T cells specific for microbial antigens. Using spatial transcriptomics and spatial T cell receptor (TCR) sequencing, we show these clonally unrelated, phenotypically distinct CD8+ T cell populations occupy discrete niches within the brain metastasis tumor microenvironment. Together, our work identifies signaling pathways within CD8+ T cells and in their surrounding environment that may be targeted for immunotherapy of brain metastases. Abstract 2: We present a protocol to localize T cell receptor clones using the Visium spatial transcriptomics platform. This approach permits simultaneous localization of both gene expression and T cell clonotypes in situ within tissue sections. T cell receptor sequences identified by this protocol are readily recapitulated by single-cell sequencing. This technique enables detailed studies of the spatial organization of the human T cell repertoire, such as the localization of infiltrating T cell clones within the tumor microenvironment. For complete details on the use and execution of this protocol, please refer to Sudmeier et al. (2022).","Keywords 1: CD8(+) T cells; TCR-sequencing; brain metastases; bystander; exhaustion; spatial transcriptomics. Keywords 2: Immunology; Molecular Biology; Sequence analysis.","True","Visium","1298","36601"
"GSE179572_GSM5420753","human","brain","35584630,35707680","Title 1: Distinct phenotypic states and spatial distribution of CD8+ T cell clonotypes in human brain metastases. Title 2: Localization of T cell clonotypes using the Visium spatial transcriptomics platform.","Abstract 1: Metastatic disease in the brain is difficult to control and predicts poor prognosis. Here, we analyze human brain metastases and demonstrate their robust infiltration by CD8+ T cell subsets with distinct antigen specificities, phenotypic states, and spatial localization within the tumor microenvironment. Brain metastases are densely infiltrated by T cells; the majority of infiltrating CD8+ T cells express PD-1. Single-cell RNA sequencing shows significant clonal overlap between proliferating and exhausted CD8+ T cells, but these subsets have minimal clonal overlap with circulating and other tumor-infiltrating CD8+ T cells, including bystander CD8+ T cells specific for microbial antigens. Using spatial transcriptomics and spatial T cell receptor (TCR) sequencing, we show these clonally unrelated, phenotypically distinct CD8+ T cell populations occupy discrete niches within the brain metastasis tumor microenvironment. Together, our work identifies signaling pathways within CD8+ T cells and in their surrounding environment that may be targeted for immunotherapy of brain metastases. Abstract 2: We present a protocol to localize T cell receptor clones using the Visium spatial transcriptomics platform. This approach permits simultaneous localization of both gene expression and T cell clonotypes in situ within tissue sections. T cell receptor sequences identified by this protocol are readily recapitulated by single-cell sequencing. This technique enables detailed studies of the spatial organization of the human T cell repertoire, such as the localization of infiltrating T cell clones within the tumor microenvironment. For complete details on the use and execution of this protocol, please refer to Sudmeier et al. (2022).","Keywords 1: CD8(+) T cells; TCR-sequencing; brain metastases; bystander; exhaustion; spatial transcriptomics. Keywords 2: Immunology; Molecular Biology; Sequence analysis.","True","Visium","4342","36601"
"GSE179572_GSM5420754","human","brain","35584630,35707680","Title 1: Distinct phenotypic states and spatial distribution of CD8+ T cell clonotypes in human brain metastases. Title 2: Localization of T cell clonotypes using the Visium spatial transcriptomics platform.","Abstract 1: Metastatic disease in the brain is difficult to control and predicts poor prognosis. Here, we analyze human brain metastases and demonstrate their robust infiltration by CD8+ T cell subsets with distinct antigen specificities, phenotypic states, and spatial localization within the tumor microenvironment. Brain metastases are densely infiltrated by T cells; the majority of infiltrating CD8+ T cells express PD-1. Single-cell RNA sequencing shows significant clonal overlap between proliferating and exhausted CD8+ T cells, but these subsets have minimal clonal overlap with circulating and other tumor-infiltrating CD8+ T cells, including bystander CD8+ T cells specific for microbial antigens. Using spatial transcriptomics and spatial T cell receptor (TCR) sequencing, we show these clonally unrelated, phenotypically distinct CD8+ T cell populations occupy discrete niches within the brain metastasis tumor microenvironment. Together, our work identifies signaling pathways within CD8+ T cells and in their surrounding environment that may be targeted for immunotherapy of brain metastases. Abstract 2: We present a protocol to localize T cell receptor clones using the Visium spatial transcriptomics platform. This approach permits simultaneous localization of both gene expression and T cell clonotypes in situ within tissue sections. T cell receptor sequences identified by this protocol are readily recapitulated by single-cell sequencing. This technique enables detailed studies of the spatial organization of the human T cell repertoire, such as the localization of infiltrating T cell clones within the tumor microenvironment. For complete details on the use and execution of this protocol, please refer to Sudmeier et al. (2022).","Keywords 1: CD8(+) T cells; TCR-sequencing; brain metastases; bystander; exhaustion; spatial transcriptomics. Keywords 2: Immunology; Molecular Biology; Sequence analysis.","True","Visium","2152","36601"
"GSE180682_GSM5467944","mouse","digit","35616636","Spatial transcriptomics reveals metabolic changes underly age-dependent declines in digit regeneration","De novo limb regeneration after amputation is restricted in mammals to the distal digit tip. Central to this regenerative process is the blastema, a heterogeneous population of lineage-restricted, dedifferentiated cells that ultimately orchestrates regeneration of the amputated bone and surrounding soft tissue. To investigate skeletal regeneration, we made use of spatial transcriptomics to characterize the transcriptional profile specifically within the blastema. Using this technique, we generated a gene signature with high specificity for the blastema in both our spatial data, as well as other previously published single-cell RNA-sequencing transcriptomic studies. To elucidate potential mechanisms distinguishing regenerative from non-regenerative healing, we applied spatial transcriptomics to an aging model. Consistent with other forms of repair, our digit amputation mouse model showed a significant impairment in regeneration in aged mice. Contrasting young and aged mice, spatial analysis revealed a metabolic shift in aged blastema associated with an increased bioenergetic requirement. This enhanced metabolic turnover was associated with increased hypoxia and angiogenic signaling, leading to excessive vascularization and altered regenerated bone architecture in aged mice. Administration of the metabolite oxaloacetate decreased the oxygen consumption rate of the aged blastema and increased WNT signaling, leading to enhanced in vivo bone regeneration. Thus, targeting cell metabolism may be a promising strategy to mitigate aging-induced declines in tissue regeneration.","aging; bone regeneration; cell biology; cell metabolism; digit regeneration; mouse; oxaloacetate; spatial transcriptomics.","False","Visium","1007","31053"
"GSE180682_GSM5467945","mouse","digit","35616636","Spatial transcriptomics reveals metabolic changes underly age-dependent declines in digit regeneration","De novo limb regeneration after amputation is restricted in mammals to the distal digit tip. Central to this regenerative process is the blastema, a heterogeneous population of lineage-restricted, dedifferentiated cells that ultimately orchestrates regeneration of the amputated bone and surrounding soft tissue. To investigate skeletal regeneration, we made use of spatial transcriptomics to characterize the transcriptional profile specifically within the blastema. Using this technique, we generated a gene signature with high specificity for the blastema in both our spatial data, as well as other previously published single-cell RNA-sequencing transcriptomic studies. To elucidate potential mechanisms distinguishing regenerative from non-regenerative healing, we applied spatial transcriptomics to an aging model. Consistent with other forms of repair, our digit amputation mouse model showed a significant impairment in regeneration in aged mice. Contrasting young and aged mice, spatial analysis revealed a metabolic shift in aged blastema associated with an increased bioenergetic requirement. This enhanced metabolic turnover was associated with increased hypoxia and angiogenic signaling, leading to excessive vascularization and altered regenerated bone architecture in aged mice. Administration of the metabolite oxaloacetate decreased the oxygen consumption rate of the aged blastema and increased WNT signaling, leading to enhanced in vivo bone regeneration. Thus, targeting cell metabolism may be a promising strategy to mitigate aging-induced declines in tissue regeneration.","aging; bone regeneration; cell biology; cell metabolism; digit regeneration; mouse; oxaloacetate; spatial transcriptomics.","False","Visium","1234","31053"
"GSE180682_GSM5467946","mouse","digit","35616636","Spatial transcriptomics reveals metabolic changes underly age-dependent declines in digit regeneration","De novo limb regeneration after amputation is restricted in mammals to the distal digit tip. Central to this regenerative process is the blastema, a heterogeneous population of lineage-restricted, dedifferentiated cells that ultimately orchestrates regeneration of the amputated bone and surrounding soft tissue. To investigate skeletal regeneration, we made use of spatial transcriptomics to characterize the transcriptional profile specifically within the blastema. Using this technique, we generated a gene signature with high specificity for the blastema in both our spatial data, as well as other previously published single-cell RNA-sequencing transcriptomic studies. To elucidate potential mechanisms distinguishing regenerative from non-regenerative healing, we applied spatial transcriptomics to an aging model. Consistent with other forms of repair, our digit amputation mouse model showed a significant impairment in regeneration in aged mice. Contrasting young and aged mice, spatial analysis revealed a metabolic shift in aged blastema associated with an increased bioenergetic requirement. This enhanced metabolic turnover was associated with increased hypoxia and angiogenic signaling, leading to excessive vascularization and altered regenerated bone architecture in aged mice. Administration of the metabolite oxaloacetate decreased the oxygen consumption rate of the aged blastema and increased WNT signaling, leading to enhanced in vivo bone regeneration. Thus, targeting cell metabolism may be a promising strategy to mitigate aging-induced declines in tissue regeneration.","aging; bone regeneration; cell biology; cell metabolism; digit regeneration; mouse; oxaloacetate; spatial transcriptomics.","False","Visium","993","31053"
"GSE180682_GSM5467947","mouse","digit","35616636","Spatial transcriptomics reveals metabolic changes underly age-dependent declines in digit regeneration","De novo limb regeneration after amputation is restricted in mammals to the distal digit tip. Central to this regenerative process is the blastema, a heterogeneous population of lineage-restricted, dedifferentiated cells that ultimately orchestrates regeneration of the amputated bone and surrounding soft tissue. To investigate skeletal regeneration, we made use of spatial transcriptomics to characterize the transcriptional profile specifically within the blastema. Using this technique, we generated a gene signature with high specificity for the blastema in both our spatial data, as well as other previously published single-cell RNA-sequencing transcriptomic studies. To elucidate potential mechanisms distinguishing regenerative from non-regenerative healing, we applied spatial transcriptomics to an aging model. Consistent with other forms of repair, our digit amputation mouse model showed a significant impairment in regeneration in aged mice. Contrasting young and aged mice, spatial analysis revealed a metabolic shift in aged blastema associated with an increased bioenergetic requirement. This enhanced metabolic turnover was associated with increased hypoxia and angiogenic signaling, leading to excessive vascularization and altered regenerated bone architecture in aged mice. Administration of the metabolite oxaloacetate decreased the oxygen consumption rate of the aged blastema and increased WNT signaling, leading to enhanced in vivo bone regeneration. Thus, targeting cell metabolism may be a promising strategy to mitigate aging-induced declines in tissue regeneration.","aging; bone regeneration; cell biology; cell metabolism; digit regeneration; mouse; oxaloacetate; spatial transcriptomics.","False","Visium","1304","31053"
"GSE180682_GSM5896868","mouse","digit","35616636","Spatial transcriptomics reveals metabolic changes underly age-dependent declines in digit regeneration","De novo limb regeneration after amputation is restricted in mammals to the distal digit tip. Central to this regenerative process is the blastema, a heterogeneous population of lineage-restricted, dedifferentiated cells that ultimately orchestrates regeneration of the amputated bone and surrounding soft tissue. To investigate skeletal regeneration, we made use of spatial transcriptomics to characterize the transcriptional profile specifically within the blastema. Using this technique, we generated a gene signature with high specificity for the blastema in both our spatial data, as well as other previously published single-cell RNA-sequencing transcriptomic studies. To elucidate potential mechanisms distinguishing regenerative from non-regenerative healing, we applied spatial transcriptomics to an aging model. Consistent with other forms of repair, our digit amputation mouse model showed a significant impairment in regeneration in aged mice. Contrasting young and aged mice, spatial analysis revealed a metabolic shift in aged blastema associated with an increased bioenergetic requirement. This enhanced metabolic turnover was associated with increased hypoxia and angiogenic signaling, leading to excessive vascularization and altered regenerated bone architecture in aged mice. Administration of the metabolite oxaloacetate decreased the oxygen consumption rate of the aged blastema and increased WNT signaling, leading to enhanced in vivo bone regeneration. Thus, targeting cell metabolism may be a promising strategy to mitigate aging-induced declines in tissue regeneration.","aging; bone regeneration; cell biology; cell metabolism; digit regeneration; mouse; oxaloacetate; spatial transcriptomics.","False","Visium","1538","32285"
"GSE180682_GSM5896869","mouse","digit","35616636","Spatial transcriptomics reveals metabolic changes underly age-dependent declines in digit regeneration","De novo limb regeneration after amputation is restricted in mammals to the distal digit tip. Central to this regenerative process is the blastema, a heterogeneous population of lineage-restricted, dedifferentiated cells that ultimately orchestrates regeneration of the amputated bone and surrounding soft tissue. To investigate skeletal regeneration, we made use of spatial transcriptomics to characterize the transcriptional profile specifically within the blastema. Using this technique, we generated a gene signature with high specificity for the blastema in both our spatial data, as well as other previously published single-cell RNA-sequencing transcriptomic studies. To elucidate potential mechanisms distinguishing regenerative from non-regenerative healing, we applied spatial transcriptomics to an aging model. Consistent with other forms of repair, our digit amputation mouse model showed a significant impairment in regeneration in aged mice. Contrasting young and aged mice, spatial analysis revealed a metabolic shift in aged blastema associated with an increased bioenergetic requirement. This enhanced metabolic turnover was associated with increased hypoxia and angiogenic signaling, leading to excessive vascularization and altered regenerated bone architecture in aged mice. Administration of the metabolite oxaloacetate decreased the oxygen consumption rate of the aged blastema and increased WNT signaling, leading to enhanced in vivo bone regeneration. Thus, targeting cell metabolism may be a promising strategy to mitigate aging-induced declines in tissue regeneration.","aging; bone regeneration; cell biology; cell metabolism; digit regeneration; mouse; oxaloacetate; spatial transcriptomics.","False","Visium","1541","32285"
"GSE180682_GSM5896870","mouse","digit","35616636","Spatial transcriptomics reveals metabolic changes underly age-dependent declines in digit regeneration","De novo limb regeneration after amputation is restricted in mammals to the distal digit tip. Central to this regenerative process is the blastema, a heterogeneous population of lineage-restricted, dedifferentiated cells that ultimately orchestrates regeneration of the amputated bone and surrounding soft tissue. To investigate skeletal regeneration, we made use of spatial transcriptomics to characterize the transcriptional profile specifically within the blastema. Using this technique, we generated a gene signature with high specificity for the blastema in both our spatial data, as well as other previously published single-cell RNA-sequencing transcriptomic studies. To elucidate potential mechanisms distinguishing regenerative from non-regenerative healing, we applied spatial transcriptomics to an aging model. Consistent with other forms of repair, our digit amputation mouse model showed a significant impairment in regeneration in aged mice. Contrasting young and aged mice, spatial analysis revealed a metabolic shift in aged blastema associated with an increased bioenergetic requirement. This enhanced metabolic turnover was associated with increased hypoxia and angiogenic signaling, leading to excessive vascularization and altered regenerated bone architecture in aged mice. Administration of the metabolite oxaloacetate decreased the oxygen consumption rate of the aged blastema and increased WNT signaling, leading to enhanced in vivo bone regeneration. Thus, targeting cell metabolism may be a promising strategy to mitigate aging-induced declines in tissue regeneration.","aging; bone regeneration; cell biology; cell metabolism; digit regeneration; mouse; oxaloacetate; spatial transcriptomics.","False","Visium","1314","32285"
"GSE180682_GSM5896871","mouse","digit","35616636","Spatial transcriptomics reveals metabolic changes underly age-dependent declines in digit regeneration","De novo limb regeneration after amputation is restricted in mammals to the distal digit tip. Central to this regenerative process is the blastema, a heterogeneous population of lineage-restricted, dedifferentiated cells that ultimately orchestrates regeneration of the amputated bone and surrounding soft tissue. To investigate skeletal regeneration, we made use of spatial transcriptomics to characterize the transcriptional profile specifically within the blastema. Using this technique, we generated a gene signature with high specificity for the blastema in both our spatial data, as well as other previously published single-cell RNA-sequencing transcriptomic studies. To elucidate potential mechanisms distinguishing regenerative from non-regenerative healing, we applied spatial transcriptomics to an aging model. Consistent with other forms of repair, our digit amputation mouse model showed a significant impairment in regeneration in aged mice. Contrasting young and aged mice, spatial analysis revealed a metabolic shift in aged blastema associated with an increased bioenergetic requirement. This enhanced metabolic turnover was associated with increased hypoxia and angiogenic signaling, leading to excessive vascularization and altered regenerated bone architecture in aged mice. Administration of the metabolite oxaloacetate decreased the oxygen consumption rate of the aged blastema and increased WNT signaling, leading to enhanced in vivo bone regeneration. Thus, targeting cell metabolism may be a promising strategy to mitigate aging-induced declines in tissue regeneration.","aging; bone regeneration; cell biology; cell metabolism; digit regeneration; mouse; oxaloacetate; spatial transcriptomics.","False","Visium","1137","32285"
"GSE182208_GSM5531131","human","skin","35192691","Endothelial Phospholipase Cγ2 Improves Outcomes of Diabetic Ischemic Limb Rescue Following VEGF Therapy","Therapeutic vascular endothelial growth factor (VEGF) replenishment has met with limited success for the management of critical limb-threatening ischemia. To improve outcomes of VEGF therapy, we applied single-cell RNA sequencing (scRNA-seq) technology to study the endothelial cells of the human diabetic skin. Single-cell suspensions were generated from the human skin followed by cDNA preparation using the Chromium Next GEM Single-cell 3' Kit v3.1. Using appropriate quality control measures, 36,487 cells were chosen for downstream analysis. scRNA-seq studies identified that although VEGF signaling was not significantly altered in diabetic versus nondiabetic skin, phospholipase Cγ2 (PLCγ2) was downregulated. The significance of PLCγ2 in VEGF-mediated increase in endothelial cell metabolism and function was assessed in cultured human microvascular endothelial cells. In these cells, VEGF enhanced mitochondrial function, as indicated by elevation in oxygen consumption rate and extracellular acidification rate. The VEGF-dependent increase in cell metabolism was blunted in response to PLCγ2 inhibition. Follow-up rescue studies therefore focused on understanding the significance of VEGF therapy in presence or absence of endothelial PLCγ2 in type 1 (streptozotocin-injected) and type 2 (db/db) diabetic ischemic tissue. Nonviral topical tissue nanotransfection technology (TNT) delivery of CDH5 promoter-driven PLCγ2 open reading frame promoted the rescue of hindlimb ischemia in diabetic mice. Improvement of blood flow was also associated with higher abundance of VWF+/CD31+ and VWF+/SMA+ immunohistochemical staining. TNT-based gene delivery was not associated with tissue edema, a commonly noted complication associated with proangiogenic gene therapies. Taken together, our study demonstrates that TNT-mediated delivery of endothelial PLCγ2, as part of combination gene therapy, is effective in diabetic ischemic limb rescue.","","False","Visium","2603","33538"
"GSE182939_GSM5543479","mouse","kidney","34853151","Spatially Resolved Transcriptomic Analysis of Acute Kidney Injury in a Female Murine Model","Background: Single-cell sequencing technologies have advanced our understanding of kidney biology and disease, but the loss of spatial information in these datasets hinders our interpretation of intercellular communication networks and regional gene expression patterns. New spatial transcriptomic sequencing platforms make it possible to measure the topography of gene expression at genome depth.

Methods: We optimized and validated a female bilateral ischemia-reperfusion injury model. Using the 10× Genomics Visium Spatial Gene Expression solution, we generated spatial maps of gene expression across the injury and repair time course, and applied two open-source computational tools, Giotto and SPOTlight, to increase resolution and measure cell-cell interaction dynamics.

Results: An ischemia time of 34 minutes in a female murine model resulted in comparable injury to 22 minutes for males. We report a total of 16,856 unique genes mapped across our injury and repair time course. Giotto, a computational toolbox for spatial data analysis, enabled increased resolution mapping of genes and cell types. Using a seeded nonnegative matrix regression (SPOTlight) to deconvolute the dynamic landscape of cell-cell interactions, we found that injured proximal tubule cells were characterized by increasing macrophage and lymphocyte interactions even 6 weeks after injury, potentially reflecting the AKI to CKD transition.

Conclusions: In this transcriptomic atlas, we defined region-specific and injury-induced loss of differentiation markers and their re-expression during repair, as well as region-specific injury and repair transcriptional responses. Lastly, we created an interactive data visualization application for the scientific community to explore these results (http://humphreyslab.com/SingleCell/).","AKI; spatial; transcriptomics.","False","Visium","1617","32285"
"GSE182939_GSM5543480","mouse","kidney","34853151","Spatially Resolved Transcriptomic Analysis of Acute Kidney Injury in a Female Murine Model","Background: Single-cell sequencing technologies have advanced our understanding of kidney biology and disease, but the loss of spatial information in these datasets hinders our interpretation of intercellular communication networks and regional gene expression patterns. New spatial transcriptomic sequencing platforms make it possible to measure the topography of gene expression at genome depth.

Methods: We optimized and validated a female bilateral ischemia-reperfusion injury model. Using the 10× Genomics Visium Spatial Gene Expression solution, we generated spatial maps of gene expression across the injury and repair time course, and applied two open-source computational tools, Giotto and SPOTlight, to increase resolution and measure cell-cell interaction dynamics.

Results: An ischemia time of 34 minutes in a female murine model resulted in comparable injury to 22 minutes for males. We report a total of 16,856 unique genes mapped across our injury and repair time course. Giotto, a computational toolbox for spatial data analysis, enabled increased resolution mapping of genes and cell types. Using a seeded nonnegative matrix regression (SPOTlight) to deconvolute the dynamic landscape of cell-cell interactions, we found that injured proximal tubule cells were characterized by increasing macrophage and lymphocyte interactions even 6 weeks after injury, potentially reflecting the AKI to CKD transition.

Conclusions: In this transcriptomic atlas, we defined region-specific and injury-induced loss of differentiation markers and their re-expression during repair, as well as region-specific injury and repair transcriptional responses. Lastly, we created an interactive data visualization application for the scientific community to explore these results (http://humphreyslab.com/SingleCell/).","AKI; spatial; transcriptomics.","False","Visium","2219","32285"
"GSE182939_GSM5543481","mouse","kidney","34853151","Spatially Resolved Transcriptomic Analysis of Acute Kidney Injury in a Female Murine Model","Background: Single-cell sequencing technologies have advanced our understanding of kidney biology and disease, but the loss of spatial information in these datasets hinders our interpretation of intercellular communication networks and regional gene expression patterns. New spatial transcriptomic sequencing platforms make it possible to measure the topography of gene expression at genome depth.

Methods: We optimized and validated a female bilateral ischemia-reperfusion injury model. Using the 10× Genomics Visium Spatial Gene Expression solution, we generated spatial maps of gene expression across the injury and repair time course, and applied two open-source computational tools, Giotto and SPOTlight, to increase resolution and measure cell-cell interaction dynamics.

Results: An ischemia time of 34 minutes in a female murine model resulted in comparable injury to 22 minutes for males. We report a total of 16,856 unique genes mapped across our injury and repair time course. Giotto, a computational toolbox for spatial data analysis, enabled increased resolution mapping of genes and cell types. Using a seeded nonnegative matrix regression (SPOTlight) to deconvolute the dynamic landscape of cell-cell interactions, we found that injured proximal tubule cells were characterized by increasing macrophage and lymphocyte interactions even 6 weeks after injury, potentially reflecting the AKI to CKD transition.

Conclusions: In this transcriptomic atlas, we defined region-specific and injury-induced loss of differentiation markers and their re-expression during repair, as well as region-specific injury and repair transcriptional responses. Lastly, we created an interactive data visualization application for the scientific community to explore these results (http://humphreyslab.com/SingleCell/).","AKI; spatial; transcriptomics.","False","Visium","1893","32285"
"GSE182939_GSM5543482","mouse","kidney","34853151","Spatially Resolved Transcriptomic Analysis of Acute Kidney Injury in a Female Murine Model","Background: Single-cell sequencing technologies have advanced our understanding of kidney biology and disease, but the loss of spatial information in these datasets hinders our interpretation of intercellular communication networks and regional gene expression patterns. New spatial transcriptomic sequencing platforms make it possible to measure the topography of gene expression at genome depth.

Methods: We optimized and validated a female bilateral ischemia-reperfusion injury model. Using the 10× Genomics Visium Spatial Gene Expression solution, we generated spatial maps of gene expression across the injury and repair time course, and applied two open-source computational tools, Giotto and SPOTlight, to increase resolution and measure cell-cell interaction dynamics.

Results: An ischemia time of 34 minutes in a female murine model resulted in comparable injury to 22 minutes for males. We report a total of 16,856 unique genes mapped across our injury and repair time course. Giotto, a computational toolbox for spatial data analysis, enabled increased resolution mapping of genes and cell types. Using a seeded nonnegative matrix regression (SPOTlight) to deconvolute the dynamic landscape of cell-cell interactions, we found that injured proximal tubule cells were characterized by increasing macrophage and lymphocyte interactions even 6 weeks after injury, potentially reflecting the AKI to CKD transition.

Conclusions: In this transcriptomic atlas, we defined region-specific and injury-induced loss of differentiation markers and their re-expression during repair, as well as region-specific injury and repair transcriptional responses. Lastly, we created an interactive data visualization application for the scientific community to explore these results (http://humphreyslab.com/SingleCell/).","AKI; spatial; transcriptomics.","False","Visium","2212","32285"
"GSE182939_GSM5543483","mouse","kidney","34853151","Spatially Resolved Transcriptomic Analysis of Acute Kidney Injury in a Female Murine Model","Background: Single-cell sequencing technologies have advanced our understanding of kidney biology and disease, but the loss of spatial information in these datasets hinders our interpretation of intercellular communication networks and regional gene expression patterns. New spatial transcriptomic sequencing platforms make it possible to measure the topography of gene expression at genome depth.

Methods: We optimized and validated a female bilateral ischemia-reperfusion injury model. Using the 10× Genomics Visium Spatial Gene Expression solution, we generated spatial maps of gene expression across the injury and repair time course, and applied two open-source computational tools, Giotto and SPOTlight, to increase resolution and measure cell-cell interaction dynamics.

Results: An ischemia time of 34 minutes in a female murine model resulted in comparable injury to 22 minutes for males. We report a total of 16,856 unique genes mapped across our injury and repair time course. Giotto, a computational toolbox for spatial data analysis, enabled increased resolution mapping of genes and cell types. Using a seeded nonnegative matrix regression (SPOTlight) to deconvolute the dynamic landscape of cell-cell interactions, we found that injured proximal tubule cells were characterized by increasing macrophage and lymphocyte interactions even 6 weeks after injury, potentially reflecting the AKI to CKD transition.

Conclusions: In this transcriptomic atlas, we defined region-specific and injury-induced loss of differentiation markers and their re-expression during repair, as well as region-specific injury and repair transcriptional responses. Lastly, we created an interactive data visualization application for the scientific community to explore these results (http://humphreyslab.com/SingleCell/).","AKI; spatial; transcriptomics.","False","Visium","1598","32285"
"GSE183456_GSM6047774","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","3007","33538"
"GSE183456_GSM6047775","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","3627","36601"
"GSE183456_GSM6047776","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","4166","36601"
"GSE183456_GSM6047777","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","2627","36601"
"GSE183456_GSM6047778","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","956","36601"
"GSE183456_GSM6047779","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","1034","36601"
"GSE183456_GSM6047780","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","1322","36601"
"GSE183456_GSM6047781","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","673","36601"
"GSE183456_GSM6047782","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","673","36601"
"GSE183456_GSM6047783","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","560","36601"
"GSE183456_GSM6047784","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","534","36601"
"GSE183456_GSM6047785","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","453","36601"
"GSE183456_GSM6047786","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","461","36601"
"GSE183456_GSM6047787","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","904","36601"
"GSE183456_GSM6047788","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","601","36601"
"GSE183456_GSM6047789","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","787","36601"
"GSE183456_GSM6047790","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","407","36601"
"GSE183456_GSM6047791","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","317","36601"
"GSE183456_GSM6047792","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","645","36601"
"GSE183456_GSM6047793","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","673","36601"
"GSE183456_GSM6047794","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","640","36601"
"GSE183456_GSM6047795","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","507","36601"
"GSE183456_GSM6047796","human","kidney","37468583","An atlas of healthy and injured cell states and niches in the human kidney","Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","","False","Visium","370","36601"
"GSE184510_GSM5591748","human","brain","37081484","Size matters: the impact of nucleus size on results from spatial transcriptomics","Background: Visium Spatial Gene Expression (ST) is a method combining histological spatial information with transcriptomics profiles directly from tissue sections. The use of spatial information has made it possible to discover new modes of gene expression regulations. However, in the ST experiment, the nucleus size of cells may exceed the thickness of a tissue slice. This may, in turn, negatively affect comprehensive capturing the transcriptomics profile in a single slice, especially for tissues having large differences in the size of nuclei.

Methods: Here, we defined the effect of Consecutive Slices Data Integration (CSDI) on unveiling accurate spot clustering and deconvolution of spatial transcriptomic spots in human postmortem brains. By considering the histological information as reference, we assessed the improvement of unsupervised clustering and single nuclei RNA-seq and ST data integration before and after CSDI.

Results: Apart from the escalated number of defined clusters representing neuronal layers, the pattern of clusters in consecutive sections was concordant only after CSDI. Besides, the assigned cell labels to spots matches the histological pattern of tissue sections after CSDI.

Conclusion: CSDI can be applied to investigate consecutive sections studied with ST in the human cerebral cortex, avoiding misinterpretation of spot clustering and annotation, increasing accuracy of cell recognition as well as improvement in uncovering the layers of grey matter in the human brain.","Cerebral cortex; Consecutive tissue sections; Data integration; Neuronal nuclei; Spatial transcriptomics.","False","Visium","2466","36601"
"GSE184510_GSM5591749","human","brain","37081484","Size matters: the impact of nucleus size on results from spatial transcriptomics","Background: Visium Spatial Gene Expression (ST) is a method combining histological spatial information with transcriptomics profiles directly from tissue sections. The use of spatial information has made it possible to discover new modes of gene expression regulations. However, in the ST experiment, the nucleus size of cells may exceed the thickness of a tissue slice. This may, in turn, negatively affect comprehensive capturing the transcriptomics profile in a single slice, especially for tissues having large differences in the size of nuclei.

Methods: Here, we defined the effect of Consecutive Slices Data Integration (CSDI) on unveiling accurate spot clustering and deconvolution of spatial transcriptomic spots in human postmortem brains. By considering the histological information as reference, we assessed the improvement of unsupervised clustering and single nuclei RNA-seq and ST data integration before and after CSDI.

Results: Apart from the escalated number of defined clusters representing neuronal layers, the pattern of clusters in consecutive sections was concordant only after CSDI. Besides, the assigned cell labels to spots matches the histological pattern of tissue sections after CSDI.

Conclusion: CSDI can be applied to investigate consecutive sections studied with ST in the human cerebral cortex, avoiding misinterpretation of spot clustering and annotation, increasing accuracy of cell recognition as well as improvement in uncovering the layers of grey matter in the human brain.","Cerebral cortex; Consecutive tissue sections; Data integration; Neuronal nuclei; Spatial transcriptomics.","False","Visium","3340","36601"
"GSE184510_GSM5591750","human","brain","37081484","Size matters: the impact of nucleus size on results from spatial transcriptomics","Background: Visium Spatial Gene Expression (ST) is a method combining histological spatial information with transcriptomics profiles directly from tissue sections. The use of spatial information has made it possible to discover new modes of gene expression regulations. However, in the ST experiment, the nucleus size of cells may exceed the thickness of a tissue slice. This may, in turn, negatively affect comprehensive capturing the transcriptomics profile in a single slice, especially for tissues having large differences in the size of nuclei.

Methods: Here, we defined the effect of Consecutive Slices Data Integration (CSDI) on unveiling accurate spot clustering and deconvolution of spatial transcriptomic spots in human postmortem brains. By considering the histological information as reference, we assessed the improvement of unsupervised clustering and single nuclei RNA-seq and ST data integration before and after CSDI.

Results: Apart from the escalated number of defined clusters representing neuronal layers, the pattern of clusters in consecutive sections was concordant only after CSDI. Besides, the assigned cell labels to spots matches the histological pattern of tissue sections after CSDI.

Conclusion: CSDI can be applied to investigate consecutive sections studied with ST in the human cerebral cortex, avoiding misinterpretation of spot clustering and annotation, increasing accuracy of cell recognition as well as improvement in uncovering the layers of grey matter in the human brain.","Cerebral cortex; Consecutive tissue sections; Data integration; Neuronal nuclei; Spatial transcriptomics.","False","Visium","2768","36601"
"GSE184510_GSM5591751","human","brain","37081484","Size matters: the impact of nucleus size on results from spatial transcriptomics","Background: Visium Spatial Gene Expression (ST) is a method combining histological spatial information with transcriptomics profiles directly from tissue sections. The use of spatial information has made it possible to discover new modes of gene expression regulations. However, in the ST experiment, the nucleus size of cells may exceed the thickness of a tissue slice. This may, in turn, negatively affect comprehensive capturing the transcriptomics profile in a single slice, especially for tissues having large differences in the size of nuclei.

Methods: Here, we defined the effect of Consecutive Slices Data Integration (CSDI) on unveiling accurate spot clustering and deconvolution of spatial transcriptomic spots in human postmortem brains. By considering the histological information as reference, we assessed the improvement of unsupervised clustering and single nuclei RNA-seq and ST data integration before and after CSDI.

Results: Apart from the escalated number of defined clusters representing neuronal layers, the pattern of clusters in consecutive sections was concordant only after CSDI. Besides, the assigned cell labels to spots matches the histological pattern of tissue sections after CSDI.

Conclusion: CSDI can be applied to investigate consecutive sections studied with ST in the human cerebral cortex, avoiding misinterpretation of spot clustering and annotation, increasing accuracy of cell recognition as well as improvement in uncovering the layers of grey matter in the human brain.","Cerebral cortex; Consecutive tissue sections; Data integration; Neuronal nuclei; Spatial transcriptomics.","False","Visium","2280","36601"
"GSE184510_GSM5591752","human","brain","37081484","Size matters: the impact of nucleus size on results from spatial transcriptomics","Background: Visium Spatial Gene Expression (ST) is a method combining histological spatial information with transcriptomics profiles directly from tissue sections. The use of spatial information has made it possible to discover new modes of gene expression regulations. However, in the ST experiment, the nucleus size of cells may exceed the thickness of a tissue slice. This may, in turn, negatively affect comprehensive capturing the transcriptomics profile in a single slice, especially for tissues having large differences in the size of nuclei.

Methods: Here, we defined the effect of Consecutive Slices Data Integration (CSDI) on unveiling accurate spot clustering and deconvolution of spatial transcriptomic spots in human postmortem brains. By considering the histological information as reference, we assessed the improvement of unsupervised clustering and single nuclei RNA-seq and ST data integration before and after CSDI.

Results: Apart from the escalated number of defined clusters representing neuronal layers, the pattern of clusters in consecutive sections was concordant only after CSDI. Besides, the assigned cell labels to spots matches the histological pattern of tissue sections after CSDI.

Conclusion: CSDI can be applied to investigate consecutive sections studied with ST in the human cerebral cortex, avoiding misinterpretation of spot clustering and annotation, increasing accuracy of cell recognition as well as improvement in uncovering the layers of grey matter in the human brain.","Cerebral cortex; Consecutive tissue sections; Data integration; Neuronal nuclei; Spatial transcriptomics.","False","Visium","2485","36601"
"GSE184510_GSM5591753","human","brain","37081484","Size matters: the impact of nucleus size on results from spatial transcriptomics","Background: Visium Spatial Gene Expression (ST) is a method combining histological spatial information with transcriptomics profiles directly from tissue sections. The use of spatial information has made it possible to discover new modes of gene expression regulations. However, in the ST experiment, the nucleus size of cells may exceed the thickness of a tissue slice. This may, in turn, negatively affect comprehensive capturing the transcriptomics profile in a single slice, especially for tissues having large differences in the size of nuclei.

Methods: Here, we defined the effect of Consecutive Slices Data Integration (CSDI) on unveiling accurate spot clustering and deconvolution of spatial transcriptomic spots in human postmortem brains. By considering the histological information as reference, we assessed the improvement of unsupervised clustering and single nuclei RNA-seq and ST data integration before and after CSDI.

Results: Apart from the escalated number of defined clusters representing neuronal layers, the pattern of clusters in consecutive sections was concordant only after CSDI. Besides, the assigned cell labels to spots matches the histological pattern of tissue sections after CSDI.

Conclusion: CSDI can be applied to investigate consecutive sections studied with ST in the human cerebral cortex, avoiding misinterpretation of spot clustering and annotation, increasing accuracy of cell recognition as well as improvement in uncovering the layers of grey matter in the human brain.","Cerebral cortex; Consecutive tissue sections; Data integration; Neuronal nuclei; Spatial transcriptomics.","False","Visium","3349","36601"
"GSE184510_GSM5591754","human","brain","37081484","Size matters: the impact of nucleus size on results from spatial transcriptomics","Background: Visium Spatial Gene Expression (ST) is a method combining histological spatial information with transcriptomics profiles directly from tissue sections. The use of spatial information has made it possible to discover new modes of gene expression regulations. However, in the ST experiment, the nucleus size of cells may exceed the thickness of a tissue slice. This may, in turn, negatively affect comprehensive capturing the transcriptomics profile in a single slice, especially for tissues having large differences in the size of nuclei.

Methods: Here, we defined the effect of Consecutive Slices Data Integration (CSDI) on unveiling accurate spot clustering and deconvolution of spatial transcriptomic spots in human postmortem brains. By considering the histological information as reference, we assessed the improvement of unsupervised clustering and single nuclei RNA-seq and ST data integration before and after CSDI.

Results: Apart from the escalated number of defined clusters representing neuronal layers, the pattern of clusters in consecutive sections was concordant only after CSDI. Besides, the assigned cell labels to spots matches the histological pattern of tissue sections after CSDI.

Conclusion: CSDI can be applied to investigate consecutive sections studied with ST in the human cerebral cortex, avoiding misinterpretation of spot clustering and annotation, increasing accuracy of cell recognition as well as improvement in uncovering the layers of grey matter in the human brain.","Cerebral cortex; Consecutive tissue sections; Data integration; Neuronal nuclei; Spatial transcriptomics.","False","Visium","2728","36601"
"GSE184510_GSM5591755","human","brain","37081484","Size matters: the impact of nucleus size on results from spatial transcriptomics","Background: Visium Spatial Gene Expression (ST) is a method combining histological spatial information with transcriptomics profiles directly from tissue sections. The use of spatial information has made it possible to discover new modes of gene expression regulations. However, in the ST experiment, the nucleus size of cells may exceed the thickness of a tissue slice. This may, in turn, negatively affect comprehensive capturing the transcriptomics profile in a single slice, especially for tissues having large differences in the size of nuclei.

Methods: Here, we defined the effect of Consecutive Slices Data Integration (CSDI) on unveiling accurate spot clustering and deconvolution of spatial transcriptomic spots in human postmortem brains. By considering the histological information as reference, we assessed the improvement of unsupervised clustering and single nuclei RNA-seq and ST data integration before and after CSDI.

Results: Apart from the escalated number of defined clusters representing neuronal layers, the pattern of clusters in consecutive sections was concordant only after CSDI. Besides, the assigned cell labels to spots matches the histological pattern of tissue sections after CSDI.

Conclusion: CSDI can be applied to investigate consecutive sections studied with ST in the human cerebral cortex, avoiding misinterpretation of spot clustering and annotation, increasing accuracy of cell recognition as well as improvement in uncovering the layers of grey matter in the human brain.","Cerebral cortex; Consecutive tissue sections; Data integration; Neuronal nuclei; Spatial transcriptomics.","False","Visium","2337","36601"
"GSE186290_GSM5643203","mouse","stomach","34931404","MEK inhibition suppresses metastatic progression of KRAS-mutated gastric cancer","Metastatic progression of tumors is driven by genetic alterations and tumor-stroma interaction. To elucidate the mechanism underlying the oncogene-induced gastric tumor progression, we have developed an organoid-based model of gastric cancer from GAstric Neoplasia (GAN) mice, which express Wnt1 and the enzymes COX2 and microsomal prostaglandin E synthase 1 in the stomach. Both p53 knockout (GAN-p53KO) organoids and KRASG12V -expressing GAN-p53KO (GAN-KP) organoids were generated by genetic manipulation of GAN mouse-derived tumor (GAN wild-type [WT]) organoids. In contrast with GAN-WT and GAN-p53KO organoids, which manifested Wnt addiction, GAN-KP organoids showed a Wnt-independent phenotype and the ability to proliferate without formation of a Wnt-regulated three-dimensional epithelial architecture. After transplantation in syngeneic mouse stomach, GAN-p53KO cells formed only small tumors, whereas GAN-KP cells gave rise to invasive tumors associated with the development of hypoxia as well as to liver metastasis. Spatial transcriptomics analysis suggested that hypoxia signaling contributes to the metastatic progression of GAN-KP tumors. In particular, such analysis identified a cluster of stromal cells located at the tumor invasive front that expressed genes related to hypoxia signaling, angiogenesis, and cell migration. These cells were also positive for phosphorylated extracellular signal-regulated kinase (ERK), suggesting that mitogen-activated protein kinase (MAPK) signaling promotes development of both tumor and microenvironment. The MEK (MAPK kinase) inhibitor trametinib suppressed the development of GAN-KP gastric tumors, formation of a hypoxic microenvironment, tumor angiogenesis, and liver metastasis. Our findings therefore establish a rationale for application of trametinib to suppress metastatic progression of KRAS-mutated gastric cancer.","MEK; epithelial-mesenchymal transition (EMT); gastric cancer; hypoxia; mouse model.","True","Visium","2429","31054"
"GSE186818_GSM5661565","fish","brain","36306789","Prostaglandin E2 synchronizes lunar-regulated beach spawning in grass puffers","Many organisms living along the coastlines synchronize their reproduction with the lunar cycle. At the time of spring tide, thousands of grass puffers (Takifugu alboplumbeus) aggregate and vigorously tremble their bodies at the water's edge to spawn. To understand the mechanisms underlying this spectacular semilunar beach spawning, we collected the hypothalamus and pituitary from male grass puffers every week for 2 months. RNA sequencing (RNA-seq) analysis identified 125 semilunar genes, including genes crucial for reproduction (e.g., gonadotropin-releasing hormone 1 [gnrh1], luteinizing hormone β subunit [lhb]) and receptors for pheromone prostaglandin E (PGE). PGE2 is secreted into the seawater during the spawning, and its administration activates olfactory sensory neurons and triggers trembling behavior of surrounding individuals. These results suggest that PGE2 synchronizes lunar-regulated beach-spawning behavior in grass puffers. To further explore the mechanism that regulates the lunar-synchronized transcription of semilunar genes, we searched for semilunar transcription factors. Spatial transcriptomics and multiplex fluorescent in situ hybridization showed co-localization of the semilunar transcription factor CCAAT/enhancer-binding protein δ (cebpd) and gnrh1, and cebpd induced the promoter activity of gnrh1. Taken together, our study demonstrates semilunar genes that mediate lunar-synchronized beach-spawning behavior. VIDEO ABSTRACT.","beach spawning; biological clock; circalunar rhythms; grass puffer; lunar cycle; neap tide; pheromone; seasonal reproduction; semilunar rhythm; spring tide.","False","Visium","3095","26886"
"GSE188257_GSM5673398","mouse","ovary","35996587","Spatially resolved transcriptomic profiling of ovarian aging in mice","Ovarian aging precedes that of any other mammalian organ and is the primary cause of female age-related infertility. The biological mechanisms responsible for ovarian aging remain unclear. Previous studies have been limited by their use of bulk RNA-sequencing, which masks the dynamic and heterogeneous nature of the ovary. In this study, we spatially resolved the transcriptomic landscape of ovaries from young and aged outbred mice. In total, we defined eight main ovarian cell populations, all of which were characterized by significant transcriptomic changes between young and aged samples. Further sub-cluster analysis revealed separate transcriptomes for distinct granulosa cell populations found in young versus aged mice, in addition to an oocyte sub-cluster population completely absent from aged mouse ovaries. This study provides a new perspective on mammalian ovarian aging using spatial transcriptomics to achieve deeper understanding of the localization and cell-population-specific mechanisms underlying age-related fertility decline.","Cellular physiology; Omics; Physiology; Transcriptomics.","False","Visium","841","32285"
"GSE188257_GSM5673399","mouse","ovary","35996587","Spatially resolved transcriptomic profiling of ovarian aging in mice","Ovarian aging precedes that of any other mammalian organ and is the primary cause of female age-related infertility. The biological mechanisms responsible for ovarian aging remain unclear. Previous studies have been limited by their use of bulk RNA-sequencing, which masks the dynamic and heterogeneous nature of the ovary. In this study, we spatially resolved the transcriptomic landscape of ovaries from young and aged outbred mice. In total, we defined eight main ovarian cell populations, all of which were characterized by significant transcriptomic changes between young and aged samples. Further sub-cluster analysis revealed separate transcriptomes for distinct granulosa cell populations found in young versus aged mice, in addition to an oocyte sub-cluster population completely absent from aged mouse ovaries. This study provides a new perspective on mammalian ovarian aging using spatial transcriptomics to achieve deeper understanding of the localization and cell-population-specific mechanisms underlying age-related fertility decline.","Cellular physiology; Omics; Physiology; Transcriptomics.","False","Visium","1415","32285"
"GSE188257_GSM5673400","mouse","ovary","35996587","Spatially resolved transcriptomic profiling of ovarian aging in mice","Ovarian aging precedes that of any other mammalian organ and is the primary cause of female age-related infertility. The biological mechanisms responsible for ovarian aging remain unclear. Previous studies have been limited by their use of bulk RNA-sequencing, which masks the dynamic and heterogeneous nature of the ovary. In this study, we spatially resolved the transcriptomic landscape of ovaries from young and aged outbred mice. In total, we defined eight main ovarian cell populations, all of which were characterized by significant transcriptomic changes between young and aged samples. Further sub-cluster analysis revealed separate transcriptomes for distinct granulosa cell populations found in young versus aged mice, in addition to an oocyte sub-cluster population completely absent from aged mouse ovaries. This study provides a new perspective on mammalian ovarian aging using spatial transcriptomics to achieve deeper understanding of the localization and cell-population-specific mechanisms underlying age-related fertility decline.","Cellular physiology; Omics; Physiology; Transcriptomics.","False","Visium","814","32285"
"GSE188257_GSM5673401","mouse","ovary","35996587","Spatially resolved transcriptomic profiling of ovarian aging in mice","Ovarian aging precedes that of any other mammalian organ and is the primary cause of female age-related infertility. The biological mechanisms responsible for ovarian aging remain unclear. Previous studies have been limited by their use of bulk RNA-sequencing, which masks the dynamic and heterogeneous nature of the ovary. In this study, we spatially resolved the transcriptomic landscape of ovaries from young and aged outbred mice. In total, we defined eight main ovarian cell populations, all of which were characterized by significant transcriptomic changes between young and aged samples. Further sub-cluster analysis revealed separate transcriptomes for distinct granulosa cell populations found in young versus aged mice, in addition to an oocyte sub-cluster population completely absent from aged mouse ovaries. This study provides a new perspective on mammalian ovarian aging using spatial transcriptomics to achieve deeper understanding of the localization and cell-population-specific mechanisms underlying age-related fertility decline.","Cellular physiology; Omics; Physiology; Transcriptomics.","False","Visium","1193","32285"
"GSE188888_GSM5691526","mouse","heart","35050211","Characterizing Neonatal Heart Maturation, Regeneration, and Scar Resolution Using Spatial Transcriptomics","The neonatal mammalian heart exhibits a remarkable regenerative potential, which includes fibrotic scar resolution and the generation of new cardiomyocytes. To investigate the mechanisms facilitating heart repair after apical resection in neonatal mice, we conducted bulk and spatial transcriptomic analyses at regenerative and non-regenerative timepoints. Importantly, spatial transcriptomics provided near single-cell resolution, revealing distinct domains of atrial and ventricular myocardium that exhibit dynamic phenotypic alterations during postnatal heart maturation. Spatial transcriptomics also defined the cardiac scar, which transitions from a proliferative to secretory phenotype as the heart loses regenerative potential. The resolving scar is characterized by spatially and temporally restricted programs of inflammation, epicardium expansion and extracellular matrix production, metabolic reprogramming, lipogenic scar extrusion, and cardiomyocyte restoration. Finally, this study revealed the emergence of a regenerative border zone defined by immature cardiomyocyte markers and the robust expression of Sprr1a. Taken together, our study defines the spatially and temporally restricted gene programs that underlie neonatal heart regeneration and provides insight into cardio-restorative mechanisms supporting scar resolution.","fibroblast; heart; mouse; regeneration; scar; spatial transcriptomics.","False","Visium","959","32285"
"GSE188888_GSM5691527","mouse","heart","35050211","Characterizing Neonatal Heart Maturation, Regeneration, and Scar Resolution Using Spatial Transcriptomics","The neonatal mammalian heart exhibits a remarkable regenerative potential, which includes fibrotic scar resolution and the generation of new cardiomyocytes. To investigate the mechanisms facilitating heart repair after apical resection in neonatal mice, we conducted bulk and spatial transcriptomic analyses at regenerative and non-regenerative timepoints. Importantly, spatial transcriptomics provided near single-cell resolution, revealing distinct domains of atrial and ventricular myocardium that exhibit dynamic phenotypic alterations during postnatal heart maturation. Spatial transcriptomics also defined the cardiac scar, which transitions from a proliferative to secretory phenotype as the heart loses regenerative potential. The resolving scar is characterized by spatially and temporally restricted programs of inflammation, epicardium expansion and extracellular matrix production, metabolic reprogramming, lipogenic scar extrusion, and cardiomyocyte restoration. Finally, this study revealed the emergence of a regenerative border zone defined by immature cardiomyocyte markers and the robust expression of Sprr1a. Taken together, our study defines the spatially and temporally restricted gene programs that underlie neonatal heart regeneration and provides insight into cardio-restorative mechanisms supporting scar resolution.","fibroblast; heart; mouse; regeneration; scar; spatial transcriptomics.","False","Visium","1374","32285"
"GSE188888_GSM5691528","mouse","heart","35050211","Characterizing Neonatal Heart Maturation, Regeneration, and Scar Resolution Using Spatial Transcriptomics","The neonatal mammalian heart exhibits a remarkable regenerative potential, which includes fibrotic scar resolution and the generation of new cardiomyocytes. To investigate the mechanisms facilitating heart repair after apical resection in neonatal mice, we conducted bulk and spatial transcriptomic analyses at regenerative and non-regenerative timepoints. Importantly, spatial transcriptomics provided near single-cell resolution, revealing distinct domains of atrial and ventricular myocardium that exhibit dynamic phenotypic alterations during postnatal heart maturation. Spatial transcriptomics also defined the cardiac scar, which transitions from a proliferative to secretory phenotype as the heart loses regenerative potential. The resolving scar is characterized by spatially and temporally restricted programs of inflammation, epicardium expansion and extracellular matrix production, metabolic reprogramming, lipogenic scar extrusion, and cardiomyocyte restoration. Finally, this study revealed the emergence of a regenerative border zone defined by immature cardiomyocyte markers and the robust expression of Sprr1a. Taken together, our study defines the spatially and temporally restricted gene programs that underlie neonatal heart regeneration and provides insight into cardio-restorative mechanisms supporting scar resolution.","fibroblast; heart; mouse; regeneration; scar; spatial transcriptomics.","False","Visium","1722","32285"
"GSE188888_GSM5691529","mouse","heart","35050211","Characterizing Neonatal Heart Maturation, Regeneration, and Scar Resolution Using Spatial Transcriptomics","The neonatal mammalian heart exhibits a remarkable regenerative potential, which includes fibrotic scar resolution and the generation of new cardiomyocytes. To investigate the mechanisms facilitating heart repair after apical resection in neonatal mice, we conducted bulk and spatial transcriptomic analyses at regenerative and non-regenerative timepoints. Importantly, spatial transcriptomics provided near single-cell resolution, revealing distinct domains of atrial and ventricular myocardium that exhibit dynamic phenotypic alterations during postnatal heart maturation. Spatial transcriptomics also defined the cardiac scar, which transitions from a proliferative to secretory phenotype as the heart loses regenerative potential. The resolving scar is characterized by spatially and temporally restricted programs of inflammation, epicardium expansion and extracellular matrix production, metabolic reprogramming, lipogenic scar extrusion, and cardiomyocyte restoration. Finally, this study revealed the emergence of a regenerative border zone defined by immature cardiomyocyte markers and the robust expression of Sprr1a. Taken together, our study defines the spatially and temporally restricted gene programs that underlie neonatal heart regeneration and provides insight into cardio-restorative mechanisms supporting scar resolution.","fibroblast; heart; mouse; regeneration; scar; spatial transcriptomics.","False","Visium","2095","32285"
"GSE190595_GSM5726156","mouse","colon","35149721","The spatial transcriptomic landscape of the healing mouse intestine following damage","The intestinal barrier is composed of a complex cell network defining highly compartmentalized and specialized structures. Here, we use spatial transcriptomics to define how the transcriptomic landscape is spatially organized in the steady state and healing murine colon. At steady state conditions, we demonstrate a previously unappreciated molecular regionalization of the colon, which dramatically changes during mucosal healing. Here, we identified spatially-organized transcriptional programs defining compartmentalized mucosal healing, and regions with dominant wired pathways. Furthermore, we showed that decreased p53 activation defined areas with increased presence of proliferating epithelial stem cells. Finally, we mapped transcriptomics modules associated with human diseases demonstrating the translational potential of our dataset. Overall, we provide a publicly available resource defining principles of transcriptomic regionalization of the colon during mucosal healing and a framework to develop and progress further hypotheses.","","False","Visium","3934","31053"
"GSE190595_GSM5726157","mouse","colon","35149721","The spatial transcriptomic landscape of the healing mouse intestine following damage","The intestinal barrier is composed of a complex cell network defining highly compartmentalized and specialized structures. Here, we use spatial transcriptomics to define how the transcriptomic landscape is spatially organized in the steady state and healing murine colon. At steady state conditions, we demonstrate a previously unappreciated molecular regionalization of the colon, which dramatically changes during mucosal healing. Here, we identified spatially-organized transcriptional programs defining compartmentalized mucosal healing, and regions with dominant wired pathways. Furthermore, we showed that decreased p53 activation defined areas with increased presence of proliferating epithelial stem cells. Finally, we mapped transcriptomics modules associated with human diseases demonstrating the translational potential of our dataset. Overall, we provide a publicly available resource defining principles of transcriptomic regionalization of the colon during mucosal healing and a framework to develop and progress further hypotheses.","","False","Visium","3547","31053"
"GSE192742_GSM5764414","mouse","liver","35021063,36304458","Title 1: Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Title 2: The C1q-ApoE complex: A new hallmark pathology of viral hepatitis and nonalcoholic fatty liver disease.","Abstract 1: The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here we present a spatial proteogenomic atlas of the healthy and obese human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics, and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells, including a population of lipid-associated macrophages (LAMs) at the bile ducts. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and LAMs. We also uncover the respective spatially resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that LAMs are induced by local lipid exposure, leading to their induction in steatotic regions of the murine and human liver, while Kupffer cell development crucially depends on their cross-talk with hepatic stellate cells via the evolutionarily conserved ALK1-BMP9/10 axis. Abstract 2: We recently identified a high-affinity C1q-ApoE complex in human artery atherosclerotic intima lesions and in human amyloid plaques of Alzheimer's Disease brains defining a common pathogenetic pathway of two diverse diseases, i.e. atherosclerosis and dementia. C1q is the initiating and controlling protein of the classical complement cascade (CCC), which occupies a key role in multiple acute and chronic inflammatory tissue responses. C1q is largely produced by myeloid cells including Kupffer cells (KCs) and subsequently secreted into the circulation as an inactive preprotein. Its binding partner, Apolipoprotein E (ApoE), is produced by KCs and hepatocytes and it is also secreted into the circulation, where it regulates essential steps of lipid transport. In addition to its major source, ApoE can be produced by non-liver cells including immune cells and multiple other cells depending on local tissue contexts. To initiate the CCC cascade, C1q must be activated by molecules as varied as oxidized lipids, amyloid fibrils, and immune complexes. However, ApoE is mute towards inactive C1q but binds at high-affinity to its activated form. Specifically, our studies revealed that ApoE is a CCC-specific checkpoint inhibitor via the formation of the C1q-ApoE complex. We proposed that it may arise in multiple if not all CCC-associated diseases and that its presence indicates ongoing CCC activity. Here, we turned to the liver to examine C1q-ApoE complexes in human B- and C-viral hepatitis and nonalcoholic fatty liver disease (NAFLD). In addition, we used multidrug-resistance-2 gene-knockout (Mdr2-KO) mice as a model for inflammatory liver disease and hepatocellular carcinoma (HCC) pathogenesis. In normal murine and human livers, KCs were the major C1q-producing cell type while hepatocytes were the primary ApoE-forming cell type though the C1q-ApoE complex was rare or nonexistent. However, significant numbers of C1q-ApoE complexes formed in both Mdr2-KO, human viral hepatitis, and NAFLD around portal triads where immune cells had infiltrated the liver. Additionally, high numbers of C1q-ApoE complexes emerged in human livers in areas of extracellular lipid droplets across the entire liver parenchyma in NAFLD-affected patients. Thus, the C1q-ApoE complex is a new pathological hallmark of viral hepatitis B and C and NAFLD.","Keywords 1: CITE-seq; Kupffer cell; NAFLD; across species; atlas; lipid-associated macrophage; liver; multi-omic; proteogenomic; spatial transcriptomics. Keywords 2: C1q-ApoE complex; classical complement cascade (CCC); hepatocellular carcinoma (HCC); nonalcoholic fatty liver disease (NAFLD); viral hepatitis.","False","Visium","1646","31053"
"GSE192742_GSM5764415","mouse","liver","35021063,36304458","Title 1: Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Title 2: The C1q-ApoE complex: A new hallmark pathology of viral hepatitis and nonalcoholic fatty liver disease.","Abstract 1: The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here we present a spatial proteogenomic atlas of the healthy and obese human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics, and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells, including a population of lipid-associated macrophages (LAMs) at the bile ducts. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and LAMs. We also uncover the respective spatially resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that LAMs are induced by local lipid exposure, leading to their induction in steatotic regions of the murine and human liver, while Kupffer cell development crucially depends on their cross-talk with hepatic stellate cells via the evolutionarily conserved ALK1-BMP9/10 axis. Abstract 2: We recently identified a high-affinity C1q-ApoE complex in human artery atherosclerotic intima lesions and in human amyloid plaques of Alzheimer's Disease brains defining a common pathogenetic pathway of two diverse diseases, i.e. atherosclerosis and dementia. C1q is the initiating and controlling protein of the classical complement cascade (CCC), which occupies a key role in multiple acute and chronic inflammatory tissue responses. C1q is largely produced by myeloid cells including Kupffer cells (KCs) and subsequently secreted into the circulation as an inactive preprotein. Its binding partner, Apolipoprotein E (ApoE), is produced by KCs and hepatocytes and it is also secreted into the circulation, where it regulates essential steps of lipid transport. In addition to its major source, ApoE can be produced by non-liver cells including immune cells and multiple other cells depending on local tissue contexts. To initiate the CCC cascade, C1q must be activated by molecules as varied as oxidized lipids, amyloid fibrils, and immune complexes. However, ApoE is mute towards inactive C1q but binds at high-affinity to its activated form. Specifically, our studies revealed that ApoE is a CCC-specific checkpoint inhibitor via the formation of the C1q-ApoE complex. We proposed that it may arise in multiple if not all CCC-associated diseases and that its presence indicates ongoing CCC activity. Here, we turned to the liver to examine C1q-ApoE complexes in human B- and C-viral hepatitis and nonalcoholic fatty liver disease (NAFLD). In addition, we used multidrug-resistance-2 gene-knockout (Mdr2-KO) mice as a model for inflammatory liver disease and hepatocellular carcinoma (HCC) pathogenesis. In normal murine and human livers, KCs were the major C1q-producing cell type while hepatocytes were the primary ApoE-forming cell type though the C1q-ApoE complex was rare or nonexistent. However, significant numbers of C1q-ApoE complexes formed in both Mdr2-KO, human viral hepatitis, and NAFLD around portal triads where immune cells had infiltrated the liver. Additionally, high numbers of C1q-ApoE complexes emerged in human livers in areas of extracellular lipid droplets across the entire liver parenchyma in NAFLD-affected patients. Thus, the C1q-ApoE complex is a new pathological hallmark of viral hepatitis B and C and NAFLD.","Keywords 1: CITE-seq; Kupffer cell; NAFLD; across species; atlas; lipid-associated macrophage; liver; multi-omic; proteogenomic; spatial transcriptomics. Keywords 2: C1q-ApoE complex; classical complement cascade (CCC); hepatocellular carcinoma (HCC); nonalcoholic fatty liver disease (NAFLD); viral hepatitis.","False","Visium","1651","31053"
"GSE192742_GSM5764416","mouse","liver","35021063,36304458","Title 1: Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Title 2: The C1q-ApoE complex: A new hallmark pathology of viral hepatitis and nonalcoholic fatty liver disease.","Abstract 1: The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here we present a spatial proteogenomic atlas of the healthy and obese human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics, and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells, including a population of lipid-associated macrophages (LAMs) at the bile ducts. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and LAMs. We also uncover the respective spatially resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that LAMs are induced by local lipid exposure, leading to their induction in steatotic regions of the murine and human liver, while Kupffer cell development crucially depends on their cross-talk with hepatic stellate cells via the evolutionarily conserved ALK1-BMP9/10 axis. Abstract 2: We recently identified a high-affinity C1q-ApoE complex in human artery atherosclerotic intima lesions and in human amyloid plaques of Alzheimer's Disease brains defining a common pathogenetic pathway of two diverse diseases, i.e. atherosclerosis and dementia. C1q is the initiating and controlling protein of the classical complement cascade (CCC), which occupies a key role in multiple acute and chronic inflammatory tissue responses. C1q is largely produced by myeloid cells including Kupffer cells (KCs) and subsequently secreted into the circulation as an inactive preprotein. Its binding partner, Apolipoprotein E (ApoE), is produced by KCs and hepatocytes and it is also secreted into the circulation, where it regulates essential steps of lipid transport. In addition to its major source, ApoE can be produced by non-liver cells including immune cells and multiple other cells depending on local tissue contexts. To initiate the CCC cascade, C1q must be activated by molecules as varied as oxidized lipids, amyloid fibrils, and immune complexes. However, ApoE is mute towards inactive C1q but binds at high-affinity to its activated form. Specifically, our studies revealed that ApoE is a CCC-specific checkpoint inhibitor via the formation of the C1q-ApoE complex. We proposed that it may arise in multiple if not all CCC-associated diseases and that its presence indicates ongoing CCC activity. Here, we turned to the liver to examine C1q-ApoE complexes in human B- and C-viral hepatitis and nonalcoholic fatty liver disease (NAFLD). In addition, we used multidrug-resistance-2 gene-knockout (Mdr2-KO) mice as a model for inflammatory liver disease and hepatocellular carcinoma (HCC) pathogenesis. In normal murine and human livers, KCs were the major C1q-producing cell type while hepatocytes were the primary ApoE-forming cell type though the C1q-ApoE complex was rare or nonexistent. However, significant numbers of C1q-ApoE complexes formed in both Mdr2-KO, human viral hepatitis, and NAFLD around portal triads where immune cells had infiltrated the liver. Additionally, high numbers of C1q-ApoE complexes emerged in human livers in areas of extracellular lipid droplets across the entire liver parenchyma in NAFLD-affected patients. Thus, the C1q-ApoE complex is a new pathological hallmark of viral hepatitis B and C and NAFLD.","Keywords 1: CITE-seq; Kupffer cell; NAFLD; across species; atlas; lipid-associated macrophage; liver; multi-omic; proteogenomic; spatial transcriptomics. Keywords 2: C1q-ApoE complex; classical complement cascade (CCC); hepatocellular carcinoma (HCC); nonalcoholic fatty liver disease (NAFLD); viral hepatitis.","False","Visium","1604","31053"
"GSE192742_GSM5764417","mouse","liver","35021063,36304458","Title 1: Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Title 2: The C1q-ApoE complex: A new hallmark pathology of viral hepatitis and nonalcoholic fatty liver disease.","Abstract 1: The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here we present a spatial proteogenomic atlas of the healthy and obese human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics, and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells, including a population of lipid-associated macrophages (LAMs) at the bile ducts. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and LAMs. We also uncover the respective spatially resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that LAMs are induced by local lipid exposure, leading to their induction in steatotic regions of the murine and human liver, while Kupffer cell development crucially depends on their cross-talk with hepatic stellate cells via the evolutionarily conserved ALK1-BMP9/10 axis. Abstract 2: We recently identified a high-affinity C1q-ApoE complex in human artery atherosclerotic intima lesions and in human amyloid plaques of Alzheimer's Disease brains defining a common pathogenetic pathway of two diverse diseases, i.e. atherosclerosis and dementia. C1q is the initiating and controlling protein of the classical complement cascade (CCC), which occupies a key role in multiple acute and chronic inflammatory tissue responses. C1q is largely produced by myeloid cells including Kupffer cells (KCs) and subsequently secreted into the circulation as an inactive preprotein. Its binding partner, Apolipoprotein E (ApoE), is produced by KCs and hepatocytes and it is also secreted into the circulation, where it regulates essential steps of lipid transport. In addition to its major source, ApoE can be produced by non-liver cells including immune cells and multiple other cells depending on local tissue contexts. To initiate the CCC cascade, C1q must be activated by molecules as varied as oxidized lipids, amyloid fibrils, and immune complexes. However, ApoE is mute towards inactive C1q but binds at high-affinity to its activated form. Specifically, our studies revealed that ApoE is a CCC-specific checkpoint inhibitor via the formation of the C1q-ApoE complex. We proposed that it may arise in multiple if not all CCC-associated diseases and that its presence indicates ongoing CCC activity. Here, we turned to the liver to examine C1q-ApoE complexes in human B- and C-viral hepatitis and nonalcoholic fatty liver disease (NAFLD). In addition, we used multidrug-resistance-2 gene-knockout (Mdr2-KO) mice as a model for inflammatory liver disease and hepatocellular carcinoma (HCC) pathogenesis. In normal murine and human livers, KCs were the major C1q-producing cell type while hepatocytes were the primary ApoE-forming cell type though the C1q-ApoE complex was rare or nonexistent. However, significant numbers of C1q-ApoE complexes formed in both Mdr2-KO, human viral hepatitis, and NAFLD around portal triads where immune cells had infiltrated the liver. Additionally, high numbers of C1q-ApoE complexes emerged in human livers in areas of extracellular lipid droplets across the entire liver parenchyma in NAFLD-affected patients. Thus, the C1q-ApoE complex is a new pathological hallmark of viral hepatitis B and C and NAFLD.","Keywords 1: CITE-seq; Kupffer cell; NAFLD; across species; atlas; lipid-associated macrophage; liver; multi-omic; proteogenomic; spatial transcriptomics. Keywords 2: C1q-ApoE complex; classical complement cascade (CCC); hepatocellular carcinoma (HCC); nonalcoholic fatty liver disease (NAFLD); viral hepatitis.","False","Visium","2286","31053"
"GSE192742_GSM5764418","mouse","liver","35021063,36304458","Title 1: Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Title 2: The C1q-ApoE complex: A new hallmark pathology of viral hepatitis and nonalcoholic fatty liver disease.","Abstract 1: The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here we present a spatial proteogenomic atlas of the healthy and obese human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics, and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells, including a population of lipid-associated macrophages (LAMs) at the bile ducts. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and LAMs. We also uncover the respective spatially resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that LAMs are induced by local lipid exposure, leading to their induction in steatotic regions of the murine and human liver, while Kupffer cell development crucially depends on their cross-talk with hepatic stellate cells via the evolutionarily conserved ALK1-BMP9/10 axis. Abstract 2: We recently identified a high-affinity C1q-ApoE complex in human artery atherosclerotic intima lesions and in human amyloid plaques of Alzheimer's Disease brains defining a common pathogenetic pathway of two diverse diseases, i.e. atherosclerosis and dementia. C1q is the initiating and controlling protein of the classical complement cascade (CCC), which occupies a key role in multiple acute and chronic inflammatory tissue responses. C1q is largely produced by myeloid cells including Kupffer cells (KCs) and subsequently secreted into the circulation as an inactive preprotein. Its binding partner, Apolipoprotein E (ApoE), is produced by KCs and hepatocytes and it is also secreted into the circulation, where it regulates essential steps of lipid transport. In addition to its major source, ApoE can be produced by non-liver cells including immune cells and multiple other cells depending on local tissue contexts. To initiate the CCC cascade, C1q must be activated by molecules as varied as oxidized lipids, amyloid fibrils, and immune complexes. However, ApoE is mute towards inactive C1q but binds at high-affinity to its activated form. Specifically, our studies revealed that ApoE is a CCC-specific checkpoint inhibitor via the formation of the C1q-ApoE complex. We proposed that it may arise in multiple if not all CCC-associated diseases and that its presence indicates ongoing CCC activity. Here, we turned to the liver to examine C1q-ApoE complexes in human B- and C-viral hepatitis and nonalcoholic fatty liver disease (NAFLD). In addition, we used multidrug-resistance-2 gene-knockout (Mdr2-KO) mice as a model for inflammatory liver disease and hepatocellular carcinoma (HCC) pathogenesis. In normal murine and human livers, KCs were the major C1q-producing cell type while hepatocytes were the primary ApoE-forming cell type though the C1q-ApoE complex was rare or nonexistent. However, significant numbers of C1q-ApoE complexes formed in both Mdr2-KO, human viral hepatitis, and NAFLD around portal triads where immune cells had infiltrated the liver. Additionally, high numbers of C1q-ApoE complexes emerged in human livers in areas of extracellular lipid droplets across the entire liver parenchyma in NAFLD-affected patients. Thus, the C1q-ApoE complex is a new pathological hallmark of viral hepatitis B and C and NAFLD.","Keywords 1: CITE-seq; Kupffer cell; NAFLD; across species; atlas; lipid-associated macrophage; liver; multi-omic; proteogenomic; spatial transcriptomics. Keywords 2: C1q-ApoE complex; classical complement cascade (CCC); hepatocellular carcinoma (HCC); nonalcoholic fatty liver disease (NAFLD); viral hepatitis.","False","Visium","438","31053"
"GSE192742_GSM5764419","mouse","liver","35021063,36304458","Title 1: Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Title 2: The C1q-ApoE complex: A new hallmark pathology of viral hepatitis and nonalcoholic fatty liver disease.","Abstract 1: The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here we present a spatial proteogenomic atlas of the healthy and obese human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics, and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells, including a population of lipid-associated macrophages (LAMs) at the bile ducts. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and LAMs. We also uncover the respective spatially resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that LAMs are induced by local lipid exposure, leading to their induction in steatotic regions of the murine and human liver, while Kupffer cell development crucially depends on their cross-talk with hepatic stellate cells via the evolutionarily conserved ALK1-BMP9/10 axis. Abstract 2: We recently identified a high-affinity C1q-ApoE complex in human artery atherosclerotic intima lesions and in human amyloid plaques of Alzheimer's Disease brains defining a common pathogenetic pathway of two diverse diseases, i.e. atherosclerosis and dementia. C1q is the initiating and controlling protein of the classical complement cascade (CCC), which occupies a key role in multiple acute and chronic inflammatory tissue responses. C1q is largely produced by myeloid cells including Kupffer cells (KCs) and subsequently secreted into the circulation as an inactive preprotein. Its binding partner, Apolipoprotein E (ApoE), is produced by KCs and hepatocytes and it is also secreted into the circulation, where it regulates essential steps of lipid transport. In addition to its major source, ApoE can be produced by non-liver cells including immune cells and multiple other cells depending on local tissue contexts. To initiate the CCC cascade, C1q must be activated by molecules as varied as oxidized lipids, amyloid fibrils, and immune complexes. However, ApoE is mute towards inactive C1q but binds at high-affinity to its activated form. Specifically, our studies revealed that ApoE is a CCC-specific checkpoint inhibitor via the formation of the C1q-ApoE complex. We proposed that it may arise in multiple if not all CCC-associated diseases and that its presence indicates ongoing CCC activity. Here, we turned to the liver to examine C1q-ApoE complexes in human B- and C-viral hepatitis and nonalcoholic fatty liver disease (NAFLD). In addition, we used multidrug-resistance-2 gene-knockout (Mdr2-KO) mice as a model for inflammatory liver disease and hepatocellular carcinoma (HCC) pathogenesis. In normal murine and human livers, KCs were the major C1q-producing cell type while hepatocytes were the primary ApoE-forming cell type though the C1q-ApoE complex was rare or nonexistent. However, significant numbers of C1q-ApoE complexes formed in both Mdr2-KO, human viral hepatitis, and NAFLD around portal triads where immune cells had infiltrated the liver. Additionally, high numbers of C1q-ApoE complexes emerged in human livers in areas of extracellular lipid droplets across the entire liver parenchyma in NAFLD-affected patients. Thus, the C1q-ApoE complex is a new pathological hallmark of viral hepatitis B and C and NAFLD.","Keywords 1: CITE-seq; Kupffer cell; NAFLD; across species; atlas; lipid-associated macrophage; liver; multi-omic; proteogenomic; spatial transcriptomics. Keywords 2: C1q-ApoE complex; classical complement cascade (CCC); hepatocellular carcinoma (HCC); nonalcoholic fatty liver disease (NAFLD); viral hepatitis.","False","Visium","1279","31053"
"GSE192742_GSM5764420","mouse","liver","35021063,36304458","Title 1: Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Title 2: The C1q-ApoE complex: A new hallmark pathology of viral hepatitis and nonalcoholic fatty liver disease.","Abstract 1: The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here we present a spatial proteogenomic atlas of the healthy and obese human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics, and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells, including a population of lipid-associated macrophages (LAMs) at the bile ducts. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and LAMs. We also uncover the respective spatially resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that LAMs are induced by local lipid exposure, leading to their induction in steatotic regions of the murine and human liver, while Kupffer cell development crucially depends on their cross-talk with hepatic stellate cells via the evolutionarily conserved ALK1-BMP9/10 axis. Abstract 2: We recently identified a high-affinity C1q-ApoE complex in human artery atherosclerotic intima lesions and in human amyloid plaques of Alzheimer's Disease brains defining a common pathogenetic pathway of two diverse diseases, i.e. atherosclerosis and dementia. C1q is the initiating and controlling protein of the classical complement cascade (CCC), which occupies a key role in multiple acute and chronic inflammatory tissue responses. C1q is largely produced by myeloid cells including Kupffer cells (KCs) and subsequently secreted into the circulation as an inactive preprotein. Its binding partner, Apolipoprotein E (ApoE), is produced by KCs and hepatocytes and it is also secreted into the circulation, where it regulates essential steps of lipid transport. In addition to its major source, ApoE can be produced by non-liver cells including immune cells and multiple other cells depending on local tissue contexts. To initiate the CCC cascade, C1q must be activated by molecules as varied as oxidized lipids, amyloid fibrils, and immune complexes. However, ApoE is mute towards inactive C1q but binds at high-affinity to its activated form. Specifically, our studies revealed that ApoE is a CCC-specific checkpoint inhibitor via the formation of the C1q-ApoE complex. We proposed that it may arise in multiple if not all CCC-associated diseases and that its presence indicates ongoing CCC activity. Here, we turned to the liver to examine C1q-ApoE complexes in human B- and C-viral hepatitis and nonalcoholic fatty liver disease (NAFLD). In addition, we used multidrug-resistance-2 gene-knockout (Mdr2-KO) mice as a model for inflammatory liver disease and hepatocellular carcinoma (HCC) pathogenesis. In normal murine and human livers, KCs were the major C1q-producing cell type while hepatocytes were the primary ApoE-forming cell type though the C1q-ApoE complex was rare or nonexistent. However, significant numbers of C1q-ApoE complexes formed in both Mdr2-KO, human viral hepatitis, and NAFLD around portal triads where immune cells had infiltrated the liver. Additionally, high numbers of C1q-ApoE complexes emerged in human livers in areas of extracellular lipid droplets across the entire liver parenchyma in NAFLD-affected patients. Thus, the C1q-ApoE complex is a new pathological hallmark of viral hepatitis B and C and NAFLD.","Keywords 1: CITE-seq; Kupffer cell; NAFLD; across species; atlas; lipid-associated macrophage; liver; multi-omic; proteogenomic; spatial transcriptomics. Keywords 2: C1q-ApoE complex; classical complement cascade (CCC); hepatocellular carcinoma (HCC); nonalcoholic fatty liver disease (NAFLD); viral hepatitis.","False","Visium","2348","31053"
"GSE192742_GSM5764421","mouse","liver","35021063,36304458","Title 1: Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Title 2: The C1q-ApoE complex: A new hallmark pathology of viral hepatitis and nonalcoholic fatty liver disease.","Abstract 1: The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here we present a spatial proteogenomic atlas of the healthy and obese human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics, and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells, including a population of lipid-associated macrophages (LAMs) at the bile ducts. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and LAMs. We also uncover the respective spatially resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that LAMs are induced by local lipid exposure, leading to their induction in steatotic regions of the murine and human liver, while Kupffer cell development crucially depends on their cross-talk with hepatic stellate cells via the evolutionarily conserved ALK1-BMP9/10 axis. Abstract 2: We recently identified a high-affinity C1q-ApoE complex in human artery atherosclerotic intima lesions and in human amyloid plaques of Alzheimer's Disease brains defining a common pathogenetic pathway of two diverse diseases, i.e. atherosclerosis and dementia. C1q is the initiating and controlling protein of the classical complement cascade (CCC), which occupies a key role in multiple acute and chronic inflammatory tissue responses. C1q is largely produced by myeloid cells including Kupffer cells (KCs) and subsequently secreted into the circulation as an inactive preprotein. Its binding partner, Apolipoprotein E (ApoE), is produced by KCs and hepatocytes and it is also secreted into the circulation, where it regulates essential steps of lipid transport. In addition to its major source, ApoE can be produced by non-liver cells including immune cells and multiple other cells depending on local tissue contexts. To initiate the CCC cascade, C1q must be activated by molecules as varied as oxidized lipids, amyloid fibrils, and immune complexes. However, ApoE is mute towards inactive C1q but binds at high-affinity to its activated form. Specifically, our studies revealed that ApoE is a CCC-specific checkpoint inhibitor via the formation of the C1q-ApoE complex. We proposed that it may arise in multiple if not all CCC-associated diseases and that its presence indicates ongoing CCC activity. Here, we turned to the liver to examine C1q-ApoE complexes in human B- and C-viral hepatitis and nonalcoholic fatty liver disease (NAFLD). In addition, we used multidrug-resistance-2 gene-knockout (Mdr2-KO) mice as a model for inflammatory liver disease and hepatocellular carcinoma (HCC) pathogenesis. In normal murine and human livers, KCs were the major C1q-producing cell type while hepatocytes were the primary ApoE-forming cell type though the C1q-ApoE complex was rare or nonexistent. However, significant numbers of C1q-ApoE complexes formed in both Mdr2-KO, human viral hepatitis, and NAFLD around portal triads where immune cells had infiltrated the liver. Additionally, high numbers of C1q-ApoE complexes emerged in human livers in areas of extracellular lipid droplets across the entire liver parenchyma in NAFLD-affected patients. Thus, the C1q-ApoE complex is a new pathological hallmark of viral hepatitis B and C and NAFLD.","Keywords 1: CITE-seq; Kupffer cell; NAFLD; across species; atlas; lipid-associated macrophage; liver; multi-omic; proteogenomic; spatial transcriptomics. Keywords 2: C1q-ApoE complex; classical complement cascade (CCC); hepatocellular carcinoma (HCC); nonalcoholic fatty liver disease (NAFLD); viral hepatitis.","False","Visium","2076","31053"
"GSE192742_GSM5764422","mouse","liver","35021063,36304458","Title 1: Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Title 2: The C1q-ApoE complex: A new hallmark pathology of viral hepatitis and nonalcoholic fatty liver disease.","Abstract 1: The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here we present a spatial proteogenomic atlas of the healthy and obese human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics, and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells, including a population of lipid-associated macrophages (LAMs) at the bile ducts. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and LAMs. We also uncover the respective spatially resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that LAMs are induced by local lipid exposure, leading to their induction in steatotic regions of the murine and human liver, while Kupffer cell development crucially depends on their cross-talk with hepatic stellate cells via the evolutionarily conserved ALK1-BMP9/10 axis. Abstract 2: We recently identified a high-affinity C1q-ApoE complex in human artery atherosclerotic intima lesions and in human amyloid plaques of Alzheimer's Disease brains defining a common pathogenetic pathway of two diverse diseases, i.e. atherosclerosis and dementia. C1q is the initiating and controlling protein of the classical complement cascade (CCC), which occupies a key role in multiple acute and chronic inflammatory tissue responses. C1q is largely produced by myeloid cells including Kupffer cells (KCs) and subsequently secreted into the circulation as an inactive preprotein. Its binding partner, Apolipoprotein E (ApoE), is produced by KCs and hepatocytes and it is also secreted into the circulation, where it regulates essential steps of lipid transport. In addition to its major source, ApoE can be produced by non-liver cells including immune cells and multiple other cells depending on local tissue contexts. To initiate the CCC cascade, C1q must be activated by molecules as varied as oxidized lipids, amyloid fibrils, and immune complexes. However, ApoE is mute towards inactive C1q but binds at high-affinity to its activated form. Specifically, our studies revealed that ApoE is a CCC-specific checkpoint inhibitor via the formation of the C1q-ApoE complex. We proposed that it may arise in multiple if not all CCC-associated diseases and that its presence indicates ongoing CCC activity. Here, we turned to the liver to examine C1q-ApoE complexes in human B- and C-viral hepatitis and nonalcoholic fatty liver disease (NAFLD). In addition, we used multidrug-resistance-2 gene-knockout (Mdr2-KO) mice as a model for inflammatory liver disease and hepatocellular carcinoma (HCC) pathogenesis. In normal murine and human livers, KCs were the major C1q-producing cell type while hepatocytes were the primary ApoE-forming cell type though the C1q-ApoE complex was rare or nonexistent. However, significant numbers of C1q-ApoE complexes formed in both Mdr2-KO, human viral hepatitis, and NAFLD around portal triads where immune cells had infiltrated the liver. Additionally, high numbers of C1q-ApoE complexes emerged in human livers in areas of extracellular lipid droplets across the entire liver parenchyma in NAFLD-affected patients. Thus, the C1q-ApoE complex is a new pathological hallmark of viral hepatitis B and C and NAFLD.","Keywords 1: CITE-seq; Kupffer cell; NAFLD; across species; atlas; lipid-associated macrophage; liver; multi-omic; proteogenomic; spatial transcriptomics. Keywords 2: C1q-ApoE complex; classical complement cascade (CCC); hepatocellular carcinoma (HCC); nonalcoholic fatty liver disease (NAFLD); viral hepatitis.","False","Visium","2254","31053"
"GSE192742_GSM5764424","human","liver","35021063,36304458","Title 1: Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Title 2: The C1q-ApoE complex: A new hallmark pathology of viral hepatitis and nonalcoholic fatty liver disease.","Abstract 1: The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here we present a spatial proteogenomic atlas of the healthy and obese human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics, and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells, including a population of lipid-associated macrophages (LAMs) at the bile ducts. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and LAMs. We also uncover the respective spatially resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that LAMs are induced by local lipid exposure, leading to their induction in steatotic regions of the murine and human liver, while Kupffer cell development crucially depends on their cross-talk with hepatic stellate cells via the evolutionarily conserved ALK1-BMP9/10 axis. Abstract 2: We recently identified a high-affinity C1q-ApoE complex in human artery atherosclerotic intima lesions and in human amyloid plaques of Alzheimer's Disease brains defining a common pathogenetic pathway of two diverse diseases, i.e. atherosclerosis and dementia. C1q is the initiating and controlling protein of the classical complement cascade (CCC), which occupies a key role in multiple acute and chronic inflammatory tissue responses. C1q is largely produced by myeloid cells including Kupffer cells (KCs) and subsequently secreted into the circulation as an inactive preprotein. Its binding partner, Apolipoprotein E (ApoE), is produced by KCs and hepatocytes and it is also secreted into the circulation, where it regulates essential steps of lipid transport. In addition to its major source, ApoE can be produced by non-liver cells including immune cells and multiple other cells depending on local tissue contexts. To initiate the CCC cascade, C1q must be activated by molecules as varied as oxidized lipids, amyloid fibrils, and immune complexes. However, ApoE is mute towards inactive C1q but binds at high-affinity to its activated form. Specifically, our studies revealed that ApoE is a CCC-specific checkpoint inhibitor via the formation of the C1q-ApoE complex. We proposed that it may arise in multiple if not all CCC-associated diseases and that its presence indicates ongoing CCC activity. Here, we turned to the liver to examine C1q-ApoE complexes in human B- and C-viral hepatitis and nonalcoholic fatty liver disease (NAFLD). In addition, we used multidrug-resistance-2 gene-knockout (Mdr2-KO) mice as a model for inflammatory liver disease and hepatocellular carcinoma (HCC) pathogenesis. In normal murine and human livers, KCs were the major C1q-producing cell type while hepatocytes were the primary ApoE-forming cell type though the C1q-ApoE complex was rare or nonexistent. However, significant numbers of C1q-ApoE complexes formed in both Mdr2-KO, human viral hepatitis, and NAFLD around portal triads where immune cells had infiltrated the liver. Additionally, high numbers of C1q-ApoE complexes emerged in human livers in areas of extracellular lipid droplets across the entire liver parenchyma in NAFLD-affected patients. Thus, the C1q-ApoE complex is a new pathological hallmark of viral hepatitis B and C and NAFLD.","Keywords 1: CITE-seq; Kupffer cell; NAFLD; across species; atlas; lipid-associated macrophage; liver; multi-omic; proteogenomic; spatial transcriptomics. Keywords 2: C1q-ApoE complex; classical complement cascade (CCC); hepatocellular carcinoma (HCC); nonalcoholic fatty liver disease (NAFLD); viral hepatitis.","False","Visium","1248","32738"
"GSE192742_GSM5764425","human","liver","35021063,36304458","Title 1: Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Title 2: The C1q-ApoE complex: A new hallmark pathology of viral hepatitis and nonalcoholic fatty liver disease.","Abstract 1: The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here we present a spatial proteogenomic atlas of the healthy and obese human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics, and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells, including a population of lipid-associated macrophages (LAMs) at the bile ducts. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and LAMs. We also uncover the respective spatially resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that LAMs are induced by local lipid exposure, leading to their induction in steatotic regions of the murine and human liver, while Kupffer cell development crucially depends on their cross-talk with hepatic stellate cells via the evolutionarily conserved ALK1-BMP9/10 axis. Abstract 2: We recently identified a high-affinity C1q-ApoE complex in human artery atherosclerotic intima lesions and in human amyloid plaques of Alzheimer's Disease brains defining a common pathogenetic pathway of two diverse diseases, i.e. atherosclerosis and dementia. C1q is the initiating and controlling protein of the classical complement cascade (CCC), which occupies a key role in multiple acute and chronic inflammatory tissue responses. C1q is largely produced by myeloid cells including Kupffer cells (KCs) and subsequently secreted into the circulation as an inactive preprotein. Its binding partner, Apolipoprotein E (ApoE), is produced by KCs and hepatocytes and it is also secreted into the circulation, where it regulates essential steps of lipid transport. In addition to its major source, ApoE can be produced by non-liver cells including immune cells and multiple other cells depending on local tissue contexts. To initiate the CCC cascade, C1q must be activated by molecules as varied as oxidized lipids, amyloid fibrils, and immune complexes. However, ApoE is mute towards inactive C1q but binds at high-affinity to its activated form. Specifically, our studies revealed that ApoE is a CCC-specific checkpoint inhibitor via the formation of the C1q-ApoE complex. We proposed that it may arise in multiple if not all CCC-associated diseases and that its presence indicates ongoing CCC activity. Here, we turned to the liver to examine C1q-ApoE complexes in human B- and C-viral hepatitis and nonalcoholic fatty liver disease (NAFLD). In addition, we used multidrug-resistance-2 gene-knockout (Mdr2-KO) mice as a model for inflammatory liver disease and hepatocellular carcinoma (HCC) pathogenesis. In normal murine and human livers, KCs were the major C1q-producing cell type while hepatocytes were the primary ApoE-forming cell type though the C1q-ApoE complex was rare or nonexistent. However, significant numbers of C1q-ApoE complexes formed in both Mdr2-KO, human viral hepatitis, and NAFLD around portal triads where immune cells had infiltrated the liver. Additionally, high numbers of C1q-ApoE complexes emerged in human livers in areas of extracellular lipid droplets across the entire liver parenchyma in NAFLD-affected patients. Thus, the C1q-ApoE complex is a new pathological hallmark of viral hepatitis B and C and NAFLD.","Keywords 1: CITE-seq; Kupffer cell; NAFLD; across species; atlas; lipid-associated macrophage; liver; multi-omic; proteogenomic; spatial transcriptomics. Keywords 2: C1q-ApoE complex; classical complement cascade (CCC); hepatocellular carcinoma (HCC); nonalcoholic fatty liver disease (NAFLD); viral hepatitis.","False","Visium","532","32738"
"GSE192742_GSM5764426","human","liver","35021063,36304458","Title 1: Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Title 2: The C1q-ApoE complex: A new hallmark pathology of viral hepatitis and nonalcoholic fatty liver disease.","Abstract 1: The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here we present a spatial proteogenomic atlas of the healthy and obese human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics, and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells, including a population of lipid-associated macrophages (LAMs) at the bile ducts. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and LAMs. We also uncover the respective spatially resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that LAMs are induced by local lipid exposure, leading to their induction in steatotic regions of the murine and human liver, while Kupffer cell development crucially depends on their cross-talk with hepatic stellate cells via the evolutionarily conserved ALK1-BMP9/10 axis. Abstract 2: We recently identified a high-affinity C1q-ApoE complex in human artery atherosclerotic intima lesions and in human amyloid plaques of Alzheimer's Disease brains defining a common pathogenetic pathway of two diverse diseases, i.e. atherosclerosis and dementia. C1q is the initiating and controlling protein of the classical complement cascade (CCC), which occupies a key role in multiple acute and chronic inflammatory tissue responses. C1q is largely produced by myeloid cells including Kupffer cells (KCs) and subsequently secreted into the circulation as an inactive preprotein. Its binding partner, Apolipoprotein E (ApoE), is produced by KCs and hepatocytes and it is also secreted into the circulation, where it regulates essential steps of lipid transport. In addition to its major source, ApoE can be produced by non-liver cells including immune cells and multiple other cells depending on local tissue contexts. To initiate the CCC cascade, C1q must be activated by molecules as varied as oxidized lipids, amyloid fibrils, and immune complexes. However, ApoE is mute towards inactive C1q but binds at high-affinity to its activated form. Specifically, our studies revealed that ApoE is a CCC-specific checkpoint inhibitor via the formation of the C1q-ApoE complex. We proposed that it may arise in multiple if not all CCC-associated diseases and that its presence indicates ongoing CCC activity. Here, we turned to the liver to examine C1q-ApoE complexes in human B- and C-viral hepatitis and nonalcoholic fatty liver disease (NAFLD). In addition, we used multidrug-resistance-2 gene-knockout (Mdr2-KO) mice as a model for inflammatory liver disease and hepatocellular carcinoma (HCC) pathogenesis. In normal murine and human livers, KCs were the major C1q-producing cell type while hepatocytes were the primary ApoE-forming cell type though the C1q-ApoE complex was rare or nonexistent. However, significant numbers of C1q-ApoE complexes formed in both Mdr2-KO, human viral hepatitis, and NAFLD around portal triads where immune cells had infiltrated the liver. Additionally, high numbers of C1q-ApoE complexes emerged in human livers in areas of extracellular lipid droplets across the entire liver parenchyma in NAFLD-affected patients. Thus, the C1q-ApoE complex is a new pathological hallmark of viral hepatitis B and C and NAFLD.","Keywords 1: CITE-seq; Kupffer cell; NAFLD; across species; atlas; lipid-associated macrophage; liver; multi-omic; proteogenomic; spatial transcriptomics. Keywords 2: C1q-ApoE complex; classical complement cascade (CCC); hepatocellular carcinoma (HCC); nonalcoholic fatty liver disease (NAFLD); viral hepatitis.","False","Visium","2501","32738"
"GSE192742_GSM5764427","human","liver","35021063,36304458","Title 1: Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Title 2: The C1q-ApoE complex: A new hallmark pathology of viral hepatitis and nonalcoholic fatty liver disease.","Abstract 1: The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here we present a spatial proteogenomic atlas of the healthy and obese human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics, and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells, including a population of lipid-associated macrophages (LAMs) at the bile ducts. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and LAMs. We also uncover the respective spatially resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that LAMs are induced by local lipid exposure, leading to their induction in steatotic regions of the murine and human liver, while Kupffer cell development crucially depends on their cross-talk with hepatic stellate cells via the evolutionarily conserved ALK1-BMP9/10 axis. Abstract 2: We recently identified a high-affinity C1q-ApoE complex in human artery atherosclerotic intima lesions and in human amyloid plaques of Alzheimer's Disease brains defining a common pathogenetic pathway of two diverse diseases, i.e. atherosclerosis and dementia. C1q is the initiating and controlling protein of the classical complement cascade (CCC), which occupies a key role in multiple acute and chronic inflammatory tissue responses. C1q is largely produced by myeloid cells including Kupffer cells (KCs) and subsequently secreted into the circulation as an inactive preprotein. Its binding partner, Apolipoprotein E (ApoE), is produced by KCs and hepatocytes and it is also secreted into the circulation, where it regulates essential steps of lipid transport. In addition to its major source, ApoE can be produced by non-liver cells including immune cells and multiple other cells depending on local tissue contexts. To initiate the CCC cascade, C1q must be activated by molecules as varied as oxidized lipids, amyloid fibrils, and immune complexes. However, ApoE is mute towards inactive C1q but binds at high-affinity to its activated form. Specifically, our studies revealed that ApoE is a CCC-specific checkpoint inhibitor via the formation of the C1q-ApoE complex. We proposed that it may arise in multiple if not all CCC-associated diseases and that its presence indicates ongoing CCC activity. Here, we turned to the liver to examine C1q-ApoE complexes in human B- and C-viral hepatitis and nonalcoholic fatty liver disease (NAFLD). In addition, we used multidrug-resistance-2 gene-knockout (Mdr2-KO) mice as a model for inflammatory liver disease and hepatocellular carcinoma (HCC) pathogenesis. In normal murine and human livers, KCs were the major C1q-producing cell type while hepatocytes were the primary ApoE-forming cell type though the C1q-ApoE complex was rare or nonexistent. However, significant numbers of C1q-ApoE complexes formed in both Mdr2-KO, human viral hepatitis, and NAFLD around portal triads where immune cells had infiltrated the liver. Additionally, high numbers of C1q-ApoE complexes emerged in human livers in areas of extracellular lipid droplets across the entire liver parenchyma in NAFLD-affected patients. Thus, the C1q-ApoE complex is a new pathological hallmark of viral hepatitis B and C and NAFLD.","Keywords 1: CITE-seq; Kupffer cell; NAFLD; across species; atlas; lipid-associated macrophage; liver; multi-omic; proteogenomic; spatial transcriptomics. Keywords 2: C1q-ApoE complex; classical complement cascade (CCC); hepatocellular carcinoma (HCC); nonalcoholic fatty liver disease (NAFLD); viral hepatitis.","False","Visium","1185","32738"
"GSE192742_GSM5764428","human","liver","35021063,36304458","Title 1: Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Title 2: The C1q-ApoE complex: A new hallmark pathology of viral hepatitis and nonalcoholic fatty liver disease.","Abstract 1: The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here we present a spatial proteogenomic atlas of the healthy and obese human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics, and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells, including a population of lipid-associated macrophages (LAMs) at the bile ducts. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and LAMs. We also uncover the respective spatially resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that LAMs are induced by local lipid exposure, leading to their induction in steatotic regions of the murine and human liver, while Kupffer cell development crucially depends on their cross-talk with hepatic stellate cells via the evolutionarily conserved ALK1-BMP9/10 axis. Abstract 2: We recently identified a high-affinity C1q-ApoE complex in human artery atherosclerotic intima lesions and in human amyloid plaques of Alzheimer's Disease brains defining a common pathogenetic pathway of two diverse diseases, i.e. atherosclerosis and dementia. C1q is the initiating and controlling protein of the classical complement cascade (CCC), which occupies a key role in multiple acute and chronic inflammatory tissue responses. C1q is largely produced by myeloid cells including Kupffer cells (KCs) and subsequently secreted into the circulation as an inactive preprotein. Its binding partner, Apolipoprotein E (ApoE), is produced by KCs and hepatocytes and it is also secreted into the circulation, where it regulates essential steps of lipid transport. In addition to its major source, ApoE can be produced by non-liver cells including immune cells and multiple other cells depending on local tissue contexts. To initiate the CCC cascade, C1q must be activated by molecules as varied as oxidized lipids, amyloid fibrils, and immune complexes. However, ApoE is mute towards inactive C1q but binds at high-affinity to its activated form. Specifically, our studies revealed that ApoE is a CCC-specific checkpoint inhibitor via the formation of the C1q-ApoE complex. We proposed that it may arise in multiple if not all CCC-associated diseases and that its presence indicates ongoing CCC activity. Here, we turned to the liver to examine C1q-ApoE complexes in human B- and C-viral hepatitis and nonalcoholic fatty liver disease (NAFLD). In addition, we used multidrug-resistance-2 gene-knockout (Mdr2-KO) mice as a model for inflammatory liver disease and hepatocellular carcinoma (HCC) pathogenesis. In normal murine and human livers, KCs were the major C1q-producing cell type while hepatocytes were the primary ApoE-forming cell type though the C1q-ApoE complex was rare or nonexistent. However, significant numbers of C1q-ApoE complexes formed in both Mdr2-KO, human viral hepatitis, and NAFLD around portal triads where immune cells had infiltrated the liver. Additionally, high numbers of C1q-ApoE complexes emerged in human livers in areas of extracellular lipid droplets across the entire liver parenchyma in NAFLD-affected patients. Thus, the C1q-ApoE complex is a new pathological hallmark of viral hepatitis B and C and NAFLD.","Keywords 1: CITE-seq; Kupffer cell; NAFLD; across species; atlas; lipid-associated macrophage; liver; multi-omic; proteogenomic; spatial transcriptomics. Keywords 2: C1q-ApoE complex; classical complement cascade (CCC); hepatocellular carcinoma (HCC); nonalcoholic fatty liver disease (NAFLD); viral hepatitis.","False","Visium","1423","32738"
"GSE193460_GSM5808054","mouse","lung","35290801","Spatial CRISPR genomics identifies regulators of the tumor microenvironment","While CRISPR screens are helping uncover genes regulating many cell-intrinsic processes, existing approaches are suboptimal for identifying extracellular gene functions, particularly in the tissue context. Here, we developed an approach for spatial functional genomics called Perturb-map. We applied Perturb-map to knock out dozens of genes in parallel in a mouse model of lung cancer and simultaneously assessed how each knockout influenced tumor growth, histopathology, and immune composition. Moreover, we paired Perturb-map and spatial transcriptomics for unbiased analysis of CRISPR-edited tumors. We found that in Tgfbr2 knockout tumors, the tumor microenvironment (TME) was converted to a fibro-mucinous state, and T cells excluded, concomitant with upregulated TGFβ and TGFβ-mediated fibroblast activation, indicating that TGFβ-receptor loss on cancer cells increased TGFβ bioavailability and its immunosuppressive effects on the TME. These studies establish Perturb-map for functional genomics within the tissue at single-cell resolution with spatial architecture preserved and provide insight into how TGFβ responsiveness of cancer cells can affect the TME.","CRISPR screens; Socs1; TGF beta; cancer immunology; interferon gamma; lung cancer; spatial genomics; spatial transcriptomics; tumor clonality; tumor microenvironment.","True","Visium","1906","32289"
"GSE193460_GSM5808055","mouse","lung","35290801","Spatial CRISPR genomics identifies regulators of the tumor microenvironment","While CRISPR screens are helping uncover genes regulating many cell-intrinsic processes, existing approaches are suboptimal for identifying extracellular gene functions, particularly in the tissue context. Here, we developed an approach for spatial functional genomics called Perturb-map. We applied Perturb-map to knock out dozens of genes in parallel in a mouse model of lung cancer and simultaneously assessed how each knockout influenced tumor growth, histopathology, and immune composition. Moreover, we paired Perturb-map and spatial transcriptomics for unbiased analysis of CRISPR-edited tumors. We found that in Tgfbr2 knockout tumors, the tumor microenvironment (TME) was converted to a fibro-mucinous state, and T cells excluded, concomitant with upregulated TGFβ and TGFβ-mediated fibroblast activation, indicating that TGFβ-receptor loss on cancer cells increased TGFβ bioavailability and its immunosuppressive effects on the TME. These studies establish Perturb-map for functional genomics within the tissue at single-cell resolution with spatial architecture preserved and provide insight into how TGFβ responsiveness of cancer cells can affect the TME.","CRISPR screens; Socs1; TGF beta; cancer immunology; interferon gamma; lung cancer; spatial genomics; spatial transcriptomics; tumor clonality; tumor microenvironment.","True","Visium","1931","32289"
"GSE193460_GSM5808056","mouse","lung","35290801","Spatial CRISPR genomics identifies regulators of the tumor microenvironment","While CRISPR screens are helping uncover genes regulating many cell-intrinsic processes, existing approaches are suboptimal for identifying extracellular gene functions, particularly in the tissue context. Here, we developed an approach for spatial functional genomics called Perturb-map. We applied Perturb-map to knock out dozens of genes in parallel in a mouse model of lung cancer and simultaneously assessed how each knockout influenced tumor growth, histopathology, and immune composition. Moreover, we paired Perturb-map and spatial transcriptomics for unbiased analysis of CRISPR-edited tumors. We found that in Tgfbr2 knockout tumors, the tumor microenvironment (TME) was converted to a fibro-mucinous state, and T cells excluded, concomitant with upregulated TGFβ and TGFβ-mediated fibroblast activation, indicating that TGFβ-receptor loss on cancer cells increased TGFβ bioavailability and its immunosuppressive effects on the TME. These studies establish Perturb-map for functional genomics within the tissue at single-cell resolution with spatial architecture preserved and provide insight into how TGFβ responsiveness of cancer cells can affect the TME.","CRISPR screens; Socs1; TGF beta; cancer immunology; interferon gamma; lung cancer; spatial genomics; spatial transcriptomics; tumor clonality; tumor microenvironment.","True","Visium","1244","32289"
"GSE193460_GSM5808057","mouse","lung","35290801","Spatial CRISPR genomics identifies regulators of the tumor microenvironment","While CRISPR screens are helping uncover genes regulating many cell-intrinsic processes, existing approaches are suboptimal for identifying extracellular gene functions, particularly in the tissue context. Here, we developed an approach for spatial functional genomics called Perturb-map. We applied Perturb-map to knock out dozens of genes in parallel in a mouse model of lung cancer and simultaneously assessed how each knockout influenced tumor growth, histopathology, and immune composition. Moreover, we paired Perturb-map and spatial transcriptomics for unbiased analysis of CRISPR-edited tumors. We found that in Tgfbr2 knockout tumors, the tumor microenvironment (TME) was converted to a fibro-mucinous state, and T cells excluded, concomitant with upregulated TGFβ and TGFβ-mediated fibroblast activation, indicating that TGFβ-receptor loss on cancer cells increased TGFβ bioavailability and its immunosuppressive effects on the TME. These studies establish Perturb-map for functional genomics within the tissue at single-cell resolution with spatial architecture preserved and provide insight into how TGFβ responsiveness of cancer cells can affect the TME.","CRISPR screens; Socs1; TGF beta; cancer immunology; interferon gamma; lung cancer; spatial genomics; spatial transcriptomics; tumor clonality; tumor microenvironment.","True","Visium","2546","32289"
"GSE194329_GSM5833528","human","glioma","36823172","Spatial transcriptomics reveals niche-specific enrichment and vulnerabilities of radial glial stem-like cells in malignant gliomas","Diffuse midline glioma-H3K27M mutant (DMG) and glioblastoma (GBM) are the most lethal brain tumors that primarily occur in pediatric and adult patients, respectively. Both tumors exhibit significant heterogeneity, shaped by distinct genetic/epigenetic drivers, transcriptional programs including RNA splicing, and microenvironmental cues in glioma niches. However, the spatial organization of cellular states and niche-specific regulatory programs remain to be investigated. Here, we perform a spatial profiling of DMG and GBM combining short- and long-read spatial transcriptomics, and single-cell transcriptomic datasets. We identify clinically relevant transcriptional programs, RNA isoform diversity, and multi-cellular ecosystems across different glioma niches. We find that while the tumor core enriches for oligodendrocyte precursor-like cells, radial glial stem-like (RG-like) cells are enriched in the neuron-rich invasive niche in both DMG and GBM. Further, we identify niche-specific regulatory programs for RG-like cells, and functionally confirm that FAM20C mediates invasive growth of RG-like cells in a neuron-rich microenvironment in a human neural stem cell derived orthotopic DMG model. Together, our results provide a blueprint for understanding the spatial architecture and niche-specific vulnerabilities of DMG and GBM.","","True","Visium","4337","36601"
"GSE194329_GSM5833529","human","glioma","36823172","Spatial transcriptomics reveals niche-specific enrichment and vulnerabilities of radial glial stem-like cells in malignant gliomas","Diffuse midline glioma-H3K27M mutant (DMG) and glioblastoma (GBM) are the most lethal brain tumors that primarily occur in pediatric and adult patients, respectively. Both tumors exhibit significant heterogeneity, shaped by distinct genetic/epigenetic drivers, transcriptional programs including RNA splicing, and microenvironmental cues in glioma niches. However, the spatial organization of cellular states and niche-specific regulatory programs remain to be investigated. Here, we perform a spatial profiling of DMG and GBM combining short- and long-read spatial transcriptomics, and single-cell transcriptomic datasets. We identify clinically relevant transcriptional programs, RNA isoform diversity, and multi-cellular ecosystems across different glioma niches. We find that while the tumor core enriches for oligodendrocyte precursor-like cells, radial glial stem-like (RG-like) cells are enriched in the neuron-rich invasive niche in both DMG and GBM. Further, we identify niche-specific regulatory programs for RG-like cells, and functionally confirm that FAM20C mediates invasive growth of RG-like cells in a neuron-rich microenvironment in a human neural stem cell derived orthotopic DMG model. Together, our results provide a blueprint for understanding the spatial architecture and niche-specific vulnerabilities of DMG and GBM.","","True","Visium","2305","36601"
"GSE194329_GSM5833530","human","glioma","36823172","Spatial transcriptomics reveals niche-specific enrichment and vulnerabilities of radial glial stem-like cells in malignant gliomas","Diffuse midline glioma-H3K27M mutant (DMG) and glioblastoma (GBM) are the most lethal brain tumors that primarily occur in pediatric and adult patients, respectively. Both tumors exhibit significant heterogeneity, shaped by distinct genetic/epigenetic drivers, transcriptional programs including RNA splicing, and microenvironmental cues in glioma niches. However, the spatial organization of cellular states and niche-specific regulatory programs remain to be investigated. Here, we perform a spatial profiling of DMG and GBM combining short- and long-read spatial transcriptomics, and single-cell transcriptomic datasets. We identify clinically relevant transcriptional programs, RNA isoform diversity, and multi-cellular ecosystems across different glioma niches. We find that while the tumor core enriches for oligodendrocyte precursor-like cells, radial glial stem-like (RG-like) cells are enriched in the neuron-rich invasive niche in both DMG and GBM. Further, we identify niche-specific regulatory programs for RG-like cells, and functionally confirm that FAM20C mediates invasive growth of RG-like cells in a neuron-rich microenvironment in a human neural stem cell derived orthotopic DMG model. Together, our results provide a blueprint for understanding the spatial architecture and niche-specific vulnerabilities of DMG and GBM.","","True","Visium","1063","36601"
"GSE194329_GSM5833531","human","glioma","36823172","Spatial transcriptomics reveals niche-specific enrichment and vulnerabilities of radial glial stem-like cells in malignant gliomas","Diffuse midline glioma-H3K27M mutant (DMG) and glioblastoma (GBM) are the most lethal brain tumors that primarily occur in pediatric and adult patients, respectively. Both tumors exhibit significant heterogeneity, shaped by distinct genetic/epigenetic drivers, transcriptional programs including RNA splicing, and microenvironmental cues in glioma niches. However, the spatial organization of cellular states and niche-specific regulatory programs remain to be investigated. Here, we perform a spatial profiling of DMG and GBM combining short- and long-read spatial transcriptomics, and single-cell transcriptomic datasets. We identify clinically relevant transcriptional programs, RNA isoform diversity, and multi-cellular ecosystems across different glioma niches. We find that while the tumor core enriches for oligodendrocyte precursor-like cells, radial glial stem-like (RG-like) cells are enriched in the neuron-rich invasive niche in both DMG and GBM. Further, we identify niche-specific regulatory programs for RG-like cells, and functionally confirm that FAM20C mediates invasive growth of RG-like cells in a neuron-rich microenvironment in a human neural stem cell derived orthotopic DMG model. Together, our results provide a blueprint for understanding the spatial architecture and niche-specific vulnerabilities of DMG and GBM.","","True","Visium","1056","36601"
"GSE194329_GSM5833532","human","glioma","36823172","Spatial transcriptomics reveals niche-specific enrichment and vulnerabilities of radial glial stem-like cells in malignant gliomas","Diffuse midline glioma-H3K27M mutant (DMG) and glioblastoma (GBM) are the most lethal brain tumors that primarily occur in pediatric and adult patients, respectively. Both tumors exhibit significant heterogeneity, shaped by distinct genetic/epigenetic drivers, transcriptional programs including RNA splicing, and microenvironmental cues in glioma niches. However, the spatial organization of cellular states and niche-specific regulatory programs remain to be investigated. Here, we perform a spatial profiling of DMG and GBM combining short- and long-read spatial transcriptomics, and single-cell transcriptomic datasets. We identify clinically relevant transcriptional programs, RNA isoform diversity, and multi-cellular ecosystems across different glioma niches. We find that while the tumor core enriches for oligodendrocyte precursor-like cells, radial glial stem-like (RG-like) cells are enriched in the neuron-rich invasive niche in both DMG and GBM. Further, we identify niche-specific regulatory programs for RG-like cells, and functionally confirm that FAM20C mediates invasive growth of RG-like cells in a neuron-rich microenvironment in a human neural stem cell derived orthotopic DMG model. Together, our results provide a blueprint for understanding the spatial architecture and niche-specific vulnerabilities of DMG and GBM.","","True","Visium","2128","36601"
"GSE194329_GSM5833533","human","glioma","36823172","Spatial transcriptomics reveals niche-specific enrichment and vulnerabilities of radial glial stem-like cells in malignant gliomas","Diffuse midline glioma-H3K27M mutant (DMG) and glioblastoma (GBM) are the most lethal brain tumors that primarily occur in pediatric and adult patients, respectively. Both tumors exhibit significant heterogeneity, shaped by distinct genetic/epigenetic drivers, transcriptional programs including RNA splicing, and microenvironmental cues in glioma niches. However, the spatial organization of cellular states and niche-specific regulatory programs remain to be investigated. Here, we perform a spatial profiling of DMG and GBM combining short- and long-read spatial transcriptomics, and single-cell transcriptomic datasets. We identify clinically relevant transcriptional programs, RNA isoform diversity, and multi-cellular ecosystems across different glioma niches. We find that while the tumor core enriches for oligodendrocyte precursor-like cells, radial glial stem-like (RG-like) cells are enriched in the neuron-rich invasive niche in both DMG and GBM. Further, we identify niche-specific regulatory programs for RG-like cells, and functionally confirm that FAM20C mediates invasive growth of RG-like cells in a neuron-rich microenvironment in a human neural stem cell derived orthotopic DMG model. Together, our results provide a blueprint for understanding the spatial architecture and niche-specific vulnerabilities of DMG and GBM.","","True","Visium","4764","36601"
"GSE194329_GSM5833534","human","glioma","36823172","Spatial transcriptomics reveals niche-specific enrichment and vulnerabilities of radial glial stem-like cells in malignant gliomas","Diffuse midline glioma-H3K27M mutant (DMG) and glioblastoma (GBM) are the most lethal brain tumors that primarily occur in pediatric and adult patients, respectively. Both tumors exhibit significant heterogeneity, shaped by distinct genetic/epigenetic drivers, transcriptional programs including RNA splicing, and microenvironmental cues in glioma niches. However, the spatial organization of cellular states and niche-specific regulatory programs remain to be investigated. Here, we perform a spatial profiling of DMG and GBM combining short- and long-read spatial transcriptomics, and single-cell transcriptomic datasets. We identify clinically relevant transcriptional programs, RNA isoform diversity, and multi-cellular ecosystems across different glioma niches. We find that while the tumor core enriches for oligodendrocyte precursor-like cells, radial glial stem-like (RG-like) cells are enriched in the neuron-rich invasive niche in both DMG and GBM. Further, we identify niche-specific regulatory programs for RG-like cells, and functionally confirm that FAM20C mediates invasive growth of RG-like cells in a neuron-rich microenvironment in a human neural stem cell derived orthotopic DMG model. Together, our results provide a blueprint for understanding the spatial architecture and niche-specific vulnerabilities of DMG and GBM.","","True","Visium","2891","36601"
"GSE194329_GSM5833535","human","glioma","36823172","Spatial transcriptomics reveals niche-specific enrichment and vulnerabilities of radial glial stem-like cells in malignant gliomas","Diffuse midline glioma-H3K27M mutant (DMG) and glioblastoma (GBM) are the most lethal brain tumors that primarily occur in pediatric and adult patients, respectively. Both tumors exhibit significant heterogeneity, shaped by distinct genetic/epigenetic drivers, transcriptional programs including RNA splicing, and microenvironmental cues in glioma niches. However, the spatial organization of cellular states and niche-specific regulatory programs remain to be investigated. Here, we perform a spatial profiling of DMG and GBM combining short- and long-read spatial transcriptomics, and single-cell transcriptomic datasets. We identify clinically relevant transcriptional programs, RNA isoform diversity, and multi-cellular ecosystems across different glioma niches. We find that while the tumor core enriches for oligodendrocyte precursor-like cells, radial glial stem-like (RG-like) cells are enriched in the neuron-rich invasive niche in both DMG and GBM. Further, we identify niche-specific regulatory programs for RG-like cells, and functionally confirm that FAM20C mediates invasive growth of RG-like cells in a neuron-rich microenvironment in a human neural stem cell derived orthotopic DMG model. Together, our results provide a blueprint for understanding the spatial architecture and niche-specific vulnerabilities of DMG and GBM.","","True","Visium","1775","36601"
"GSE194329_GSM5833536","human","glioma","36823172","Spatial transcriptomics reveals niche-specific enrichment and vulnerabilities of radial glial stem-like cells in malignant gliomas","Diffuse midline glioma-H3K27M mutant (DMG) and glioblastoma (GBM) are the most lethal brain tumors that primarily occur in pediatric and adult patients, respectively. Both tumors exhibit significant heterogeneity, shaped by distinct genetic/epigenetic drivers, transcriptional programs including RNA splicing, and microenvironmental cues in glioma niches. However, the spatial organization of cellular states and niche-specific regulatory programs remain to be investigated. Here, we perform a spatial profiling of DMG and GBM combining short- and long-read spatial transcriptomics, and single-cell transcriptomic datasets. We identify clinically relevant transcriptional programs, RNA isoform diversity, and multi-cellular ecosystems across different glioma niches. We find that while the tumor core enriches for oligodendrocyte precursor-like cells, radial glial stem-like (RG-like) cells are enriched in the neuron-rich invasive niche in both DMG and GBM. Further, we identify niche-specific regulatory programs for RG-like cells, and functionally confirm that FAM20C mediates invasive growth of RG-like cells in a neuron-rich microenvironment in a human neural stem cell derived orthotopic DMG model. Together, our results provide a blueprint for understanding the spatial architecture and niche-specific vulnerabilities of DMG and GBM.","","True","Visium","2540","36601"
"GSE194329_GSM5833537","human","glioma","36823172","Spatial transcriptomics reveals niche-specific enrichment and vulnerabilities of radial glial stem-like cells in malignant gliomas","Diffuse midline glioma-H3K27M mutant (DMG) and glioblastoma (GBM) are the most lethal brain tumors that primarily occur in pediatric and adult patients, respectively. Both tumors exhibit significant heterogeneity, shaped by distinct genetic/epigenetic drivers, transcriptional programs including RNA splicing, and microenvironmental cues in glioma niches. However, the spatial organization of cellular states and niche-specific regulatory programs remain to be investigated. Here, we perform a spatial profiling of DMG and GBM combining short- and long-read spatial transcriptomics, and single-cell transcriptomic datasets. We identify clinically relevant transcriptional programs, RNA isoform diversity, and multi-cellular ecosystems across different glioma niches. We find that while the tumor core enriches for oligodendrocyte precursor-like cells, radial glial stem-like (RG-like) cells are enriched in the neuron-rich invasive niche in both DMG and GBM. Further, we identify niche-specific regulatory programs for RG-like cells, and functionally confirm that FAM20C mediates invasive growth of RG-like cells in a neuron-rich microenvironment in a human neural stem cell derived orthotopic DMG model. Together, our results provide a blueprint for understanding the spatial architecture and niche-specific vulnerabilities of DMG and GBM.","","True","Visium","2203","36601"
"GSE194329_GSM5833538","human","glioma","36823172","Spatial transcriptomics reveals niche-specific enrichment and vulnerabilities of radial glial stem-like cells in malignant gliomas","Diffuse midline glioma-H3K27M mutant (DMG) and glioblastoma (GBM) are the most lethal brain tumors that primarily occur in pediatric and adult patients, respectively. Both tumors exhibit significant heterogeneity, shaped by distinct genetic/epigenetic drivers, transcriptional programs including RNA splicing, and microenvironmental cues in glioma niches. However, the spatial organization of cellular states and niche-specific regulatory programs remain to be investigated. Here, we perform a spatial profiling of DMG and GBM combining short- and long-read spatial transcriptomics, and single-cell transcriptomic datasets. We identify clinically relevant transcriptional programs, RNA isoform diversity, and multi-cellular ecosystems across different glioma niches. We find that while the tumor core enriches for oligodendrocyte precursor-like cells, radial glial stem-like (RG-like) cells are enriched in the neuron-rich invasive niche in both DMG and GBM. Further, we identify niche-specific regulatory programs for RG-like cells, and functionally confirm that FAM20C mediates invasive growth of RG-like cells in a neuron-rich microenvironment in a human neural stem cell derived orthotopic DMG model. Together, our results provide a blueprint for understanding the spatial architecture and niche-specific vulnerabilities of DMG and GBM.","","True","Visium","2171","36601"
"GSE194338_GSM5833739","mouse","brain","","Excitatory SST neurons in the medial paralemniscal nucleus control repetitive self-grooming and encode reward","","","False","Visium","3651","32286"
"GSE195598_GSM5841550","mouse","lymph node","37691918","Multi-omics analysis identifies IgG2b class-switching with ALCAM-CD6 co-stimulation in joint-draining lymph nodes during advanced inflammatory-erosive arthritis","Introduction: Defective lymphatic drainage and translocation of B-cells in inflamed (Bin) joint-draining lymph node sinuses are pathogenic phenomena in patients with severe rheumatoid arthritis (RA). However, the molecular mechanisms underlying this lymphatic dysfunction remain poorly understood. Herein, we utilized multi-omic spatial and single-cell transcriptomics to evaluate altered cellular composition (including lymphatic endothelial cells, macrophages, B-cells, and T-cells) in the joint-draining lymph node sinuses and their associated phenotypic changes and cell-cell interactions during RA development using the tumor necrosis factor transgenic (TNF-Tg) mouse model.

Methods: Popliteal lymph nodes (PLNs) from wild-type (n=10) and TNF-Tg male mice with """"""""""""""""""""""""""""""""Early"""""""""""""""""""""""""""""""" (5 to 6-months of age; n=6) and """"""""""""""""""""""""""""""""Advanced"""""""""""""""""""""""""""""""" (>8-months of age; n=12) arthritis were harvested and processed for spatial transcriptomics. Single-cell RNA sequencing (scRNAseq) was performed in PLNs from the TNF-Tg cohorts (n=6 PLNs pooled/cohort). PLN histopathology and ELISPOT along with ankle histology and micro-CT were evaluated. Histopathology of human lymph nodes and synovia was performed for clinical correlation.

Results: Advanced PLN sinuses exhibited an increased Ighg2b/Ighm expression ratio (Early 0.5 ± 0.1 vs Advanced 1.4 ± 0.5 counts/counts; p<0.001) that significantly correlated with reduced talus bone volumes in the afferent ankle (R2 = 0.54, p<0.001). Integration of single-cell and spatial transcriptomics revealed the increased IgG2b+ plasma cells localized in MARCO+ peri-follicular medullary sinuses. A concomitant decreased Fth1 expression (Early 2.5 ± 0.74 vs Advanced 1.0 ± 0.50 counts, p<0.001) within Advanced PLN sinuses was associated with accumulation of iron-laden Prussian blue positive macrophages in lymph nodes and synovium of Advanced TNF-Tg mice, and further validated in RA clinical samples. T-cells were increased 8-fold in Advanced PLNs, and bioinformatic pathway assessment identified the interaction between ALCAM+ macrophages and CD6+ T-cells as a plausible co-stimulatory mechanism to promote IgG2b class-switching.

Discussion: Collectively, these data support a model of flare in chronic TNF-induced arthritis in which loss of lymphatic flow through affected joint-draining lymph nodes facilitates the interaction between effluxing macrophages and T-cells via ALCAM-CD6 co-stimulation, initiating IgG2b class-switching and plasma cell differentiation of the expanded Bin population. Future work is warranted to investigate immunoglobulin clonality and potential autoimmune consequences, as well as the efficacy of anti-CD6 therapy to prevent these pathogenic events.","B-cells; arthritis; lymph node; lymphatics; plasma cells; single-cell RNA sequencing; spatial transcriptomics.","True","Visium","833","32287"
"GSE195598_GSM5841551","mouse","lymph node","37691918","Multi-omics analysis identifies IgG2b class-switching with ALCAM-CD6 co-stimulation in joint-draining lymph nodes during advanced inflammatory-erosive arthritis","Introduction: Defective lymphatic drainage and translocation of B-cells in inflamed (Bin) joint-draining lymph node sinuses are pathogenic phenomena in patients with severe rheumatoid arthritis (RA). However, the molecular mechanisms underlying this lymphatic dysfunction remain poorly understood. Herein, we utilized multi-omic spatial and single-cell transcriptomics to evaluate altered cellular composition (including lymphatic endothelial cells, macrophages, B-cells, and T-cells) in the joint-draining lymph node sinuses and their associated phenotypic changes and cell-cell interactions during RA development using the tumor necrosis factor transgenic (TNF-Tg) mouse model.

Methods: Popliteal lymph nodes (PLNs) from wild-type (n=10) and TNF-Tg male mice with """"""""""""""""""""""""""""""""Early"""""""""""""""""""""""""""""""" (5 to 6-months of age; n=6) and """"""""""""""""""""""""""""""""Advanced"""""""""""""""""""""""""""""""" (>8-months of age; n=12) arthritis were harvested and processed for spatial transcriptomics. Single-cell RNA sequencing (scRNAseq) was performed in PLNs from the TNF-Tg cohorts (n=6 PLNs pooled/cohort). PLN histopathology and ELISPOT along with ankle histology and micro-CT were evaluated. Histopathology of human lymph nodes and synovia was performed for clinical correlation.

Results: Advanced PLN sinuses exhibited an increased Ighg2b/Ighm expression ratio (Early 0.5 ± 0.1 vs Advanced 1.4 ± 0.5 counts/counts; p<0.001) that significantly correlated with reduced talus bone volumes in the afferent ankle (R2 = 0.54, p<0.001). Integration of single-cell and spatial transcriptomics revealed the increased IgG2b+ plasma cells localized in MARCO+ peri-follicular medullary sinuses. A concomitant decreased Fth1 expression (Early 2.5 ± 0.74 vs Advanced 1.0 ± 0.50 counts, p<0.001) within Advanced PLN sinuses was associated with accumulation of iron-laden Prussian blue positive macrophages in lymph nodes and synovium of Advanced TNF-Tg mice, and further validated in RA clinical samples. T-cells were increased 8-fold in Advanced PLNs, and bioinformatic pathway assessment identified the interaction between ALCAM+ macrophages and CD6+ T-cells as a plausible co-stimulatory mechanism to promote IgG2b class-switching.

Discussion: Collectively, these data support a model of flare in chronic TNF-induced arthritis in which loss of lymphatic flow through affected joint-draining lymph nodes facilitates the interaction between effluxing macrophages and T-cells via ALCAM-CD6 co-stimulation, initiating IgG2b class-switching and plasma cell differentiation of the expanded Bin population. Future work is warranted to investigate immunoglobulin clonality and potential autoimmune consequences, as well as the efficacy of anti-CD6 therapy to prevent these pathogenic events.","B-cells; arthritis; lymph node; lymphatics; plasma cells; single-cell RNA sequencing; spatial transcriptomics.","True","Visium","1489","32287"
"GSE195598_GSM5841552","mouse","lymph node","37691918","Multi-omics analysis identifies IgG2b class-switching with ALCAM-CD6 co-stimulation in joint-draining lymph nodes during advanced inflammatory-erosive arthritis","Introduction: Defective lymphatic drainage and translocation of B-cells in inflamed (Bin) joint-draining lymph node sinuses are pathogenic phenomena in patients with severe rheumatoid arthritis (RA). However, the molecular mechanisms underlying this lymphatic dysfunction remain poorly understood. Herein, we utilized multi-omic spatial and single-cell transcriptomics to evaluate altered cellular composition (including lymphatic endothelial cells, macrophages, B-cells, and T-cells) in the joint-draining lymph node sinuses and their associated phenotypic changes and cell-cell interactions during RA development using the tumor necrosis factor transgenic (TNF-Tg) mouse model.

Methods: Popliteal lymph nodes (PLNs) from wild-type (n=10) and TNF-Tg male mice with """"""""""""""""""""""""""""""""Early"""""""""""""""""""""""""""""""" (5 to 6-months of age; n=6) and """"""""""""""""""""""""""""""""Advanced"""""""""""""""""""""""""""""""" (>8-months of age; n=12) arthritis were harvested and processed for spatial transcriptomics. Single-cell RNA sequencing (scRNAseq) was performed in PLNs from the TNF-Tg cohorts (n=6 PLNs pooled/cohort). PLN histopathology and ELISPOT along with ankle histology and micro-CT were evaluated. Histopathology of human lymph nodes and synovia was performed for clinical correlation.

Results: Advanced PLN sinuses exhibited an increased Ighg2b/Ighm expression ratio (Early 0.5 ± 0.1 vs Advanced 1.4 ± 0.5 counts/counts; p<0.001) that significantly correlated with reduced talus bone volumes in the afferent ankle (R2 = 0.54, p<0.001). Integration of single-cell and spatial transcriptomics revealed the increased IgG2b+ plasma cells localized in MARCO+ peri-follicular medullary sinuses. A concomitant decreased Fth1 expression (Early 2.5 ± 0.74 vs Advanced 1.0 ± 0.50 counts, p<0.001) within Advanced PLN sinuses was associated with accumulation of iron-laden Prussian blue positive macrophages in lymph nodes and synovium of Advanced TNF-Tg mice, and further validated in RA clinical samples. T-cells were increased 8-fold in Advanced PLNs, and bioinformatic pathway assessment identified the interaction between ALCAM+ macrophages and CD6+ T-cells as a plausible co-stimulatory mechanism to promote IgG2b class-switching.

Discussion: Collectively, these data support a model of flare in chronic TNF-induced arthritis in which loss of lymphatic flow through affected joint-draining lymph nodes facilitates the interaction between effluxing macrophages and T-cells via ALCAM-CD6 co-stimulation, initiating IgG2b class-switching and plasma cell differentiation of the expanded Bin population. Future work is warranted to investigate immunoglobulin clonality and potential autoimmune consequences, as well as the efficacy of anti-CD6 therapy to prevent these pathogenic events.","B-cells; arthritis; lymph node; lymphatics; plasma cells; single-cell RNA sequencing; spatial transcriptomics.","True","Visium","2015","32287"
"GSE195598_GSM5841553","mouse","lymph node","37691918","Multi-omics analysis identifies IgG2b class-switching with ALCAM-CD6 co-stimulation in joint-draining lymph nodes during advanced inflammatory-erosive arthritis","Introduction: Defective lymphatic drainage and translocation of B-cells in inflamed (Bin) joint-draining lymph node sinuses are pathogenic phenomena in patients with severe rheumatoid arthritis (RA). However, the molecular mechanisms underlying this lymphatic dysfunction remain poorly understood. Herein, we utilized multi-omic spatial and single-cell transcriptomics to evaluate altered cellular composition (including lymphatic endothelial cells, macrophages, B-cells, and T-cells) in the joint-draining lymph node sinuses and their associated phenotypic changes and cell-cell interactions during RA development using the tumor necrosis factor transgenic (TNF-Tg) mouse model.

Methods: Popliteal lymph nodes (PLNs) from wild-type (n=10) and TNF-Tg male mice with """"""""""""""""""""""""""""""""Early"""""""""""""""""""""""""""""""" (5 to 6-months of age; n=6) and """"""""""""""""""""""""""""""""Advanced"""""""""""""""""""""""""""""""" (>8-months of age; n=12) arthritis were harvested and processed for spatial transcriptomics. Single-cell RNA sequencing (scRNAseq) was performed in PLNs from the TNF-Tg cohorts (n=6 PLNs pooled/cohort). PLN histopathology and ELISPOT along with ankle histology and micro-CT were evaluated. Histopathology of human lymph nodes and synovia was performed for clinical correlation.

Results: Advanced PLN sinuses exhibited an increased Ighg2b/Ighm expression ratio (Early 0.5 ± 0.1 vs Advanced 1.4 ± 0.5 counts/counts; p<0.001) that significantly correlated with reduced talus bone volumes in the afferent ankle (R2 = 0.54, p<0.001). Integration of single-cell and spatial transcriptomics revealed the increased IgG2b+ plasma cells localized in MARCO+ peri-follicular medullary sinuses. A concomitant decreased Fth1 expression (Early 2.5 ± 0.74 vs Advanced 1.0 ± 0.50 counts, p<0.001) within Advanced PLN sinuses was associated with accumulation of iron-laden Prussian blue positive macrophages in lymph nodes and synovium of Advanced TNF-Tg mice, and further validated in RA clinical samples. T-cells were increased 8-fold in Advanced PLNs, and bioinformatic pathway assessment identified the interaction between ALCAM+ macrophages and CD6+ T-cells as a plausible co-stimulatory mechanism to promote IgG2b class-switching.

Discussion: Collectively, these data support a model of flare in chronic TNF-induced arthritis in which loss of lymphatic flow through affected joint-draining lymph nodes facilitates the interaction between effluxing macrophages and T-cells via ALCAM-CD6 co-stimulation, initiating IgG2b class-switching and plasma cell differentiation of the expanded Bin population. Future work is warranted to investigate immunoglobulin clonality and potential autoimmune consequences, as well as the efficacy of anti-CD6 therapy to prevent these pathogenic events.","B-cells; arthritis; lymph node; lymphatics; plasma cells; single-cell RNA sequencing; spatial transcriptomics.","True","Visium","1782","32287"
"GSE197023_GSM5907077","human","skin","37312623","Spatial transcriptomics combined with single-cell RNA-sequencing unravels the complex inflammatory cell network in atopic dermatitis","Background: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully understood.

Methods: Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics sequencing to characterize the cellular infiltrate in lesional skin. For single-cell analysis, we analyzed the single-cell data from suction blister material from AD lesions and HC skin at the antecubital fossa skin (4 ADs and 5 HCs) and full-thickness skin biopsies (4 ADs and 2 HCs). The multiple proximity extension assays were performed in the serum samples from 36 AD patients and 28 HCs.

Results: The single-cell analysis identified unique clusters of fibroblasts, dendritic cells, and macrophages in the lesional AD skin. Spatial transcriptomics analysis showed the upregulation of COL6A5, COL4A1, TNC, and CCL19 in COL18A1-expressing fibroblasts in the leukocyte-infiltrated areas in AD skin. CCR7-expressing dendritic cells (DCs) showed a similar distribution in the lesions. Additionally, M2 macrophages expressed CCL13 and CCL18 in this area. Ligand-receptor interaction analysis of the spatial transcriptome identified neighboring infiltration and interaction between activated COL18A1-expressing fibroblasts, CCL13- and CCL18-expressing M2 macrophages, CCR7- and LAMP3-expressing DCs, and T cells. As observed in skin lesions, serum levels of TNC and CCL18 were significantly elevated in AD, and correlated with clinical disease severity.

Conclusion: In this study, we show the unknown cellular crosstalk in leukocyte-infiltrated area in lesional skin. Our findings provide a comprehensive in-depth knowledge of the nature of AD skin lesions to guide the development of better treatments.","atopic dermatitis; single-cell transcriptomics; spatial transcriptomics; targeted proteomics.","False","Visium","188","21587"
"GSE197023_GSM5907078","human","skin","37312623","Spatial transcriptomics combined with single-cell RNA-sequencing unravels the complex inflammatory cell network in atopic dermatitis","Background: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully understood.

Methods: Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics sequencing to characterize the cellular infiltrate in lesional skin. For single-cell analysis, we analyzed the single-cell data from suction blister material from AD lesions and HC skin at the antecubital fossa skin (4 ADs and 5 HCs) and full-thickness skin biopsies (4 ADs and 2 HCs). The multiple proximity extension assays were performed in the serum samples from 36 AD patients and 28 HCs.

Results: The single-cell analysis identified unique clusters of fibroblasts, dendritic cells, and macrophages in the lesional AD skin. Spatial transcriptomics analysis showed the upregulation of COL6A5, COL4A1, TNC, and CCL19 in COL18A1-expressing fibroblasts in the leukocyte-infiltrated areas in AD skin. CCR7-expressing dendritic cells (DCs) showed a similar distribution in the lesions. Additionally, M2 macrophages expressed CCL13 and CCL18 in this area. Ligand-receptor interaction analysis of the spatial transcriptome identified neighboring infiltration and interaction between activated COL18A1-expressing fibroblasts, CCL13- and CCL18-expressing M2 macrophages, CCR7- and LAMP3-expressing DCs, and T cells. As observed in skin lesions, serum levels of TNC and CCL18 were significantly elevated in AD, and correlated with clinical disease severity.

Conclusion: In this study, we show the unknown cellular crosstalk in leukocyte-infiltrated area in lesional skin. Our findings provide a comprehensive in-depth knowledge of the nature of AD skin lesions to guide the development of better treatments.","atopic dermatitis; single-cell transcriptomics; spatial transcriptomics; targeted proteomics.","False","Visium","335","21587"
"GSE197023_GSM5907079","human","skin","37312623","Spatial transcriptomics combined with single-cell RNA-sequencing unravels the complex inflammatory cell network in atopic dermatitis","Background: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully understood.

Methods: Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics sequencing to characterize the cellular infiltrate in lesional skin. For single-cell analysis, we analyzed the single-cell data from suction blister material from AD lesions and HC skin at the antecubital fossa skin (4 ADs and 5 HCs) and full-thickness skin biopsies (4 ADs and 2 HCs). The multiple proximity extension assays were performed in the serum samples from 36 AD patients and 28 HCs.

Results: The single-cell analysis identified unique clusters of fibroblasts, dendritic cells, and macrophages in the lesional AD skin. Spatial transcriptomics analysis showed the upregulation of COL6A5, COL4A1, TNC, and CCL19 in COL18A1-expressing fibroblasts in the leukocyte-infiltrated areas in AD skin. CCR7-expressing dendritic cells (DCs) showed a similar distribution in the lesions. Additionally, M2 macrophages expressed CCL13 and CCL18 in this area. Ligand-receptor interaction analysis of the spatial transcriptome identified neighboring infiltration and interaction between activated COL18A1-expressing fibroblasts, CCL13- and CCL18-expressing M2 macrophages, CCR7- and LAMP3-expressing DCs, and T cells. As observed in skin lesions, serum levels of TNC and CCL18 were significantly elevated in AD, and correlated with clinical disease severity.

Conclusion: In this study, we show the unknown cellular crosstalk in leukocyte-infiltrated area in lesional skin. Our findings provide a comprehensive in-depth knowledge of the nature of AD skin lesions to guide the development of better treatments.","atopic dermatitis; single-cell transcriptomics; spatial transcriptomics; targeted proteomics.","False","Visium","791","21587"
"GSE197023_GSM5907080","human","skin","37312623","Spatial transcriptomics combined with single-cell RNA-sequencing unravels the complex inflammatory cell network in atopic dermatitis","Background: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully understood.

Methods: Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics sequencing to characterize the cellular infiltrate in lesional skin. For single-cell analysis, we analyzed the single-cell data from suction blister material from AD lesions and HC skin at the antecubital fossa skin (4 ADs and 5 HCs) and full-thickness skin biopsies (4 ADs and 2 HCs). The multiple proximity extension assays were performed in the serum samples from 36 AD patients and 28 HCs.

Results: The single-cell analysis identified unique clusters of fibroblasts, dendritic cells, and macrophages in the lesional AD skin. Spatial transcriptomics analysis showed the upregulation of COL6A5, COL4A1, TNC, and CCL19 in COL18A1-expressing fibroblasts in the leukocyte-infiltrated areas in AD skin. CCR7-expressing dendritic cells (DCs) showed a similar distribution in the lesions. Additionally, M2 macrophages expressed CCL13 and CCL18 in this area. Ligand-receptor interaction analysis of the spatial transcriptome identified neighboring infiltration and interaction between activated COL18A1-expressing fibroblasts, CCL13- and CCL18-expressing M2 macrophages, CCR7- and LAMP3-expressing DCs, and T cells. As observed in skin lesions, serum levels of TNC and CCL18 were significantly elevated in AD, and correlated with clinical disease severity.

Conclusion: In this study, we show the unknown cellular crosstalk in leukocyte-infiltrated area in lesional skin. Our findings provide a comprehensive in-depth knowledge of the nature of AD skin lesions to guide the development of better treatments.","atopic dermatitis; single-cell transcriptomics; spatial transcriptomics; targeted proteomics.","False","Visium","1177","21587"
"GSE197023_GSM5907081","human","skin","37312623","Spatial transcriptomics combined with single-cell RNA-sequencing unravels the complex inflammatory cell network in atopic dermatitis","Background: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully understood.

Methods: Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics sequencing to characterize the cellular infiltrate in lesional skin. For single-cell analysis, we analyzed the single-cell data from suction blister material from AD lesions and HC skin at the antecubital fossa skin (4 ADs and 5 HCs) and full-thickness skin biopsies (4 ADs and 2 HCs). The multiple proximity extension assays were performed in the serum samples from 36 AD patients and 28 HCs.

Results: The single-cell analysis identified unique clusters of fibroblasts, dendritic cells, and macrophages in the lesional AD skin. Spatial transcriptomics analysis showed the upregulation of COL6A5, COL4A1, TNC, and CCL19 in COL18A1-expressing fibroblasts in the leukocyte-infiltrated areas in AD skin. CCR7-expressing dendritic cells (DCs) showed a similar distribution in the lesions. Additionally, M2 macrophages expressed CCL13 and CCL18 in this area. Ligand-receptor interaction analysis of the spatial transcriptome identified neighboring infiltration and interaction between activated COL18A1-expressing fibroblasts, CCL13- and CCL18-expressing M2 macrophages, CCR7- and LAMP3-expressing DCs, and T cells. As observed in skin lesions, serum levels of TNC and CCL18 were significantly elevated in AD, and correlated with clinical disease severity.

Conclusion: In this study, we show the unknown cellular crosstalk in leukocyte-infiltrated area in lesional skin. Our findings provide a comprehensive in-depth knowledge of the nature of AD skin lesions to guide the development of better treatments.","atopic dermatitis; single-cell transcriptomics; spatial transcriptomics; targeted proteomics.","False","Visium","1081","21587"
"GSE197023_GSM5907082","human","skin","37312623","Spatial transcriptomics combined with single-cell RNA-sequencing unravels the complex inflammatory cell network in atopic dermatitis","Background: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully understood.

Methods: Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics sequencing to characterize the cellular infiltrate in lesional skin. For single-cell analysis, we analyzed the single-cell data from suction blister material from AD lesions and HC skin at the antecubital fossa skin (4 ADs and 5 HCs) and full-thickness skin biopsies (4 ADs and 2 HCs). The multiple proximity extension assays were performed in the serum samples from 36 AD patients and 28 HCs.

Results: The single-cell analysis identified unique clusters of fibroblasts, dendritic cells, and macrophages in the lesional AD skin. Spatial transcriptomics analysis showed the upregulation of COL6A5, COL4A1, TNC, and CCL19 in COL18A1-expressing fibroblasts in the leukocyte-infiltrated areas in AD skin. CCR7-expressing dendritic cells (DCs) showed a similar distribution in the lesions. Additionally, M2 macrophages expressed CCL13 and CCL18 in this area. Ligand-receptor interaction analysis of the spatial transcriptome identified neighboring infiltration and interaction between activated COL18A1-expressing fibroblasts, CCL13- and CCL18-expressing M2 macrophages, CCR7- and LAMP3-expressing DCs, and T cells. As observed in skin lesions, serum levels of TNC and CCL18 were significantly elevated in AD, and correlated with clinical disease severity.

Conclusion: In this study, we show the unknown cellular crosstalk in leukocyte-infiltrated area in lesional skin. Our findings provide a comprehensive in-depth knowledge of the nature of AD skin lesions to guide the development of better treatments.","atopic dermatitis; single-cell transcriptomics; spatial transcriptomics; targeted proteomics.","False","Visium","1045","21587"
"GSE197023_GSM5907083","human","skin","37312623","Spatial transcriptomics combined with single-cell RNA-sequencing unravels the complex inflammatory cell network in atopic dermatitis","Background: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully understood.

Methods: Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics sequencing to characterize the cellular infiltrate in lesional skin. For single-cell analysis, we analyzed the single-cell data from suction blister material from AD lesions and HC skin at the antecubital fossa skin (4 ADs and 5 HCs) and full-thickness skin biopsies (4 ADs and 2 HCs). The multiple proximity extension assays were performed in the serum samples from 36 AD patients and 28 HCs.

Results: The single-cell analysis identified unique clusters of fibroblasts, dendritic cells, and macrophages in the lesional AD skin. Spatial transcriptomics analysis showed the upregulation of COL6A5, COL4A1, TNC, and CCL19 in COL18A1-expressing fibroblasts in the leukocyte-infiltrated areas in AD skin. CCR7-expressing dendritic cells (DCs) showed a similar distribution in the lesions. Additionally, M2 macrophages expressed CCL13 and CCL18 in this area. Ligand-receptor interaction analysis of the spatial transcriptome identified neighboring infiltration and interaction between activated COL18A1-expressing fibroblasts, CCL13- and CCL18-expressing M2 macrophages, CCR7- and LAMP3-expressing DCs, and T cells. As observed in skin lesions, serum levels of TNC and CCL18 were significantly elevated in AD, and correlated with clinical disease severity.

Conclusion: In this study, we show the unknown cellular crosstalk in leukocyte-infiltrated area in lesional skin. Our findings provide a comprehensive in-depth knowledge of the nature of AD skin lesions to guide the development of better treatments.","atopic dermatitis; single-cell transcriptomics; spatial transcriptomics; targeted proteomics.","False","Visium","1032","21587"
"GSE197023_GSM5907084","human","skin","37312623","Spatial transcriptomics combined with single-cell RNA-sequencing unravels the complex inflammatory cell network in atopic dermatitis","Background: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully understood.

Methods: Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics sequencing to characterize the cellular infiltrate in lesional skin. For single-cell analysis, we analyzed the single-cell data from suction blister material from AD lesions and HC skin at the antecubital fossa skin (4 ADs and 5 HCs) and full-thickness skin biopsies (4 ADs and 2 HCs). The multiple proximity extension assays were performed in the serum samples from 36 AD patients and 28 HCs.

Results: The single-cell analysis identified unique clusters of fibroblasts, dendritic cells, and macrophages in the lesional AD skin. Spatial transcriptomics analysis showed the upregulation of COL6A5, COL4A1, TNC, and CCL19 in COL18A1-expressing fibroblasts in the leukocyte-infiltrated areas in AD skin. CCR7-expressing dendritic cells (DCs) showed a similar distribution in the lesions. Additionally, M2 macrophages expressed CCL13 and CCL18 in this area. Ligand-receptor interaction analysis of the spatial transcriptome identified neighboring infiltration and interaction between activated COL18A1-expressing fibroblasts, CCL13- and CCL18-expressing M2 macrophages, CCR7- and LAMP3-expressing DCs, and T cells. As observed in skin lesions, serum levels of TNC and CCL18 were significantly elevated in AD, and correlated with clinical disease severity.

Conclusion: In this study, we show the unknown cellular crosstalk in leukocyte-infiltrated area in lesional skin. Our findings provide a comprehensive in-depth knowledge of the nature of AD skin lesions to guide the development of better treatments.","atopic dermatitis; single-cell transcriptomics; spatial transcriptomics; targeted proteomics.","False","Visium","95","21587"
"GSE197023_GSM5907085","human","skin","37312623","Spatial transcriptomics combined with single-cell RNA-sequencing unravels the complex inflammatory cell network in atopic dermatitis","Background: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully understood.

Methods: Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics sequencing to characterize the cellular infiltrate in lesional skin. For single-cell analysis, we analyzed the single-cell data from suction blister material from AD lesions and HC skin at the antecubital fossa skin (4 ADs and 5 HCs) and full-thickness skin biopsies (4 ADs and 2 HCs). The multiple proximity extension assays were performed in the serum samples from 36 AD patients and 28 HCs.

Results: The single-cell analysis identified unique clusters of fibroblasts, dendritic cells, and macrophages in the lesional AD skin. Spatial transcriptomics analysis showed the upregulation of COL6A5, COL4A1, TNC, and CCL19 in COL18A1-expressing fibroblasts in the leukocyte-infiltrated areas in AD skin. CCR7-expressing dendritic cells (DCs) showed a similar distribution in the lesions. Additionally, M2 macrophages expressed CCL13 and CCL18 in this area. Ligand-receptor interaction analysis of the spatial transcriptome identified neighboring infiltration and interaction between activated COL18A1-expressing fibroblasts, CCL13- and CCL18-expressing M2 macrophages, CCR7- and LAMP3-expressing DCs, and T cells. As observed in skin lesions, serum levels of TNC and CCL18 were significantly elevated in AD, and correlated with clinical disease severity.

Conclusion: In this study, we show the unknown cellular crosstalk in leukocyte-infiltrated area in lesional skin. Our findings provide a comprehensive in-depth knowledge of the nature of AD skin lesions to guide the development of better treatments.","atopic dermatitis; single-cell transcriptomics; spatial transcriptomics; targeted proteomics.","False","Visium","419","21587"
"GSE197023_GSM5907086","human","skin","37312623","Spatial transcriptomics combined with single-cell RNA-sequencing unravels the complex inflammatory cell network in atopic dermatitis","Background: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully understood.

Methods: Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics sequencing to characterize the cellular infiltrate in lesional skin. For single-cell analysis, we analyzed the single-cell data from suction blister material from AD lesions and HC skin at the antecubital fossa skin (4 ADs and 5 HCs) and full-thickness skin biopsies (4 ADs and 2 HCs). The multiple proximity extension assays were performed in the serum samples from 36 AD patients and 28 HCs.

Results: The single-cell analysis identified unique clusters of fibroblasts, dendritic cells, and macrophages in the lesional AD skin. Spatial transcriptomics analysis showed the upregulation of COL6A5, COL4A1, TNC, and CCL19 in COL18A1-expressing fibroblasts in the leukocyte-infiltrated areas in AD skin. CCR7-expressing dendritic cells (DCs) showed a similar distribution in the lesions. Additionally, M2 macrophages expressed CCL13 and CCL18 in this area. Ligand-receptor interaction analysis of the spatial transcriptome identified neighboring infiltration and interaction between activated COL18A1-expressing fibroblasts, CCL13- and CCL18-expressing M2 macrophages, CCR7- and LAMP3-expressing DCs, and T cells. As observed in skin lesions, serum levels of TNC and CCL18 were significantly elevated in AD, and correlated with clinical disease severity.

Conclusion: In this study, we show the unknown cellular crosstalk in leukocyte-infiltrated area in lesional skin. Our findings provide a comprehensive in-depth knowledge of the nature of AD skin lesions to guide the development of better treatments.","atopic dermatitis; single-cell transcriptomics; spatial transcriptomics; targeted proteomics.","False","Visium","754","21587"
"GSE197023_GSM5907088","human","skin","37312623","Spatial transcriptomics combined with single-cell RNA-sequencing unravels the complex inflammatory cell network in atopic dermatitis","Background: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully understood.

Methods: Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics sequencing to characterize the cellular infiltrate in lesional skin. For single-cell analysis, we analyzed the single-cell data from suction blister material from AD lesions and HC skin at the antecubital fossa skin (4 ADs and 5 HCs) and full-thickness skin biopsies (4 ADs and 2 HCs). The multiple proximity extension assays were performed in the serum samples from 36 AD patients and 28 HCs.

Results: The single-cell analysis identified unique clusters of fibroblasts, dendritic cells, and macrophages in the lesional AD skin. Spatial transcriptomics analysis showed the upregulation of COL6A5, COL4A1, TNC, and CCL19 in COL18A1-expressing fibroblasts in the leukocyte-infiltrated areas in AD skin. CCR7-expressing dendritic cells (DCs) showed a similar distribution in the lesions. Additionally, M2 macrophages expressed CCL13 and CCL18 in this area. Ligand-receptor interaction analysis of the spatial transcriptome identified neighboring infiltration and interaction between activated COL18A1-expressing fibroblasts, CCL13- and CCL18-expressing M2 macrophages, CCR7- and LAMP3-expressing DCs, and T cells. As observed in skin lesions, serum levels of TNC and CCL18 were significantly elevated in AD, and correlated with clinical disease severity.

Conclusion: In this study, we show the unknown cellular crosstalk in leukocyte-infiltrated area in lesional skin. Our findings provide a comprehensive in-depth knowledge of the nature of AD skin lesions to guide the development of better treatments.","atopic dermatitis; single-cell transcriptomics; spatial transcriptomics; targeted proteomics.","False","Visium","975","21587"
"GSE197023_GSM5907089","human","skin","37312623","Spatial transcriptomics combined with single-cell RNA-sequencing unravels the complex inflammatory cell network in atopic dermatitis","Background: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully understood.

Methods: Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics sequencing to characterize the cellular infiltrate in lesional skin. For single-cell analysis, we analyzed the single-cell data from suction blister material from AD lesions and HC skin at the antecubital fossa skin (4 ADs and 5 HCs) and full-thickness skin biopsies (4 ADs and 2 HCs). The multiple proximity extension assays were performed in the serum samples from 36 AD patients and 28 HCs.

Results: The single-cell analysis identified unique clusters of fibroblasts, dendritic cells, and macrophages in the lesional AD skin. Spatial transcriptomics analysis showed the upregulation of COL6A5, COL4A1, TNC, and CCL19 in COL18A1-expressing fibroblasts in the leukocyte-infiltrated areas in AD skin. CCR7-expressing dendritic cells (DCs) showed a similar distribution in the lesions. Additionally, M2 macrophages expressed CCL13 and CCL18 in this area. Ligand-receptor interaction analysis of the spatial transcriptome identified neighboring infiltration and interaction between activated COL18A1-expressing fibroblasts, CCL13- and CCL18-expressing M2 macrophages, CCR7- and LAMP3-expressing DCs, and T cells. As observed in skin lesions, serum levels of TNC and CCL18 were significantly elevated in AD, and correlated with clinical disease severity.

Conclusion: In this study, we show the unknown cellular crosstalk in leukocyte-infiltrated area in lesional skin. Our findings provide a comprehensive in-depth knowledge of the nature of AD skin lesions to guide the development of better treatments.","atopic dermatitis; single-cell transcriptomics; spatial transcriptomics; targeted proteomics.","False","Visium","815","21587"
"GSE197023_GSM5907090","human","skin","37312623","Spatial transcriptomics combined with single-cell RNA-sequencing unravels the complex inflammatory cell network in atopic dermatitis","Background: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully understood.

Methods: Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics sequencing to characterize the cellular infiltrate in lesional skin. For single-cell analysis, we analyzed the single-cell data from suction blister material from AD lesions and HC skin at the antecubital fossa skin (4 ADs and 5 HCs) and full-thickness skin biopsies (4 ADs and 2 HCs). The multiple proximity extension assays were performed in the serum samples from 36 AD patients and 28 HCs.

Results: The single-cell analysis identified unique clusters of fibroblasts, dendritic cells, and macrophages in the lesional AD skin. Spatial transcriptomics analysis showed the upregulation of COL6A5, COL4A1, TNC, and CCL19 in COL18A1-expressing fibroblasts in the leukocyte-infiltrated areas in AD skin. CCR7-expressing dendritic cells (DCs) showed a similar distribution in the lesions. Additionally, M2 macrophages expressed CCL13 and CCL18 in this area. Ligand-receptor interaction analysis of the spatial transcriptome identified neighboring infiltration and interaction between activated COL18A1-expressing fibroblasts, CCL13- and CCL18-expressing M2 macrophages, CCR7- and LAMP3-expressing DCs, and T cells. As observed in skin lesions, serum levels of TNC and CCL18 were significantly elevated in AD, and correlated with clinical disease severity.

Conclusion: In this study, we show the unknown cellular crosstalk in leukocyte-infiltrated area in lesional skin. Our findings provide a comprehensive in-depth knowledge of the nature of AD skin lesions to guide the development of better treatments.","atopic dermatitis; single-cell transcriptomics; spatial transcriptomics; targeted proteomics.","False","Visium","878","21587"
"GSE197023_GSM5907091","human","skin","37312623","Spatial transcriptomics combined with single-cell RNA-sequencing unravels the complex inflammatory cell network in atopic dermatitis","Background: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully understood.

Methods: Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics sequencing to characterize the cellular infiltrate in lesional skin. For single-cell analysis, we analyzed the single-cell data from suction blister material from AD lesions and HC skin at the antecubital fossa skin (4 ADs and 5 HCs) and full-thickness skin biopsies (4 ADs and 2 HCs). The multiple proximity extension assays were performed in the serum samples from 36 AD patients and 28 HCs.

Results: The single-cell analysis identified unique clusters of fibroblasts, dendritic cells, and macrophages in the lesional AD skin. Spatial transcriptomics analysis showed the upregulation of COL6A5, COL4A1, TNC, and CCL19 in COL18A1-expressing fibroblasts in the leukocyte-infiltrated areas in AD skin. CCR7-expressing dendritic cells (DCs) showed a similar distribution in the lesions. Additionally, M2 macrophages expressed CCL13 and CCL18 in this area. Ligand-receptor interaction analysis of the spatial transcriptome identified neighboring infiltration and interaction between activated COL18A1-expressing fibroblasts, CCL13- and CCL18-expressing M2 macrophages, CCR7- and LAMP3-expressing DCs, and T cells. As observed in skin lesions, serum levels of TNC and CCL18 were significantly elevated in AD, and correlated with clinical disease severity.

Conclusion: In this study, we show the unknown cellular crosstalk in leukocyte-infiltrated area in lesional skin. Our findings provide a comprehensive in-depth knowledge of the nature of AD skin lesions to guide the development of better treatments.","atopic dermatitis; single-cell transcriptomics; spatial transcriptomics; targeted proteomics.","False","Visium","527","21587"
"GSE197023_GSM5907092","human","skin","37312623","Spatial transcriptomics combined with single-cell RNA-sequencing unravels the complex inflammatory cell network in atopic dermatitis","Background: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully understood.

Methods: Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics sequencing to characterize the cellular infiltrate in lesional skin. For single-cell analysis, we analyzed the single-cell data from suction blister material from AD lesions and HC skin at the antecubital fossa skin (4 ADs and 5 HCs) and full-thickness skin biopsies (4 ADs and 2 HCs). The multiple proximity extension assays were performed in the serum samples from 36 AD patients and 28 HCs.

Results: The single-cell analysis identified unique clusters of fibroblasts, dendritic cells, and macrophages in the lesional AD skin. Spatial transcriptomics analysis showed the upregulation of COL6A5, COL4A1, TNC, and CCL19 in COL18A1-expressing fibroblasts in the leukocyte-infiltrated areas in AD skin. CCR7-expressing dendritic cells (DCs) showed a similar distribution in the lesions. Additionally, M2 macrophages expressed CCL13 and CCL18 in this area. Ligand-receptor interaction analysis of the spatial transcriptome identified neighboring infiltration and interaction between activated COL18A1-expressing fibroblasts, CCL13- and CCL18-expressing M2 macrophages, CCR7- and LAMP3-expressing DCs, and T cells. As observed in skin lesions, serum levels of TNC and CCL18 were significantly elevated in AD, and correlated with clinical disease severity.

Conclusion: In this study, we show the unknown cellular crosstalk in leukocyte-infiltrated area in lesional skin. Our findings provide a comprehensive in-depth knowledge of the nature of AD skin lesions to guide the development of better treatments.","atopic dermatitis; single-cell transcriptomics; spatial transcriptomics; targeted proteomics.","False","Visium","722","21587"
"GSE197023_GSM5907093","human","skin","37312623","Spatial transcriptomics combined with single-cell RNA-sequencing unravels the complex inflammatory cell network in atopic dermatitis","Background: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully understood.

Methods: Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics sequencing to characterize the cellular infiltrate in lesional skin. For single-cell analysis, we analyzed the single-cell data from suction blister material from AD lesions and HC skin at the antecubital fossa skin (4 ADs and 5 HCs) and full-thickness skin biopsies (4 ADs and 2 HCs). The multiple proximity extension assays were performed in the serum samples from 36 AD patients and 28 HCs.

Results: The single-cell analysis identified unique clusters of fibroblasts, dendritic cells, and macrophages in the lesional AD skin. Spatial transcriptomics analysis showed the upregulation of COL6A5, COL4A1, TNC, and CCL19 in COL18A1-expressing fibroblasts in the leukocyte-infiltrated areas in AD skin. CCR7-expressing dendritic cells (DCs) showed a similar distribution in the lesions. Additionally, M2 macrophages expressed CCL13 and CCL18 in this area. Ligand-receptor interaction analysis of the spatial transcriptome identified neighboring infiltration and interaction between activated COL18A1-expressing fibroblasts, CCL13- and CCL18-expressing M2 macrophages, CCR7- and LAMP3-expressing DCs, and T cells. As observed in skin lesions, serum levels of TNC and CCL18 were significantly elevated in AD, and correlated with clinical disease severity.

Conclusion: In this study, we show the unknown cellular crosstalk in leukocyte-infiltrated area in lesional skin. Our findings provide a comprehensive in-depth knowledge of the nature of AD skin lesions to guide the development of better treatments.","atopic dermatitis; single-cell transcriptomics; spatial transcriptomics; targeted proteomics.","False","Visium","629","21587"
"GSE197023_GSM5907094","human","skin","37312623","Spatial transcriptomics combined with single-cell RNA-sequencing unravels the complex inflammatory cell network in atopic dermatitis","Background: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully understood.

Methods: Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics sequencing to characterize the cellular infiltrate in lesional skin. For single-cell analysis, we analyzed the single-cell data from suction blister material from AD lesions and HC skin at the antecubital fossa skin (4 ADs and 5 HCs) and full-thickness skin biopsies (4 ADs and 2 HCs). The multiple proximity extension assays were performed in the serum samples from 36 AD patients and 28 HCs.

Results: The single-cell analysis identified unique clusters of fibroblasts, dendritic cells, and macrophages in the lesional AD skin. Spatial transcriptomics analysis showed the upregulation of COL6A5, COL4A1, TNC, and CCL19 in COL18A1-expressing fibroblasts in the leukocyte-infiltrated areas in AD skin. CCR7-expressing dendritic cells (DCs) showed a similar distribution in the lesions. Additionally, M2 macrophages expressed CCL13 and CCL18 in this area. Ligand-receptor interaction analysis of the spatial transcriptome identified neighboring infiltration and interaction between activated COL18A1-expressing fibroblasts, CCL13- and CCL18-expressing M2 macrophages, CCR7- and LAMP3-expressing DCs, and T cells. As observed in skin lesions, serum levels of TNC and CCL18 were significantly elevated in AD, and correlated with clinical disease severity.

Conclusion: In this study, we show the unknown cellular crosstalk in leukocyte-infiltrated area in lesional skin. Our findings provide a comprehensive in-depth knowledge of the nature of AD skin lesions to guide the development of better treatments.","atopic dermatitis; single-cell transcriptomics; spatial transcriptomics; targeted proteomics.","False","Visium","838","21587"
"GSE197023_GSM5907095","human","skin","37312623","Spatial transcriptomics combined with single-cell RNA-sequencing unravels the complex inflammatory cell network in atopic dermatitis","Background: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully understood.

Methods: Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics sequencing to characterize the cellular infiltrate in lesional skin. For single-cell analysis, we analyzed the single-cell data from suction blister material from AD lesions and HC skin at the antecubital fossa skin (4 ADs and 5 HCs) and full-thickness skin biopsies (4 ADs and 2 HCs). The multiple proximity extension assays were performed in the serum samples from 36 AD patients and 28 HCs.

Results: The single-cell analysis identified unique clusters of fibroblasts, dendritic cells, and macrophages in the lesional AD skin. Spatial transcriptomics analysis showed the upregulation of COL6A5, COL4A1, TNC, and CCL19 in COL18A1-expressing fibroblasts in the leukocyte-infiltrated areas in AD skin. CCR7-expressing dendritic cells (DCs) showed a similar distribution in the lesions. Additionally, M2 macrophages expressed CCL13 and CCL18 in this area. Ligand-receptor interaction analysis of the spatial transcriptome identified neighboring infiltration and interaction between activated COL18A1-expressing fibroblasts, CCL13- and CCL18-expressing M2 macrophages, CCR7- and LAMP3-expressing DCs, and T cells. As observed in skin lesions, serum levels of TNC and CCL18 were significantly elevated in AD, and correlated with clinical disease severity.

Conclusion: In this study, we show the unknown cellular crosstalk in leukocyte-infiltrated area in lesional skin. Our findings provide a comprehensive in-depth knowledge of the nature of AD skin lesions to guide the development of better treatments.","atopic dermatitis; single-cell transcriptomics; spatial transcriptomics; targeted proteomics.","False","Visium","653","21587"
"GSE197023_GSM5907096","human","skin","37312623","Spatial transcriptomics combined with single-cell RNA-sequencing unravels the complex inflammatory cell network in atopic dermatitis","Background: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully understood.

Methods: Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics sequencing to characterize the cellular infiltrate in lesional skin. For single-cell analysis, we analyzed the single-cell data from suction blister material from AD lesions and HC skin at the antecubital fossa skin (4 ADs and 5 HCs) and full-thickness skin biopsies (4 ADs and 2 HCs). The multiple proximity extension assays were performed in the serum samples from 36 AD patients and 28 HCs.

Results: The single-cell analysis identified unique clusters of fibroblasts, dendritic cells, and macrophages in the lesional AD skin. Spatial transcriptomics analysis showed the upregulation of COL6A5, COL4A1, TNC, and CCL19 in COL18A1-expressing fibroblasts in the leukocyte-infiltrated areas in AD skin. CCR7-expressing dendritic cells (DCs) showed a similar distribution in the lesions. Additionally, M2 macrophages expressed CCL13 and CCL18 in this area. Ligand-receptor interaction analysis of the spatial transcriptome identified neighboring infiltration and interaction between activated COL18A1-expressing fibroblasts, CCL13- and CCL18-expressing M2 macrophages, CCR7- and LAMP3-expressing DCs, and T cells. As observed in skin lesions, serum levels of TNC and CCL18 were significantly elevated in AD, and correlated with clinical disease severity.

Conclusion: In this study, we show the unknown cellular crosstalk in leukocyte-infiltrated area in lesional skin. Our findings provide a comprehensive in-depth knowledge of the nature of AD skin lesions to guide the development of better treatments.","atopic dermatitis; single-cell transcriptomics; spatial transcriptomics; targeted proteomics.","False","Visium","788","21587"
"GSE197317_GSM5914539","human","pancreas","36513063","Single-cell transcriptomic and spatial landscapes of the developing human pancreas","Current differentiation protocols have not been successful in reproducibly generating fully functional human beta cells in vitro, partly due to incomplete understanding of human pancreas development. Here, we present detailed transcriptomic analysis of the various cell types of the developing human pancreas, including their spatial gene patterns. We integrated single-cell RNA sequencing with spatial transcriptomics at multiple developmental time points and revealed distinct temporal-spatial gene cascades. Cell trajectory inference identified endocrine progenitor populations and branch-specific genes as the progenitors differentiate toward alpha or beta cells. Spatial differentiation trajectories indicated that Schwann cells are spatially co-located with endocrine progenitors, and cell-cell connectivity analysis predicted that they may interact via L1CAM-EPHB2 signaling. Our integrated approach enabled us to identify heterogeneity and multiple lineage dynamics within the mesenchyme, showing that it contributed to the exocrine acinar cell state. Finally, we have generated an interactive web resource for investigating human pancreas development for the research community.","Schwann cells; Visium; beta cell development; endocrine progenitors; human fetal pancreas; scRNA-seq; spatial transcriptomics; trajectory inference.","False","Visium","294","36601"
"GSE197317_GSM5914540","human","pancreas","36513063","Single-cell transcriptomic and spatial landscapes of the developing human pancreas","Current differentiation protocols have not been successful in reproducibly generating fully functional human beta cells in vitro, partly due to incomplete understanding of human pancreas development. Here, we present detailed transcriptomic analysis of the various cell types of the developing human pancreas, including their spatial gene patterns. We integrated single-cell RNA sequencing with spatial transcriptomics at multiple developmental time points and revealed distinct temporal-spatial gene cascades. Cell trajectory inference identified endocrine progenitor populations and branch-specific genes as the progenitors differentiate toward alpha or beta cells. Spatial differentiation trajectories indicated that Schwann cells are spatially co-located with endocrine progenitors, and cell-cell connectivity analysis predicted that they may interact via L1CAM-EPHB2 signaling. Our integrated approach enabled us to identify heterogeneity and multiple lineage dynamics within the mesenchyme, showing that it contributed to the exocrine acinar cell state. Finally, we have generated an interactive web resource for investigating human pancreas development for the research community.","Schwann cells; Visium; beta cell development; endocrine progenitors; human fetal pancreas; scRNA-seq; spatial transcriptomics; trajectory inference.","False","Visium","294","36601"
"GSE197317_GSM5914541","human","pancreas","36513063","Single-cell transcriptomic and spatial landscapes of the developing human pancreas","Current differentiation protocols have not been successful in reproducibly generating fully functional human beta cells in vitro, partly due to incomplete understanding of human pancreas development. Here, we present detailed transcriptomic analysis of the various cell types of the developing human pancreas, including their spatial gene patterns. We integrated single-cell RNA sequencing with spatial transcriptomics at multiple developmental time points and revealed distinct temporal-spatial gene cascades. Cell trajectory inference identified endocrine progenitor populations and branch-specific genes as the progenitors differentiate toward alpha or beta cells. Spatial differentiation trajectories indicated that Schwann cells are spatially co-located with endocrine progenitors, and cell-cell connectivity analysis predicted that they may interact via L1CAM-EPHB2 signaling. Our integrated approach enabled us to identify heterogeneity and multiple lineage dynamics within the mesenchyme, showing that it contributed to the exocrine acinar cell state. Finally, we have generated an interactive web resource for investigating human pancreas development for the research community.","Schwann cells; Visium; beta cell development; endocrine progenitors; human fetal pancreas; scRNA-seq; spatial transcriptomics; trajectory inference.","False","Visium","1550","36601"
"GSE197317_GSM5914542","human","pancreas","36513063","Single-cell transcriptomic and spatial landscapes of the developing human pancreas","Current differentiation protocols have not been successful in reproducibly generating fully functional human beta cells in vitro, partly due to incomplete understanding of human pancreas development. Here, we present detailed transcriptomic analysis of the various cell types of the developing human pancreas, including their spatial gene patterns. We integrated single-cell RNA sequencing with spatial transcriptomics at multiple developmental time points and revealed distinct temporal-spatial gene cascades. Cell trajectory inference identified endocrine progenitor populations and branch-specific genes as the progenitors differentiate toward alpha or beta cells. Spatial differentiation trajectories indicated that Schwann cells are spatially co-located with endocrine progenitors, and cell-cell connectivity analysis predicted that they may interact via L1CAM-EPHB2 signaling. Our integrated approach enabled us to identify heterogeneity and multiple lineage dynamics within the mesenchyme, showing that it contributed to the exocrine acinar cell state. Finally, we have generated an interactive web resource for investigating human pancreas development for the research community.","Schwann cells; Visium; beta cell development; endocrine progenitors; human fetal pancreas; scRNA-seq; spatial transcriptomics; trajectory inference.","False","Visium","1546","36601"
"GSE197317_GSM5914543","human","pancreas","36513063","Single-cell transcriptomic and spatial landscapes of the developing human pancreas","Current differentiation protocols have not been successful in reproducibly generating fully functional human beta cells in vitro, partly due to incomplete understanding of human pancreas development. Here, we present detailed transcriptomic analysis of the various cell types of the developing human pancreas, including their spatial gene patterns. We integrated single-cell RNA sequencing with spatial transcriptomics at multiple developmental time points and revealed distinct temporal-spatial gene cascades. Cell trajectory inference identified endocrine progenitor populations and branch-specific genes as the progenitors differentiate toward alpha or beta cells. Spatial differentiation trajectories indicated that Schwann cells are spatially co-located with endocrine progenitors, and cell-cell connectivity analysis predicted that they may interact via L1CAM-EPHB2 signaling. Our integrated approach enabled us to identify heterogeneity and multiple lineage dynamics within the mesenchyme, showing that it contributed to the exocrine acinar cell state. Finally, we have generated an interactive web resource for investigating human pancreas development for the research community.","Schwann cells; Visium; beta cell development; endocrine progenitors; human fetal pancreas; scRNA-seq; spatial transcriptomics; trajectory inference.","False","Visium","1498","36601"
"GSE197317_GSM5914544","human","pancreas","36513063","Single-cell transcriptomic and spatial landscapes of the developing human pancreas","Current differentiation protocols have not been successful in reproducibly generating fully functional human beta cells in vitro, partly due to incomplete understanding of human pancreas development. Here, we present detailed transcriptomic analysis of the various cell types of the developing human pancreas, including their spatial gene patterns. We integrated single-cell RNA sequencing with spatial transcriptomics at multiple developmental time points and revealed distinct temporal-spatial gene cascades. Cell trajectory inference identified endocrine progenitor populations and branch-specific genes as the progenitors differentiate toward alpha or beta cells. Spatial differentiation trajectories indicated that Schwann cells are spatially co-located with endocrine progenitors, and cell-cell connectivity analysis predicted that they may interact via L1CAM-EPHB2 signaling. Our integrated approach enabled us to identify heterogeneity and multiple lineage dynamics within the mesenchyme, showing that it contributed to the exocrine acinar cell state. Finally, we have generated an interactive web resource for investigating human pancreas development for the research community.","Schwann cells; Visium; beta cell development; endocrine progenitors; human fetal pancreas; scRNA-seq; spatial transcriptomics; trajectory inference.","False","Visium","1196","36601"
"GSE197317_GSM5914545","human","pancreas","36513063","Single-cell transcriptomic and spatial landscapes of the developing human pancreas","Current differentiation protocols have not been successful in reproducibly generating fully functional human beta cells in vitro, partly due to incomplete understanding of human pancreas development. Here, we present detailed transcriptomic analysis of the various cell types of the developing human pancreas, including their spatial gene patterns. We integrated single-cell RNA sequencing with spatial transcriptomics at multiple developmental time points and revealed distinct temporal-spatial gene cascades. Cell trajectory inference identified endocrine progenitor populations and branch-specific genes as the progenitors differentiate toward alpha or beta cells. Spatial differentiation trajectories indicated that Schwann cells are spatially co-located with endocrine progenitors, and cell-cell connectivity analysis predicted that they may interact via L1CAM-EPHB2 signaling. Our integrated approach enabled us to identify heterogeneity and multiple lineage dynamics within the mesenchyme, showing that it contributed to the exocrine acinar cell state. Finally, we have generated an interactive web resource for investigating human pancreas development for the research community.","Schwann cells; Visium; beta cell development; endocrine progenitors; human fetal pancreas; scRNA-seq; spatial transcriptomics; trajectory inference.","False","Visium","2126","36601"
"GSE197317_GSM5914546","human","pancreas","36513063","Single-cell transcriptomic and spatial landscapes of the developing human pancreas","Current differentiation protocols have not been successful in reproducibly generating fully functional human beta cells in vitro, partly due to incomplete understanding of human pancreas development. Here, we present detailed transcriptomic analysis of the various cell types of the developing human pancreas, including their spatial gene patterns. We integrated single-cell RNA sequencing with spatial transcriptomics at multiple developmental time points and revealed distinct temporal-spatial gene cascades. Cell trajectory inference identified endocrine progenitor populations and branch-specific genes as the progenitors differentiate toward alpha or beta cells. Spatial differentiation trajectories indicated that Schwann cells are spatially co-located with endocrine progenitors, and cell-cell connectivity analysis predicted that they may interact via L1CAM-EPHB2 signaling. Our integrated approach enabled us to identify heterogeneity and multiple lineage dynamics within the mesenchyme, showing that it contributed to the exocrine acinar cell state. Finally, we have generated an interactive web resource for investigating human pancreas development for the research community.","Schwann cells; Visium; beta cell development; endocrine progenitors; human fetal pancreas; scRNA-seq; spatial transcriptomics; trajectory inference.","False","Visium","1719","36601"
"GSE199659_GSM5979972","mouse","muscle","37582915","Spatial transcriptomics reveal markers of histopathological changes in Duchenne muscular dystrophy mouse models","Duchenne muscular dystrophy is caused by mutations in the DMD gene, leading to lack of dystrophin. Chronic muscle damage eventually leads to histological alterations in skeletal muscles. The identification of genes and cell types driving tissue remodeling is a key step to developing effective therapies. Here we use spatial transcriptomics in two Duchenne muscular dystrophy mouse models differing in disease severity to identify gene expression signatures underlying skeletal muscle pathology and to directly link gene expression to muscle histology. We perform deconvolution analysis to identify cell types contributing to histological alterations. We show increased expression of specific genes in areas of muscle regeneration (Myl4, Sparc, Hspg2), fibrosis (Vim, Fn1, Thbs4) and calcification (Bgn, Ctsk, Spp1). These findings are confirmed by smFISH. Finally, we use differentiation dynamic analysis in the D2-mdx muscle to identify muscle fibers in the present state that are predicted to become affected in the future state.","","False","Visium","2348","32285"
"GSE199659_GSM5979973","mouse","muscle","37582915","Spatial transcriptomics reveal markers of histopathological changes in Duchenne muscular dystrophy mouse models","Duchenne muscular dystrophy is caused by mutations in the DMD gene, leading to lack of dystrophin. Chronic muscle damage eventually leads to histological alterations in skeletal muscles. The identification of genes and cell types driving tissue remodeling is a key step to developing effective therapies. Here we use spatial transcriptomics in two Duchenne muscular dystrophy mouse models differing in disease severity to identify gene expression signatures underlying skeletal muscle pathology and to directly link gene expression to muscle histology. We perform deconvolution analysis to identify cell types contributing to histological alterations. We show increased expression of specific genes in areas of muscle regeneration (Myl4, Sparc, Hspg2), fibrosis (Vim, Fn1, Thbs4) and calcification (Bgn, Ctsk, Spp1). These findings are confirmed by smFISH. Finally, we use differentiation dynamic analysis in the D2-mdx muscle to identify muscle fibers in the present state that are predicted to become affected in the future state.","","False","Visium","1426","32285"
"GSE199659_GSM5979974","mouse","muscle","37582915","Spatial transcriptomics reveal markers of histopathological changes in Duchenne muscular dystrophy mouse models","Duchenne muscular dystrophy is caused by mutations in the DMD gene, leading to lack of dystrophin. Chronic muscle damage eventually leads to histological alterations in skeletal muscles. The identification of genes and cell types driving tissue remodeling is a key step to developing effective therapies. Here we use spatial transcriptomics in two Duchenne muscular dystrophy mouse models differing in disease severity to identify gene expression signatures underlying skeletal muscle pathology and to directly link gene expression to muscle histology. We perform deconvolution analysis to identify cell types contributing to histological alterations. We show increased expression of specific genes in areas of muscle regeneration (Myl4, Sparc, Hspg2), fibrosis (Vim, Fn1, Thbs4) and calcification (Bgn, Ctsk, Spp1). These findings are confirmed by smFISH. Finally, we use differentiation dynamic analysis in the D2-mdx muscle to identify muscle fibers in the present state that are predicted to become affected in the future state.","","False","Visium","1884","32285"
"GSE199659_GSM5979975","mouse","muscle","37582915","Spatial transcriptomics reveal markers of histopathological changes in Duchenne muscular dystrophy mouse models","Duchenne muscular dystrophy is caused by mutations in the DMD gene, leading to lack of dystrophin. Chronic muscle damage eventually leads to histological alterations in skeletal muscles. The identification of genes and cell types driving tissue remodeling is a key step to developing effective therapies. Here we use spatial transcriptomics in two Duchenne muscular dystrophy mouse models differing in disease severity to identify gene expression signatures underlying skeletal muscle pathology and to directly link gene expression to muscle histology. We perform deconvolution analysis to identify cell types contributing to histological alterations. We show increased expression of specific genes in areas of muscle regeneration (Myl4, Sparc, Hspg2), fibrosis (Vim, Fn1, Thbs4) and calcification (Bgn, Ctsk, Spp1). These findings are confirmed by smFISH. Finally, we use differentiation dynamic analysis in the D2-mdx muscle to identify muscle fibers in the present state that are predicted to become affected in the future state.","","False","Visium","1370","32285"
"GSE200720_GSM6042726","mouse","spleen","36450262","Spatial transcriptomics demonstrates the role of CD4 T cells in effector CD8 T cell differentiation during chronic viral infection","CD4 T cell help is critical to sustain effector CD8 T cell responses during chronic infection, notably via T follicular helper (Tfh)-derived interleukin-21 (IL-21). Conversely, CD4 depletion results in severe CD8 T cell dysfunction and lifelong viremia despite CD4 T cell reemergence following transient depletion. These observations suggest that repopulating CD4 subsets are functionally or numerically insufficient to orchestrate a robust CD8 response. We utilize spatial transcriptomics and single-cell RNA sequencing (scRNA-seq) to investigate CD4 T cell heterogeneity under CD4-replete and -deplete conditions and explore cellular interactions during chronic infection. Although IL-21-producing Tfh cells repopulate following transient CD4 depletion, they are outnumbered by immunomodulatory CD4 T cells. Moreover, the splenic architecture appears perturbed, with decreases in white pulp regions, coinciding with germinal center losses. These disruptions in splenic architecture are associated with diminished Tfh and progenitor CD8 T cell colocalization, providing a potential mechanism for impaired progenitor-to-effector CD8 T cell differentiation during """"""""""""""""""""""""""""""""un-helped"""""""""""""""""""""""""""""""" conditions.","CD4 T cells; CD8 T cells; CP: Immunology; IL-21; LCMV; T follicular helper cells; single-cell RNA sequencing; spatial transcriptomics.","False","Visium","2774","32285"
"GSE200720_GSM6042727","mouse","spleen","36450262","Spatial transcriptomics demonstrates the role of CD4 T cells in effector CD8 T cell differentiation during chronic viral infection","CD4 T cell help is critical to sustain effector CD8 T cell responses during chronic infection, notably via T follicular helper (Tfh)-derived interleukin-21 (IL-21). Conversely, CD4 depletion results in severe CD8 T cell dysfunction and lifelong viremia despite CD4 T cell reemergence following transient depletion. These observations suggest that repopulating CD4 subsets are functionally or numerically insufficient to orchestrate a robust CD8 response. We utilize spatial transcriptomics and single-cell RNA sequencing (scRNA-seq) to investigate CD4 T cell heterogeneity under CD4-replete and -deplete conditions and explore cellular interactions during chronic infection. Although IL-21-producing Tfh cells repopulate following transient CD4 depletion, they are outnumbered by immunomodulatory CD4 T cells. Moreover, the splenic architecture appears perturbed, with decreases in white pulp regions, coinciding with germinal center losses. These disruptions in splenic architecture are associated with diminished Tfh and progenitor CD8 T cell colocalization, providing a potential mechanism for impaired progenitor-to-effector CD8 T cell differentiation during """"""""""""""""""""""""""""""""un-helped"""""""""""""""""""""""""""""""" conditions.","CD4 T cells; CD8 T cells; CP: Immunology; IL-21; LCMV; T follicular helper cells; single-cell RNA sequencing; spatial transcriptomics.","False","Visium","2177","32285"
"GSE200720_GSM6042728","mouse","spleen","36450262","Spatial transcriptomics demonstrates the role of CD4 T cells in effector CD8 T cell differentiation during chronic viral infection","CD4 T cell help is critical to sustain effector CD8 T cell responses during chronic infection, notably via T follicular helper (Tfh)-derived interleukin-21 (IL-21). Conversely, CD4 depletion results in severe CD8 T cell dysfunction and lifelong viremia despite CD4 T cell reemergence following transient depletion. These observations suggest that repopulating CD4 subsets are functionally or numerically insufficient to orchestrate a robust CD8 response. We utilize spatial transcriptomics and single-cell RNA sequencing (scRNA-seq) to investigate CD4 T cell heterogeneity under CD4-replete and -deplete conditions and explore cellular interactions during chronic infection. Although IL-21-producing Tfh cells repopulate following transient CD4 depletion, they are outnumbered by immunomodulatory CD4 T cells. Moreover, the splenic architecture appears perturbed, with decreases in white pulp regions, coinciding with germinal center losses. These disruptions in splenic architecture are associated with diminished Tfh and progenitor CD8 T cell colocalization, providing a potential mechanism for impaired progenitor-to-effector CD8 T cell differentiation during """"""""""""""""""""""""""""""""un-helped"""""""""""""""""""""""""""""""" conditions.","CD4 T cells; CD8 T cells; CP: Immunology; IL-21; LCMV; T follicular helper cells; single-cell RNA sequencing; spatial transcriptomics.","False","Visium","2659","32285"
"GSE200720_GSM6042729","mouse","spleen","36450262","Spatial transcriptomics demonstrates the role of CD4 T cells in effector CD8 T cell differentiation during chronic viral infection","CD4 T cell help is critical to sustain effector CD8 T cell responses during chronic infection, notably via T follicular helper (Tfh)-derived interleukin-21 (IL-21). Conversely, CD4 depletion results in severe CD8 T cell dysfunction and lifelong viremia despite CD4 T cell reemergence following transient depletion. These observations suggest that repopulating CD4 subsets are functionally or numerically insufficient to orchestrate a robust CD8 response. We utilize spatial transcriptomics and single-cell RNA sequencing (scRNA-seq) to investigate CD4 T cell heterogeneity under CD4-replete and -deplete conditions and explore cellular interactions during chronic infection. Although IL-21-producing Tfh cells repopulate following transient CD4 depletion, they are outnumbered by immunomodulatory CD4 T cells. Moreover, the splenic architecture appears perturbed, with decreases in white pulp regions, coinciding with germinal center losses. These disruptions in splenic architecture are associated with diminished Tfh and progenitor CD8 T cell colocalization, providing a potential mechanism for impaired progenitor-to-effector CD8 T cell differentiation during """"""""""""""""""""""""""""""""un-helped"""""""""""""""""""""""""""""""" conditions.","CD4 T cells; CD8 T cells; CP: Immunology; IL-21; LCMV; T follicular helper cells; single-cell RNA sequencing; spatial transcriptomics.","False","Visium","2495","32285"
"GSE200720_GSM6042730","mouse","spleen","36450262","Spatial transcriptomics demonstrates the role of CD4 T cells in effector CD8 T cell differentiation during chronic viral infection","CD4 T cell help is critical to sustain effector CD8 T cell responses during chronic infection, notably via T follicular helper (Tfh)-derived interleukin-21 (IL-21). Conversely, CD4 depletion results in severe CD8 T cell dysfunction and lifelong viremia despite CD4 T cell reemergence following transient depletion. These observations suggest that repopulating CD4 subsets are functionally or numerically insufficient to orchestrate a robust CD8 response. We utilize spatial transcriptomics and single-cell RNA sequencing (scRNA-seq) to investigate CD4 T cell heterogeneity under CD4-replete and -deplete conditions and explore cellular interactions during chronic infection. Although IL-21-producing Tfh cells repopulate following transient CD4 depletion, they are outnumbered by immunomodulatory CD4 T cells. Moreover, the splenic architecture appears perturbed, with decreases in white pulp regions, coinciding with germinal center losses. These disruptions in splenic architecture are associated with diminished Tfh and progenitor CD8 T cell colocalization, providing a potential mechanism for impaired progenitor-to-effector CD8 T cell differentiation during """"""""""""""""""""""""""""""""un-helped"""""""""""""""""""""""""""""""" conditions.","CD4 T cells; CD8 T cells; CP: Immunology; IL-21; LCMV; T follicular helper cells; single-cell RNA sequencing; spatial transcriptomics.","False","Visium","2254","32285"
"GSE200720_GSM6042731","mouse","spleen","36450262","Spatial transcriptomics demonstrates the role of CD4 T cells in effector CD8 T cell differentiation during chronic viral infection","CD4 T cell help is critical to sustain effector CD8 T cell responses during chronic infection, notably via T follicular helper (Tfh)-derived interleukin-21 (IL-21). Conversely, CD4 depletion results in severe CD8 T cell dysfunction and lifelong viremia despite CD4 T cell reemergence following transient depletion. These observations suggest that repopulating CD4 subsets are functionally or numerically insufficient to orchestrate a robust CD8 response. We utilize spatial transcriptomics and single-cell RNA sequencing (scRNA-seq) to investigate CD4 T cell heterogeneity under CD4-replete and -deplete conditions and explore cellular interactions during chronic infection. Although IL-21-producing Tfh cells repopulate following transient CD4 depletion, they are outnumbered by immunomodulatory CD4 T cells. Moreover, the splenic architecture appears perturbed, with decreases in white pulp regions, coinciding with germinal center losses. These disruptions in splenic architecture are associated with diminished Tfh and progenitor CD8 T cell colocalization, providing a potential mechanism for impaired progenitor-to-effector CD8 T cell differentiation during """"""""""""""""""""""""""""""""un-helped"""""""""""""""""""""""""""""""" conditions.","CD4 T cells; CD8 T cells; CP: Immunology; IL-21; LCMV; T follicular helper cells; single-cell RNA sequencing; spatial transcriptomics.","False","Visium","2714","32285"
"GSE200720_GSM6042732","mouse","spleen","36450262","Spatial transcriptomics demonstrates the role of CD4 T cells in effector CD8 T cell differentiation during chronic viral infection","CD4 T cell help is critical to sustain effector CD8 T cell responses during chronic infection, notably via T follicular helper (Tfh)-derived interleukin-21 (IL-21). Conversely, CD4 depletion results in severe CD8 T cell dysfunction and lifelong viremia despite CD4 T cell reemergence following transient depletion. These observations suggest that repopulating CD4 subsets are functionally or numerically insufficient to orchestrate a robust CD8 response. We utilize spatial transcriptomics and single-cell RNA sequencing (scRNA-seq) to investigate CD4 T cell heterogeneity under CD4-replete and -deplete conditions and explore cellular interactions during chronic infection. Although IL-21-producing Tfh cells repopulate following transient CD4 depletion, they are outnumbered by immunomodulatory CD4 T cells. Moreover, the splenic architecture appears perturbed, with decreases in white pulp regions, coinciding with germinal center losses. These disruptions in splenic architecture are associated with diminished Tfh and progenitor CD8 T cell colocalization, providing a potential mechanism for impaired progenitor-to-effector CD8 T cell differentiation during """"""""""""""""""""""""""""""""un-helped"""""""""""""""""""""""""""""""" conditions.","CD4 T cells; CD8 T cells; CP: Immunology; IL-21; LCMV; T follicular helper cells; single-cell RNA sequencing; spatial transcriptomics.","False","Visium","3018","32285"
"GSE200720_GSM6042733","mouse","spleen","36450262","Spatial transcriptomics demonstrates the role of CD4 T cells in effector CD8 T cell differentiation during chronic viral infection","CD4 T cell help is critical to sustain effector CD8 T cell responses during chronic infection, notably via T follicular helper (Tfh)-derived interleukin-21 (IL-21). Conversely, CD4 depletion results in severe CD8 T cell dysfunction and lifelong viremia despite CD4 T cell reemergence following transient depletion. These observations suggest that repopulating CD4 subsets are functionally or numerically insufficient to orchestrate a robust CD8 response. We utilize spatial transcriptomics and single-cell RNA sequencing (scRNA-seq) to investigate CD4 T cell heterogeneity under CD4-replete and -deplete conditions and explore cellular interactions during chronic infection. Although IL-21-producing Tfh cells repopulate following transient CD4 depletion, they are outnumbered by immunomodulatory CD4 T cells. Moreover, the splenic architecture appears perturbed, with decreases in white pulp regions, coinciding with germinal center losses. These disruptions in splenic architecture are associated with diminished Tfh and progenitor CD8 T cell colocalization, providing a potential mechanism for impaired progenitor-to-effector CD8 T cell differentiation during """"""""""""""""""""""""""""""""un-helped"""""""""""""""""""""""""""""""" conditions.","CD4 T cells; CD8 T cells; CP: Immunology; IL-21; LCMV; T follicular helper cells; single-cell RNA sequencing; spatial transcriptomics.","False","Visium","1669","32285"
"GSE200751_GSM7164986","mouse","melanoma","37605008","MacroH2A restricts inflammatory gene expression in melanoma cancer-associated fibroblasts by coordinating chromatin looping","MacroH2A has established tumour suppressive functions in melanoma and other cancers, but an unappreciated role in the tumour microenvironment. Using an autochthonous, immunocompetent mouse model of melanoma, we demonstrate that mice devoid of macroH2A variants exhibit increased tumour burden compared with wild-type counterparts. MacroH2A-deficient tumours accumulate immunosuppressive monocytes and are depleted of functional cytotoxic T cells, characteristics consistent with a compromised anti-tumour response. Single cell and spatial transcriptomics identify increased dedifferentiation along the neural crest lineage of the tumour compartment and increased frequency and activation of cancer-associated fibroblasts following macroH2A loss. Mechanistically, macroH2A-deficient cancer-associated fibroblasts display increased myeloid chemoattractant activity as a consequence of hyperinducible expression of inflammatory genes, which is enforced by increased chromatin looping of their promoters to enhancers that gain H3K27ac. In summary, we reveal a tumour suppressive role for macroH2A variants through the regulation of chromatin architecture in the tumour stroma with potential implications for human melanoma.","","True","Visium","1330","32285"
"GSE200751_GSM7164987","mouse","melanoma","37605008","MacroH2A restricts inflammatory gene expression in melanoma cancer-associated fibroblasts by coordinating chromatin looping","MacroH2A has established tumour suppressive functions in melanoma and other cancers, but an unappreciated role in the tumour microenvironment. Using an autochthonous, immunocompetent mouse model of melanoma, we demonstrate that mice devoid of macroH2A variants exhibit increased tumour burden compared with wild-type counterparts. MacroH2A-deficient tumours accumulate immunosuppressive monocytes and are depleted of functional cytotoxic T cells, characteristics consistent with a compromised anti-tumour response. Single cell and spatial transcriptomics identify increased dedifferentiation along the neural crest lineage of the tumour compartment and increased frequency and activation of cancer-associated fibroblasts following macroH2A loss. Mechanistically, macroH2A-deficient cancer-associated fibroblasts display increased myeloid chemoattractant activity as a consequence of hyperinducible expression of inflammatory genes, which is enforced by increased chromatin looping of their promoters to enhancers that gain H3K27ac. In summary, we reveal a tumour suppressive role for macroH2A variants through the regulation of chromatin architecture in the tumour stroma with potential implications for human melanoma.","","True","Visium","1739","32285"
"GSE201610_GSM6068593","mouse","brain","37773230","Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation","Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.","","False","Visium","2663","32285"
"GSE201610_GSM6068594","mouse","brain","37773230","Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation","Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.","","False","Visium","2663","32285"
"GSE201610_GSM6068595","mouse","brain","37773230","Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation","Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.","","False","Visium","2838","32285"
"GSE201610_GSM6068596","mouse","brain","37773230","Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation","Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.","","False","Visium","2550","32285"
"GSE201610_GSM6068597","mouse","brain","37773230","Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation","Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.","","False","Visium","2936","32285"
"GSE201610_GSM6068598","mouse","brain","37773230","Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation","Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.","","False","Visium","2778","32285"
"GSE202322_GSM6108346","mouse","lung","37852965","A spatial sequencing atlas of age-induced changes in the lung during influenza infection","Influenza virus infection causes increased morbidity and mortality in the elderly. Aging impairs the immune response to influenza, both intrinsically and because of altered interactions with endothelial and pulmonary epithelial cells. To characterize these changes, we performed single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk RNA sequencing (bulk RNA-seq) on lung tissue from young and aged female mice at days 0, 3, and 9 post-influenza infection. Our analyses identified dozens of key genes differentially expressed in kinetic, age-dependent, and cell type-specific manners. Aged immune cells exhibited altered inflammatory, memory, and chemotactic profiles. Aged endothelial cells demonstrated characteristics of reduced vascular wound healing and a prothrombotic state. Spatial transcriptomics identified novel profibrotic and antifibrotic markers expressed by epithelial and non-epithelial cells, highlighting the complex networks that promote fibrosis in aged lungs. Bulk RNA-seq generated a timeline of global transcriptional activity, showing increased expression of genes involved in inflammation and coagulation in aged lungs. Our work provides an atlas of high-throughput sequencing methodologies that can be used to investigate age-related changes in the response to influenza virus, identify novel cell-cell interactions for further study, and ultimately uncover potential therapeutic targets to improve health outcomes in the elderly following influenza infection.","","False","Visium","1986","32285"
"GSE202322_GSM6108347","mouse","lung","37852965","A spatial sequencing atlas of age-induced changes in the lung during influenza infection","Influenza virus infection causes increased morbidity and mortality in the elderly. Aging impairs the immune response to influenza, both intrinsically and because of altered interactions with endothelial and pulmonary epithelial cells. To characterize these changes, we performed single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk RNA sequencing (bulk RNA-seq) on lung tissue from young and aged female mice at days 0, 3, and 9 post-influenza infection. Our analyses identified dozens of key genes differentially expressed in kinetic, age-dependent, and cell type-specific manners. Aged immune cells exhibited altered inflammatory, memory, and chemotactic profiles. Aged endothelial cells demonstrated characteristics of reduced vascular wound healing and a prothrombotic state. Spatial transcriptomics identified novel profibrotic and antifibrotic markers expressed by epithelial and non-epithelial cells, highlighting the complex networks that promote fibrosis in aged lungs. Bulk RNA-seq generated a timeline of global transcriptional activity, showing increased expression of genes involved in inflammation and coagulation in aged lungs. Our work provides an atlas of high-throughput sequencing methodologies that can be used to investigate age-related changes in the response to influenza virus, identify novel cell-cell interactions for further study, and ultimately uncover potential therapeutic targets to improve health outcomes in the elderly following influenza infection.","","False","Visium","2218","32285"
"GSE202322_GSM6108348","mouse","lung","37852965","A spatial sequencing atlas of age-induced changes in the lung during influenza infection","Influenza virus infection causes increased morbidity and mortality in the elderly. Aging impairs the immune response to influenza, both intrinsically and because of altered interactions with endothelial and pulmonary epithelial cells. To characterize these changes, we performed single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk RNA sequencing (bulk RNA-seq) on lung tissue from young and aged female mice at days 0, 3, and 9 post-influenza infection. Our analyses identified dozens of key genes differentially expressed in kinetic, age-dependent, and cell type-specific manners. Aged immune cells exhibited altered inflammatory, memory, and chemotactic profiles. Aged endothelial cells demonstrated characteristics of reduced vascular wound healing and a prothrombotic state. Spatial transcriptomics identified novel profibrotic and antifibrotic markers expressed by epithelial and non-epithelial cells, highlighting the complex networks that promote fibrosis in aged lungs. Bulk RNA-seq generated a timeline of global transcriptional activity, showing increased expression of genes involved in inflammation and coagulation in aged lungs. Our work provides an atlas of high-throughput sequencing methodologies that can be used to investigate age-related changes in the response to influenza virus, identify novel cell-cell interactions for further study, and ultimately uncover potential therapeutic targets to improve health outcomes in the elderly following influenza infection.","","False","Visium","2486","32285"
"GSE202322_GSM6108349","mouse","lung","37852965","A spatial sequencing atlas of age-induced changes in the lung during influenza infection","Influenza virus infection causes increased morbidity and mortality in the elderly. Aging impairs the immune response to influenza, both intrinsically and because of altered interactions with endothelial and pulmonary epithelial cells. To characterize these changes, we performed single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk RNA sequencing (bulk RNA-seq) on lung tissue from young and aged female mice at days 0, 3, and 9 post-influenza infection. Our analyses identified dozens of key genes differentially expressed in kinetic, age-dependent, and cell type-specific manners. Aged immune cells exhibited altered inflammatory, memory, and chemotactic profiles. Aged endothelial cells demonstrated characteristics of reduced vascular wound healing and a prothrombotic state. Spatial transcriptomics identified novel profibrotic and antifibrotic markers expressed by epithelial and non-epithelial cells, highlighting the complex networks that promote fibrosis in aged lungs. Bulk RNA-seq generated a timeline of global transcriptional activity, showing increased expression of genes involved in inflammation and coagulation in aged lungs. Our work provides an atlas of high-throughput sequencing methodologies that can be used to investigate age-related changes in the response to influenza virus, identify novel cell-cell interactions for further study, and ultimately uncover potential therapeutic targets to improve health outcomes in the elderly following influenza infection.","","False","Visium","2520","32285"
"GSE202322_GSM6108350","mouse","lung","37852965","A spatial sequencing atlas of age-induced changes in the lung during influenza infection","Influenza virus infection causes increased morbidity and mortality in the elderly. Aging impairs the immune response to influenza, both intrinsically and because of altered interactions with endothelial and pulmonary epithelial cells. To characterize these changes, we performed single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk RNA sequencing (bulk RNA-seq) on lung tissue from young and aged female mice at days 0, 3, and 9 post-influenza infection. Our analyses identified dozens of key genes differentially expressed in kinetic, age-dependent, and cell type-specific manners. Aged immune cells exhibited altered inflammatory, memory, and chemotactic profiles. Aged endothelial cells demonstrated characteristics of reduced vascular wound healing and a prothrombotic state. Spatial transcriptomics identified novel profibrotic and antifibrotic markers expressed by epithelial and non-epithelial cells, highlighting the complex networks that promote fibrosis in aged lungs. Bulk RNA-seq generated a timeline of global transcriptional activity, showing increased expression of genes involved in inflammation and coagulation in aged lungs. Our work provides an atlas of high-throughput sequencing methodologies that can be used to investigate age-related changes in the response to influenza virus, identify novel cell-cell interactions for further study, and ultimately uncover potential therapeutic targets to improve health outcomes in the elderly following influenza infection.","","False","Visium","2847","32285"
"GSE202322_GSM6108351","mouse","lung","37852965","A spatial sequencing atlas of age-induced changes in the lung during influenza infection","Influenza virus infection causes increased morbidity and mortality in the elderly. Aging impairs the immune response to influenza, both intrinsically and because of altered interactions with endothelial and pulmonary epithelial cells. To characterize these changes, we performed single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk RNA sequencing (bulk RNA-seq) on lung tissue from young and aged female mice at days 0, 3, and 9 post-influenza infection. Our analyses identified dozens of key genes differentially expressed in kinetic, age-dependent, and cell type-specific manners. Aged immune cells exhibited altered inflammatory, memory, and chemotactic profiles. Aged endothelial cells demonstrated characteristics of reduced vascular wound healing and a prothrombotic state. Spatial transcriptomics identified novel profibrotic and antifibrotic markers expressed by epithelial and non-epithelial cells, highlighting the complex networks that promote fibrosis in aged lungs. Bulk RNA-seq generated a timeline of global transcriptional activity, showing increased expression of genes involved in inflammation and coagulation in aged lungs. Our work provides an atlas of high-throughput sequencing methodologies that can be used to investigate age-related changes in the response to influenza virus, identify novel cell-cell interactions for further study, and ultimately uncover potential therapeutic targets to improve health outcomes in the elderly following influenza infection.","","False","Visium","2969","32285"
"GSE203424_GSM6171782","mouse","brain","36448627","Microglial INPP5D limits plaque formation and glial reactivity in the PSAPP mouse model of Alzheimer's disease","Introduction: The inositol polyphosphate-5-phosphatase D (INPP5D) gene encodes a dual-specificity phosphatase that can dephosphorylate both phospholipids and phosphoproteins. Single nucleotide polymorphisms in INPP5D impact risk for developing late onset sporadic Alzheimer's disease (LOAD).

Methods: To assess the consequences of inducible Inpp5d knockdown in microglia of APPKM670/671NL /PSEN1Δexon9 (PSAPP) mice, we injected 3-month-old Inpp5dfl/fl /Cx3cr1CreER/+ and PSAPP/Inpp5dfl/fl /Cx3cr1CreER/+ mice with either tamoxifen (TAM) or corn oil (CO) to induce recombination.

Results: At age 6 months, we found that the percent area of 6E10+ deposits and plaque-associated microglia in Inpp5d knockdown mice were increased compared to controls. Spatial transcriptomics identified a plaque-specific expression profile that was extensively altered by Inpp5d knockdown.

Discussion: These results demonstrate that conditional Inpp5d downregulation in the PSAPP mouse increases plaque burden and recruitment of microglia to plaques. Spatial transcriptomics highlighted an extended gene expression signature associated with plaques and identified CST7 (cystatin F) as a novel marker of plaques.

Highlights: Inpp5d knockdown increases plaque burden and plaque-associated microglia number. Spatial transcriptomics identifies an expanded plaque-specific gene expression profile. Plaque-induced gene expression is altered by Inpp5d knockdown in microglia. Our plaque-associated gene signature overlaps with human Alzheimer's disease gene networks.","Alzheimer's disease; Inpp5d; SHIP1; cystatin F; microglia; oligomer; spatial transcriptomics.","False","Visium","2549","32285"
"GSE203424_GSM6171783","mouse","brain","36448627","Microglial INPP5D limits plaque formation and glial reactivity in the PSAPP mouse model of Alzheimer's disease","Introduction: The inositol polyphosphate-5-phosphatase D (INPP5D) gene encodes a dual-specificity phosphatase that can dephosphorylate both phospholipids and phosphoproteins. Single nucleotide polymorphisms in INPP5D impact risk for developing late onset sporadic Alzheimer's disease (LOAD).

Methods: To assess the consequences of inducible Inpp5d knockdown in microglia of APPKM670/671NL /PSEN1Δexon9 (PSAPP) mice, we injected 3-month-old Inpp5dfl/fl /Cx3cr1CreER/+ and PSAPP/Inpp5dfl/fl /Cx3cr1CreER/+ mice with either tamoxifen (TAM) or corn oil (CO) to induce recombination.

Results: At age 6 months, we found that the percent area of 6E10+ deposits and plaque-associated microglia in Inpp5d knockdown mice were increased compared to controls. Spatial transcriptomics identified a plaque-specific expression profile that was extensively altered by Inpp5d knockdown.

Discussion: These results demonstrate that conditional Inpp5d downregulation in the PSAPP mouse increases plaque burden and recruitment of microglia to plaques. Spatial transcriptomics highlighted an extended gene expression signature associated with plaques and identified CST7 (cystatin F) as a novel marker of plaques.

Highlights: Inpp5d knockdown increases plaque burden and plaque-associated microglia number. Spatial transcriptomics identifies an expanded plaque-specific gene expression profile. Plaque-induced gene expression is altered by Inpp5d knockdown in microglia. Our plaque-associated gene signature overlaps with human Alzheimer's disease gene networks.","Alzheimer's disease; Inpp5d; SHIP1; cystatin F; microglia; oligomer; spatial transcriptomics.","False","Visium","3025","32285"
"GSE203424_GSM6171784","mouse","brain","36448627","Microglial INPP5D limits plaque formation and glial reactivity in the PSAPP mouse model of Alzheimer's disease","Introduction: The inositol polyphosphate-5-phosphatase D (INPP5D) gene encodes a dual-specificity phosphatase that can dephosphorylate both phospholipids and phosphoproteins. Single nucleotide polymorphisms in INPP5D impact risk for developing late onset sporadic Alzheimer's disease (LOAD).

Methods: To assess the consequences of inducible Inpp5d knockdown in microglia of APPKM670/671NL /PSEN1Δexon9 (PSAPP) mice, we injected 3-month-old Inpp5dfl/fl /Cx3cr1CreER/+ and PSAPP/Inpp5dfl/fl /Cx3cr1CreER/+ mice with either tamoxifen (TAM) or corn oil (CO) to induce recombination.

Results: At age 6 months, we found that the percent area of 6E10+ deposits and plaque-associated microglia in Inpp5d knockdown mice were increased compared to controls. Spatial transcriptomics identified a plaque-specific expression profile that was extensively altered by Inpp5d knockdown.

Discussion: These results demonstrate that conditional Inpp5d downregulation in the PSAPP mouse increases plaque burden and recruitment of microglia to plaques. Spatial transcriptomics highlighted an extended gene expression signature associated with plaques and identified CST7 (cystatin F) as a novel marker of plaques.

Highlights: Inpp5d knockdown increases plaque burden and plaque-associated microglia number. Spatial transcriptomics identifies an expanded plaque-specific gene expression profile. Plaque-induced gene expression is altered by Inpp5d knockdown in microglia. Our plaque-associated gene signature overlaps with human Alzheimer's disease gene networks.","Alzheimer's disease; Inpp5d; SHIP1; cystatin F; microglia; oligomer; spatial transcriptomics.","False","Visium","2731","32285"
"GSE203424_GSM6171785","mouse","brain","36448627","Microglial INPP5D limits plaque formation and glial reactivity in the PSAPP mouse model of Alzheimer's disease","Introduction: The inositol polyphosphate-5-phosphatase D (INPP5D) gene encodes a dual-specificity phosphatase that can dephosphorylate both phospholipids and phosphoproteins. Single nucleotide polymorphisms in INPP5D impact risk for developing late onset sporadic Alzheimer's disease (LOAD).

Methods: To assess the consequences of inducible Inpp5d knockdown in microglia of APPKM670/671NL /PSEN1Δexon9 (PSAPP) mice, we injected 3-month-old Inpp5dfl/fl /Cx3cr1CreER/+ and PSAPP/Inpp5dfl/fl /Cx3cr1CreER/+ mice with either tamoxifen (TAM) or corn oil (CO) to induce recombination.

Results: At age 6 months, we found that the percent area of 6E10+ deposits and plaque-associated microglia in Inpp5d knockdown mice were increased compared to controls. Spatial transcriptomics identified a plaque-specific expression profile that was extensively altered by Inpp5d knockdown.

Discussion: These results demonstrate that conditional Inpp5d downregulation in the PSAPP mouse increases plaque burden and recruitment of microglia to plaques. Spatial transcriptomics highlighted an extended gene expression signature associated with plaques and identified CST7 (cystatin F) as a novel marker of plaques.

Highlights: Inpp5d knockdown increases plaque burden and plaque-associated microglia number. Spatial transcriptomics identifies an expanded plaque-specific gene expression profile. Plaque-induced gene expression is altered by Inpp5d knockdown in microglia. Our plaque-associated gene signature overlaps with human Alzheimer's disease gene networks.","Alzheimer's disease; Inpp5d; SHIP1; cystatin F; microglia; oligomer; spatial transcriptomics.","False","Visium","3513","32285"
"GSE203424_GSM6171786","mouse","brain","36448627","Microglial INPP5D limits plaque formation and glial reactivity in the PSAPP mouse model of Alzheimer's disease","Introduction: The inositol polyphosphate-5-phosphatase D (INPP5D) gene encodes a dual-specificity phosphatase that can dephosphorylate both phospholipids and phosphoproteins. Single nucleotide polymorphisms in INPP5D impact risk for developing late onset sporadic Alzheimer's disease (LOAD).

Methods: To assess the consequences of inducible Inpp5d knockdown in microglia of APPKM670/671NL /PSEN1Δexon9 (PSAPP) mice, we injected 3-month-old Inpp5dfl/fl /Cx3cr1CreER/+ and PSAPP/Inpp5dfl/fl /Cx3cr1CreER/+ mice with either tamoxifen (TAM) or corn oil (CO) to induce recombination.

Results: At age 6 months, we found that the percent area of 6E10+ deposits and plaque-associated microglia in Inpp5d knockdown mice were increased compared to controls. Spatial transcriptomics identified a plaque-specific expression profile that was extensively altered by Inpp5d knockdown.

Discussion: These results demonstrate that conditional Inpp5d downregulation in the PSAPP mouse increases plaque burden and recruitment of microglia to plaques. Spatial transcriptomics highlighted an extended gene expression signature associated with plaques and identified CST7 (cystatin F) as a novel marker of plaques.

Highlights: Inpp5d knockdown increases plaque burden and plaque-associated microglia number. Spatial transcriptomics identifies an expanded plaque-specific gene expression profile. Plaque-induced gene expression is altered by Inpp5d knockdown in microglia. Our plaque-associated gene signature overlaps with human Alzheimer's disease gene networks.","Alzheimer's disease; Inpp5d; SHIP1; cystatin F; microglia; oligomer; spatial transcriptomics.","False","Visium","2841","32285"
"GSE203424_GSM6171787","mouse","brain","36448627","Microglial INPP5D limits plaque formation and glial reactivity in the PSAPP mouse model of Alzheimer's disease","Introduction: The inositol polyphosphate-5-phosphatase D (INPP5D) gene encodes a dual-specificity phosphatase that can dephosphorylate both phospholipids and phosphoproteins. Single nucleotide polymorphisms in INPP5D impact risk for developing late onset sporadic Alzheimer's disease (LOAD).

Methods: To assess the consequences of inducible Inpp5d knockdown in microglia of APPKM670/671NL /PSEN1Δexon9 (PSAPP) mice, we injected 3-month-old Inpp5dfl/fl /Cx3cr1CreER/+ and PSAPP/Inpp5dfl/fl /Cx3cr1CreER/+ mice with either tamoxifen (TAM) or corn oil (CO) to induce recombination.

Results: At age 6 months, we found that the percent area of 6E10+ deposits and plaque-associated microglia in Inpp5d knockdown mice were increased compared to controls. Spatial transcriptomics identified a plaque-specific expression profile that was extensively altered by Inpp5d knockdown.

Discussion: These results demonstrate that conditional Inpp5d downregulation in the PSAPP mouse increases plaque burden and recruitment of microglia to plaques. Spatial transcriptomics highlighted an extended gene expression signature associated with plaques and identified CST7 (cystatin F) as a novel marker of plaques.

Highlights: Inpp5d knockdown increases plaque burden and plaque-associated microglia number. Spatial transcriptomics identifies an expanded plaque-specific gene expression profile. Plaque-induced gene expression is altered by Inpp5d knockdown in microglia. Our plaque-associated gene signature overlaps with human Alzheimer's disease gene networks.","Alzheimer's disease; Inpp5d; SHIP1; cystatin F; microglia; oligomer; spatial transcriptomics.","False","Visium","3126","32285"
"GSE203424_GSM6171788","mouse","brain","36448627","Microglial INPP5D limits plaque formation and glial reactivity in the PSAPP mouse model of Alzheimer's disease","Introduction: The inositol polyphosphate-5-phosphatase D (INPP5D) gene encodes a dual-specificity phosphatase that can dephosphorylate both phospholipids and phosphoproteins. Single nucleotide polymorphisms in INPP5D impact risk for developing late onset sporadic Alzheimer's disease (LOAD).

Methods: To assess the consequences of inducible Inpp5d knockdown in microglia of APPKM670/671NL /PSEN1Δexon9 (PSAPP) mice, we injected 3-month-old Inpp5dfl/fl /Cx3cr1CreER/+ and PSAPP/Inpp5dfl/fl /Cx3cr1CreER/+ mice with either tamoxifen (TAM) or corn oil (CO) to induce recombination.

Results: At age 6 months, we found that the percent area of 6E10+ deposits and plaque-associated microglia in Inpp5d knockdown mice were increased compared to controls. Spatial transcriptomics identified a plaque-specific expression profile that was extensively altered by Inpp5d knockdown.

Discussion: These results demonstrate that conditional Inpp5d downregulation in the PSAPP mouse increases plaque burden and recruitment of microglia to plaques. Spatial transcriptomics highlighted an extended gene expression signature associated with plaques and identified CST7 (cystatin F) as a novel marker of plaques.

Highlights: Inpp5d knockdown increases plaque burden and plaque-associated microglia number. Spatial transcriptomics identifies an expanded plaque-specific gene expression profile. Plaque-induced gene expression is altered by Inpp5d knockdown in microglia. Our plaque-associated gene signature overlaps with human Alzheimer's disease gene networks.","Alzheimer's disease; Inpp5d; SHIP1; cystatin F; microglia; oligomer; spatial transcriptomics.","False","Visium","2601","32285"
"GSE203424_GSM6171789","mouse","brain","36448627","Microglial INPP5D limits plaque formation and glial reactivity in the PSAPP mouse model of Alzheimer's disease","Introduction: The inositol polyphosphate-5-phosphatase D (INPP5D) gene encodes a dual-specificity phosphatase that can dephosphorylate both phospholipids and phosphoproteins. Single nucleotide polymorphisms in INPP5D impact risk for developing late onset sporadic Alzheimer's disease (LOAD).

Methods: To assess the consequences of inducible Inpp5d knockdown in microglia of APPKM670/671NL /PSEN1Δexon9 (PSAPP) mice, we injected 3-month-old Inpp5dfl/fl /Cx3cr1CreER/+ and PSAPP/Inpp5dfl/fl /Cx3cr1CreER/+ mice with either tamoxifen (TAM) or corn oil (CO) to induce recombination.

Results: At age 6 months, we found that the percent area of 6E10+ deposits and plaque-associated microglia in Inpp5d knockdown mice were increased compared to controls. Spatial transcriptomics identified a plaque-specific expression profile that was extensively altered by Inpp5d knockdown.

Discussion: These results demonstrate that conditional Inpp5d downregulation in the PSAPP mouse increases plaque burden and recruitment of microglia to plaques. Spatial transcriptomics highlighted an extended gene expression signature associated with plaques and identified CST7 (cystatin F) as a novel marker of plaques.

Highlights: Inpp5d knockdown increases plaque burden and plaque-associated microglia number. Spatial transcriptomics identifies an expanded plaque-specific gene expression profile. Plaque-induced gene expression is altered by Inpp5d knockdown in microglia. Our plaque-associated gene signature overlaps with human Alzheimer's disease gene networks.","Alzheimer's disease; Inpp5d; SHIP1; cystatin F; microglia; oligomer; spatial transcriptomics.","False","Visium","3078","32285"
"GSE203424_GSM6171790","mouse","brain","36448627","Microglial INPP5D limits plaque formation and glial reactivity in the PSAPP mouse model of Alzheimer's disease","Introduction: The inositol polyphosphate-5-phosphatase D (INPP5D) gene encodes a dual-specificity phosphatase that can dephosphorylate both phospholipids and phosphoproteins. Single nucleotide polymorphisms in INPP5D impact risk for developing late onset sporadic Alzheimer's disease (LOAD).

Methods: To assess the consequences of inducible Inpp5d knockdown in microglia of APPKM670/671NL /PSEN1Δexon9 (PSAPP) mice, we injected 3-month-old Inpp5dfl/fl /Cx3cr1CreER/+ and PSAPP/Inpp5dfl/fl /Cx3cr1CreER/+ mice with either tamoxifen (TAM) or corn oil (CO) to induce recombination.

Results: At age 6 months, we found that the percent area of 6E10+ deposits and plaque-associated microglia in Inpp5d knockdown mice were increased compared to controls. Spatial transcriptomics identified a plaque-specific expression profile that was extensively altered by Inpp5d knockdown.

Discussion: These results demonstrate that conditional Inpp5d downregulation in the PSAPP mouse increases plaque burden and recruitment of microglia to plaques. Spatial transcriptomics highlighted an extended gene expression signature associated with plaques and identified CST7 (cystatin F) as a novel marker of plaques.

Highlights: Inpp5d knockdown increases plaque burden and plaque-associated microglia number. Spatial transcriptomics identifies an expanded plaque-specific gene expression profile. Plaque-induced gene expression is altered by Inpp5d knockdown in microglia. Our plaque-associated gene signature overlaps with human Alzheimer's disease gene networks.","Alzheimer's disease; Inpp5d; SHIP1; cystatin F; microglia; oligomer; spatial transcriptomics.","False","Visium","2647","32285"
"GSE203424_GSM6171791","mouse","brain","36448627","Microglial INPP5D limits plaque formation and glial reactivity in the PSAPP mouse model of Alzheimer's disease","Introduction: The inositol polyphosphate-5-phosphatase D (INPP5D) gene encodes a dual-specificity phosphatase that can dephosphorylate both phospholipids and phosphoproteins. Single nucleotide polymorphisms in INPP5D impact risk for developing late onset sporadic Alzheimer's disease (LOAD).

Methods: To assess the consequences of inducible Inpp5d knockdown in microglia of APPKM670/671NL /PSEN1Δexon9 (PSAPP) mice, we injected 3-month-old Inpp5dfl/fl /Cx3cr1CreER/+ and PSAPP/Inpp5dfl/fl /Cx3cr1CreER/+ mice with either tamoxifen (TAM) or corn oil (CO) to induce recombination.

Results: At age 6 months, we found that the percent area of 6E10+ deposits and plaque-associated microglia in Inpp5d knockdown mice were increased compared to controls. Spatial transcriptomics identified a plaque-specific expression profile that was extensively altered by Inpp5d knockdown.

Discussion: These results demonstrate that conditional Inpp5d downregulation in the PSAPP mouse increases plaque burden and recruitment of microglia to plaques. Spatial transcriptomics highlighted an extended gene expression signature associated with plaques and identified CST7 (cystatin F) as a novel marker of plaques.

Highlights: Inpp5d knockdown increases plaque burden and plaque-associated microglia number. Spatial transcriptomics identifies an expanded plaque-specific gene expression profile. Plaque-induced gene expression is altered by Inpp5d knockdown in microglia. Our plaque-associated gene signature overlaps with human Alzheimer's disease gene networks.","Alzheimer's disease; Inpp5d; SHIP1; cystatin F; microglia; oligomer; spatial transcriptomics.","False","Visium","2731","32285"
"GSE203424_GSM6171792","mouse","brain","36448627","Microglial INPP5D limits plaque formation and glial reactivity in the PSAPP mouse model of Alzheimer's disease","Introduction: The inositol polyphosphate-5-phosphatase D (INPP5D) gene encodes a dual-specificity phosphatase that can dephosphorylate both phospholipids and phosphoproteins. Single nucleotide polymorphisms in INPP5D impact risk for developing late onset sporadic Alzheimer's disease (LOAD).

Methods: To assess the consequences of inducible Inpp5d knockdown in microglia of APPKM670/671NL /PSEN1Δexon9 (PSAPP) mice, we injected 3-month-old Inpp5dfl/fl /Cx3cr1CreER/+ and PSAPP/Inpp5dfl/fl /Cx3cr1CreER/+ mice with either tamoxifen (TAM) or corn oil (CO) to induce recombination.

Results: At age 6 months, we found that the percent area of 6E10+ deposits and plaque-associated microglia in Inpp5d knockdown mice were increased compared to controls. Spatial transcriptomics identified a plaque-specific expression profile that was extensively altered by Inpp5d knockdown.

Discussion: These results demonstrate that conditional Inpp5d downregulation in the PSAPP mouse increases plaque burden and recruitment of microglia to plaques. Spatial transcriptomics highlighted an extended gene expression signature associated with plaques and identified CST7 (cystatin F) as a novel marker of plaques.

Highlights: Inpp5d knockdown increases plaque burden and plaque-associated microglia number. Spatial transcriptomics identifies an expanded plaque-specific gene expression profile. Plaque-induced gene expression is altered by Inpp5d knockdown in microglia. Our plaque-associated gene signature overlaps with human Alzheimer's disease gene networks.","Alzheimer's disease; Inpp5d; SHIP1; cystatin F; microglia; oligomer; spatial transcriptomics.","False","Visium","2416","32285"
"GSE203424_GSM6171793","mouse","brain","36448627","Microglial INPP5D limits plaque formation and glial reactivity in the PSAPP mouse model of Alzheimer's disease","Introduction: The inositol polyphosphate-5-phosphatase D (INPP5D) gene encodes a dual-specificity phosphatase that can dephosphorylate both phospholipids and phosphoproteins. Single nucleotide polymorphisms in INPP5D impact risk for developing late onset sporadic Alzheimer's disease (LOAD).

Methods: To assess the consequences of inducible Inpp5d knockdown in microglia of APPKM670/671NL /PSEN1Δexon9 (PSAPP) mice, we injected 3-month-old Inpp5dfl/fl /Cx3cr1CreER/+ and PSAPP/Inpp5dfl/fl /Cx3cr1CreER/+ mice with either tamoxifen (TAM) or corn oil (CO) to induce recombination.

Results: At age 6 months, we found that the percent area of 6E10+ deposits and plaque-associated microglia in Inpp5d knockdown mice were increased compared to controls. Spatial transcriptomics identified a plaque-specific expression profile that was extensively altered by Inpp5d knockdown.

Discussion: These results demonstrate that conditional Inpp5d downregulation in the PSAPP mouse increases plaque burden and recruitment of microglia to plaques. Spatial transcriptomics highlighted an extended gene expression signature associated with plaques and identified CST7 (cystatin F) as a novel marker of plaques.

Highlights: Inpp5d knockdown increases plaque burden and plaque-associated microglia number. Spatial transcriptomics identifies an expanded plaque-specific gene expression profile. Plaque-induced gene expression is altered by Inpp5d knockdown in microglia. Our plaque-associated gene signature overlaps with human Alzheimer's disease gene networks.","Alzheimer's disease; Inpp5d; SHIP1; cystatin F; microglia; oligomer; spatial transcriptomics.","False","Visium","2775","32285"
"GSE203612_GSM6177599","human","breast","35931863","Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment","Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including 'stress', 'interferon response', 'epithelial-mesenchymal transition', 'metal response', 'basal' and 'ciliated'. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.","","True","Visium","2384","33538"
"GSE203612_GSM6177601","human","breast","35931863","Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment","Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including 'stress', 'interferon response', 'epithelial-mesenchymal transition', 'metal response', 'basal' and 'ciliated'. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.","","True","Visium","1863","33538"
"GSE203612_GSM6177603","human","breast","35931863","Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment","Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including 'stress', 'interferon response', 'epithelial-mesenchymal transition', 'metal response', 'basal' and 'ciliated'. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.","","True","Visium","2346","33538"
"GSE203612_GSM6177607","human","stomach","35931863","Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment","Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including 'stress', 'interferon response', 'epithelial-mesenchymal transition', 'metal response', 'basal' and 'ciliated'. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.","","True","Visium","2624","33538"
"GSE203612_GSM6177609","human","stomach","35931863","Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment","Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including 'stress', 'interferon response', 'epithelial-mesenchymal transition', 'metal response', 'basal' and 'ciliated'. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.","","True","Visium","2493","33538"
"GSE203612_GSM6177612","human","liver","35931863","Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment","Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including 'stress', 'interferon response', 'epithelial-mesenchymal transition', 'metal response', 'basal' and 'ciliated'. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.","","True","Visium","1661","33538"
"GSE203612_GSM6177614","human","ovary","35931863","Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment","Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including 'stress', 'interferon response', 'epithelial-mesenchymal transition', 'metal response', 'basal' and 'ciliated'. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.","","True","Visium","1762","33538"
"GSE203612_GSM6177617","human","ovary","35931863","Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment","Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including 'stress', 'interferon response', 'epithelial-mesenchymal transition', 'metal response', 'basal' and 'ciliated'. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.","","True","Visium","1661","33538"
"GSE203612_GSM6177618","human","ovary","35931863","Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment","Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including 'stress', 'interferon response', 'epithelial-mesenchymal transition', 'metal response', 'basal' and 'ciliated'. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.","","True","Visium","1789","33538"
"GSE203612_GSM6177623","human","endometrium","35931863","Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment","Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including 'stress', 'interferon response', 'epithelial-mesenchymal transition', 'metal response', 'basal' and 'ciliated'. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.","","True","Visium","1351","33538"
"GSE203612_GSM6177624","human & mouse","xenograft","35931863","Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment","Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including 'stress', 'interferon response', 'epithelial-mesenchymal transition', 'metal response', 'basal' and 'ciliated'. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.","","True","Visium","3039","41797"
"GSE203612_GSM6177625","human & mouse","xenograft","35931863","Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment","Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including 'stress', 'interferon response', 'epithelial-mesenchymal transition', 'metal response', 'basal' and 'ciliated'. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.","","True","Visium","2556","41797"
"GSE205306_GSM6210023","mouse","tissue specimen","38424167","Single-cell RNA sequencing reveals myeloid and T cell co-stimulation mediated by IL-7 anti-cancer immunotherapy","Background: Immune checkpoint inhibitors unleash inhibitory signals on T cells conferred by tumors and surrounding stromal cells. Despite the clinical efficacy of checkpoint inhibitors, the lack of target expression and persistence of immunosuppressive cells limit the pervasive effectiveness of the therapy. These limitations may be overcome by alternative approaches that co-stimulate T cells and the immune microenvironment.

Methods: We analyzed single-cell RNA sequencing data from multiple human cancers and a mouse tumor transplant model to discover the pleiotropic expression of the Interleukin 7 (IL-7) receptor on T cells, macrophages, and dendritic cells.

Results: Our experiment on the mouse model demonstrated that recombinant IL-7 therapy induces tumor regression, expansion of effector CD8 T cells, and pro-inflammatory activation of macrophages. Moreover, spatial transcriptomic data support immunostimulatory interactions between macrophages and T cells.

Conclusion: These results indicate that IL-7 therapy induces anti-tumor immunity by activating T cells and pro-inflammatory myeloid cells, which may have diverse therapeutic applicability.","","True","Visium","2221","32285"
"GSE205306_GSM6210024","mouse","tissue specimen","38424167","Single-cell RNA sequencing reveals myeloid and T cell co-stimulation mediated by IL-7 anti-cancer immunotherapy","Background: Immune checkpoint inhibitors unleash inhibitory signals on T cells conferred by tumors and surrounding stromal cells. Despite the clinical efficacy of checkpoint inhibitors, the lack of target expression and persistence of immunosuppressive cells limit the pervasive effectiveness of the therapy. These limitations may be overcome by alternative approaches that co-stimulate T cells and the immune microenvironment.

Methods: We analyzed single-cell RNA sequencing data from multiple human cancers and a mouse tumor transplant model to discover the pleiotropic expression of the Interleukin 7 (IL-7) receptor on T cells, macrophages, and dendritic cells.

Results: Our experiment on the mouse model demonstrated that recombinant IL-7 therapy induces tumor regression, expansion of effector CD8 T cells, and pro-inflammatory activation of macrophages. Moreover, spatial transcriptomic data support immunostimulatory interactions between macrophages and T cells.

Conclusion: These results indicate that IL-7 therapy induces anti-tumor immunity by activating T cells and pro-inflammatory myeloid cells, which may have diverse therapeutic applicability.","","True","Visium","1951","32285"
"GSE206306_GSM6250307","human","kidney","37468493","A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease","Kidney stone disease causes significant morbidity and increases health care utilization. In this work, we decipher the cellular and molecular niche of the human renal papilla in patients with calcium oxalate (CaOx) stone disease and healthy subjects. In addition to identifying cell types important in papillary physiology, we characterize collecting duct cell subtypes and an undifferentiated epithelial cell type that was more prevalent in stone patients. Despite the focal nature of mineral deposition in nephrolithiasis, we uncover a global injury signature characterized by immune activation, oxidative stress and extracellular matrix remodeling. We also identify the association of MMP7 and MMP9 expression with stone disease and mineral deposition, respectively. MMP7 and MMP9 are significantly increased in the urine of patients with CaOx stone disease, and their levels correlate with disease activity. Our results define the spatial molecular landscape and specific pathways contributing to stone-mediated injury in the human papilla and identify associated urinary biomarkers.","","False","Visium","2438","36601"
"GSE206306_GSM6250308","human","kidney","37468493","A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease","Kidney stone disease causes significant morbidity and increases health care utilization. In this work, we decipher the cellular and molecular niche of the human renal papilla in patients with calcium oxalate (CaOx) stone disease and healthy subjects. In addition to identifying cell types important in papillary physiology, we characterize collecting duct cell subtypes and an undifferentiated epithelial cell type that was more prevalent in stone patients. Despite the focal nature of mineral deposition in nephrolithiasis, we uncover a global injury signature characterized by immune activation, oxidative stress and extracellular matrix remodeling. We also identify the association of MMP7 and MMP9 expression with stone disease and mineral deposition, respectively. MMP7 and MMP9 are significantly increased in the urine of patients with CaOx stone disease, and their levels correlate with disease activity. Our results define the spatial molecular landscape and specific pathways contributing to stone-mediated injury in the human papilla and identify associated urinary biomarkers.","","False","Visium","817","36601"
"GSE206306_GSM6250309","human","kidney","37468493","A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease","Kidney stone disease causes significant morbidity and increases health care utilization. In this work, we decipher the cellular and molecular niche of the human renal papilla in patients with calcium oxalate (CaOx) stone disease and healthy subjects. In addition to identifying cell types important in papillary physiology, we characterize collecting duct cell subtypes and an undifferentiated epithelial cell type that was more prevalent in stone patients. Despite the focal nature of mineral deposition in nephrolithiasis, we uncover a global injury signature characterized by immune activation, oxidative stress and extracellular matrix remodeling. We also identify the association of MMP7 and MMP9 expression with stone disease and mineral deposition, respectively. MMP7 and MMP9 are significantly increased in the urine of patients with CaOx stone disease, and their levels correlate with disease activity. Our results define the spatial molecular landscape and specific pathways contributing to stone-mediated injury in the human papilla and identify associated urinary biomarkers.","","False","Visium","939","36601"
"GSE206306_GSM6250310","human","kidney","37468493","A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease","Kidney stone disease causes significant morbidity and increases health care utilization. In this work, we decipher the cellular and molecular niche of the human renal papilla in patients with calcium oxalate (CaOx) stone disease and healthy subjects. In addition to identifying cell types important in papillary physiology, we characterize collecting duct cell subtypes and an undifferentiated epithelial cell type that was more prevalent in stone patients. Despite the focal nature of mineral deposition in nephrolithiasis, we uncover a global injury signature characterized by immune activation, oxidative stress and extracellular matrix remodeling. We also identify the association of MMP7 and MMP9 expression with stone disease and mineral deposition, respectively. MMP7 and MMP9 are significantly increased in the urine of patients with CaOx stone disease, and their levels correlate with disease activity. Our results define the spatial molecular landscape and specific pathways contributing to stone-mediated injury in the human papilla and identify associated urinary biomarkers.","","False","Visium","1085","36601"
"GSE206306_GSM7166170","human","kidney","37468493","A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease","Kidney stone disease causes significant morbidity and increases health care utilization. In this work, we decipher the cellular and molecular niche of the human renal papilla in patients with calcium oxalate (CaOx) stone disease and healthy subjects. In addition to identifying cell types important in papillary physiology, we characterize collecting duct cell subtypes and an undifferentiated epithelial cell type that was more prevalent in stone patients. Despite the focal nature of mineral deposition in nephrolithiasis, we uncover a global injury signature characterized by immune activation, oxidative stress and extracellular matrix remodeling. We also identify the association of MMP7 and MMP9 expression with stone disease and mineral deposition, respectively. MMP7 and MMP9 are significantly increased in the urine of patients with CaOx stone disease, and their levels correlate with disease activity. Our results define the spatial molecular landscape and specific pathways contributing to stone-mediated injury in the human papilla and identify associated urinary biomarkers.","","False","Visium","2468","36601"
"GSE206552_GSM6256810","human","liver","37157887","Spatial resolution of cellular senescence dynamics in human colorectal liver metastasis","Hepatic metastasis is a clinical challenge for colorectal cancer (CRC). Senescent cancer cells accumulate in CRC favoring tumor dissemination. Whether this mechanism progresses also in metastasis is unexplored. Here, we integrated spatial transcriptomics, 3D-microscopy, and multicellular transcriptomics to study the role of cellular senescence in human colorectal liver metastasis (CRLM). We discovered two distinct senescent metastatic cancer cell (SMCC) subtypes, transcriptionally located at the opposite pole of epithelial (e) to mesenchymal (m) transition. SMCCs differ in chemotherapy susceptibility, biological program, and prognostic roles. Mechanistically, epithelial (e)SMCC initiation relies on nucleolar stress, whereby c-myc dependent oncogene hyperactivation induces ribosomal RPL11 accumulation and DNA damage response. In a 2D pre-clinical model, we demonstrated that RPL11 co-localized with HDM2, a p53-specific ubiquitin ligase, leading to senescence activation in (e)SMCCs. On the contrary, mesenchymal (m)SMCCs undergo TGFβ paracrine activation of NOX4-p15 effectors. SMCCs display opposing effects also in the immune regulation of neighboring cells, establishing an immunosuppressive environment or leading to an active immune workflow. Both SMCC signatures are predictive biomarkers whose unbalanced ratio determined the clinical outcome in CRLM and CRC patients. Altogether, we provide a comprehensive new understanding of the role of SMCCs in CRLM and highlight their potential as new therapeutic targets to limit CRLM progression.","EMT; cellular senescence; colorectal cancer liver metastasis; prognostic role; senescence-associated secretory phenotype; spatial transcriptomics.","True","Visium","3498","36601"
"GSE206552_GSM6256811","human","liver","37157887","Spatial resolution of cellular senescence dynamics in human colorectal liver metastasis","Hepatic metastasis is a clinical challenge for colorectal cancer (CRC). Senescent cancer cells accumulate in CRC favoring tumor dissemination. Whether this mechanism progresses also in metastasis is unexplored. Here, we integrated spatial transcriptomics, 3D-microscopy, and multicellular transcriptomics to study the role of cellular senescence in human colorectal liver metastasis (CRLM). We discovered two distinct senescent metastatic cancer cell (SMCC) subtypes, transcriptionally located at the opposite pole of epithelial (e) to mesenchymal (m) transition. SMCCs differ in chemotherapy susceptibility, biological program, and prognostic roles. Mechanistically, epithelial (e)SMCC initiation relies on nucleolar stress, whereby c-myc dependent oncogene hyperactivation induces ribosomal RPL11 accumulation and DNA damage response. In a 2D pre-clinical model, we demonstrated that RPL11 co-localized with HDM2, a p53-specific ubiquitin ligase, leading to senescence activation in (e)SMCCs. On the contrary, mesenchymal (m)SMCCs undergo TGFβ paracrine activation of NOX4-p15 effectors. SMCCs display opposing effects also in the immune regulation of neighboring cells, establishing an immunosuppressive environment or leading to an active immune workflow. Both SMCC signatures are predictive biomarkers whose unbalanced ratio determined the clinical outcome in CRLM and CRC patients. Altogether, we provide a comprehensive new understanding of the role of SMCCs in CRLM and highlight their potential as new therapeutic targets to limit CRLM progression.","EMT; cellular senescence; colorectal cancer liver metastasis; prognostic role; senescence-associated secretory phenotype; spatial transcriptomics.","True","Visium","3776","36601"
"GSE206552_GSM6256812","human","liver","37157887","Spatial resolution of cellular senescence dynamics in human colorectal liver metastasis","Hepatic metastasis is a clinical challenge for colorectal cancer (CRC). Senescent cancer cells accumulate in CRC favoring tumor dissemination. Whether this mechanism progresses also in metastasis is unexplored. Here, we integrated spatial transcriptomics, 3D-microscopy, and multicellular transcriptomics to study the role of cellular senescence in human colorectal liver metastasis (CRLM). We discovered two distinct senescent metastatic cancer cell (SMCC) subtypes, transcriptionally located at the opposite pole of epithelial (e) to mesenchymal (m) transition. SMCCs differ in chemotherapy susceptibility, biological program, and prognostic roles. Mechanistically, epithelial (e)SMCC initiation relies on nucleolar stress, whereby c-myc dependent oncogene hyperactivation induces ribosomal RPL11 accumulation and DNA damage response. In a 2D pre-clinical model, we demonstrated that RPL11 co-localized with HDM2, a p53-specific ubiquitin ligase, leading to senescence activation in (e)SMCCs. On the contrary, mesenchymal (m)SMCCs undergo TGFβ paracrine activation of NOX4-p15 effectors. SMCCs display opposing effects also in the immune regulation of neighboring cells, establishing an immunosuppressive environment or leading to an active immune workflow. Both SMCC signatures are predictive biomarkers whose unbalanced ratio determined the clinical outcome in CRLM and CRC patients. Altogether, we provide a comprehensive new understanding of the role of SMCCs in CRLM and highlight their potential as new therapeutic targets to limit CRLM progression.","EMT; cellular senescence; colorectal cancer liver metastasis; prognostic role; senescence-associated secretory phenotype; spatial transcriptomics.","True","Visium","1248","36601"
"GSE206552_GSM6256813","human","liver","37157887","Spatial resolution of cellular senescence dynamics in human colorectal liver metastasis","Hepatic metastasis is a clinical challenge for colorectal cancer (CRC). Senescent cancer cells accumulate in CRC favoring tumor dissemination. Whether this mechanism progresses also in metastasis is unexplored. Here, we integrated spatial transcriptomics, 3D-microscopy, and multicellular transcriptomics to study the role of cellular senescence in human colorectal liver metastasis (CRLM). We discovered two distinct senescent metastatic cancer cell (SMCC) subtypes, transcriptionally located at the opposite pole of epithelial (e) to mesenchymal (m) transition. SMCCs differ in chemotherapy susceptibility, biological program, and prognostic roles. Mechanistically, epithelial (e)SMCC initiation relies on nucleolar stress, whereby c-myc dependent oncogene hyperactivation induces ribosomal RPL11 accumulation and DNA damage response. In a 2D pre-clinical model, we demonstrated that RPL11 co-localized with HDM2, a p53-specific ubiquitin ligase, leading to senescence activation in (e)SMCCs. On the contrary, mesenchymal (m)SMCCs undergo TGFβ paracrine activation of NOX4-p15 effectors. SMCCs display opposing effects also in the immune regulation of neighboring cells, establishing an immunosuppressive environment or leading to an active immune workflow. Both SMCC signatures are predictive biomarkers whose unbalanced ratio determined the clinical outcome in CRLM and CRC patients. Altogether, we provide a comprehensive new understanding of the role of SMCCs in CRLM and highlight their potential as new therapeutic targets to limit CRLM progression.","EMT; cellular senescence; colorectal cancer liver metastasis; prognostic role; senescence-associated secretory phenotype; spatial transcriptomics.","True","Visium","1537","36601"
"GSE206621_GSM6258251","human","mouth","36648332","Spatially resolved transcriptomics reveals pro-inflammatory fibroblast involved in lymphocyte recruitment through CXCL8 and CXCL10","The interplay among different cells in a tissue is essential for maintaining homeostasis. Although disease states have been traditionally attributed to individual cell types, increasing evidence and new therapeutic options have demonstrated the primary role of multicellular functions to understand health and disease, opening new avenues to understand pathogenesis and develop new treatment strategies. We recently described the cellular composition and dynamics of the human oral mucosa; however, the spatial arrangement of cells is needed to better understand a morphologically complex tissue. Here, we link single-cell RNA sequencing, spatial transcriptomics, and high-resolution multiplex fluorescence in situ hybridisation to characterise human oral mucosa in health and oral chronic inflammatory disease. We deconvolved expression for resolution enhancement of spatial transcriptomic data and defined highly specialised epithelial and stromal compartments describing location-specific immune programs. Furthermore, we spatially mapped a rare pathogenic fibroblast population localised in a highly immunogenic region, responsible for lymphocyte recruitment through CXCL8 and CXCL10 and with a possible role in pathological angiogenesis through ALOX5AP. Collectively, our study provides a comprehensive reference for the study of oral chronic disease pathogenesis.","cell biology; fibroblast; gingiva; human; immunology; inflammation; oral mucosa; periodontal disease; spatial genomics.","False","Visium","231","36601"
"GSE206621_GSM6258252","human","mouth","36648332","Spatially resolved transcriptomics reveals pro-inflammatory fibroblast involved in lymphocyte recruitment through CXCL8 and CXCL10","The interplay among different cells in a tissue is essential for maintaining homeostasis. Although disease states have been traditionally attributed to individual cell types, increasing evidence and new therapeutic options have demonstrated the primary role of multicellular functions to understand health and disease, opening new avenues to understand pathogenesis and develop new treatment strategies. We recently described the cellular composition and dynamics of the human oral mucosa; however, the spatial arrangement of cells is needed to better understand a morphologically complex tissue. Here, we link single-cell RNA sequencing, spatial transcriptomics, and high-resolution multiplex fluorescence in situ hybridisation to characterise human oral mucosa in health and oral chronic inflammatory disease. We deconvolved expression for resolution enhancement of spatial transcriptomic data and defined highly specialised epithelial and stromal compartments describing location-specific immune programs. Furthermore, we spatially mapped a rare pathogenic fibroblast population localised in a highly immunogenic region, responsible for lymphocyte recruitment through CXCL8 and CXCL10 and with a possible role in pathological angiogenesis through ALOX5AP. Collectively, our study provides a comprehensive reference for the study of oral chronic disease pathogenesis.","cell biology; fibroblast; gingiva; human; immunology; inflammation; oral mucosa; periodontal disease; spatial genomics.","False","Visium","284","36601"
"GSE206621_GSM6258253","human","mouth","36648332","Spatially resolved transcriptomics reveals pro-inflammatory fibroblast involved in lymphocyte recruitment through CXCL8 and CXCL10","The interplay among different cells in a tissue is essential for maintaining homeostasis. Although disease states have been traditionally attributed to individual cell types, increasing evidence and new therapeutic options have demonstrated the primary role of multicellular functions to understand health and disease, opening new avenues to understand pathogenesis and develop new treatment strategies. We recently described the cellular composition and dynamics of the human oral mucosa; however, the spatial arrangement of cells is needed to better understand a morphologically complex tissue. Here, we link single-cell RNA sequencing, spatial transcriptomics, and high-resolution multiplex fluorescence in situ hybridisation to characterise human oral mucosa in health and oral chronic inflammatory disease. We deconvolved expression for resolution enhancement of spatial transcriptomic data and defined highly specialised epithelial and stromal compartments describing location-specific immune programs. Furthermore, we spatially mapped a rare pathogenic fibroblast population localised in a highly immunogenic region, responsible for lymphocyte recruitment through CXCL8 and CXCL10 and with a possible role in pathological angiogenesis through ALOX5AP. Collectively, our study provides a comprehensive reference for the study of oral chronic disease pathogenesis.","cell biology; fibroblast; gingiva; human; immunology; inflammation; oral mucosa; periodontal disease; spatial genomics.","False","Visium","295","36601"
"GSE206621_GSM6258254","human","mouth","36648332","Spatially resolved transcriptomics reveals pro-inflammatory fibroblast involved in lymphocyte recruitment through CXCL8 and CXCL10","The interplay among different cells in a tissue is essential for maintaining homeostasis. Although disease states have been traditionally attributed to individual cell types, increasing evidence and new therapeutic options have demonstrated the primary role of multicellular functions to understand health and disease, opening new avenues to understand pathogenesis and develop new treatment strategies. We recently described the cellular composition and dynamics of the human oral mucosa; however, the spatial arrangement of cells is needed to better understand a morphologically complex tissue. Here, we link single-cell RNA sequencing, spatial transcriptomics, and high-resolution multiplex fluorescence in situ hybridisation to characterise human oral mucosa in health and oral chronic inflammatory disease. We deconvolved expression for resolution enhancement of spatial transcriptomic data and defined highly specialised epithelial and stromal compartments describing location-specific immune programs. Furthermore, we spatially mapped a rare pathogenic fibroblast population localised in a highly immunogenic region, responsible for lymphocyte recruitment through CXCL8 and CXCL10 and with a possible role in pathological angiogenesis through ALOX5AP. Collectively, our study provides a comprehensive reference for the study of oral chronic disease pathogenesis.","cell biology; fibroblast; gingiva; human; immunology; inflammation; oral mucosa; periodontal disease; spatial genomics.","False","Visium","319","36601"
"GSE206621_GSM6258255","human","mouth","36648332","Spatially resolved transcriptomics reveals pro-inflammatory fibroblast involved in lymphocyte recruitment through CXCL8 and CXCL10","The interplay among different cells in a tissue is essential for maintaining homeostasis. Although disease states have been traditionally attributed to individual cell types, increasing evidence and new therapeutic options have demonstrated the primary role of multicellular functions to understand health and disease, opening new avenues to understand pathogenesis and develop new treatment strategies. We recently described the cellular composition and dynamics of the human oral mucosa; however, the spatial arrangement of cells is needed to better understand a morphologically complex tissue. Here, we link single-cell RNA sequencing, spatial transcriptomics, and high-resolution multiplex fluorescence in situ hybridisation to characterise human oral mucosa in health and oral chronic inflammatory disease. We deconvolved expression for resolution enhancement of spatial transcriptomic data and defined highly specialised epithelial and stromal compartments describing location-specific immune programs. Furthermore, we spatially mapped a rare pathogenic fibroblast population localised in a highly immunogenic region, responsible for lymphocyte recruitment through CXCL8 and CXCL10 and with a possible role in pathological angiogenesis through ALOX5AP. Collectively, our study provides a comprehensive reference for the study of oral chronic disease pathogenesis.","cell biology; fibroblast; gingiva; human; immunology; inflammation; oral mucosa; periodontal disease; spatial genomics.","False","Visium","1088","36945"
"GSE206621_GSM6258256","human","mouth","36648332","Spatially resolved transcriptomics reveals pro-inflammatory fibroblast involved in lymphocyte recruitment through CXCL8 and CXCL10","The interplay among different cells in a tissue is essential for maintaining homeostasis. Although disease states have been traditionally attributed to individual cell types, increasing evidence and new therapeutic options have demonstrated the primary role of multicellular functions to understand health and disease, opening new avenues to understand pathogenesis and develop new treatment strategies. We recently described the cellular composition and dynamics of the human oral mucosa; however, the spatial arrangement of cells is needed to better understand a morphologically complex tissue. Here, we link single-cell RNA sequencing, spatial transcriptomics, and high-resolution multiplex fluorescence in situ hybridisation to characterise human oral mucosa in health and oral chronic inflammatory disease. We deconvolved expression for resolution enhancement of spatial transcriptomic data and defined highly specialised epithelial and stromal compartments describing location-specific immune programs. Furthermore, we spatially mapped a rare pathogenic fibroblast population localised in a highly immunogenic region, responsible for lymphocyte recruitment through CXCL8 and CXCL10 and with a possible role in pathological angiogenesis through ALOX5AP. Collectively, our study provides a comprehensive reference for the study of oral chronic disease pathogenesis.","cell biology; fibroblast; gingiva; human; immunology; inflammation; oral mucosa; periodontal disease; spatial genomics.","False","Visium","415","36945"
"GSE206621_GSM6258257","human","mouth","36648332","Spatially resolved transcriptomics reveals pro-inflammatory fibroblast involved in lymphocyte recruitment through CXCL8 and CXCL10","The interplay among different cells in a tissue is essential for maintaining homeostasis. Although disease states have been traditionally attributed to individual cell types, increasing evidence and new therapeutic options have demonstrated the primary role of multicellular functions to understand health and disease, opening new avenues to understand pathogenesis and develop new treatment strategies. We recently described the cellular composition and dynamics of the human oral mucosa; however, the spatial arrangement of cells is needed to better understand a morphologically complex tissue. Here, we link single-cell RNA sequencing, spatial transcriptomics, and high-resolution multiplex fluorescence in situ hybridisation to characterise human oral mucosa in health and oral chronic inflammatory disease. We deconvolved expression for resolution enhancement of spatial transcriptomic data and defined highly specialised epithelial and stromal compartments describing location-specific immune programs. Furthermore, we spatially mapped a rare pathogenic fibroblast population localised in a highly immunogenic region, responsible for lymphocyte recruitment through CXCL8 and CXCL10 and with a possible role in pathological angiogenesis through ALOX5AP. Collectively, our study provides a comprehensive reference for the study of oral chronic disease pathogenesis.","cell biology; fibroblast; gingiva; human; immunology; inflammation; oral mucosa; periodontal disease; spatial genomics.","False","Visium","160","36945"
"GSE206621_GSM6258258","human","mouth","36648332","Spatially resolved transcriptomics reveals pro-inflammatory fibroblast involved in lymphocyte recruitment through CXCL8 and CXCL10","The interplay among different cells in a tissue is essential for maintaining homeostasis. Although disease states have been traditionally attributed to individual cell types, increasing evidence and new therapeutic options have demonstrated the primary role of multicellular functions to understand health and disease, opening new avenues to understand pathogenesis and develop new treatment strategies. We recently described the cellular composition and dynamics of the human oral mucosa; however, the spatial arrangement of cells is needed to better understand a morphologically complex tissue. Here, we link single-cell RNA sequencing, spatial transcriptomics, and high-resolution multiplex fluorescence in situ hybridisation to characterise human oral mucosa in health and oral chronic inflammatory disease. We deconvolved expression for resolution enhancement of spatial transcriptomic data and defined highly specialised epithelial and stromal compartments describing location-specific immune programs. Furthermore, we spatially mapped a rare pathogenic fibroblast population localised in a highly immunogenic region, responsible for lymphocyte recruitment through CXCL8 and CXCL10 and with a possible role in pathological angiogenesis through ALOX5AP. Collectively, our study provides a comprehensive reference for the study of oral chronic disease pathogenesis.","cell biology; fibroblast; gingiva; human; immunology; inflammation; oral mucosa; periodontal disease; spatial genomics.","False","Visium","501","36945"
"GSE206787_GSM7898096","mouse","heart","38129410","Spatiotemporal signaling underlies progressive vascular rarefaction in myocardial infarction","Therapeutic angiogenesis represents a promising avenue to revascularize the ischemic heart. Its limited success is partly due to our poor understanding of the cardiac stroma, specifically mural cells, and their response to ischemic injury. Here, we combine single-cell and positional transcriptomics to assess the behavior of mural cells within the healing heart. In response to myocardial infarction, mural cells adopt an altered state closely associated with the infarct and retain a distinct lineage from fibroblasts. This response is concurrent with vascular rarefaction and reduced vascular coverage by mural cells. Positional transcriptomics reveals that the infarcted heart is governed by regional-dependent and temporally regulated programs. While the remote zone acts as an important source of pro-angiogenic signals, the infarct zone is accentuated by chronic activation of anti-angiogenic, pro-fibrotic, and inflammatory cues. Together, our work unveils the spatiotemporal programs underlying cardiac repair and establishes an association between vascular deterioration and mural cell dysfunction.","","False","Visium","591","19465"
"GSE206787_GSM7898097","mouse","heart","38129410","Spatiotemporal signaling underlies progressive vascular rarefaction in myocardial infarction","Therapeutic angiogenesis represents a promising avenue to revascularize the ischemic heart. Its limited success is partly due to our poor understanding of the cardiac stroma, specifically mural cells, and their response to ischemic injury. Here, we combine single-cell and positional transcriptomics to assess the behavior of mural cells within the healing heart. In response to myocardial infarction, mural cells adopt an altered state closely associated with the infarct and retain a distinct lineage from fibroblasts. This response is concurrent with vascular rarefaction and reduced vascular coverage by mural cells. Positional transcriptomics reveals that the infarcted heart is governed by regional-dependent and temporally regulated programs. While the remote zone acts as an important source of pro-angiogenic signals, the infarct zone is accentuated by chronic activation of anti-angiogenic, pro-fibrotic, and inflammatory cues. Together, our work unveils the spatiotemporal programs underlying cardiac repair and establishes an association between vascular deterioration and mural cell dysfunction.","","False","Visium","1068","19465"
"GSE206787_GSM7898098","mouse","heart","38129410","Spatiotemporal signaling underlies progressive vascular rarefaction in myocardial infarction","Therapeutic angiogenesis represents a promising avenue to revascularize the ischemic heart. Its limited success is partly due to our poor understanding of the cardiac stroma, specifically mural cells, and their response to ischemic injury. Here, we combine single-cell and positional transcriptomics to assess the behavior of mural cells within the healing heart. In response to myocardial infarction, mural cells adopt an altered state closely associated with the infarct and retain a distinct lineage from fibroblasts. This response is concurrent with vascular rarefaction and reduced vascular coverage by mural cells. Positional transcriptomics reveals that the infarcted heart is governed by regional-dependent and temporally regulated programs. While the remote zone acts as an important source of pro-angiogenic signals, the infarct zone is accentuated by chronic activation of anti-angiogenic, pro-fibrotic, and inflammatory cues. Together, our work unveils the spatiotemporal programs underlying cardiac repair and establishes an association between vascular deterioration and mural cell dysfunction.","","False","Visium","1245","19465"
"GSE206787_GSM7898099","mouse","heart","38129410","Spatiotemporal signaling underlies progressive vascular rarefaction in myocardial infarction","Therapeutic angiogenesis represents a promising avenue to revascularize the ischemic heart. Its limited success is partly due to our poor understanding of the cardiac stroma, specifically mural cells, and their response to ischemic injury. Here, we combine single-cell and positional transcriptomics to assess the behavior of mural cells within the healing heart. In response to myocardial infarction, mural cells adopt an altered state closely associated with the infarct and retain a distinct lineage from fibroblasts. This response is concurrent with vascular rarefaction and reduced vascular coverage by mural cells. Positional transcriptomics reveals that the infarcted heart is governed by regional-dependent and temporally regulated programs. While the remote zone acts as an important source of pro-angiogenic signals, the infarct zone is accentuated by chronic activation of anti-angiogenic, pro-fibrotic, and inflammatory cues. Together, our work unveils the spatiotemporal programs underlying cardiac repair and establishes an association between vascular deterioration and mural cell dysfunction.","","False","Visium","948","19465"
"GSE207205_GSM6281320","human","thymus","36741401","Multimodal human thymic profiling reveals trajectories and cellular milieu for T agonist selection","To prevent autoimmunity, thymocytes expressing self-reactive T cell receptors (TCRs) are negatively selected, however, divergence into tolerogenic, agonist selected lineages represent an alternative fate. As thymocyte development, selection, and lineage choices are dependent on spatial context and cell-to-cell interactions, we have performed Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) and spatial transcriptomics on paediatric human thymu​​s. Thymocytes expressing markers of strong TCR signalling diverged from the conventional developmental trajectory prior to CD4+ or CD8+ lineage commitment, while markers of different agonist selected T cell populations (CD8αα(I), CD8αα(II), T(agonist), Treg(diff), and Treg) exhibited variable timing of induction. Expression profiles of chemokines and co-stimulatory molecules, together with spatial localisation, supported that dendritic cells, B cells, and stromal cells contribute to agonist selection, with different subsets influencing thymocytes at specific developmental stages within distinct spatial niches. Understanding factors influencing agonist T cells is needed to benefit from their immunoregulatory effects in clinical use.","T agonist selection; T cell development; antigen-presenting cells; autoimmunity; human thymus; multi-modal; single-cell RNA sequencing; spatial transcriptomics.","False","Visium","2276","36601"
"GSE207205_GSM6281321","human","thymus","36741401","Multimodal human thymic profiling reveals trajectories and cellular milieu for T agonist selection","To prevent autoimmunity, thymocytes expressing self-reactive T cell receptors (TCRs) are negatively selected, however, divergence into tolerogenic, agonist selected lineages represent an alternative fate. As thymocyte development, selection, and lineage choices are dependent on spatial context and cell-to-cell interactions, we have performed Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) and spatial transcriptomics on paediatric human thymu​​s. Thymocytes expressing markers of strong TCR signalling diverged from the conventional developmental trajectory prior to CD4+ or CD8+ lineage commitment, while markers of different agonist selected T cell populations (CD8αα(I), CD8αα(II), T(agonist), Treg(diff), and Treg) exhibited variable timing of induction. Expression profiles of chemokines and co-stimulatory molecules, together with spatial localisation, supported that dendritic cells, B cells, and stromal cells contribute to agonist selection, with different subsets influencing thymocytes at specific developmental stages within distinct spatial niches. Understanding factors influencing agonist T cells is needed to benefit from their immunoregulatory effects in clinical use.","T agonist selection; T cell development; antigen-presenting cells; autoimmunity; human thymus; multi-modal; single-cell RNA sequencing; spatial transcriptomics.","False","Visium","2594","36601"
"GSE207205_GSM6281322","human","thymus","36741401","Multimodal human thymic profiling reveals trajectories and cellular milieu for T agonist selection","To prevent autoimmunity, thymocytes expressing self-reactive T cell receptors (TCRs) are negatively selected, however, divergence into tolerogenic, agonist selected lineages represent an alternative fate. As thymocyte development, selection, and lineage choices are dependent on spatial context and cell-to-cell interactions, we have performed Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) and spatial transcriptomics on paediatric human thymu​​s. Thymocytes expressing markers of strong TCR signalling diverged from the conventional developmental trajectory prior to CD4+ or CD8+ lineage commitment, while markers of different agonist selected T cell populations (CD8αα(I), CD8αα(II), T(agonist), Treg(diff), and Treg) exhibited variable timing of induction. Expression profiles of chemokines and co-stimulatory molecules, together with spatial localisation, supported that dendritic cells, B cells, and stromal cells contribute to agonist selection, with different subsets influencing thymocytes at specific developmental stages within distinct spatial niches. Understanding factors influencing agonist T cells is needed to benefit from their immunoregulatory effects in clinical use.","T agonist selection; T cell development; antigen-presenting cells; autoimmunity; human thymus; multi-modal; single-cell RNA sequencing; spatial transcriptomics.","False","Visium","2269","36601"
"GSE207205_GSM6281323","human","thymus","36741401","Multimodal human thymic profiling reveals trajectories and cellular milieu for T agonist selection","To prevent autoimmunity, thymocytes expressing self-reactive T cell receptors (TCRs) are negatively selected, however, divergence into tolerogenic, agonist selected lineages represent an alternative fate. As thymocyte development, selection, and lineage choices are dependent on spatial context and cell-to-cell interactions, we have performed Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) and spatial transcriptomics on paediatric human thymu​​s. Thymocytes expressing markers of strong TCR signalling diverged from the conventional developmental trajectory prior to CD4+ or CD8+ lineage commitment, while markers of different agonist selected T cell populations (CD8αα(I), CD8αα(II), T(agonist), Treg(diff), and Treg) exhibited variable timing of induction. Expression profiles of chemokines and co-stimulatory molecules, together with spatial localisation, supported that dendritic cells, B cells, and stromal cells contribute to agonist selection, with different subsets influencing thymocytes at specific developmental stages within distinct spatial niches. Understanding factors influencing agonist T cells is needed to benefit from their immunoregulatory effects in clinical use.","T agonist selection; T cell development; antigen-presenting cells; autoimmunity; human thymus; multi-modal; single-cell RNA sequencing; spatial transcriptomics.","False","Visium","2582","36601"
"GSE207205_GSM6281324","human","thymus","36741401","Multimodal human thymic profiling reveals trajectories and cellular milieu for T agonist selection","To prevent autoimmunity, thymocytes expressing self-reactive T cell receptors (TCRs) are negatively selected, however, divergence into tolerogenic, agonist selected lineages represent an alternative fate. As thymocyte development, selection, and lineage choices are dependent on spatial context and cell-to-cell interactions, we have performed Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) and spatial transcriptomics on paediatric human thymu​​s. Thymocytes expressing markers of strong TCR signalling diverged from the conventional developmental trajectory prior to CD4+ or CD8+ lineage commitment, while markers of different agonist selected T cell populations (CD8αα(I), CD8αα(II), T(agonist), Treg(diff), and Treg) exhibited variable timing of induction. Expression profiles of chemokines and co-stimulatory molecules, together with spatial localisation, supported that dendritic cells, B cells, and stromal cells contribute to agonist selection, with different subsets influencing thymocytes at specific developmental stages within distinct spatial niches. Understanding factors influencing agonist T cells is needed to benefit from their immunoregulatory effects in clinical use.","T agonist selection; T cell development; antigen-presenting cells; autoimmunity; human thymus; multi-modal; single-cell RNA sequencing; spatial transcriptomics.","False","Visium","2515","36601"
"GSE207205_GSM6281325","human","thymus","36741401","Multimodal human thymic profiling reveals trajectories and cellular milieu for T agonist selection","To prevent autoimmunity, thymocytes expressing self-reactive T cell receptors (TCRs) are negatively selected, however, divergence into tolerogenic, agonist selected lineages represent an alternative fate. As thymocyte development, selection, and lineage choices are dependent on spatial context and cell-to-cell interactions, we have performed Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) and spatial transcriptomics on paediatric human thymu​​s. Thymocytes expressing markers of strong TCR signalling diverged from the conventional developmental trajectory prior to CD4+ or CD8+ lineage commitment, while markers of different agonist selected T cell populations (CD8αα(I), CD8αα(II), T(agonist), Treg(diff), and Treg) exhibited variable timing of induction. Expression profiles of chemokines and co-stimulatory molecules, together with spatial localisation, supported that dendritic cells, B cells, and stromal cells contribute to agonist selection, with different subsets influencing thymocytes at specific developmental stages within distinct spatial niches. Understanding factors influencing agonist T cells is needed to benefit from their immunoregulatory effects in clinical use.","T agonist selection; T cell development; antigen-presenting cells; autoimmunity; human thymus; multi-modal; single-cell RNA sequencing; spatial transcriptomics.","False","Visium","1623","36601"
"GSE207205_GSM6281326","human","thymus","36741401","Multimodal human thymic profiling reveals trajectories and cellular milieu for T agonist selection","To prevent autoimmunity, thymocytes expressing self-reactive T cell receptors (TCRs) are negatively selected, however, divergence into tolerogenic, agonist selected lineages represent an alternative fate. As thymocyte development, selection, and lineage choices are dependent on spatial context and cell-to-cell interactions, we have performed Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) and spatial transcriptomics on paediatric human thymu​​s. Thymocytes expressing markers of strong TCR signalling diverged from the conventional developmental trajectory prior to CD4+ or CD8+ lineage commitment, while markers of different agonist selected T cell populations (CD8αα(I), CD8αα(II), T(agonist), Treg(diff), and Treg) exhibited variable timing of induction. Expression profiles of chemokines and co-stimulatory molecules, together with spatial localisation, supported that dendritic cells, B cells, and stromal cells contribute to agonist selection, with different subsets influencing thymocytes at specific developmental stages within distinct spatial niches. Understanding factors influencing agonist T cells is needed to benefit from their immunoregulatory effects in clinical use.","T agonist selection; T cell development; antigen-presenting cells; autoimmunity; human thymus; multi-modal; single-cell RNA sequencing; spatial transcriptomics.","False","Visium","2227","36601"
"GSE207205_GSM6281327","human","thymus","36741401","Multimodal human thymic profiling reveals trajectories and cellular milieu for T agonist selection","To prevent autoimmunity, thymocytes expressing self-reactive T cell receptors (TCRs) are negatively selected, however, divergence into tolerogenic, agonist selected lineages represent an alternative fate. As thymocyte development, selection, and lineage choices are dependent on spatial context and cell-to-cell interactions, we have performed Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) and spatial transcriptomics on paediatric human thymu​​s. Thymocytes expressing markers of strong TCR signalling diverged from the conventional developmental trajectory prior to CD4+ or CD8+ lineage commitment, while markers of different agonist selected T cell populations (CD8αα(I), CD8αα(II), T(agonist), Treg(diff), and Treg) exhibited variable timing of induction. Expression profiles of chemokines and co-stimulatory molecules, together with spatial localisation, supported that dendritic cells, B cells, and stromal cells contribute to agonist selection, with different subsets influencing thymocytes at specific developmental stages within distinct spatial niches. Understanding factors influencing agonist T cells is needed to benefit from their immunoregulatory effects in clinical use.","T agonist selection; T cell development; antigen-presenting cells; autoimmunity; human thymus; multi-modal; single-cell RNA sequencing; spatial transcriptomics.","False","Visium","2243","36601"
"GSE208253_GSM6339631","human","mouth","37596273","Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response","The spatial organization of the tumor microenvironment has a profound impact on biology and therapy response. Here, we perform an integrative single-cell and spatial transcriptomic analysis on HPV-negative oral squamous cell carcinoma (OSCC) to comprehensively characterize malignant cells in tumor core (TC) and leading edge (LE) transcriptional architectures. We show that the TC and LE are characterized by unique transcriptional profiles, neighboring cellular compositions, and ligand-receptor interactions. We demonstrate that the gene expression profile associated with the LE is conserved across different cancers while the TC is tissue specific, highlighting common mechanisms underlying tumor progression and invasion. Additionally, we find our LE gene signature is associated with worse clinical outcomes while TC gene signature is associated with improved prognosis across multiple cancer types. Finally, using an in silico modeling approach, we describe spatially-regulated patterns of cell development in OSCC that are predictably associated with drug response. Our work provides pan-cancer insights into TC and LE biology and interactive spatial atlases ( http://www.pboselab.ca/spatial_OSCC/ ; http://www.pboselab.ca/dynamo_OSCC/ ) that can be foundational for developing novel targeted therapies.","","True","Visium","1185","36601"
"GSE208253_GSM6339632","human","mouth","37596273","Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response","The spatial organization of the tumor microenvironment has a profound impact on biology and therapy response. Here, we perform an integrative single-cell and spatial transcriptomic analysis on HPV-negative oral squamous cell carcinoma (OSCC) to comprehensively characterize malignant cells in tumor core (TC) and leading edge (LE) transcriptional architectures. We show that the TC and LE are characterized by unique transcriptional profiles, neighboring cellular compositions, and ligand-receptor interactions. We demonstrate that the gene expression profile associated with the LE is conserved across different cancers while the TC is tissue specific, highlighting common mechanisms underlying tumor progression and invasion. Additionally, we find our LE gene signature is associated with worse clinical outcomes while TC gene signature is associated with improved prognosis across multiple cancer types. Finally, using an in silico modeling approach, we describe spatially-regulated patterns of cell development in OSCC that are predictably associated with drug response. Our work provides pan-cancer insights into TC and LE biology and interactive spatial atlases ( http://www.pboselab.ca/spatial_OSCC/ ; http://www.pboselab.ca/dynamo_OSCC/ ) that can be foundational for developing novel targeted therapies.","","True","Visium","1836","36601"
"GSE208253_GSM6339633","human","mouth","37596273","Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response","The spatial organization of the tumor microenvironment has a profound impact on biology and therapy response. Here, we perform an integrative single-cell and spatial transcriptomic analysis on HPV-negative oral squamous cell carcinoma (OSCC) to comprehensively characterize malignant cells in tumor core (TC) and leading edge (LE) transcriptional architectures. We show that the TC and LE are characterized by unique transcriptional profiles, neighboring cellular compositions, and ligand-receptor interactions. We demonstrate that the gene expression profile associated with the LE is conserved across different cancers while the TC is tissue specific, highlighting common mechanisms underlying tumor progression and invasion. Additionally, we find our LE gene signature is associated with worse clinical outcomes while TC gene signature is associated with improved prognosis across multiple cancer types. Finally, using an in silico modeling approach, we describe spatially-regulated patterns of cell development in OSCC that are predictably associated with drug response. Our work provides pan-cancer insights into TC and LE biology and interactive spatial atlases ( http://www.pboselab.ca/spatial_OSCC/ ; http://www.pboselab.ca/dynamo_OSCC/ ) that can be foundational for developing novel targeted therapies.","","True","Visium","969","36601"
"GSE208253_GSM6339634","human","mouth","37596273","Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response","The spatial organization of the tumor microenvironment has a profound impact on biology and therapy response. Here, we perform an integrative single-cell and spatial transcriptomic analysis on HPV-negative oral squamous cell carcinoma (OSCC) to comprehensively characterize malignant cells in tumor core (TC) and leading edge (LE) transcriptional architectures. We show that the TC and LE are characterized by unique transcriptional profiles, neighboring cellular compositions, and ligand-receptor interactions. We demonstrate that the gene expression profile associated with the LE is conserved across different cancers while the TC is tissue specific, highlighting common mechanisms underlying tumor progression and invasion. Additionally, we find our LE gene signature is associated with worse clinical outcomes while TC gene signature is associated with improved prognosis across multiple cancer types. Finally, using an in silico modeling approach, we describe spatially-regulated patterns of cell development in OSCC that are predictably associated with drug response. Our work provides pan-cancer insights into TC and LE biology and interactive spatial atlases ( http://www.pboselab.ca/spatial_OSCC/ ; http://www.pboselab.ca/dynamo_OSCC/ ) that can be foundational for developing novel targeted therapies.","","True","Visium","2046","36601"
"GSE208253_GSM6339635","human","mouth","37596273","Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response","The spatial organization of the tumor microenvironment has a profound impact on biology and therapy response. Here, we perform an integrative single-cell and spatial transcriptomic analysis on HPV-negative oral squamous cell carcinoma (OSCC) to comprehensively characterize malignant cells in tumor core (TC) and leading edge (LE) transcriptional architectures. We show that the TC and LE are characterized by unique transcriptional profiles, neighboring cellular compositions, and ligand-receptor interactions. We demonstrate that the gene expression profile associated with the LE is conserved across different cancers while the TC is tissue specific, highlighting common mechanisms underlying tumor progression and invasion. Additionally, we find our LE gene signature is associated with worse clinical outcomes while TC gene signature is associated with improved prognosis across multiple cancer types. Finally, using an in silico modeling approach, we describe spatially-regulated patterns of cell development in OSCC that are predictably associated with drug response. Our work provides pan-cancer insights into TC and LE biology and interactive spatial atlases ( http://www.pboselab.ca/spatial_OSCC/ ; http://www.pboselab.ca/dynamo_OSCC/ ) that can be foundational for developing novel targeted therapies.","","True","Visium","1678","36601"
"GSE208253_GSM6339636","human","mouth","37596273","Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response","The spatial organization of the tumor microenvironment has a profound impact on biology and therapy response. Here, we perform an integrative single-cell and spatial transcriptomic analysis on HPV-negative oral squamous cell carcinoma (OSCC) to comprehensively characterize malignant cells in tumor core (TC) and leading edge (LE) transcriptional architectures. We show that the TC and LE are characterized by unique transcriptional profiles, neighboring cellular compositions, and ligand-receptor interactions. We demonstrate that the gene expression profile associated with the LE is conserved across different cancers while the TC is tissue specific, highlighting common mechanisms underlying tumor progression and invasion. Additionally, we find our LE gene signature is associated with worse clinical outcomes while TC gene signature is associated with improved prognosis across multiple cancer types. Finally, using an in silico modeling approach, we describe spatially-regulated patterns of cell development in OSCC that are predictably associated with drug response. Our work provides pan-cancer insights into TC and LE biology and interactive spatial atlases ( http://www.pboselab.ca/spatial_OSCC/ ; http://www.pboselab.ca/dynamo_OSCC/ ) that can be foundational for developing novel targeted therapies.","","True","Visium","3311","36601"
"GSE208253_GSM6339637","human","mouth","37596273","Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response","The spatial organization of the tumor microenvironment has a profound impact on biology and therapy response. Here, we perform an integrative single-cell and spatial transcriptomic analysis on HPV-negative oral squamous cell carcinoma (OSCC) to comprehensively characterize malignant cells in tumor core (TC) and leading edge (LE) transcriptional architectures. We show that the TC and LE are characterized by unique transcriptional profiles, neighboring cellular compositions, and ligand-receptor interactions. We demonstrate that the gene expression profile associated with the LE is conserved across different cancers while the TC is tissue specific, highlighting common mechanisms underlying tumor progression and invasion. Additionally, we find our LE gene signature is associated with worse clinical outcomes while TC gene signature is associated with improved prognosis across multiple cancer types. Finally, using an in silico modeling approach, we describe spatially-regulated patterns of cell development in OSCC that are predictably associated with drug response. Our work provides pan-cancer insights into TC and LE biology and interactive spatial atlases ( http://www.pboselab.ca/spatial_OSCC/ ; http://www.pboselab.ca/dynamo_OSCC/ ) that can be foundational for developing novel targeted therapies.","","True","Visium","2860","36601"
"GSE208253_GSM6339638","human","mouth","37596273","Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response","The spatial organization of the tumor microenvironment has a profound impact on biology and therapy response. Here, we perform an integrative single-cell and spatial transcriptomic analysis on HPV-negative oral squamous cell carcinoma (OSCC) to comprehensively characterize malignant cells in tumor core (TC) and leading edge (LE) transcriptional architectures. We show that the TC and LE are characterized by unique transcriptional profiles, neighboring cellular compositions, and ligand-receptor interactions. We demonstrate that the gene expression profile associated with the LE is conserved across different cancers while the TC is tissue specific, highlighting common mechanisms underlying tumor progression and invasion. Additionally, we find our LE gene signature is associated with worse clinical outcomes while TC gene signature is associated with improved prognosis across multiple cancer types. Finally, using an in silico modeling approach, we describe spatially-regulated patterns of cell development in OSCC that are predictably associated with drug response. Our work provides pan-cancer insights into TC and LE biology and interactive spatial atlases ( http://www.pboselab.ca/spatial_OSCC/ ; http://www.pboselab.ca/dynamo_OSCC/ ) that can be foundational for developing novel targeted therapies.","","True","Visium","2475","36601"
"GSE208253_GSM6339639","human","mouth","37596273","Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response","The spatial organization of the tumor microenvironment has a profound impact on biology and therapy response. Here, we perform an integrative single-cell and spatial transcriptomic analysis on HPV-negative oral squamous cell carcinoma (OSCC) to comprehensively characterize malignant cells in tumor core (TC) and leading edge (LE) transcriptional architectures. We show that the TC and LE are characterized by unique transcriptional profiles, neighboring cellular compositions, and ligand-receptor interactions. We demonstrate that the gene expression profile associated with the LE is conserved across different cancers while the TC is tissue specific, highlighting common mechanisms underlying tumor progression and invasion. Additionally, we find our LE gene signature is associated with worse clinical outcomes while TC gene signature is associated with improved prognosis across multiple cancer types. Finally, using an in silico modeling approach, we describe spatially-regulated patterns of cell development in OSCC that are predictably associated with drug response. Our work provides pan-cancer insights into TC and LE biology and interactive spatial atlases ( http://www.pboselab.ca/spatial_OSCC/ ; http://www.pboselab.ca/dynamo_OSCC/ ) that can be foundational for developing novel targeted therapies.","","True","Visium","3591","36601"
"GSE208253_GSM6339640","human","mouth","37596273","Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response","The spatial organization of the tumor microenvironment has a profound impact on biology and therapy response. Here, we perform an integrative single-cell and spatial transcriptomic analysis on HPV-negative oral squamous cell carcinoma (OSCC) to comprehensively characterize malignant cells in tumor core (TC) and leading edge (LE) transcriptional architectures. We show that the TC and LE are characterized by unique transcriptional profiles, neighboring cellular compositions, and ligand-receptor interactions. We demonstrate that the gene expression profile associated with the LE is conserved across different cancers while the TC is tissue specific, highlighting common mechanisms underlying tumor progression and invasion. Additionally, we find our LE gene signature is associated with worse clinical outcomes while TC gene signature is associated with improved prognosis across multiple cancer types. Finally, using an in silico modeling approach, we describe spatially-regulated patterns of cell development in OSCC that are predictably associated with drug response. Our work provides pan-cancer insights into TC and LE biology and interactive spatial atlases ( http://www.pboselab.ca/spatial_OSCC/ ; http://www.pboselab.ca/dynamo_OSCC/ ) that can be foundational for developing novel targeted therapies.","","True","Visium","2731","36601"
"GSE208253_GSM6339641","human","mouth","37596273","Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response","The spatial organization of the tumor microenvironment has a profound impact on biology and therapy response. Here, we perform an integrative single-cell and spatial transcriptomic analysis on HPV-negative oral squamous cell carcinoma (OSCC) to comprehensively characterize malignant cells in tumor core (TC) and leading edge (LE) transcriptional architectures. We show that the TC and LE are characterized by unique transcriptional profiles, neighboring cellular compositions, and ligand-receptor interactions. We demonstrate that the gene expression profile associated with the LE is conserved across different cancers while the TC is tissue specific, highlighting common mechanisms underlying tumor progression and invasion. Additionally, we find our LE gene signature is associated with worse clinical outcomes while TC gene signature is associated with improved prognosis across multiple cancer types. Finally, using an in silico modeling approach, we describe spatially-regulated patterns of cell development in OSCC that are predictably associated with drug response. Our work provides pan-cancer insights into TC and LE biology and interactive spatial atlases ( http://www.pboselab.ca/spatial_OSCC/ ; http://www.pboselab.ca/dynamo_OSCC/ ) that can be foundational for developing novel targeted therapies.","","True","Visium","2130","36601"
"GSE208253_GSM6339642","human","mouth","37596273","Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response","The spatial organization of the tumor microenvironment has a profound impact on biology and therapy response. Here, we perform an integrative single-cell and spatial transcriptomic analysis on HPV-negative oral squamous cell carcinoma (OSCC) to comprehensively characterize malignant cells in tumor core (TC) and leading edge (LE) transcriptional architectures. We show that the TC and LE are characterized by unique transcriptional profiles, neighboring cellular compositions, and ligand-receptor interactions. We demonstrate that the gene expression profile associated with the LE is conserved across different cancers while the TC is tissue specific, highlighting common mechanisms underlying tumor progression and invasion. Additionally, we find our LE gene signature is associated with worse clinical outcomes while TC gene signature is associated with improved prognosis across multiple cancer types. Finally, using an in silico modeling approach, we describe spatially-regulated patterns of cell development in OSCC that are predictably associated with drug response. Our work provides pan-cancer insights into TC and LE biology and interactive spatial atlases ( http://www.pboselab.ca/spatial_OSCC/ ; http://www.pboselab.ca/dynamo_OSCC/ ) that can be foundational for developing novel targeted therapies.","","True","Visium","1559","36601"
"GSE209583_GSM6380080","mouse","brain","38155736","Human neural stem cells restore spatial memory in a transgenic Alzheimer's disease mouse model by an immunomodulating mechanism","Introduction: Stem cells are a promising therapeutic in Alzheimer's disease (AD) given the complex pathophysiologic pathways involved. However, the therapeutic mechanisms of stem cells remain unclear. Here, we used spatial transcriptomics to elucidate therapeutic mechanisms of human neural stem cells (hNSCs) in an animal model of AD.

Methods: hNSCs were transplanted into the fimbria fornix of the hippocampus using the 5XFAD mouse model. Spatial memory was assessed by Morris water maze. Amyloid plaque burden was quantified. Spatial transcriptomics was performed and differentially expressed genes (DEGs) identified both globally and within the hippocampus. Subsequent pathway enrichment and ligand-receptor network analysis was performed.

Results: hNSC transplantation restored learning curves of 5XFAD mice. However, there were no changes in amyloid plaque burden. Spatial transcriptomics showed 1,061 DEGs normalized in hippocampal subregions. Plaque induced genes in microglia, along with populations of stage 1 and stage 2 disease associated microglia (DAM), were normalized upon hNSC transplantation. Pathologic signaling between hippocampus and DAM was also restored.

Discussion: hNSCs normalized many dysregulated genes, although this was not mediated by a change in amyloid plaque levels. Rather, hNSCs appear to exert beneficial effects in part by modulating microglia-mediated neuroinflammation and signaling in AD.","Alzheimer’s disease; cell communication; disease-associated microglia; immunomodulation; microglia; neural stem cell; spatial transcriptomics; stem cell therapy.","False","Visium","2380","32285"
"GSE209583_GSM6380081","mouse","brain","38155736","Human neural stem cells restore spatial memory in a transgenic Alzheimer's disease mouse model by an immunomodulating mechanism","Introduction: Stem cells are a promising therapeutic in Alzheimer's disease (AD) given the complex pathophysiologic pathways involved. However, the therapeutic mechanisms of stem cells remain unclear. Here, we used spatial transcriptomics to elucidate therapeutic mechanisms of human neural stem cells (hNSCs) in an animal model of AD.

Methods: hNSCs were transplanted into the fimbria fornix of the hippocampus using the 5XFAD mouse model. Spatial memory was assessed by Morris water maze. Amyloid plaque burden was quantified. Spatial transcriptomics was performed and differentially expressed genes (DEGs) identified both globally and within the hippocampus. Subsequent pathway enrichment and ligand-receptor network analysis was performed.

Results: hNSC transplantation restored learning curves of 5XFAD mice. However, there were no changes in amyloid plaque burden. Spatial transcriptomics showed 1,061 DEGs normalized in hippocampal subregions. Plaque induced genes in microglia, along with populations of stage 1 and stage 2 disease associated microglia (DAM), were normalized upon hNSC transplantation. Pathologic signaling between hippocampus and DAM was also restored.

Discussion: hNSCs normalized many dysregulated genes, although this was not mediated by a change in amyloid plaque levels. Rather, hNSCs appear to exert beneficial effects in part by modulating microglia-mediated neuroinflammation and signaling in AD.","Alzheimer’s disease; cell communication; disease-associated microglia; immunomodulation; microglia; neural stem cell; spatial transcriptomics; stem cell therapy.","False","Visium","2171","32285"
"GSE209583_GSM6380082","mouse","brain","38155736","Human neural stem cells restore spatial memory in a transgenic Alzheimer's disease mouse model by an immunomodulating mechanism","Introduction: Stem cells are a promising therapeutic in Alzheimer's disease (AD) given the complex pathophysiologic pathways involved. However, the therapeutic mechanisms of stem cells remain unclear. Here, we used spatial transcriptomics to elucidate therapeutic mechanisms of human neural stem cells (hNSCs) in an animal model of AD.

Methods: hNSCs were transplanted into the fimbria fornix of the hippocampus using the 5XFAD mouse model. Spatial memory was assessed by Morris water maze. Amyloid plaque burden was quantified. Spatial transcriptomics was performed and differentially expressed genes (DEGs) identified both globally and within the hippocampus. Subsequent pathway enrichment and ligand-receptor network analysis was performed.

Results: hNSC transplantation restored learning curves of 5XFAD mice. However, there were no changes in amyloid plaque burden. Spatial transcriptomics showed 1,061 DEGs normalized in hippocampal subregions. Plaque induced genes in microglia, along with populations of stage 1 and stage 2 disease associated microglia (DAM), were normalized upon hNSC transplantation. Pathologic signaling between hippocampus and DAM was also restored.

Discussion: hNSCs normalized many dysregulated genes, although this was not mediated by a change in amyloid plaque levels. Rather, hNSCs appear to exert beneficial effects in part by modulating microglia-mediated neuroinflammation and signaling in AD.","Alzheimer’s disease; cell communication; disease-associated microglia; immunomodulation; microglia; neural stem cell; spatial transcriptomics; stem cell therapy.","False","Visium","1794","32285"
"GSE209583_GSM6380083","mouse","brain","38155736","Human neural stem cells restore spatial memory in a transgenic Alzheimer's disease mouse model by an immunomodulating mechanism","Introduction: Stem cells are a promising therapeutic in Alzheimer's disease (AD) given the complex pathophysiologic pathways involved. However, the therapeutic mechanisms of stem cells remain unclear. Here, we used spatial transcriptomics to elucidate therapeutic mechanisms of human neural stem cells (hNSCs) in an animal model of AD.

Methods: hNSCs were transplanted into the fimbria fornix of the hippocampus using the 5XFAD mouse model. Spatial memory was assessed by Morris water maze. Amyloid plaque burden was quantified. Spatial transcriptomics was performed and differentially expressed genes (DEGs) identified both globally and within the hippocampus. Subsequent pathway enrichment and ligand-receptor network analysis was performed.

Results: hNSC transplantation restored learning curves of 5XFAD mice. However, there were no changes in amyloid plaque burden. Spatial transcriptomics showed 1,061 DEGs normalized in hippocampal subregions. Plaque induced genes in microglia, along with populations of stage 1 and stage 2 disease associated microglia (DAM), were normalized upon hNSC transplantation. Pathologic signaling between hippocampus and DAM was also restored.

Discussion: hNSCs normalized many dysregulated genes, although this was not mediated by a change in amyloid plaque levels. Rather, hNSCs appear to exert beneficial effects in part by modulating microglia-mediated neuroinflammation and signaling in AD.","Alzheimer’s disease; cell communication; disease-associated microglia; immunomodulation; microglia; neural stem cell; spatial transcriptomics; stem cell therapy.","False","Visium","2410","32285"
"GSE209583_GSM6380084","mouse","brain","38155736","Human neural stem cells restore spatial memory in a transgenic Alzheimer's disease mouse model by an immunomodulating mechanism","Introduction: Stem cells are a promising therapeutic in Alzheimer's disease (AD) given the complex pathophysiologic pathways involved. However, the therapeutic mechanisms of stem cells remain unclear. Here, we used spatial transcriptomics to elucidate therapeutic mechanisms of human neural stem cells (hNSCs) in an animal model of AD.

Methods: hNSCs were transplanted into the fimbria fornix of the hippocampus using the 5XFAD mouse model. Spatial memory was assessed by Morris water maze. Amyloid plaque burden was quantified. Spatial transcriptomics was performed and differentially expressed genes (DEGs) identified both globally and within the hippocampus. Subsequent pathway enrichment and ligand-receptor network analysis was performed.

Results: hNSC transplantation restored learning curves of 5XFAD mice. However, there were no changes in amyloid plaque burden. Spatial transcriptomics showed 1,061 DEGs normalized in hippocampal subregions. Plaque induced genes in microglia, along with populations of stage 1 and stage 2 disease associated microglia (DAM), were normalized upon hNSC transplantation. Pathologic signaling between hippocampus and DAM was also restored.

Discussion: hNSCs normalized many dysregulated genes, although this was not mediated by a change in amyloid plaque levels. Rather, hNSCs appear to exert beneficial effects in part by modulating microglia-mediated neuroinflammation and signaling in AD.","Alzheimer’s disease; cell communication; disease-associated microglia; immunomodulation; microglia; neural stem cell; spatial transcriptomics; stem cell therapy.","False","Visium","2484","32285"
"GSE209583_GSM6380085","mouse","brain","38155736","Human neural stem cells restore spatial memory in a transgenic Alzheimer's disease mouse model by an immunomodulating mechanism","Introduction: Stem cells are a promising therapeutic in Alzheimer's disease (AD) given the complex pathophysiologic pathways involved. However, the therapeutic mechanisms of stem cells remain unclear. Here, we used spatial transcriptomics to elucidate therapeutic mechanisms of human neural stem cells (hNSCs) in an animal model of AD.

Methods: hNSCs were transplanted into the fimbria fornix of the hippocampus using the 5XFAD mouse model. Spatial memory was assessed by Morris water maze. Amyloid plaque burden was quantified. Spatial transcriptomics was performed and differentially expressed genes (DEGs) identified both globally and within the hippocampus. Subsequent pathway enrichment and ligand-receptor network analysis was performed.

Results: hNSC transplantation restored learning curves of 5XFAD mice. However, there were no changes in amyloid plaque burden. Spatial transcriptomics showed 1,061 DEGs normalized in hippocampal subregions. Plaque induced genes in microglia, along with populations of stage 1 and stage 2 disease associated microglia (DAM), were normalized upon hNSC transplantation. Pathologic signaling between hippocampus and DAM was also restored.

Discussion: hNSCs normalized many dysregulated genes, although this was not mediated by a change in amyloid plaque levels. Rather, hNSCs appear to exert beneficial effects in part by modulating microglia-mediated neuroinflammation and signaling in AD.","Alzheimer’s disease; cell communication; disease-associated microglia; immunomodulation; microglia; neural stem cell; spatial transcriptomics; stem cell therapy.","False","Visium","2732","32285"
"GSE209583_GSM6380086","mouse","brain","38155736","Human neural stem cells restore spatial memory in a transgenic Alzheimer's disease mouse model by an immunomodulating mechanism","Introduction: Stem cells are a promising therapeutic in Alzheimer's disease (AD) given the complex pathophysiologic pathways involved. However, the therapeutic mechanisms of stem cells remain unclear. Here, we used spatial transcriptomics to elucidate therapeutic mechanisms of human neural stem cells (hNSCs) in an animal model of AD.

Methods: hNSCs were transplanted into the fimbria fornix of the hippocampus using the 5XFAD mouse model. Spatial memory was assessed by Morris water maze. Amyloid plaque burden was quantified. Spatial transcriptomics was performed and differentially expressed genes (DEGs) identified both globally and within the hippocampus. Subsequent pathway enrichment and ligand-receptor network analysis was performed.

Results: hNSC transplantation restored learning curves of 5XFAD mice. However, there were no changes in amyloid plaque burden. Spatial transcriptomics showed 1,061 DEGs normalized in hippocampal subregions. Plaque induced genes in microglia, along with populations of stage 1 and stage 2 disease associated microglia (DAM), were normalized upon hNSC transplantation. Pathologic signaling between hippocampus and DAM was also restored.

Discussion: hNSCs normalized many dysregulated genes, although this was not mediated by a change in amyloid plaque levels. Rather, hNSCs appear to exert beneficial effects in part by modulating microglia-mediated neuroinflammation and signaling in AD.","Alzheimer’s disease; cell communication; disease-associated microglia; immunomodulation; microglia; neural stem cell; spatial transcriptomics; stem cell therapy.","False","Visium","2936","32285"
"GSE209583_GSM6380087","mouse","brain","38155736","Human neural stem cells restore spatial memory in a transgenic Alzheimer's disease mouse model by an immunomodulating mechanism","Introduction: Stem cells are a promising therapeutic in Alzheimer's disease (AD) given the complex pathophysiologic pathways involved. However, the therapeutic mechanisms of stem cells remain unclear. Here, we used spatial transcriptomics to elucidate therapeutic mechanisms of human neural stem cells (hNSCs) in an animal model of AD.

Methods: hNSCs were transplanted into the fimbria fornix of the hippocampus using the 5XFAD mouse model. Spatial memory was assessed by Morris water maze. Amyloid plaque burden was quantified. Spatial transcriptomics was performed and differentially expressed genes (DEGs) identified both globally and within the hippocampus. Subsequent pathway enrichment and ligand-receptor network analysis was performed.

Results: hNSC transplantation restored learning curves of 5XFAD mice. However, there were no changes in amyloid plaque burden. Spatial transcriptomics showed 1,061 DEGs normalized in hippocampal subregions. Plaque induced genes in microglia, along with populations of stage 1 and stage 2 disease associated microglia (DAM), were normalized upon hNSC transplantation. Pathologic signaling between hippocampus and DAM was also restored.

Discussion: hNSCs normalized many dysregulated genes, although this was not mediated by a change in amyloid plaque levels. Rather, hNSCs appear to exert beneficial effects in part by modulating microglia-mediated neuroinflammation and signaling in AD.","Alzheimer’s disease; cell communication; disease-associated microglia; immunomodulation; microglia; neural stem cell; spatial transcriptomics; stem cell therapy.","False","Visium","2459","32285"
"GSE209583_GSM6380088","mouse","brain","38155736","Human neural stem cells restore spatial memory in a transgenic Alzheimer's disease mouse model by an immunomodulating mechanism","Introduction: Stem cells are a promising therapeutic in Alzheimer's disease (AD) given the complex pathophysiologic pathways involved. However, the therapeutic mechanisms of stem cells remain unclear. Here, we used spatial transcriptomics to elucidate therapeutic mechanisms of human neural stem cells (hNSCs) in an animal model of AD.

Methods: hNSCs were transplanted into the fimbria fornix of the hippocampus using the 5XFAD mouse model. Spatial memory was assessed by Morris water maze. Amyloid plaque burden was quantified. Spatial transcriptomics was performed and differentially expressed genes (DEGs) identified both globally and within the hippocampus. Subsequent pathway enrichment and ligand-receptor network analysis was performed.

Results: hNSC transplantation restored learning curves of 5XFAD mice. However, there were no changes in amyloid plaque burden. Spatial transcriptomics showed 1,061 DEGs normalized in hippocampal subregions. Plaque induced genes in microglia, along with populations of stage 1 and stage 2 disease associated microglia (DAM), were normalized upon hNSC transplantation. Pathologic signaling between hippocampus and DAM was also restored.

Discussion: hNSCs normalized many dysregulated genes, although this was not mediated by a change in amyloid plaque levels. Rather, hNSCs appear to exert beneficial effects in part by modulating microglia-mediated neuroinflammation and signaling in AD.","Alzheimer’s disease; cell communication; disease-associated microglia; immunomodulation; microglia; neural stem cell; spatial transcriptomics; stem cell therapy.","False","Visium","1661","32285"
"GSE209583_GSM6380089","mouse","brain","38155736","Human neural stem cells restore spatial memory in a transgenic Alzheimer's disease mouse model by an immunomodulating mechanism","Introduction: Stem cells are a promising therapeutic in Alzheimer's disease (AD) given the complex pathophysiologic pathways involved. However, the therapeutic mechanisms of stem cells remain unclear. Here, we used spatial transcriptomics to elucidate therapeutic mechanisms of human neural stem cells (hNSCs) in an animal model of AD.

Methods: hNSCs were transplanted into the fimbria fornix of the hippocampus using the 5XFAD mouse model. Spatial memory was assessed by Morris water maze. Amyloid plaque burden was quantified. Spatial transcriptomics was performed and differentially expressed genes (DEGs) identified both globally and within the hippocampus. Subsequent pathway enrichment and ligand-receptor network analysis was performed.

Results: hNSC transplantation restored learning curves of 5XFAD mice. However, there were no changes in amyloid plaque burden. Spatial transcriptomics showed 1,061 DEGs normalized in hippocampal subregions. Plaque induced genes in microglia, along with populations of stage 1 and stage 2 disease associated microglia (DAM), were normalized upon hNSC transplantation. Pathologic signaling between hippocampus and DAM was also restored.

Discussion: hNSCs normalized many dysregulated genes, although this was not mediated by a change in amyloid plaque levels. Rather, hNSCs appear to exert beneficial effects in part by modulating microglia-mediated neuroinflammation and signaling in AD.","Alzheimer’s disease; cell communication; disease-associated microglia; immunomodulation; microglia; neural stem cell; spatial transcriptomics; stem cell therapy.","False","Visium","1854","32285"
"GSE209583_GSM6380090","mouse","brain","38155736","Human neural stem cells restore spatial memory in a transgenic Alzheimer's disease mouse model by an immunomodulating mechanism","Introduction: Stem cells are a promising therapeutic in Alzheimer's disease (AD) given the complex pathophysiologic pathways involved. However, the therapeutic mechanisms of stem cells remain unclear. Here, we used spatial transcriptomics to elucidate therapeutic mechanisms of human neural stem cells (hNSCs) in an animal model of AD.

Methods: hNSCs were transplanted into the fimbria fornix of the hippocampus using the 5XFAD mouse model. Spatial memory was assessed by Morris water maze. Amyloid plaque burden was quantified. Spatial transcriptomics was performed and differentially expressed genes (DEGs) identified both globally and within the hippocampus. Subsequent pathway enrichment and ligand-receptor network analysis was performed.

Results: hNSC transplantation restored learning curves of 5XFAD mice. However, there were no changes in amyloid plaque burden. Spatial transcriptomics showed 1,061 DEGs normalized in hippocampal subregions. Plaque induced genes in microglia, along with populations of stage 1 and stage 2 disease associated microglia (DAM), were normalized upon hNSC transplantation. Pathologic signaling between hippocampus and DAM was also restored.

Discussion: hNSCs normalized many dysregulated genes, although this was not mediated by a change in amyloid plaque levels. Rather, hNSCs appear to exert beneficial effects in part by modulating microglia-mediated neuroinflammation and signaling in AD.","Alzheimer’s disease; cell communication; disease-associated microglia; immunomodulation; microglia; neural stem cell; spatial transcriptomics; stem cell therapy.","False","Visium","1723","32285"
"GSE209583_GSM6380091","mouse","brain","38155736","Human neural stem cells restore spatial memory in a transgenic Alzheimer's disease mouse model by an immunomodulating mechanism","Introduction: Stem cells are a promising therapeutic in Alzheimer's disease (AD) given the complex pathophysiologic pathways involved. However, the therapeutic mechanisms of stem cells remain unclear. Here, we used spatial transcriptomics to elucidate therapeutic mechanisms of human neural stem cells (hNSCs) in an animal model of AD.

Methods: hNSCs were transplanted into the fimbria fornix of the hippocampus using the 5XFAD mouse model. Spatial memory was assessed by Morris water maze. Amyloid plaque burden was quantified. Spatial transcriptomics was performed and differentially expressed genes (DEGs) identified both globally and within the hippocampus. Subsequent pathway enrichment and ligand-receptor network analysis was performed.

Results: hNSC transplantation restored learning curves of 5XFAD mice. However, there were no changes in amyloid plaque burden. Spatial transcriptomics showed 1,061 DEGs normalized in hippocampal subregions. Plaque induced genes in microglia, along with populations of stage 1 and stage 2 disease associated microglia (DAM), were normalized upon hNSC transplantation. Pathologic signaling between hippocampus and DAM was also restored.

Discussion: hNSCs normalized many dysregulated genes, although this was not mediated by a change in amyloid plaque levels. Rather, hNSCs appear to exert beneficial effects in part by modulating microglia-mediated neuroinflammation and signaling in AD.","Alzheimer’s disease; cell communication; disease-associated microglia; immunomodulation; microglia; neural stem cell; spatial transcriptomics; stem cell therapy.","False","Visium","1513","32285"
"GSE209583_GSM6380092","mouse","brain","38155736","Human neural stem cells restore spatial memory in a transgenic Alzheimer's disease mouse model by an immunomodulating mechanism","Introduction: Stem cells are a promising therapeutic in Alzheimer's disease (AD) given the complex pathophysiologic pathways involved. However, the therapeutic mechanisms of stem cells remain unclear. Here, we used spatial transcriptomics to elucidate therapeutic mechanisms of human neural stem cells (hNSCs) in an animal model of AD.

Methods: hNSCs were transplanted into the fimbria fornix of the hippocampus using the 5XFAD mouse model. Spatial memory was assessed by Morris water maze. Amyloid plaque burden was quantified. Spatial transcriptomics was performed and differentially expressed genes (DEGs) identified both globally and within the hippocampus. Subsequent pathway enrichment and ligand-receptor network analysis was performed.

Results: hNSC transplantation restored learning curves of 5XFAD mice. However, there were no changes in amyloid plaque burden. Spatial transcriptomics showed 1,061 DEGs normalized in hippocampal subregions. Plaque induced genes in microglia, along with populations of stage 1 and stage 2 disease associated microglia (DAM), were normalized upon hNSC transplantation. Pathologic signaling between hippocampus and DAM was also restored.

Discussion: hNSCs normalized many dysregulated genes, although this was not mediated by a change in amyloid plaque levels. Rather, hNSCs appear to exert beneficial effects in part by modulating microglia-mediated neuroinflammation and signaling in AD.","Alzheimer’s disease; cell communication; disease-associated microglia; immunomodulation; microglia; neural stem cell; spatial transcriptomics; stem cell therapy.","False","Visium","2345","32285"
"GSE209583_GSM6380093","mouse","brain","38155736","Human neural stem cells restore spatial memory in a transgenic Alzheimer's disease mouse model by an immunomodulating mechanism","Introduction: Stem cells are a promising therapeutic in Alzheimer's disease (AD) given the complex pathophysiologic pathways involved. However, the therapeutic mechanisms of stem cells remain unclear. Here, we used spatial transcriptomics to elucidate therapeutic mechanisms of human neural stem cells (hNSCs) in an animal model of AD.

Methods: hNSCs were transplanted into the fimbria fornix of the hippocampus using the 5XFAD mouse model. Spatial memory was assessed by Morris water maze. Amyloid plaque burden was quantified. Spatial transcriptomics was performed and differentially expressed genes (DEGs) identified both globally and within the hippocampus. Subsequent pathway enrichment and ligand-receptor network analysis was performed.

Results: hNSC transplantation restored learning curves of 5XFAD mice. However, there were no changes in amyloid plaque burden. Spatial transcriptomics showed 1,061 DEGs normalized in hippocampal subregions. Plaque induced genes in microglia, along with populations of stage 1 and stage 2 disease associated microglia (DAM), were normalized upon hNSC transplantation. Pathologic signaling between hippocampus and DAM was also restored.

Discussion: hNSCs normalized many dysregulated genes, although this was not mediated by a change in amyloid plaque levels. Rather, hNSCs appear to exert beneficial effects in part by modulating microglia-mediated neuroinflammation and signaling in AD.","Alzheimer’s disease; cell communication; disease-associated microglia; immunomodulation; microglia; neural stem cell; spatial transcriptomics; stem cell therapy.","False","Visium","2287","32285"
"GSE210380_GSM6429503","mouse","lacrimal gland","36341342","Spatial transcriptomics of the lacrimal gland features macrophage activity and epithelium metabolism as key alterations during chronic inflammation","The lacrimal gland (LG) is an exocrine gland that produces the watery part of the tear film that lubricates the ocular surface. Chronic inflammation, such as Sjögren's syndrome (SS), is one of the leading causes of aqueous-deficiency dry eye (ADDE) disease worldwide. In this study we analyzed the chronic inflammation in the LGs of the NOD.B10Sn-H2b/J (NOD.H-2b) mice, a mouse model of SS, utilizing bulk RNAseq and Visium spatial gene expression. With Seurat we performed unsupervised clustering and analyzed the spatial cell distribution and gene expression changes in all cell clusters within the LG sections. Moreover, for the first time, we analyzed and validated specific pathways defined by bulk RNAseq using Visium technology to determine activation of these pathways within the LG sections. This analysis suggests that altered metabolism and the hallmarks of inflammatory responses from both epithelial and immune cells drive inflammation. The most significant pathway enriched in upregulated DEGs was the """"""""""""""""""""""""""""""""TYROBP Causal Network"""""""""""""""""""""""""""""""", that has not been described previously in SS. We also noted a significant decrease in lipid metabolism in the LG of the NOD.H-2b mice. Our data suggests that modulation of these pathways can provide a therapeutic strategy to treat ADDE.","RNA sequencing; TYROBP; chronic inflammation; lacrimal gland; lipid metabolism; macrophages; spatial transcriptomics; visium.","False","Visium","1135","32285"
"GSE210380_GSM6429504","mouse","lacrimal gland","36341342","Spatial transcriptomics of the lacrimal gland features macrophage activity and epithelium metabolism as key alterations during chronic inflammation","The lacrimal gland (LG) is an exocrine gland that produces the watery part of the tear film that lubricates the ocular surface. Chronic inflammation, such as Sjögren's syndrome (SS), is one of the leading causes of aqueous-deficiency dry eye (ADDE) disease worldwide. In this study we analyzed the chronic inflammation in the LGs of the NOD.B10Sn-H2b/J (NOD.H-2b) mice, a mouse model of SS, utilizing bulk RNAseq and Visium spatial gene expression. With Seurat we performed unsupervised clustering and analyzed the spatial cell distribution and gene expression changes in all cell clusters within the LG sections. Moreover, for the first time, we analyzed and validated specific pathways defined by bulk RNAseq using Visium technology to determine activation of these pathways within the LG sections. This analysis suggests that altered metabolism and the hallmarks of inflammatory responses from both epithelial and immune cells drive inflammation. The most significant pathway enriched in upregulated DEGs was the """"""""""""""""""""""""""""""""TYROBP Causal Network"""""""""""""""""""""""""""""""", that has not been described previously in SS. We also noted a significant decrease in lipid metabolism in the LG of the NOD.H-2b mice. Our data suggests that modulation of these pathways can provide a therapeutic strategy to treat ADDE.","RNA sequencing; TYROBP; chronic inflammation; lacrimal gland; lipid metabolism; macrophages; spatial transcriptomics; visium.","False","Visium","1106","32285"
"GSE210380_GSM6429505","mouse","lacrimal gland","36341342","Spatial transcriptomics of the lacrimal gland features macrophage activity and epithelium metabolism as key alterations during chronic inflammation","The lacrimal gland (LG) is an exocrine gland that produces the watery part of the tear film that lubricates the ocular surface. Chronic inflammation, such as Sjögren's syndrome (SS), is one of the leading causes of aqueous-deficiency dry eye (ADDE) disease worldwide. In this study we analyzed the chronic inflammation in the LGs of the NOD.B10Sn-H2b/J (NOD.H-2b) mice, a mouse model of SS, utilizing bulk RNAseq and Visium spatial gene expression. With Seurat we performed unsupervised clustering and analyzed the spatial cell distribution and gene expression changes in all cell clusters within the LG sections. Moreover, for the first time, we analyzed and validated specific pathways defined by bulk RNAseq using Visium technology to determine activation of these pathways within the LG sections. This analysis suggests that altered metabolism and the hallmarks of inflammatory responses from both epithelial and immune cells drive inflammation. The most significant pathway enriched in upregulated DEGs was the """"""""""""""""""""""""""""""""TYROBP Causal Network"""""""""""""""""""""""""""""""", that has not been described previously in SS. We also noted a significant decrease in lipid metabolism in the LG of the NOD.H-2b mice. Our data suggests that modulation of these pathways can provide a therapeutic strategy to treat ADDE.","RNA sequencing; TYROBP; chronic inflammation; lacrimal gland; lipid metabolism; macrophages; spatial transcriptomics; visium.","False","Visium","1530","32285"
"GSE210380_GSM6429506","mouse","lacrimal gland","36341342","Spatial transcriptomics of the lacrimal gland features macrophage activity and epithelium metabolism as key alterations during chronic inflammation","The lacrimal gland (LG) is an exocrine gland that produces the watery part of the tear film that lubricates the ocular surface. Chronic inflammation, such as Sjögren's syndrome (SS), is one of the leading causes of aqueous-deficiency dry eye (ADDE) disease worldwide. In this study we analyzed the chronic inflammation in the LGs of the NOD.B10Sn-H2b/J (NOD.H-2b) mice, a mouse model of SS, utilizing bulk RNAseq and Visium spatial gene expression. With Seurat we performed unsupervised clustering and analyzed the spatial cell distribution and gene expression changes in all cell clusters within the LG sections. Moreover, for the first time, we analyzed and validated specific pathways defined by bulk RNAseq using Visium technology to determine activation of these pathways within the LG sections. This analysis suggests that altered metabolism and the hallmarks of inflammatory responses from both epithelial and immune cells drive inflammation. The most significant pathway enriched in upregulated DEGs was the """"""""""""""""""""""""""""""""TYROBP Causal Network"""""""""""""""""""""""""""""""", that has not been described previously in SS. We also noted a significant decrease in lipid metabolism in the LG of the NOD.H-2b mice. Our data suggests that modulation of these pathways can provide a therapeutic strategy to treat ADDE.","RNA sequencing; TYROBP; chronic inflammation; lacrimal gland; lipid metabolism; macrophages; spatial transcriptomics; visium.","False","Visium","1505","32285"
"GSE210616_GSM6433585","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1289","36601"
"GSE210616_GSM6433586","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1376","36601"
"GSE210616_GSM6433587","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1654","36601"
"GSE210616_GSM6433588","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1178","36601"
"GSE210616_GSM6433589","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","2117","36601"
"GSE210616_GSM6433590","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1532","36601"
"GSE210616_GSM6433591","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","772","36601"
"GSE210616_GSM6433592","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1325","36601"
"GSE210616_GSM6433593","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1542","36601"
"GSE210616_GSM6433594","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1391","36601"
"GSE210616_GSM6433595","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1264","36601"
"GSE210616_GSM6433596","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1167","36601"
"GSE210616_GSM6433597","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1109","36601"
"GSE210616_GSM6433598","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1058","36601"
"GSE210616_GSM6433599","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1949","36601"
"GSE210616_GSM6433600","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","754","36601"
"GSE210616_GSM6433601","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1593","36601"
"GSE210616_GSM6433602","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1554","36601"
"GSE210616_GSM6433603","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1127","36601"
"GSE210616_GSM6433604","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1111","36601"
"GSE210616_GSM6433605","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","558","36601"
"GSE210616_GSM6433606","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","850","36601"
"GSE210616_GSM6433607","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","2088","36601"
"GSE210616_GSM6433608","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","3116","36601"
"GSE210616_GSM6433609","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1518","36601"
"GSE210616_GSM6433610","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1511","36601"
"GSE210616_GSM6433611","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1359","36601"
"GSE210616_GSM6433612","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","844","36601"
"GSE210616_GSM6433613","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1868","36601"
"GSE210616_GSM6433614","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1199","36601"
"GSE210616_GSM6433615","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1048","36601"
"GSE210616_GSM6433616","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","921","36601"
"GSE210616_GSM6433617","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","554","36601"
"GSE210616_GSM6433618","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","2207","36601"
"GSE210616_GSM6433619","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","2196","36601"
"GSE210616_GSM6433620","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","926","36601"
"GSE210616_GSM6433621","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","821","36601"
"GSE210616_GSM6433622","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1298","36601"
"GSE210616_GSM6433623","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","901","36601"
"GSE210616_GSM6433624","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1266","36601"
"GSE210616_GSM6433625","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","635","36601"
"GSE210616_GSM6433626","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","897","36601"
"GSE210616_GSM6433627","human","breast","36283023","Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer","Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.","","True","Visium","1124","36601"
"GSE211895_GSM6505118","rattus norvegicus","colon","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","1428","25399"
"GSE211895_GSM6505119","rattus norvegicus","colon","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","1375","25399"
"GSE211895_GSM6505120","rattus norvegicus","colon","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","1453","25399"
"GSE211895_GSM6505121","rattus norvegicus","colon","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","1416","25399"
"GSE211895_GSM6505122","rattus norvegicus","colon","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","1393","25399"
"GSE211895_GSM6505123","rattus norvegicus","colon","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","1138","25399"
"GSE211895_GSM6505124","rattus norvegicus","colon","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","1150","25399"
"GSE211895_GSM6505125","rattus norvegicus","colon","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","1302","25399"
"GSE211895_GSM6505126","rattus norvegicus","colon","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","1286","25399"
"GSE211895_GSM6505127","rattus norvegicus","colon","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","1372","25399"
"GSE211895_GSM6505128","rattus norvegicus","colon","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","1124","25399"
"GSE211895_GSM6505129","rattus norvegicus","colon","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","1276","25399"
"GSE211895_GSM6505130","rattus norvegicus","colon","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","1345","25399"
"GSE211895_GSM6505131","rattus norvegicus","colon","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","1299","25399"
"GSE211895_GSM6505132","rattus norvegicus","colon","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","1388","25399"
"GSE211895_GSM6505133","human","pancreas","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","2204","18035"
"GSE211895_GSM6505134","human","pancreas","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","3232","18035"
"GSE211895_GSM6505135","human","pancreas","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","2261","18035"
"GSE211895_GSM6505136","mouse","b16f10 syngeneic tumor","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","1469","32285"
"GSE211895_GSM6505137","mouse","b16f10 syngeneic tumor","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","2421","32285"
"GSE211895_GSM6505138","mouse","b16f10 syngeneic tumor","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","2188","32285"
"GSE211895_GSM6505139","mouse","b16f10 syngeneic tumor","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","2897","32285"
"GSE211895_GSM6505140","mouse","b16f10 syngeneic tumor","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","3090","32285"
"GSE211895_GSM6505141","mouse","b16f10 syngeneic tumor","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","3444","32285"
"GSE211895_GSM6505142","mouse","colon","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","1378","32285"
"GSE211895_GSM6505143","mouse","colon","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","1372","32285"
"GSE211895_GSM6505144","mouse","colon","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","1982","32285"
"GSE211895_GSM6505145","mouse","colon","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","1909","32285"
"GSE211895_GSM6505146","mouse","colon","36452860","Assessment of spatial transcriptomics for oncology discovery","Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.","biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.","True","Visium","3600","32285"
"GSE212323_GSM6523976","mouse","brain","36871219","APOE modulates microglial immunometabolism in response to age, amyloid pathology, and inflammatory challenge","The E4 allele of Apolipoprotein E (APOE) is associated with both metabolic dysfunction and a heightened pro-inflammatory response: two findings that may be intrinsically linked through the concept of immunometabolism. Here, we combined bulk, single-cell, and spatial transcriptomics with cell-specific and spatially resolved metabolic analyses in mice expressing human APOE to systematically address the role of APOE across age, neuroinflammation, and AD pathology. RNA sequencing (RNA-seq) highlighted immunometabolic changes across the APOE4 glial transcriptome, specifically in subsets of metabolically distinct microglia enriched in the E4 brain during aging or following an inflammatory challenge. E4 microglia display increased Hif1α expression and a disrupted tricarboxylic acid (TCA) cycle and are inherently pro-glycolytic, while spatial transcriptomics and mass spectrometry imaging highlight an E4-specific response to amyloid that is characterized by widespread alterations in lipid metabolism. Taken together, our findings emphasize a central role for APOE in regulating microglial immunometabolism and provide valuable, interactive resources for discovery and validation research.","APOE; Apolipoprotein E; CP: Neuroscience; DAM; LPS; aging; amyloid; immunometabolism; microglia; scRNA-seq; spatial transcriptomics.","False","Visium","2773","68886"
"GSE212323_GSM6523977","mouse","brain","36871219","APOE modulates microglial immunometabolism in response to age, amyloid pathology, and inflammatory challenge","The E4 allele of Apolipoprotein E (APOE) is associated with both metabolic dysfunction and a heightened pro-inflammatory response: two findings that may be intrinsically linked through the concept of immunometabolism. Here, we combined bulk, single-cell, and spatial transcriptomics with cell-specific and spatially resolved metabolic analyses in mice expressing human APOE to systematically address the role of APOE across age, neuroinflammation, and AD pathology. RNA sequencing (RNA-seq) highlighted immunometabolic changes across the APOE4 glial transcriptome, specifically in subsets of metabolically distinct microglia enriched in the E4 brain during aging or following an inflammatory challenge. E4 microglia display increased Hif1α expression and a disrupted tricarboxylic acid (TCA) cycle and are inherently pro-glycolytic, while spatial transcriptomics and mass spectrometry imaging highlight an E4-specific response to amyloid that is characterized by widespread alterations in lipid metabolism. Taken together, our findings emphasize a central role for APOE in regulating microglial immunometabolism and provide valuable, interactive resources for discovery and validation research.","APOE; Apolipoprotein E; CP: Neuroscience; DAM; LPS; aging; amyloid; immunometabolism; microglia; scRNA-seq; spatial transcriptomics.","False","Visium","2660","68886"
"GSE212323_GSM6523978","mouse","brain","36871219","APOE modulates microglial immunometabolism in response to age, amyloid pathology, and inflammatory challenge","The E4 allele of Apolipoprotein E (APOE) is associated with both metabolic dysfunction and a heightened pro-inflammatory response: two findings that may be intrinsically linked through the concept of immunometabolism. Here, we combined bulk, single-cell, and spatial transcriptomics with cell-specific and spatially resolved metabolic analyses in mice expressing human APOE to systematically address the role of APOE across age, neuroinflammation, and AD pathology. RNA sequencing (RNA-seq) highlighted immunometabolic changes across the APOE4 glial transcriptome, specifically in subsets of metabolically distinct microglia enriched in the E4 brain during aging or following an inflammatory challenge. E4 microglia display increased Hif1α expression and a disrupted tricarboxylic acid (TCA) cycle and are inherently pro-glycolytic, while spatial transcriptomics and mass spectrometry imaging highlight an E4-specific response to amyloid that is characterized by widespread alterations in lipid metabolism. Taken together, our findings emphasize a central role for APOE in regulating microglial immunometabolism and provide valuable, interactive resources for discovery and validation research.","APOE; Apolipoprotein E; CP: Neuroscience; DAM; LPS; aging; amyloid; immunometabolism; microglia; scRNA-seq; spatial transcriptomics.","False","Visium","2670","68886"
"GSE212323_GSM6523979","mouse","brain","36871219","APOE modulates microglial immunometabolism in response to age, amyloid pathology, and inflammatory challenge","The E4 allele of Apolipoprotein E (APOE) is associated with both metabolic dysfunction and a heightened pro-inflammatory response: two findings that may be intrinsically linked through the concept of immunometabolism. Here, we combined bulk, single-cell, and spatial transcriptomics with cell-specific and spatially resolved metabolic analyses in mice expressing human APOE to systematically address the role of APOE across age, neuroinflammation, and AD pathology. RNA sequencing (RNA-seq) highlighted immunometabolic changes across the APOE4 glial transcriptome, specifically in subsets of metabolically distinct microglia enriched in the E4 brain during aging or following an inflammatory challenge. E4 microglia display increased Hif1α expression and a disrupted tricarboxylic acid (TCA) cycle and are inherently pro-glycolytic, while spatial transcriptomics and mass spectrometry imaging highlight an E4-specific response to amyloid that is characterized by widespread alterations in lipid metabolism. Taken together, our findings emphasize a central role for APOE in regulating microglial immunometabolism and provide valuable, interactive resources for discovery and validation research.","APOE; Apolipoprotein E; CP: Neuroscience; DAM; LPS; aging; amyloid; immunometabolism; microglia; scRNA-seq; spatial transcriptomics.","False","Visium","2367","68886"
"GSE212323_GSM6523980","mouse","brain","36871219","APOE modulates microglial immunometabolism in response to age, amyloid pathology, and inflammatory challenge","The E4 allele of Apolipoprotein E (APOE) is associated with both metabolic dysfunction and a heightened pro-inflammatory response: two findings that may be intrinsically linked through the concept of immunometabolism. Here, we combined bulk, single-cell, and spatial transcriptomics with cell-specific and spatially resolved metabolic analyses in mice expressing human APOE to systematically address the role of APOE across age, neuroinflammation, and AD pathology. RNA sequencing (RNA-seq) highlighted immunometabolic changes across the APOE4 glial transcriptome, specifically in subsets of metabolically distinct microglia enriched in the E4 brain during aging or following an inflammatory challenge. E4 microglia display increased Hif1α expression and a disrupted tricarboxylic acid (TCA) cycle and are inherently pro-glycolytic, while spatial transcriptomics and mass spectrometry imaging highlight an E4-specific response to amyloid that is characterized by widespread alterations in lipid metabolism. Taken together, our findings emphasize a central role for APOE in regulating microglial immunometabolism and provide valuable, interactive resources for discovery and validation research.","APOE; Apolipoprotein E; CP: Neuroscience; DAM; LPS; aging; amyloid; immunometabolism; microglia; scRNA-seq; spatial transcriptomics.","False","Visium","2366","68886"
"GSE212323_GSM6523981","mouse","brain","36871219","APOE modulates microglial immunometabolism in response to age, amyloid pathology, and inflammatory challenge","The E4 allele of Apolipoprotein E (APOE) is associated with both metabolic dysfunction and a heightened pro-inflammatory response: two findings that may be intrinsically linked through the concept of immunometabolism. Here, we combined bulk, single-cell, and spatial transcriptomics with cell-specific and spatially resolved metabolic analyses in mice expressing human APOE to systematically address the role of APOE across age, neuroinflammation, and AD pathology. RNA sequencing (RNA-seq) highlighted immunometabolic changes across the APOE4 glial transcriptome, specifically in subsets of metabolically distinct microglia enriched in the E4 brain during aging or following an inflammatory challenge. E4 microglia display increased Hif1α expression and a disrupted tricarboxylic acid (TCA) cycle and are inherently pro-glycolytic, while spatial transcriptomics and mass spectrometry imaging highlight an E4-specific response to amyloid that is characterized by widespread alterations in lipid metabolism. Taken together, our findings emphasize a central role for APOE in regulating microglial immunometabolism and provide valuable, interactive resources for discovery and validation research.","APOE; Apolipoprotein E; CP: Neuroscience; DAM; LPS; aging; amyloid; immunometabolism; microglia; scRNA-seq; spatial transcriptomics.","False","Visium","1688","68886"
"GSE212482_GSM6543813","mouse","breast","36270275","Multiomic analysis reveals conservation of cancer-associated fibroblast phenotypes across species and tissue of origin","Cancer-associated fibroblasts (CAFs) are integral to the solid tumor microenvironment. CAFs were once thought to be a relatively uniform population of matrix-producing cells, but single-cell RNA sequencing has revealed diverse CAF phenotypes. Here, we further probed CAF heterogeneity with a comprehensive multiomics approach. Using paired, same-cell chromatin accessibility and transcriptome analysis, we provided an integrated analysis of CAF subpopulations over a complex spatial transcriptomic and proteomic landscape to identify three superclusters: steady state-like (SSL), mechanoresponsive (MR), and immunomodulatory (IM) CAFs. These superclusters are recapitulated across multiple tissue types and species. Selective disruption of underlying mechanical force or immune checkpoint inhibition therapy results in shifts in CAF subpopulation distributions and affected tumor growth. As such, the balance among CAF superclusters may have considerable translational implications. Collectively, this research expands our understanding of CAF biology, identifying regulatory pathways in CAF differentiation and elucidating therapeutic targets in a species- and tumor-agnostic manner.","ATAC-seq; CODEX; RNA-seq; cancer; fibroblasts; mechanotransduction; multi-omics; single cell; spatial transcriptomics.","True","Visium","1671","32285"
"GSE212482_GSM6543814","mouse","breast","36270275","Multiomic analysis reveals conservation of cancer-associated fibroblast phenotypes across species and tissue of origin","Cancer-associated fibroblasts (CAFs) are integral to the solid tumor microenvironment. CAFs were once thought to be a relatively uniform population of matrix-producing cells, but single-cell RNA sequencing has revealed diverse CAF phenotypes. Here, we further probed CAF heterogeneity with a comprehensive multiomics approach. Using paired, same-cell chromatin accessibility and transcriptome analysis, we provided an integrated analysis of CAF subpopulations over a complex spatial transcriptomic and proteomic landscape to identify three superclusters: steady state-like (SSL), mechanoresponsive (MR), and immunomodulatory (IM) CAFs. These superclusters are recapitulated across multiple tissue types and species. Selective disruption of underlying mechanical force or immune checkpoint inhibition therapy results in shifts in CAF subpopulation distributions and affected tumor growth. As such, the balance among CAF superclusters may have considerable translational implications. Collectively, this research expands our understanding of CAF biology, identifying regulatory pathways in CAF differentiation and elucidating therapeutic targets in a species- and tumor-agnostic manner.","ATAC-seq; CODEX; RNA-seq; cancer; fibroblasts; mechanotransduction; multi-omics; single cell; spatial transcriptomics.","True","Visium","617","32285"
"GSE212482_GSM6543815","mouse","breast","36270275","Multiomic analysis reveals conservation of cancer-associated fibroblast phenotypes across species and tissue of origin","Cancer-associated fibroblasts (CAFs) are integral to the solid tumor microenvironment. CAFs were once thought to be a relatively uniform population of matrix-producing cells, but single-cell RNA sequencing has revealed diverse CAF phenotypes. Here, we further probed CAF heterogeneity with a comprehensive multiomics approach. Using paired, same-cell chromatin accessibility and transcriptome analysis, we provided an integrated analysis of CAF subpopulations over a complex spatial transcriptomic and proteomic landscape to identify three superclusters: steady state-like (SSL), mechanoresponsive (MR), and immunomodulatory (IM) CAFs. These superclusters are recapitulated across multiple tissue types and species. Selective disruption of underlying mechanical force or immune checkpoint inhibition therapy results in shifts in CAF subpopulation distributions and affected tumor growth. As such, the balance among CAF superclusters may have considerable translational implications. Collectively, this research expands our understanding of CAF biology, identifying regulatory pathways in CAF differentiation and elucidating therapeutic targets in a species- and tumor-agnostic manner.","ATAC-seq; CODEX; RNA-seq; cancer; fibroblasts; mechanotransduction; multi-omics; single cell; spatial transcriptomics.","True","Visium","1550","32285"
"GSE212482_GSM6543816","mouse","breast","36270275","Multiomic analysis reveals conservation of cancer-associated fibroblast phenotypes across species and tissue of origin","Cancer-associated fibroblasts (CAFs) are integral to the solid tumor microenvironment. CAFs were once thought to be a relatively uniform population of matrix-producing cells, but single-cell RNA sequencing has revealed diverse CAF phenotypes. Here, we further probed CAF heterogeneity with a comprehensive multiomics approach. Using paired, same-cell chromatin accessibility and transcriptome analysis, we provided an integrated analysis of CAF subpopulations over a complex spatial transcriptomic and proteomic landscape to identify three superclusters: steady state-like (SSL), mechanoresponsive (MR), and immunomodulatory (IM) CAFs. These superclusters are recapitulated across multiple tissue types and species. Selective disruption of underlying mechanical force or immune checkpoint inhibition therapy results in shifts in CAF subpopulation distributions and affected tumor growth. As such, the balance among CAF superclusters may have considerable translational implications. Collectively, this research expands our understanding of CAF biology, identifying regulatory pathways in CAF differentiation and elucidating therapeutic targets in a species- and tumor-agnostic manner.","ATAC-seq; CODEX; RNA-seq; cancer; fibroblasts; mechanotransduction; multi-omics; single cell; spatial transcriptomics.","True","Visium","1716","32285"
"GSE212482_GSM6543817","mouse","breast","36270275","Multiomic analysis reveals conservation of cancer-associated fibroblast phenotypes across species and tissue of origin","Cancer-associated fibroblasts (CAFs) are integral to the solid tumor microenvironment. CAFs were once thought to be a relatively uniform population of matrix-producing cells, but single-cell RNA sequencing has revealed diverse CAF phenotypes. Here, we further probed CAF heterogeneity with a comprehensive multiomics approach. Using paired, same-cell chromatin accessibility and transcriptome analysis, we provided an integrated analysis of CAF subpopulations over a complex spatial transcriptomic and proteomic landscape to identify three superclusters: steady state-like (SSL), mechanoresponsive (MR), and immunomodulatory (IM) CAFs. These superclusters are recapitulated across multiple tissue types and species. Selective disruption of underlying mechanical force or immune checkpoint inhibition therapy results in shifts in CAF subpopulation distributions and affected tumor growth. As such, the balance among CAF superclusters may have considerable translational implications. Collectively, this research expands our understanding of CAF biology, identifying regulatory pathways in CAF differentiation and elucidating therapeutic targets in a species- and tumor-agnostic manner.","ATAC-seq; CODEX; RNA-seq; cancer; fibroblasts; mechanotransduction; multi-omics; single cell; spatial transcriptomics.","True","Visium","1640","32285"
"GSE212482_GSM6543818","mouse","breast","36270275","Multiomic analysis reveals conservation of cancer-associated fibroblast phenotypes across species and tissue of origin","Cancer-associated fibroblasts (CAFs) are integral to the solid tumor microenvironment. CAFs were once thought to be a relatively uniform population of matrix-producing cells, but single-cell RNA sequencing has revealed diverse CAF phenotypes. Here, we further probed CAF heterogeneity with a comprehensive multiomics approach. Using paired, same-cell chromatin accessibility and transcriptome analysis, we provided an integrated analysis of CAF subpopulations over a complex spatial transcriptomic and proteomic landscape to identify three superclusters: steady state-like (SSL), mechanoresponsive (MR), and immunomodulatory (IM) CAFs. These superclusters are recapitulated across multiple tissue types and species. Selective disruption of underlying mechanical force or immune checkpoint inhibition therapy results in shifts in CAF subpopulation distributions and affected tumor growth. As such, the balance among CAF superclusters may have considerable translational implications. Collectively, this research expands our understanding of CAF biology, identifying regulatory pathways in CAF differentiation and elucidating therapeutic targets in a species- and tumor-agnostic manner.","ATAC-seq; CODEX; RNA-seq; cancer; fibroblasts; mechanotransduction; multi-omics; single cell; spatial transcriptomics.","True","Visium","480","32285"
"GSE212526_GSM6534007","human","undifferentiated pleomorphic sarcoma","38429415","Sarcoma microenvironment cell states and ecosystems are associated with prognosis and predict response to immunotherapy","Characterization of the diverse malignant and stromal cell states that make up soft tissue sarcomas and their correlation with patient outcomes has proven difficult using fixed clinical specimens. Here, we employed EcoTyper, a machine-learning framework, to identify the fundamental cell states and cellular ecosystems that make up sarcomas on a large scale using bulk transcriptomes with clinical annotations. We identified and validated 23 sarcoma-specific, transcriptionally defined cell states, many of which were highly prognostic of patient outcomes across independent datasets. We discovered three conserved cellular communities or ecotypes associated with underlying genomic alterations and distinct clinical outcomes. We show that one ecotype defined by tumor-associated macrophages and epithelial-like malignant cells predicts response to immune-checkpoint inhibition but not chemotherapy and validate our findings in an independent cohort. Our results may enable identification of patients with soft tissue sarcomas who could benefit from immunotherapy and help develop new therapeutic strategies.","","True","Visium","1648","36601"
"GSE212526_GSM6534008","human","leiomyosarcoma","38429415","Sarcoma microenvironment cell states and ecosystems are associated with prognosis and predict response to immunotherapy","Characterization of the diverse malignant and stromal cell states that make up soft tissue sarcomas and their correlation with patient outcomes has proven difficult using fixed clinical specimens. Here, we employed EcoTyper, a machine-learning framework, to identify the fundamental cell states and cellular ecosystems that make up sarcomas on a large scale using bulk transcriptomes with clinical annotations. We identified and validated 23 sarcoma-specific, transcriptionally defined cell states, many of which were highly prognostic of patient outcomes across independent datasets. We discovered three conserved cellular communities or ecotypes associated with underlying genomic alterations and distinct clinical outcomes. We show that one ecotype defined by tumor-associated macrophages and epithelial-like malignant cells predicts response to immune-checkpoint inhibition but not chemotherapy and validate our findings in an independent cohort. Our results may enable identification of patients with soft tissue sarcomas who could benefit from immunotherapy and help develop new therapeutic strategies.","","True","Visium","2103","36601"
"GSE212526_GSM6534009","human","undifferentiated pleomorphic sarcoma","38429415","Sarcoma microenvironment cell states and ecosystems are associated with prognosis and predict response to immunotherapy","Characterization of the diverse malignant and stromal cell states that make up soft tissue sarcomas and their correlation with patient outcomes has proven difficult using fixed clinical specimens. Here, we employed EcoTyper, a machine-learning framework, to identify the fundamental cell states and cellular ecosystems that make up sarcomas on a large scale using bulk transcriptomes with clinical annotations. We identified and validated 23 sarcoma-specific, transcriptionally defined cell states, many of which were highly prognostic of patient outcomes across independent datasets. We discovered three conserved cellular communities or ecotypes associated with underlying genomic alterations and distinct clinical outcomes. We show that one ecotype defined by tumor-associated macrophages and epithelial-like malignant cells predicts response to immune-checkpoint inhibition but not chemotherapy and validate our findings in an independent cohort. Our results may enable identification of patients with soft tissue sarcomas who could benefit from immunotherapy and help develop new therapeutic strategies.","","True","Visium","2179","36601"
"GSE212526_GSM6534010","human","undifferentiated pleomorphic sarcoma","38429415","Sarcoma microenvironment cell states and ecosystems are associated with prognosis and predict response to immunotherapy","Characterization of the diverse malignant and stromal cell states that make up soft tissue sarcomas and their correlation with patient outcomes has proven difficult using fixed clinical specimens. Here, we employed EcoTyper, a machine-learning framework, to identify the fundamental cell states and cellular ecosystems that make up sarcomas on a large scale using bulk transcriptomes with clinical annotations. We identified and validated 23 sarcoma-specific, transcriptionally defined cell states, many of which were highly prognostic of patient outcomes across independent datasets. We discovered three conserved cellular communities or ecotypes associated with underlying genomic alterations and distinct clinical outcomes. We show that one ecotype defined by tumor-associated macrophages and epithelial-like malignant cells predicts response to immune-checkpoint inhibition but not chemotherapy and validate our findings in an independent cohort. Our results may enable identification of patients with soft tissue sarcomas who could benefit from immunotherapy and help develop new therapeutic strategies.","","True","Visium","1446","36601"
"GSE213666_GSM6591749","mouse","heart","36577384","Multi-omics profiling visualizes dynamics of cardiac development and functions","Cardiogenesis is a tightly regulated dynamic process through a continuum of differentiation and proliferation events. Key factors and pathways governing this process remain incompletely understood. Here, we investigate mice hearts from embryonic day 10.5 to postnatal week 8 and dissect developmental changes in phosphoproteome-, proteome-, metabolome-, and transcriptome-encompassing cardiogenesis and cardiac maturation. We identify mitogen-activated protein kinases as core kinases involved in transcriptional regulation by mediating the phosphorylation of chromatin remodeling proteins during early cardiogenesis. We construct the reciprocal regulatory network of transcription factors (TFs) and identify a series of TFs controlling early cardiogenesis involved in cycling-dependent proliferation. After birth, we identify cardiac resident macrophages with high arachidonic acid metabolism activities likely involved in the clearance of injured apoptotic cardiomyocytes. Together, our comprehensive multi-omics data offer a panoramic view of cardiac development and maturation that provides a resource for further in-depth functional exploration.","CP: Developmental biology; cardiac maturation; cardiogenesis; efferocytosis; macrophages; multi-omics; protein phosphorylation; transcriptional regulation.","False","Visium","332","32285"
"GSE213688_GSM6592049","human","breast","37813278","Spatial Transcriptomics Reveal Pitfalls and Opportunities for the Detection of Rare High-Plasticity Breast Cancer Subtypes","Breast cancer is one of the most prominent types of cancers, in which therapeutic resistance is a major clinical concern. Specific subtypes, such as claudin-low and metaplastic breast carcinoma (MpBC), have been associated with high nongenetic plasticity, which can facilitate resistance. The similarities and differences between these orthogonal subtypes, identified by molecular and histopathological analyses, respectively, remain insufficiently characterized. Furthermore, adequate methods to identify high-plasticity tumors to better anticipate resistance are lacking. Here, we analyzed 11 triple-negative breast tumors, including 3 claudin-low and 4 MpBC, via high-resolution spatial transcriptomics. We combined pathological annotations and deconvolution approaches to precisely identify tumor spots, on which we performed signature enrichment, differential expression, and copy number analyses. We used The Cancer Genome Atlas and Cancer Cell Line Encyclopedia public databases for external validation of expression markers. By focusing our spatial transcriptomic analyses on tumor cells in MpBC samples, we bypassed the negative impact of stromal contamination and identified specific markers that are neither expressed in other breast cancer subtypes nor expressed in stromal cells. Three markers (BMPER, POPDC3, and SH3RF3) were validated in external expression databases encompassing bulk tumor material and stroma-free cell lines. We unveiled that existing bulk expression signatures of high-plasticity breast cancers are relevant in mesenchymal transdifferentiated compartments but can be hindered by abundant stromal cells in tumor samples, negatively impacting their clinical applicability. Spatial transcriptomic analyses constitute powerful tools to identify specific expression markers and could thus enhance diagnosis and clinical care of rare high-plasticity breast cancers.","diagnostic markers; integrative approaches; plasticity; rare subtypes; spatial transcriptomics.","True","Visium","1848","36601"
"GSE213688_GSM6592050","human","breast","37813278","Spatial Transcriptomics Reveal Pitfalls and Opportunities for the Detection of Rare High-Plasticity Breast Cancer Subtypes","Breast cancer is one of the most prominent types of cancers, in which therapeutic resistance is a major clinical concern. Specific subtypes, such as claudin-low and metaplastic breast carcinoma (MpBC), have been associated with high nongenetic plasticity, which can facilitate resistance. The similarities and differences between these orthogonal subtypes, identified by molecular and histopathological analyses, respectively, remain insufficiently characterized. Furthermore, adequate methods to identify high-plasticity tumors to better anticipate resistance are lacking. Here, we analyzed 11 triple-negative breast tumors, including 3 claudin-low and 4 MpBC, via high-resolution spatial transcriptomics. We combined pathological annotations and deconvolution approaches to precisely identify tumor spots, on which we performed signature enrichment, differential expression, and copy number analyses. We used The Cancer Genome Atlas and Cancer Cell Line Encyclopedia public databases for external validation of expression markers. By focusing our spatial transcriptomic analyses on tumor cells in MpBC samples, we bypassed the negative impact of stromal contamination and identified specific markers that are neither expressed in other breast cancer subtypes nor expressed in stromal cells. Three markers (BMPER, POPDC3, and SH3RF3) were validated in external expression databases encompassing bulk tumor material and stroma-free cell lines. We unveiled that existing bulk expression signatures of high-plasticity breast cancers are relevant in mesenchymal transdifferentiated compartments but can be hindered by abundant stromal cells in tumor samples, negatively impacting their clinical applicability. Spatial transcriptomic analyses constitute powerful tools to identify specific expression markers and could thus enhance diagnosis and clinical care of rare high-plasticity breast cancers.","diagnostic markers; integrative approaches; plasticity; rare subtypes; spatial transcriptomics.","True","Visium","2417","36601"
"GSE213688_GSM6592051","human","breast","37813278","Spatial Transcriptomics Reveal Pitfalls and Opportunities for the Detection of Rare High-Plasticity Breast Cancer Subtypes","Breast cancer is one of the most prominent types of cancers, in which therapeutic resistance is a major clinical concern. Specific subtypes, such as claudin-low and metaplastic breast carcinoma (MpBC), have been associated with high nongenetic plasticity, which can facilitate resistance. The similarities and differences between these orthogonal subtypes, identified by molecular and histopathological analyses, respectively, remain insufficiently characterized. Furthermore, adequate methods to identify high-plasticity tumors to better anticipate resistance are lacking. Here, we analyzed 11 triple-negative breast tumors, including 3 claudin-low and 4 MpBC, via high-resolution spatial transcriptomics. We combined pathological annotations and deconvolution approaches to precisely identify tumor spots, on which we performed signature enrichment, differential expression, and copy number analyses. We used The Cancer Genome Atlas and Cancer Cell Line Encyclopedia public databases for external validation of expression markers. By focusing our spatial transcriptomic analyses on tumor cells in MpBC samples, we bypassed the negative impact of stromal contamination and identified specific markers that are neither expressed in other breast cancer subtypes nor expressed in stromal cells. Three markers (BMPER, POPDC3, and SH3RF3) were validated in external expression databases encompassing bulk tumor material and stroma-free cell lines. We unveiled that existing bulk expression signatures of high-plasticity breast cancers are relevant in mesenchymal transdifferentiated compartments but can be hindered by abundant stromal cells in tumor samples, negatively impacting their clinical applicability. Spatial transcriptomic analyses constitute powerful tools to identify specific expression markers and could thus enhance diagnosis and clinical care of rare high-plasticity breast cancers.","diagnostic markers; integrative approaches; plasticity; rare subtypes; spatial transcriptomics.","True","Visium","1564","36601"
"GSE213688_GSM6592052","human","breast","37813278","Spatial Transcriptomics Reveal Pitfalls and Opportunities for the Detection of Rare High-Plasticity Breast Cancer Subtypes","Breast cancer is one of the most prominent types of cancers, in which therapeutic resistance is a major clinical concern. Specific subtypes, such as claudin-low and metaplastic breast carcinoma (MpBC), have been associated with high nongenetic plasticity, which can facilitate resistance. The similarities and differences between these orthogonal subtypes, identified by molecular and histopathological analyses, respectively, remain insufficiently characterized. Furthermore, adequate methods to identify high-plasticity tumors to better anticipate resistance are lacking. Here, we analyzed 11 triple-negative breast tumors, including 3 claudin-low and 4 MpBC, via high-resolution spatial transcriptomics. We combined pathological annotations and deconvolution approaches to precisely identify tumor spots, on which we performed signature enrichment, differential expression, and copy number analyses. We used The Cancer Genome Atlas and Cancer Cell Line Encyclopedia public databases for external validation of expression markers. By focusing our spatial transcriptomic analyses on tumor cells in MpBC samples, we bypassed the negative impact of stromal contamination and identified specific markers that are neither expressed in other breast cancer subtypes nor expressed in stromal cells. Three markers (BMPER, POPDC3, and SH3RF3) were validated in external expression databases encompassing bulk tumor material and stroma-free cell lines. We unveiled that existing bulk expression signatures of high-plasticity breast cancers are relevant in mesenchymal transdifferentiated compartments but can be hindered by abundant stromal cells in tumor samples, negatively impacting their clinical applicability. Spatial transcriptomic analyses constitute powerful tools to identify specific expression markers and could thus enhance diagnosis and clinical care of rare high-plasticity breast cancers.","diagnostic markers; integrative approaches; plasticity; rare subtypes; spatial transcriptomics.","True","Visium","2104","36601"
"GSE213688_GSM6592053","human","breast","37813278","Spatial Transcriptomics Reveal Pitfalls and Opportunities for the Detection of Rare High-Plasticity Breast Cancer Subtypes","Breast cancer is one of the most prominent types of cancers, in which therapeutic resistance is a major clinical concern. Specific subtypes, such as claudin-low and metaplastic breast carcinoma (MpBC), have been associated with high nongenetic plasticity, which can facilitate resistance. The similarities and differences between these orthogonal subtypes, identified by molecular and histopathological analyses, respectively, remain insufficiently characterized. Furthermore, adequate methods to identify high-plasticity tumors to better anticipate resistance are lacking. Here, we analyzed 11 triple-negative breast tumors, including 3 claudin-low and 4 MpBC, via high-resolution spatial transcriptomics. We combined pathological annotations and deconvolution approaches to precisely identify tumor spots, on which we performed signature enrichment, differential expression, and copy number analyses. We used The Cancer Genome Atlas and Cancer Cell Line Encyclopedia public databases for external validation of expression markers. By focusing our spatial transcriptomic analyses on tumor cells in MpBC samples, we bypassed the negative impact of stromal contamination and identified specific markers that are neither expressed in other breast cancer subtypes nor expressed in stromal cells. Three markers (BMPER, POPDC3, and SH3RF3) were validated in external expression databases encompassing bulk tumor material and stroma-free cell lines. We unveiled that existing bulk expression signatures of high-plasticity breast cancers are relevant in mesenchymal transdifferentiated compartments but can be hindered by abundant stromal cells in tumor samples, negatively impacting their clinical applicability. Spatial transcriptomic analyses constitute powerful tools to identify specific expression markers and could thus enhance diagnosis and clinical care of rare high-plasticity breast cancers.","diagnostic markers; integrative approaches; plasticity; rare subtypes; spatial transcriptomics.","True","Visium","1731","36601"
"GSE213688_GSM6592054","human","breast","37813278","Spatial Transcriptomics Reveal Pitfalls and Opportunities for the Detection of Rare High-Plasticity Breast Cancer Subtypes","Breast cancer is one of the most prominent types of cancers, in which therapeutic resistance is a major clinical concern. Specific subtypes, such as claudin-low and metaplastic breast carcinoma (MpBC), have been associated with high nongenetic plasticity, which can facilitate resistance. The similarities and differences between these orthogonal subtypes, identified by molecular and histopathological analyses, respectively, remain insufficiently characterized. Furthermore, adequate methods to identify high-plasticity tumors to better anticipate resistance are lacking. Here, we analyzed 11 triple-negative breast tumors, including 3 claudin-low and 4 MpBC, via high-resolution spatial transcriptomics. We combined pathological annotations and deconvolution approaches to precisely identify tumor spots, on which we performed signature enrichment, differential expression, and copy number analyses. We used The Cancer Genome Atlas and Cancer Cell Line Encyclopedia public databases for external validation of expression markers. By focusing our spatial transcriptomic analyses on tumor cells in MpBC samples, we bypassed the negative impact of stromal contamination and identified specific markers that are neither expressed in other breast cancer subtypes nor expressed in stromal cells. Three markers (BMPER, POPDC3, and SH3RF3) were validated in external expression databases encompassing bulk tumor material and stroma-free cell lines. We unveiled that existing bulk expression signatures of high-plasticity breast cancers are relevant in mesenchymal transdifferentiated compartments but can be hindered by abundant stromal cells in tumor samples, negatively impacting their clinical applicability. Spatial transcriptomic analyses constitute powerful tools to identify specific expression markers and could thus enhance diagnosis and clinical care of rare high-plasticity breast cancers.","diagnostic markers; integrative approaches; plasticity; rare subtypes; spatial transcriptomics.","True","Visium","1596","36601"
"GSE213688_GSM6592055","human","breast","37813278","Spatial Transcriptomics Reveal Pitfalls and Opportunities for the Detection of Rare High-Plasticity Breast Cancer Subtypes","Breast cancer is one of the most prominent types of cancers, in which therapeutic resistance is a major clinical concern. Specific subtypes, such as claudin-low and metaplastic breast carcinoma (MpBC), have been associated with high nongenetic plasticity, which can facilitate resistance. The similarities and differences between these orthogonal subtypes, identified by molecular and histopathological analyses, respectively, remain insufficiently characterized. Furthermore, adequate methods to identify high-plasticity tumors to better anticipate resistance are lacking. Here, we analyzed 11 triple-negative breast tumors, including 3 claudin-low and 4 MpBC, via high-resolution spatial transcriptomics. We combined pathological annotations and deconvolution approaches to precisely identify tumor spots, on which we performed signature enrichment, differential expression, and copy number analyses. We used The Cancer Genome Atlas and Cancer Cell Line Encyclopedia public databases for external validation of expression markers. By focusing our spatial transcriptomic analyses on tumor cells in MpBC samples, we bypassed the negative impact of stromal contamination and identified specific markers that are neither expressed in other breast cancer subtypes nor expressed in stromal cells. Three markers (BMPER, POPDC3, and SH3RF3) were validated in external expression databases encompassing bulk tumor material and stroma-free cell lines. We unveiled that existing bulk expression signatures of high-plasticity breast cancers are relevant in mesenchymal transdifferentiated compartments but can be hindered by abundant stromal cells in tumor samples, negatively impacting their clinical applicability. Spatial transcriptomic analyses constitute powerful tools to identify specific expression markers and could thus enhance diagnosis and clinical care of rare high-plasticity breast cancers.","diagnostic markers; integrative approaches; plasticity; rare subtypes; spatial transcriptomics.","True","Visium","1055","36601"
"GSE213688_GSM6592056","human","breast","37813278","Spatial Transcriptomics Reveal Pitfalls and Opportunities for the Detection of Rare High-Plasticity Breast Cancer Subtypes","Breast cancer is one of the most prominent types of cancers, in which therapeutic resistance is a major clinical concern. Specific subtypes, such as claudin-low and metaplastic breast carcinoma (MpBC), have been associated with high nongenetic plasticity, which can facilitate resistance. The similarities and differences between these orthogonal subtypes, identified by molecular and histopathological analyses, respectively, remain insufficiently characterized. Furthermore, adequate methods to identify high-plasticity tumors to better anticipate resistance are lacking. Here, we analyzed 11 triple-negative breast tumors, including 3 claudin-low and 4 MpBC, via high-resolution spatial transcriptomics. We combined pathological annotations and deconvolution approaches to precisely identify tumor spots, on which we performed signature enrichment, differential expression, and copy number analyses. We used The Cancer Genome Atlas and Cancer Cell Line Encyclopedia public databases for external validation of expression markers. By focusing our spatial transcriptomic analyses on tumor cells in MpBC samples, we bypassed the negative impact of stromal contamination and identified specific markers that are neither expressed in other breast cancer subtypes nor expressed in stromal cells. Three markers (BMPER, POPDC3, and SH3RF3) were validated in external expression databases encompassing bulk tumor material and stroma-free cell lines. We unveiled that existing bulk expression signatures of high-plasticity breast cancers are relevant in mesenchymal transdifferentiated compartments but can be hindered by abundant stromal cells in tumor samples, negatively impacting their clinical applicability. Spatial transcriptomic analyses constitute powerful tools to identify specific expression markers and could thus enhance diagnosis and clinical care of rare high-plasticity breast cancers.","diagnostic markers; integrative approaches; plasticity; rare subtypes; spatial transcriptomics.","True","Visium","1258","36601"
"GSE213688_GSM6592057","human","breast","37813278","Spatial Transcriptomics Reveal Pitfalls and Opportunities for the Detection of Rare High-Plasticity Breast Cancer Subtypes","Breast cancer is one of the most prominent types of cancers, in which therapeutic resistance is a major clinical concern. Specific subtypes, such as claudin-low and metaplastic breast carcinoma (MpBC), have been associated with high nongenetic plasticity, which can facilitate resistance. The similarities and differences between these orthogonal subtypes, identified by molecular and histopathological analyses, respectively, remain insufficiently characterized. Furthermore, adequate methods to identify high-plasticity tumors to better anticipate resistance are lacking. Here, we analyzed 11 triple-negative breast tumors, including 3 claudin-low and 4 MpBC, via high-resolution spatial transcriptomics. We combined pathological annotations and deconvolution approaches to precisely identify tumor spots, on which we performed signature enrichment, differential expression, and copy number analyses. We used The Cancer Genome Atlas and Cancer Cell Line Encyclopedia public databases for external validation of expression markers. By focusing our spatial transcriptomic analyses on tumor cells in MpBC samples, we bypassed the negative impact of stromal contamination and identified specific markers that are neither expressed in other breast cancer subtypes nor expressed in stromal cells. Three markers (BMPER, POPDC3, and SH3RF3) were validated in external expression databases encompassing bulk tumor material and stroma-free cell lines. We unveiled that existing bulk expression signatures of high-plasticity breast cancers are relevant in mesenchymal transdifferentiated compartments but can be hindered by abundant stromal cells in tumor samples, negatively impacting their clinical applicability. Spatial transcriptomic analyses constitute powerful tools to identify specific expression markers and could thus enhance diagnosis and clinical care of rare high-plasticity breast cancers.","diagnostic markers; integrative approaches; plasticity; rare subtypes; spatial transcriptomics.","True","Visium","370","36601"
"GSE213688_GSM6592058","human","breast","37813278","Spatial Transcriptomics Reveal Pitfalls and Opportunities for the Detection of Rare High-Plasticity Breast Cancer Subtypes","Breast cancer is one of the most prominent types of cancers, in which therapeutic resistance is a major clinical concern. Specific subtypes, such as claudin-low and metaplastic breast carcinoma (MpBC), have been associated with high nongenetic plasticity, which can facilitate resistance. The similarities and differences between these orthogonal subtypes, identified by molecular and histopathological analyses, respectively, remain insufficiently characterized. Furthermore, adequate methods to identify high-plasticity tumors to better anticipate resistance are lacking. Here, we analyzed 11 triple-negative breast tumors, including 3 claudin-low and 4 MpBC, via high-resolution spatial transcriptomics. We combined pathological annotations and deconvolution approaches to precisely identify tumor spots, on which we performed signature enrichment, differential expression, and copy number analyses. We used The Cancer Genome Atlas and Cancer Cell Line Encyclopedia public databases for external validation of expression markers. By focusing our spatial transcriptomic analyses on tumor cells in MpBC samples, we bypassed the negative impact of stromal contamination and identified specific markers that are neither expressed in other breast cancer subtypes nor expressed in stromal cells. Three markers (BMPER, POPDC3, and SH3RF3) were validated in external expression databases encompassing bulk tumor material and stroma-free cell lines. We unveiled that existing bulk expression signatures of high-plasticity breast cancers are relevant in mesenchymal transdifferentiated compartments but can be hindered by abundant stromal cells in tumor samples, negatively impacting their clinical applicability. Spatial transcriptomic analyses constitute powerful tools to identify specific expression markers and could thus enhance diagnosis and clinical care of rare high-plasticity breast cancers.","diagnostic markers; integrative approaches; plasticity; rare subtypes; spatial transcriptomics.","True","Visium","1405","36601"
"GSE213688_GSM6592059","human","breast","37813278","Spatial Transcriptomics Reveal Pitfalls and Opportunities for the Detection of Rare High-Plasticity Breast Cancer Subtypes","Breast cancer is one of the most prominent types of cancers, in which therapeutic resistance is a major clinical concern. Specific subtypes, such as claudin-low and metaplastic breast carcinoma (MpBC), have been associated with high nongenetic plasticity, which can facilitate resistance. The similarities and differences between these orthogonal subtypes, identified by molecular and histopathological analyses, respectively, remain insufficiently characterized. Furthermore, adequate methods to identify high-plasticity tumors to better anticipate resistance are lacking. Here, we analyzed 11 triple-negative breast tumors, including 3 claudin-low and 4 MpBC, via high-resolution spatial transcriptomics. We combined pathological annotations and deconvolution approaches to precisely identify tumor spots, on which we performed signature enrichment, differential expression, and copy number analyses. We used The Cancer Genome Atlas and Cancer Cell Line Encyclopedia public databases for external validation of expression markers. By focusing our spatial transcriptomic analyses on tumor cells in MpBC samples, we bypassed the negative impact of stromal contamination and identified specific markers that are neither expressed in other breast cancer subtypes nor expressed in stromal cells. Three markers (BMPER, POPDC3, and SH3RF3) were validated in external expression databases encompassing bulk tumor material and stroma-free cell lines. We unveiled that existing bulk expression signatures of high-plasticity breast cancers are relevant in mesenchymal transdifferentiated compartments but can be hindered by abundant stromal cells in tumor samples, negatively impacting their clinical applicability. Spatial transcriptomic analyses constitute powerful tools to identify specific expression markers and could thus enhance diagnosis and clinical care of rare high-plasticity breast cancers.","diagnostic markers; integrative approaches; plasticity; rare subtypes; spatial transcriptomics.","True","Visium","2657","36601"
"GSE213688_GSM6592060","human","breast","37813278","Spatial Transcriptomics Reveal Pitfalls and Opportunities for the Detection of Rare High-Plasticity Breast Cancer Subtypes","Breast cancer is one of the most prominent types of cancers, in which therapeutic resistance is a major clinical concern. Specific subtypes, such as claudin-low and metaplastic breast carcinoma (MpBC), have been associated with high nongenetic plasticity, which can facilitate resistance. The similarities and differences between these orthogonal subtypes, identified by molecular and histopathological analyses, respectively, remain insufficiently characterized. Furthermore, adequate methods to identify high-plasticity tumors to better anticipate resistance are lacking. Here, we analyzed 11 triple-negative breast tumors, including 3 claudin-low and 4 MpBC, via high-resolution spatial transcriptomics. We combined pathological annotations and deconvolution approaches to precisely identify tumor spots, on which we performed signature enrichment, differential expression, and copy number analyses. We used The Cancer Genome Atlas and Cancer Cell Line Encyclopedia public databases for external validation of expression markers. By focusing our spatial transcriptomic analyses on tumor cells in MpBC samples, we bypassed the negative impact of stromal contamination and identified specific markers that are neither expressed in other breast cancer subtypes nor expressed in stromal cells. Three markers (BMPER, POPDC3, and SH3RF3) were validated in external expression databases encompassing bulk tumor material and stroma-free cell lines. We unveiled that existing bulk expression signatures of high-plasticity breast cancers are relevant in mesenchymal transdifferentiated compartments but can be hindered by abundant stromal cells in tumor samples, negatively impacting their clinical applicability. Spatial transcriptomic analyses constitute powerful tools to identify specific expression markers and could thus enhance diagnosis and clinical care of rare high-plasticity breast cancers.","diagnostic markers; integrative approaches; plasticity; rare subtypes; spatial transcriptomics.","True","Visium","1295","36601"
"GSE213688_GSM6592061","human","breast","37813278","Spatial Transcriptomics Reveal Pitfalls and Opportunities for the Detection of Rare High-Plasticity Breast Cancer Subtypes","Breast cancer is one of the most prominent types of cancers, in which therapeutic resistance is a major clinical concern. Specific subtypes, such as claudin-low and metaplastic breast carcinoma (MpBC), have been associated with high nongenetic plasticity, which can facilitate resistance. The similarities and differences between these orthogonal subtypes, identified by molecular and histopathological analyses, respectively, remain insufficiently characterized. Furthermore, adequate methods to identify high-plasticity tumors to better anticipate resistance are lacking. Here, we analyzed 11 triple-negative breast tumors, including 3 claudin-low and 4 MpBC, via high-resolution spatial transcriptomics. We combined pathological annotations and deconvolution approaches to precisely identify tumor spots, on which we performed signature enrichment, differential expression, and copy number analyses. We used The Cancer Genome Atlas and Cancer Cell Line Encyclopedia public databases for external validation of expression markers. By focusing our spatial transcriptomic analyses on tumor cells in MpBC samples, we bypassed the negative impact of stromal contamination and identified specific markers that are neither expressed in other breast cancer subtypes nor expressed in stromal cells. Three markers (BMPER, POPDC3, and SH3RF3) were validated in external expression databases encompassing bulk tumor material and stroma-free cell lines. We unveiled that existing bulk expression signatures of high-plasticity breast cancers are relevant in mesenchymal transdifferentiated compartments but can be hindered by abundant stromal cells in tumor samples, negatively impacting their clinical applicability. Spatial transcriptomic analyses constitute powerful tools to identify specific expression markers and could thus enhance diagnosis and clinical care of rare high-plasticity breast cancers.","diagnostic markers; integrative approaches; plasticity; rare subtypes; spatial transcriptomics.","True","Visium","3037","36601"
"GSE213688_GSM6592062","human","breast","37813278","Spatial Transcriptomics Reveal Pitfalls and Opportunities for the Detection of Rare High-Plasticity Breast Cancer Subtypes","Breast cancer is one of the most prominent types of cancers, in which therapeutic resistance is a major clinical concern. Specific subtypes, such as claudin-low and metaplastic breast carcinoma (MpBC), have been associated with high nongenetic plasticity, which can facilitate resistance. The similarities and differences between these orthogonal subtypes, identified by molecular and histopathological analyses, respectively, remain insufficiently characterized. Furthermore, adequate methods to identify high-plasticity tumors to better anticipate resistance are lacking. Here, we analyzed 11 triple-negative breast tumors, including 3 claudin-low and 4 MpBC, via high-resolution spatial transcriptomics. We combined pathological annotations and deconvolution approaches to precisely identify tumor spots, on which we performed signature enrichment, differential expression, and copy number analyses. We used The Cancer Genome Atlas and Cancer Cell Line Encyclopedia public databases for external validation of expression markers. By focusing our spatial transcriptomic analyses on tumor cells in MpBC samples, we bypassed the negative impact of stromal contamination and identified specific markers that are neither expressed in other breast cancer subtypes nor expressed in stromal cells. Three markers (BMPER, POPDC3, and SH3RF3) were validated in external expression databases encompassing bulk tumor material and stroma-free cell lines. We unveiled that existing bulk expression signatures of high-plasticity breast cancers are relevant in mesenchymal transdifferentiated compartments but can be hindered by abundant stromal cells in tumor samples, negatively impacting their clinical applicability. Spatial transcriptomic analyses constitute powerful tools to identify specific expression markers and could thus enhance diagnosis and clinical care of rare high-plasticity breast cancers.","diagnostic markers; integrative approaches; plasticity; rare subtypes; spatial transcriptomics.","True","Visium","2700","36601"
"GSE213699_GSM6592131","human","ovary","36249907","Small cell carcinoma of the ovary hypercalcemic type (SCCOHT): A review and novel case with dual germline SMARCA4 and BRCA2 mutations","Small cell carcinoma of the ovary hypercalcemic type (SCCOHT) is a rare and aggressive disease. While classically linked to mutations in SMARCA4, we describe a case in a patient with both SMARCA4 and BRCA2 germline mutations. We describe her disease presentation, histopathology and treatment with adjuvant systemic chemotherapy, interval hyperthermic intraperitoneal chemotherapy, high dose chemotherapy with stem cell rescue, and maintenance with a poly-ADP-ribose polymerase inhibitor (PARPi). Additionally, we share spatial transcriptomics completed on original tumor.","PARP inhibitor; Small cell carcinoma of the ovary; Transcriptomics.","True","Visium","3356","16129"
"GSE213699_GSM6592132","human","ovary","36249907","Small cell carcinoma of the ovary hypercalcemic type (SCCOHT): A review and novel case with dual germline SMARCA4 and BRCA2 mutations","Small cell carcinoma of the ovary hypercalcemic type (SCCOHT) is a rare and aggressive disease. While classically linked to mutations in SMARCA4, we describe a case in a patient with both SMARCA4 and BRCA2 germline mutations. We describe her disease presentation, histopathology and treatment with adjuvant systemic chemotherapy, interval hyperthermic intraperitoneal chemotherapy, high dose chemotherapy with stem cell rescue, and maintenance with a poly-ADP-ribose polymerase inhibitor (PARPi). Additionally, we share spatial transcriptomics completed on original tumor.","PARP inhibitor; Small cell carcinoma of the ovary; Transcriptomics.","True","Visium","2272","15848"
"GSE213699_GSM6592133","human","ovary","36249907","Small cell carcinoma of the ovary hypercalcemic type (SCCOHT): A review and novel case with dual germline SMARCA4 and BRCA2 mutations","Small cell carcinoma of the ovary hypercalcemic type (SCCOHT) is a rare and aggressive disease. While classically linked to mutations in SMARCA4, we describe a case in a patient with both SMARCA4 and BRCA2 germline mutations. We describe her disease presentation, histopathology and treatment with adjuvant systemic chemotherapy, interval hyperthermic intraperitoneal chemotherapy, high dose chemotherapy with stem cell rescue, and maintenance with a poly-ADP-ribose polymerase inhibitor (PARPi). Additionally, we share spatial transcriptomics completed on original tumor.","PARP inhibitor; Small cell carcinoma of the ovary; Transcriptomics.","True","Visium","2254","14799"
"GSE213699_GSM6592134","human","ovary","36249907","Small cell carcinoma of the ovary hypercalcemic type (SCCOHT): A review and novel case with dual germline SMARCA4 and BRCA2 mutations","Small cell carcinoma of the ovary hypercalcemic type (SCCOHT) is a rare and aggressive disease. While classically linked to mutations in SMARCA4, we describe a case in a patient with both SMARCA4 and BRCA2 germline mutations. We describe her disease presentation, histopathology and treatment with adjuvant systemic chemotherapy, interval hyperthermic intraperitoneal chemotherapy, high dose chemotherapy with stem cell rescue, and maintenance with a poly-ADP-ribose polymerase inhibitor (PARPi). Additionally, we share spatial transcriptomics completed on original tumor.","PARP inhibitor; Small cell carcinoma of the ovary; Transcriptomics.","True","Visium","3139","15509"
"GSE213699_GSM6592135","human","ovary","36249907","Small cell carcinoma of the ovary hypercalcemic type (SCCOHT): A review and novel case with dual germline SMARCA4 and BRCA2 mutations","Small cell carcinoma of the ovary hypercalcemic type (SCCOHT) is a rare and aggressive disease. While classically linked to mutations in SMARCA4, we describe a case in a patient with both SMARCA4 and BRCA2 germline mutations. We describe her disease presentation, histopathology and treatment with adjuvant systemic chemotherapy, interval hyperthermic intraperitoneal chemotherapy, high dose chemotherapy with stem cell rescue, and maintenance with a poly-ADP-ribose polymerase inhibitor (PARPi). Additionally, we share spatial transcriptomics completed on original tumor.","PARP inhibitor; Small cell carcinoma of the ovary; Transcriptomics.","True","Visium","2142","15005"
"GSE213699_GSM6592136","human","ovary","36249907","Small cell carcinoma of the ovary hypercalcemic type (SCCOHT): A review and novel case with dual germline SMARCA4 and BRCA2 mutations","Small cell carcinoma of the ovary hypercalcemic type (SCCOHT) is a rare and aggressive disease. While classically linked to mutations in SMARCA4, we describe a case in a patient with both SMARCA4 and BRCA2 germline mutations. We describe her disease presentation, histopathology and treatment with adjuvant systemic chemotherapy, interval hyperthermic intraperitoneal chemotherapy, high dose chemotherapy with stem cell rescue, and maintenance with a poly-ADP-ribose polymerase inhibitor (PARPi). Additionally, we share spatial transcriptomics completed on original tumor.","PARP inhibitor; Small cell carcinoma of the ovary; Transcriptomics.","True","Visium","3107","14346"
"GSE213699_GSM6592137","human","ovary","36249907","Small cell carcinoma of the ovary hypercalcemic type (SCCOHT): A review and novel case with dual germline SMARCA4 and BRCA2 mutations","Small cell carcinoma of the ovary hypercalcemic type (SCCOHT) is a rare and aggressive disease. While classically linked to mutations in SMARCA4, we describe a case in a patient with both SMARCA4 and BRCA2 germline mutations. We describe her disease presentation, histopathology and treatment with adjuvant systemic chemotherapy, interval hyperthermic intraperitoneal chemotherapy, high dose chemotherapy with stem cell rescue, and maintenance with a poly-ADP-ribose polymerase inhibitor (PARPi). Additionally, we share spatial transcriptomics completed on original tumor.","PARP inhibitor; Small cell carcinoma of the ovary; Transcriptomics.","True","Visium","2495","15359"
"GSE213699_GSM6592138","human","ovary","36249907","Small cell carcinoma of the ovary hypercalcemic type (SCCOHT): A review and novel case with dual germline SMARCA4 and BRCA2 mutations","Small cell carcinoma of the ovary hypercalcemic type (SCCOHT) is a rare and aggressive disease. While classically linked to mutations in SMARCA4, we describe a case in a patient with both SMARCA4 and BRCA2 germline mutations. We describe her disease presentation, histopathology and treatment with adjuvant systemic chemotherapy, interval hyperthermic intraperitoneal chemotherapy, high dose chemotherapy with stem cell rescue, and maintenance with a poly-ADP-ribose polymerase inhibitor (PARPi). Additionally, we share spatial transcriptomics completed on original tumor.","PARP inhibitor; Small cell carcinoma of the ovary; Transcriptomics.","True","Visium","4156","15454"
"GSE214363_GSM6604700","mouse","skin","36543251","Stromal Reprogramming through Dual PDGFRα/β Blockade Boosts the Efficacy of Anti-PD-1 Immunotherapy in Fibrotic Tumors","Excess stroma and cancer-associated fibroblasts (CAF) enhance cancer progression and facilitate immune evasion. Insights into the mechanisms by which the stroma manipulates the immune microenvironment could help improve cancer treatment. Here, we aimed to elucidate potential approaches for stromal reprogramming and improved cancer immunotherapy. Platelet-derived growth factor C (PDGFC) and D expression were significantly associated with a poor prognosis in patients with gastric cancer, and PDGF receptor beta (PDGFRβ) was predominantly expressed in diffuse-type gastric cancer stroma. CAFs stimulated with PDGFs exhibited markedly increased expression of CXCL1, CXCL3, CXCL5, and CXCL8, which are involved in polymorphonuclear myeloid-derived suppressor cell (PMN-MDSC) recruitment. Fibrotic gastric cancer xenograft tumors exhibited increased PMN-MDSC accumulation and decreased lymphocyte infiltration, as well as resistance to anti-PD-1. Single-cell RNA sequencing and spatial transcriptomics revealed that PDGFRα/β blockade reversed the immunosuppressive microenvironment through stromal modification. Finally, combining PDGFRα/β blockade and anti-PD-1 treatment synergistically suppressed the growth of fibrotic tumors. These findings highlight the impact of stromal reprogramming on immune reactivation and the potential for combined immunotherapy for patients with fibrotic cancer.

Significance: Stromal targeting with PDGFRα/β dual blockade reverses the immunosuppressive microenvironment and enhances the efficacy of immune checkpoint inhibitors in fibrotic cancer. See related commentary by Tauriello, p. 655.","","True","Visium","2380","32285"
"GSE214363_GSM6604701","mouse","skin","36543251","Stromal Reprogramming through Dual PDGFRα/β Blockade Boosts the Efficacy of Anti-PD-1 Immunotherapy in Fibrotic Tumors","Excess stroma and cancer-associated fibroblasts (CAF) enhance cancer progression and facilitate immune evasion. Insights into the mechanisms by which the stroma manipulates the immune microenvironment could help improve cancer treatment. Here, we aimed to elucidate potential approaches for stromal reprogramming and improved cancer immunotherapy. Platelet-derived growth factor C (PDGFC) and D expression were significantly associated with a poor prognosis in patients with gastric cancer, and PDGF receptor beta (PDGFRβ) was predominantly expressed in diffuse-type gastric cancer stroma. CAFs stimulated with PDGFs exhibited markedly increased expression of CXCL1, CXCL3, CXCL5, and CXCL8, which are involved in polymorphonuclear myeloid-derived suppressor cell (PMN-MDSC) recruitment. Fibrotic gastric cancer xenograft tumors exhibited increased PMN-MDSC accumulation and decreased lymphocyte infiltration, as well as resistance to anti-PD-1. Single-cell RNA sequencing and spatial transcriptomics revealed that PDGFRα/β blockade reversed the immunosuppressive microenvironment through stromal modification. Finally, combining PDGFRα/β blockade and anti-PD-1 treatment synergistically suppressed the growth of fibrotic tumors. These findings highlight the impact of stromal reprogramming on immune reactivation and the potential for combined immunotherapy for patients with fibrotic cancer.

Significance: Stromal targeting with PDGFRα/β dual blockade reverses the immunosuppressive microenvironment and enhances the efficacy of immune checkpoint inhibitors in fibrotic cancer. See related commentary by Tauriello, p. 655.","","True","Visium","1594","32285"
"GSE214571_GSM6612124","human & mouse","breast","38282415","Molecular features of luminal breast cancer defined through spatial and single-cell transcriptomics","Background: Intratumour heterogeneity is a hallmark of most solid tumours, including breast cancers. We applied spatial transcriptomics and single-cell RNA-sequencing on patient-derived xenografts (PDXs) to profile spatially resolved cell populations within oestrogen receptor-positive (ER+ ) breast cancer and to elucidate their importance in oestrogen-dependent tumour growth.

Methods: Two PDXs of 'ER-high' breast cancers with opposite oestrogen-mediated growth responses were investigated: oestrogen-suppressed GS3 (80-100% ER) and oestrogen-dependent SC31 (40-90% ER) models. The observation was validated via single-cell analyses on an 'ER-low' PDX, GS1 (5% ER). The results from our spatial and single-cell analyses were further supported by a public ER+ breast cancer single-cell dataset and protein-based dual immunohistochemistry (IHC) of SC31 examining important luminal cancer markers (i.e., ER, progesterone receptor and Ki67). The translational implication of our findings was assessed by clinical outcome analyses on publicly available cohorts.

Results: Our space-gene-function study revealed four spatially distinct compartments within ER+ breast cancers. These compartments showed functional diversity (oestrogen-responsive, proliferative, hypoxia-induced and inflammation-related). The 'proliferative' population, rather than the 'oestrogen-responsive' compartment, was crucial for oestrogen-dependent tumour growth, leading to the acquisition of luminal B-like features. The cells expressing typical oestrogen-responsive genes like PGR were not directly linked to oestrogen-dependent proliferation. Dual IHC analyses demonstrated the distinct contribution of the Ki67+ proliferative cells toward oestrogen-mediated growth and their response to a CDK4/6 inhibitor. The gene signatures derived from the proliferative, hypoxia-induced and inflammation-related compartments were significantly correlated with worse clinical outcomes, while patients with the oestrogen-responsive signature showed better prognoses, suggesting that this compartment would not be directly associated with oestrogen-dependent tumour progression.

Conclusions: Our study identified the gene signature in our 'proliferative' compartment as an important determinant of luminal cancer subtypes. This 'proliferative' cell population is a causative feature of luminal B breast cancer, contributing toward its aggressive behaviours.","breast cancer; intratumour heterogeneity; oestrogen receptor; single-cell RNA-sequencing; spatial transcriptomics.","True","Visium","2330","68886"
"GSE214571_GSM6612125","human & mouse","breast","38282415","Molecular features of luminal breast cancer defined through spatial and single-cell transcriptomics","Background: Intratumour heterogeneity is a hallmark of most solid tumours, including breast cancers. We applied spatial transcriptomics and single-cell RNA-sequencing on patient-derived xenografts (PDXs) to profile spatially resolved cell populations within oestrogen receptor-positive (ER+ ) breast cancer and to elucidate their importance in oestrogen-dependent tumour growth.

Methods: Two PDXs of 'ER-high' breast cancers with opposite oestrogen-mediated growth responses were investigated: oestrogen-suppressed GS3 (80-100% ER) and oestrogen-dependent SC31 (40-90% ER) models. The observation was validated via single-cell analyses on an 'ER-low' PDX, GS1 (5% ER). The results from our spatial and single-cell analyses were further supported by a public ER+ breast cancer single-cell dataset and protein-based dual immunohistochemistry (IHC) of SC31 examining important luminal cancer markers (i.e., ER, progesterone receptor and Ki67). The translational implication of our findings was assessed by clinical outcome analyses on publicly available cohorts.

Results: Our space-gene-function study revealed four spatially distinct compartments within ER+ breast cancers. These compartments showed functional diversity (oestrogen-responsive, proliferative, hypoxia-induced and inflammation-related). The 'proliferative' population, rather than the 'oestrogen-responsive' compartment, was crucial for oestrogen-dependent tumour growth, leading to the acquisition of luminal B-like features. The cells expressing typical oestrogen-responsive genes like PGR were not directly linked to oestrogen-dependent proliferation. Dual IHC analyses demonstrated the distinct contribution of the Ki67+ proliferative cells toward oestrogen-mediated growth and their response to a CDK4/6 inhibitor. The gene signatures derived from the proliferative, hypoxia-induced and inflammation-related compartments were significantly correlated with worse clinical outcomes, while patients with the oestrogen-responsive signature showed better prognoses, suggesting that this compartment would not be directly associated with oestrogen-dependent tumour progression.

Conclusions: Our study identified the gene signature in our 'proliferative' compartment as an important determinant of luminal cancer subtypes. This 'proliferative' cell population is a causative feature of luminal B breast cancer, contributing toward its aggressive behaviours.","breast cancer; intratumour heterogeneity; oestrogen receptor; single-cell RNA-sequencing; spatial transcriptomics.","True","Visium","2863","68886"
"GSE214571_GSM6612126","human & mouse","breast","38282415","Molecular features of luminal breast cancer defined through spatial and single-cell transcriptomics","Background: Intratumour heterogeneity is a hallmark of most solid tumours, including breast cancers. We applied spatial transcriptomics and single-cell RNA-sequencing on patient-derived xenografts (PDXs) to profile spatially resolved cell populations within oestrogen receptor-positive (ER+ ) breast cancer and to elucidate their importance in oestrogen-dependent tumour growth.

Methods: Two PDXs of 'ER-high' breast cancers with opposite oestrogen-mediated growth responses were investigated: oestrogen-suppressed GS3 (80-100% ER) and oestrogen-dependent SC31 (40-90% ER) models. The observation was validated via single-cell analyses on an 'ER-low' PDX, GS1 (5% ER). The results from our spatial and single-cell analyses were further supported by a public ER+ breast cancer single-cell dataset and protein-based dual immunohistochemistry (IHC) of SC31 examining important luminal cancer markers (i.e., ER, progesterone receptor and Ki67). The translational implication of our findings was assessed by clinical outcome analyses on publicly available cohorts.

Results: Our space-gene-function study revealed four spatially distinct compartments within ER+ breast cancers. These compartments showed functional diversity (oestrogen-responsive, proliferative, hypoxia-induced and inflammation-related). The 'proliferative' population, rather than the 'oestrogen-responsive' compartment, was crucial for oestrogen-dependent tumour growth, leading to the acquisition of luminal B-like features. The cells expressing typical oestrogen-responsive genes like PGR were not directly linked to oestrogen-dependent proliferation. Dual IHC analyses demonstrated the distinct contribution of the Ki67+ proliferative cells toward oestrogen-mediated growth and their response to a CDK4/6 inhibitor. The gene signatures derived from the proliferative, hypoxia-induced and inflammation-related compartments were significantly correlated with worse clinical outcomes, while patients with the oestrogen-responsive signature showed better prognoses, suggesting that this compartment would not be directly associated with oestrogen-dependent tumour progression.

Conclusions: Our study identified the gene signature in our 'proliferative' compartment as an important determinant of luminal cancer subtypes. This 'proliferative' cell population is a causative feature of luminal B breast cancer, contributing toward its aggressive behaviours.","breast cancer; intratumour heterogeneity; oestrogen receptor; single-cell RNA-sequencing; spatial transcriptomics.","True","Visium","1967","68886"
"GSE214571_GSM6612127","human & mouse","breast","38282415","Molecular features of luminal breast cancer defined through spatial and single-cell transcriptomics","Background: Intratumour heterogeneity is a hallmark of most solid tumours, including breast cancers. We applied spatial transcriptomics and single-cell RNA-sequencing on patient-derived xenografts (PDXs) to profile spatially resolved cell populations within oestrogen receptor-positive (ER+ ) breast cancer and to elucidate their importance in oestrogen-dependent tumour growth.

Methods: Two PDXs of 'ER-high' breast cancers with opposite oestrogen-mediated growth responses were investigated: oestrogen-suppressed GS3 (80-100% ER) and oestrogen-dependent SC31 (40-90% ER) models. The observation was validated via single-cell analyses on an 'ER-low' PDX, GS1 (5% ER). The results from our spatial and single-cell analyses were further supported by a public ER+ breast cancer single-cell dataset and protein-based dual immunohistochemistry (IHC) of SC31 examining important luminal cancer markers (i.e., ER, progesterone receptor and Ki67). The translational implication of our findings was assessed by clinical outcome analyses on publicly available cohorts.

Results: Our space-gene-function study revealed four spatially distinct compartments within ER+ breast cancers. These compartments showed functional diversity (oestrogen-responsive, proliferative, hypoxia-induced and inflammation-related). The 'proliferative' population, rather than the 'oestrogen-responsive' compartment, was crucial for oestrogen-dependent tumour growth, leading to the acquisition of luminal B-like features. The cells expressing typical oestrogen-responsive genes like PGR were not directly linked to oestrogen-dependent proliferation. Dual IHC analyses demonstrated the distinct contribution of the Ki67+ proliferative cells toward oestrogen-mediated growth and their response to a CDK4/6 inhibitor. The gene signatures derived from the proliferative, hypoxia-induced and inflammation-related compartments were significantly correlated with worse clinical outcomes, while patients with the oestrogen-responsive signature showed better prognoses, suggesting that this compartment would not be directly associated with oestrogen-dependent tumour progression.

Conclusions: Our study identified the gene signature in our 'proliferative' compartment as an important determinant of luminal cancer subtypes. This 'proliferative' cell population is a causative feature of luminal B breast cancer, contributing toward its aggressive behaviours.","breast cancer; intratumour heterogeneity; oestrogen receptor; single-cell RNA-sequencing; spatial transcriptomics.","True","Visium","2163","68886"
"GSE214611_GSM6613077","mouse","heart","","Single Cell Spatial Transcriptomics Redefines the Borderzone induced by Myocardial Infarction and Mechanical Injury","","","False","Visium","1917","32285"
"GSE214611_GSM6613078","mouse","heart","","Single Cell Spatial Transcriptomics Redefines the Borderzone induced by Myocardial Infarction and Mechanical Injury","","","False","Visium","3057","32285"
"GSE214611_GSM6613079","mouse","heart","","Single Cell Spatial Transcriptomics Redefines the Borderzone induced by Myocardial Infarction and Mechanical Injury","","","False","Visium","3123","32285"
"GSE214611_GSM6613080","mouse","heart","","Single Cell Spatial Transcriptomics Redefines the Borderzone induced by Myocardial Infarction and Mechanical Injury","","","False","Visium","3540","32285"
"GSE214611_GSM6613082","mouse","heart","","Single Cell Spatial Transcriptomics Redefines the Borderzone induced by Myocardial Infarction and Mechanical Injury","","","False","Visium","2468","32285"
"GSE214611_GSM6613083","mouse","heart","","Single Cell Spatial Transcriptomics Redefines the Borderzone induced by Myocardial Infarction and Mechanical Injury","","","False","Visium","2578","32285"
"GSE214611_GSM6613084","mouse","heart","","Single Cell Spatial Transcriptomics Redefines the Borderzone induced by Myocardial Infarction and Mechanical Injury","","","False","Visium","2251","32285"
"GSE214611_GSM6613085","mouse","heart","","Single Cell Spatial Transcriptomics Redefines the Borderzone induced by Myocardial Infarction and Mechanical Injury","","","False","Visium","2461","32285"
"GSE214611_GSM6613086","mouse","heart","","Single Cell Spatial Transcriptomics Redefines the Borderzone induced by Myocardial Infarction and Mechanical Injury","","","False","Visium","2438","32285"
"GSE214611_GSM6613088","mouse","heart","","Single Cell Spatial Transcriptomics Redefines the Borderzone induced by Myocardial Infarction and Mechanical Injury","","","False","Visium","2428","32285"
"GSE214611_GSM6613089","mouse","heart","","Single Cell Spatial Transcriptomics Redefines the Borderzone induced by Myocardial Infarction and Mechanical Injury","","","False","Visium","2363","32285"
"GSE217058_GSM6704280","mouse","brain","37143153","Probing pathways by which rhynchophylline modifies sleep using spatial transcriptomics","Background: Rhynchophylline (RHY) is an alkaloid component of Uncaria, which are plants extensively used in traditional Asian medicines. Uncaria treatments increase sleep time and quality in humans, and RHY induces sleep in rats. However, like many traditional natural treatments, the mechanisms of action of RHY and Uncaria remain evasive. Moreover, it is unknown whether RHY modifies key brain oscillations during sleep. We thus aimed at defining the effects of RHY on sleep architecture and oscillations throughout a 24-h cycle, as well as identifying the underlying molecular mechanisms. Mice received systemic RHY injections at two times of the day (beginning and end of the light period), and vigilance states were studied by electrocorticographic recordings.

Results: RHY enhanced slow wave sleep (SWS) after both injections, suppressed paradoxical sleep (PS) in the light but enhanced PS in the dark period. Furthermore, RHY modified brain oscillations during both wakefulness and SWS (including delta activity dynamics) in a time-dependent manner. Interestingly, most effects were larger in females. A brain spatial transcriptomic analysis showed that RHY modifies the expression of genes linked to cell movement, apoptosis/necrosis, and transcription/translation in a brain region-independent manner, and changes those linked to sleep regulation (e.g., Hcrt, Pmch) in a brain region-specific manner (e.g., in the hypothalamus).

Conclusions: The findings provide support to the sleep-inducing effect of RHY, expose the relevance to shape wake/sleep oscillations, and highlight its effects on the transcriptome with a high spatial resolution. The exposed molecular mechanisms underlying the effect of a natural compound should benefit sleep- and brain-related medicine.","Electrocorticographic oscillations; Hypothalamus; Molecular profiling; Sex; Sleep induction; Slow wave sleep.","False","Visium","2522","32285"
"GSE217058_GSM6704281","mouse","brain","37143153","Probing pathways by which rhynchophylline modifies sleep using spatial transcriptomics","Background: Rhynchophylline (RHY) is an alkaloid component of Uncaria, which are plants extensively used in traditional Asian medicines. Uncaria treatments increase sleep time and quality in humans, and RHY induces sleep in rats. However, like many traditional natural treatments, the mechanisms of action of RHY and Uncaria remain evasive. Moreover, it is unknown whether RHY modifies key brain oscillations during sleep. We thus aimed at defining the effects of RHY on sleep architecture and oscillations throughout a 24-h cycle, as well as identifying the underlying molecular mechanisms. Mice received systemic RHY injections at two times of the day (beginning and end of the light period), and vigilance states were studied by electrocorticographic recordings.

Results: RHY enhanced slow wave sleep (SWS) after both injections, suppressed paradoxical sleep (PS) in the light but enhanced PS in the dark period. Furthermore, RHY modified brain oscillations during both wakefulness and SWS (including delta activity dynamics) in a time-dependent manner. Interestingly, most effects were larger in females. A brain spatial transcriptomic analysis showed that RHY modifies the expression of genes linked to cell movement, apoptosis/necrosis, and transcription/translation in a brain region-independent manner, and changes those linked to sleep regulation (e.g., Hcrt, Pmch) in a brain region-specific manner (e.g., in the hypothalamus).

Conclusions: The findings provide support to the sleep-inducing effect of RHY, expose the relevance to shape wake/sleep oscillations, and highlight its effects on the transcriptome with a high spatial resolution. The exposed molecular mechanisms underlying the effect of a natural compound should benefit sleep- and brain-related medicine.","Electrocorticographic oscillations; Hypothalamus; Molecular profiling; Sex; Sleep induction; Slow wave sleep.","False","Visium","2831","32285"
"GSE217058_GSM6704282","mouse","brain","37143153","Probing pathways by which rhynchophylline modifies sleep using spatial transcriptomics","Background: Rhynchophylline (RHY) is an alkaloid component of Uncaria, which are plants extensively used in traditional Asian medicines. Uncaria treatments increase sleep time and quality in humans, and RHY induces sleep in rats. However, like many traditional natural treatments, the mechanisms of action of RHY and Uncaria remain evasive. Moreover, it is unknown whether RHY modifies key brain oscillations during sleep. We thus aimed at defining the effects of RHY on sleep architecture and oscillations throughout a 24-h cycle, as well as identifying the underlying molecular mechanisms. Mice received systemic RHY injections at two times of the day (beginning and end of the light period), and vigilance states were studied by electrocorticographic recordings.

Results: RHY enhanced slow wave sleep (SWS) after both injections, suppressed paradoxical sleep (PS) in the light but enhanced PS in the dark period. Furthermore, RHY modified brain oscillations during both wakefulness and SWS (including delta activity dynamics) in a time-dependent manner. Interestingly, most effects were larger in females. A brain spatial transcriptomic analysis showed that RHY modifies the expression of genes linked to cell movement, apoptosis/necrosis, and transcription/translation in a brain region-independent manner, and changes those linked to sleep regulation (e.g., Hcrt, Pmch) in a brain region-specific manner (e.g., in the hypothalamus).

Conclusions: The findings provide support to the sleep-inducing effect of RHY, expose the relevance to shape wake/sleep oscillations, and highlight its effects on the transcriptome with a high spatial resolution. The exposed molecular mechanisms underlying the effect of a natural compound should benefit sleep- and brain-related medicine.","Electrocorticographic oscillations; Hypothalamus; Molecular profiling; Sex; Sleep induction; Slow wave sleep.","False","Visium","2752","32285"
"GSE217058_GSM6704283","mouse","brain","37143153","Probing pathways by which rhynchophylline modifies sleep using spatial transcriptomics","Background: Rhynchophylline (RHY) is an alkaloid component of Uncaria, which are plants extensively used in traditional Asian medicines. Uncaria treatments increase sleep time and quality in humans, and RHY induces sleep in rats. However, like many traditional natural treatments, the mechanisms of action of RHY and Uncaria remain evasive. Moreover, it is unknown whether RHY modifies key brain oscillations during sleep. We thus aimed at defining the effects of RHY on sleep architecture and oscillations throughout a 24-h cycle, as well as identifying the underlying molecular mechanisms. Mice received systemic RHY injections at two times of the day (beginning and end of the light period), and vigilance states were studied by electrocorticographic recordings.

Results: RHY enhanced slow wave sleep (SWS) after both injections, suppressed paradoxical sleep (PS) in the light but enhanced PS in the dark period. Furthermore, RHY modified brain oscillations during both wakefulness and SWS (including delta activity dynamics) in a time-dependent manner. Interestingly, most effects were larger in females. A brain spatial transcriptomic analysis showed that RHY modifies the expression of genes linked to cell movement, apoptosis/necrosis, and transcription/translation in a brain region-independent manner, and changes those linked to sleep regulation (e.g., Hcrt, Pmch) in a brain region-specific manner (e.g., in the hypothalamus).

Conclusions: The findings provide support to the sleep-inducing effect of RHY, expose the relevance to shape wake/sleep oscillations, and highlight its effects on the transcriptome with a high spatial resolution. The exposed molecular mechanisms underlying the effect of a natural compound should benefit sleep- and brain-related medicine.","Electrocorticographic oscillations; Hypothalamus; Molecular profiling; Sex; Sleep induction; Slow wave sleep.","False","Visium","2816","32285"
"GSE217058_GSM6704284","mouse","brain","37143153","Probing pathways by which rhynchophylline modifies sleep using spatial transcriptomics","Background: Rhynchophylline (RHY) is an alkaloid component of Uncaria, which are plants extensively used in traditional Asian medicines. Uncaria treatments increase sleep time and quality in humans, and RHY induces sleep in rats. However, like many traditional natural treatments, the mechanisms of action of RHY and Uncaria remain evasive. Moreover, it is unknown whether RHY modifies key brain oscillations during sleep. We thus aimed at defining the effects of RHY on sleep architecture and oscillations throughout a 24-h cycle, as well as identifying the underlying molecular mechanisms. Mice received systemic RHY injections at two times of the day (beginning and end of the light period), and vigilance states were studied by electrocorticographic recordings.

Results: RHY enhanced slow wave sleep (SWS) after both injections, suppressed paradoxical sleep (PS) in the light but enhanced PS in the dark period. Furthermore, RHY modified brain oscillations during both wakefulness and SWS (including delta activity dynamics) in a time-dependent manner. Interestingly, most effects were larger in females. A brain spatial transcriptomic analysis showed that RHY modifies the expression of genes linked to cell movement, apoptosis/necrosis, and transcription/translation in a brain region-independent manner, and changes those linked to sleep regulation (e.g., Hcrt, Pmch) in a brain region-specific manner (e.g., in the hypothalamus).

Conclusions: The findings provide support to the sleep-inducing effect of RHY, expose the relevance to shape wake/sleep oscillations, and highlight its effects on the transcriptome with a high spatial resolution. The exposed molecular mechanisms underlying the effect of a natural compound should benefit sleep- and brain-related medicine.","Electrocorticographic oscillations; Hypothalamus; Molecular profiling; Sex; Sleep induction; Slow wave sleep.","False","Visium","2108","32285"
"GSE217058_GSM6704285","mouse","brain","37143153","Probing pathways by which rhynchophylline modifies sleep using spatial transcriptomics","Background: Rhynchophylline (RHY) is an alkaloid component of Uncaria, which are plants extensively used in traditional Asian medicines. Uncaria treatments increase sleep time and quality in humans, and RHY induces sleep in rats. However, like many traditional natural treatments, the mechanisms of action of RHY and Uncaria remain evasive. Moreover, it is unknown whether RHY modifies key brain oscillations during sleep. We thus aimed at defining the effects of RHY on sleep architecture and oscillations throughout a 24-h cycle, as well as identifying the underlying molecular mechanisms. Mice received systemic RHY injections at two times of the day (beginning and end of the light period), and vigilance states were studied by electrocorticographic recordings.

Results: RHY enhanced slow wave sleep (SWS) after both injections, suppressed paradoxical sleep (PS) in the light but enhanced PS in the dark period. Furthermore, RHY modified brain oscillations during both wakefulness and SWS (including delta activity dynamics) in a time-dependent manner. Interestingly, most effects were larger in females. A brain spatial transcriptomic analysis showed that RHY modifies the expression of genes linked to cell movement, apoptosis/necrosis, and transcription/translation in a brain region-independent manner, and changes those linked to sleep regulation (e.g., Hcrt, Pmch) in a brain region-specific manner (e.g., in the hypothalamus).

Conclusions: The findings provide support to the sleep-inducing effect of RHY, expose the relevance to shape wake/sleep oscillations, and highlight its effects on the transcriptome with a high spatial resolution. The exposed molecular mechanisms underlying the effect of a natural compound should benefit sleep- and brain-related medicine.","Electrocorticographic oscillations; Hypothalamus; Molecular profiling; Sex; Sleep induction; Slow wave sleep.","False","Visium","2639","32285"
"GSE217058_GSM6704286","mouse","brain","37143153","Probing pathways by which rhynchophylline modifies sleep using spatial transcriptomics","Background: Rhynchophylline (RHY) is an alkaloid component of Uncaria, which are plants extensively used in traditional Asian medicines. Uncaria treatments increase sleep time and quality in humans, and RHY induces sleep in rats. However, like many traditional natural treatments, the mechanisms of action of RHY and Uncaria remain evasive. Moreover, it is unknown whether RHY modifies key brain oscillations during sleep. We thus aimed at defining the effects of RHY on sleep architecture and oscillations throughout a 24-h cycle, as well as identifying the underlying molecular mechanisms. Mice received systemic RHY injections at two times of the day (beginning and end of the light period), and vigilance states were studied by electrocorticographic recordings.

Results: RHY enhanced slow wave sleep (SWS) after both injections, suppressed paradoxical sleep (PS) in the light but enhanced PS in the dark period. Furthermore, RHY modified brain oscillations during both wakefulness and SWS (including delta activity dynamics) in a time-dependent manner. Interestingly, most effects were larger in females. A brain spatial transcriptomic analysis showed that RHY modifies the expression of genes linked to cell movement, apoptosis/necrosis, and transcription/translation in a brain region-independent manner, and changes those linked to sleep regulation (e.g., Hcrt, Pmch) in a brain region-specific manner (e.g., in the hypothalamus).

Conclusions: The findings provide support to the sleep-inducing effect of RHY, expose the relevance to shape wake/sleep oscillations, and highlight its effects on the transcriptome with a high spatial resolution. The exposed molecular mechanisms underlying the effect of a natural compound should benefit sleep- and brain-related medicine.","Electrocorticographic oscillations; Hypothalamus; Molecular profiling; Sex; Sleep induction; Slow wave sleep.","False","Visium","2741","32285"
"GSE217058_GSM6704287","mouse","brain","37143153","Probing pathways by which rhynchophylline modifies sleep using spatial transcriptomics","Background: Rhynchophylline (RHY) is an alkaloid component of Uncaria, which are plants extensively used in traditional Asian medicines. Uncaria treatments increase sleep time and quality in humans, and RHY induces sleep in rats. However, like many traditional natural treatments, the mechanisms of action of RHY and Uncaria remain evasive. Moreover, it is unknown whether RHY modifies key brain oscillations during sleep. We thus aimed at defining the effects of RHY on sleep architecture and oscillations throughout a 24-h cycle, as well as identifying the underlying molecular mechanisms. Mice received systemic RHY injections at two times of the day (beginning and end of the light period), and vigilance states were studied by electrocorticographic recordings.

Results: RHY enhanced slow wave sleep (SWS) after both injections, suppressed paradoxical sleep (PS) in the light but enhanced PS in the dark period. Furthermore, RHY modified brain oscillations during both wakefulness and SWS (including delta activity dynamics) in a time-dependent manner. Interestingly, most effects were larger in females. A brain spatial transcriptomic analysis showed that RHY modifies the expression of genes linked to cell movement, apoptosis/necrosis, and transcription/translation in a brain region-independent manner, and changes those linked to sleep regulation (e.g., Hcrt, Pmch) in a brain region-specific manner (e.g., in the hypothalamus).

Conclusions: The findings provide support to the sleep-inducing effect of RHY, expose the relevance to shape wake/sleep oscillations, and highlight its effects on the transcriptome with a high spatial resolution. The exposed molecular mechanisms underlying the effect of a natural compound should benefit sleep- and brain-related medicine.","Electrocorticographic oscillations; Hypothalamus; Molecular profiling; Sex; Sleep induction; Slow wave sleep.","False","Visium","3128","32285"
"GSE217414_GSM6716963","human","liver","37144485","Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR","The study of cellular networks mediated by ligand-receptor interactions has attracted much attention recently owing to single-cell omics. However, rich collections of bulk data accompanied with clinical information exists and continue to be generated with no equivalent in single-cell so far. In parallel, spatial transcriptomic (ST) analyses represent a revolutionary tool in biology. A large number of ST projects rely on multicellular resolution, for instance the Visium™ platform, where several cells are analyzed at each location, thus producing localized bulk data. Here, we describe BulkSignalR, a R package to infer ligand-receptor networks from bulk data. BulkSignalR integrates ligand-receptor interactions with downstream pathways to estimate statistical significance. A range of visualization methods complement the statistics, including functions dedicated to spatial data. We demonstrate BulkSignalR relevance using different datasets, including new Visium liver metastasis ST data, with experimental validation of protein colocalization. A comparison with other ST packages shows the significantly higher quality of BulkSignalR inferences. BulkSignalR can be applied to any species thanks to its built-in generic ortholog mapping functionality.","","True","Visium","2887","17943"
"GSE217414_GSM6716964","human","liver","37144485","Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR","The study of cellular networks mediated by ligand-receptor interactions has attracted much attention recently owing to single-cell omics. However, rich collections of bulk data accompanied with clinical information exists and continue to be generated with no equivalent in single-cell so far. In parallel, spatial transcriptomic (ST) analyses represent a revolutionary tool in biology. A large number of ST projects rely on multicellular resolution, for instance the Visium™ platform, where several cells are analyzed at each location, thus producing localized bulk data. Here, we describe BulkSignalR, a R package to infer ligand-receptor networks from bulk data. BulkSignalR integrates ligand-receptor interactions with downstream pathways to estimate statistical significance. A range of visualization methods complement the statistics, including functions dedicated to spatial data. We demonstrate BulkSignalR relevance using different datasets, including new Visium liver metastasis ST data, with experimental validation of protein colocalization. A comparison with other ST packages shows the significantly higher quality of BulkSignalR inferences. BulkSignalR can be applied to any species thanks to its built-in generic ortholog mapping functionality.","","True","Visium","3379","17943"
"GSE217414_GSM6716965","human","liver","37144485","Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR","The study of cellular networks mediated by ligand-receptor interactions has attracted much attention recently owing to single-cell omics. However, rich collections of bulk data accompanied with clinical information exists and continue to be generated with no equivalent in single-cell so far. In parallel, spatial transcriptomic (ST) analyses represent a revolutionary tool in biology. A large number of ST projects rely on multicellular resolution, for instance the Visium™ platform, where several cells are analyzed at each location, thus producing localized bulk data. Here, we describe BulkSignalR, a R package to infer ligand-receptor networks from bulk data. BulkSignalR integrates ligand-receptor interactions with downstream pathways to estimate statistical significance. A range of visualization methods complement the statistics, including functions dedicated to spatial data. We demonstrate BulkSignalR relevance using different datasets, including new Visium liver metastasis ST data, with experimental validation of protein colocalization. A comparison with other ST packages shows the significantly higher quality of BulkSignalR inferences. BulkSignalR can be applied to any species thanks to its built-in generic ortholog mapping functionality.","","True","Visium","2091","17943"
"GSE217414_GSM6716966","human","liver","37144485","Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR","The study of cellular networks mediated by ligand-receptor interactions has attracted much attention recently owing to single-cell omics. However, rich collections of bulk data accompanied with clinical information exists and continue to be generated with no equivalent in single-cell so far. In parallel, spatial transcriptomic (ST) analyses represent a revolutionary tool in biology. A large number of ST projects rely on multicellular resolution, for instance the Visium™ platform, where several cells are analyzed at each location, thus producing localized bulk data. Here, we describe BulkSignalR, a R package to infer ligand-receptor networks from bulk data. BulkSignalR integrates ligand-receptor interactions with downstream pathways to estimate statistical significance. A range of visualization methods complement the statistics, including functions dedicated to spatial data. We demonstrate BulkSignalR relevance using different datasets, including new Visium liver metastasis ST data, with experimental validation of protein colocalization. A comparison with other ST packages shows the significantly higher quality of BulkSignalR inferences. BulkSignalR can be applied to any species thanks to its built-in generic ortholog mapping functionality.","","True","Visium","2317","17943"
"GSE217843_GSM6727528","mouse","pancreas","37914939","IL-1β+ macrophages fuel pathogenic inflammation in pancreatic cancer","Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease with high resistance to therapies1. Inflammatory and immunomodulatory signals co-exist in the pancreatic tumour microenvironment, leading to dysregulated repair and cytotoxic responses. Tumour-associated macrophages (TAMs) have key roles in PDAC2, but their diversity has prevented therapeutic exploitation. Here we combined single-cell and spatial genomics with functional experiments to unravel macrophage functions in pancreatic cancer. We uncovered an inflammatory loop between tumour cells and interleukin-1β (IL-1β)-expressing TAMs, a subset of macrophages elicited by a local synergy between prostaglandin E2 (PGE2) and tumour necrosis factor (TNF). Physical proximity with IL-1β+ TAMs was associated with inflammatory reprogramming and acquisition of pathogenic properties by a subset of PDAC cells. This occurrence was an early event in pancreatic tumorigenesis and led to persistent transcriptional changes associated with disease progression and poor outcomes for patients. Blocking PGE2 or IL-1β activity elicited TAM reprogramming and antagonized tumour cell-intrinsic and -extrinsic inflammation, leading to PDAC control in vivo. Targeting the PGE2-IL-1β axis may enable preventive or therapeutic strategies for reprogramming of immune dynamics in pancreatic cancer.","","True","Visium","3444","32285"
"GSE217843_GSM6727529","mouse","pancreas","37914939","IL-1β+ macrophages fuel pathogenic inflammation in pancreatic cancer","Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease with high resistance to therapies1. Inflammatory and immunomodulatory signals co-exist in the pancreatic tumour microenvironment, leading to dysregulated repair and cytotoxic responses. Tumour-associated macrophages (TAMs) have key roles in PDAC2, but their diversity has prevented therapeutic exploitation. Here we combined single-cell and spatial genomics with functional experiments to unravel macrophage functions in pancreatic cancer. We uncovered an inflammatory loop between tumour cells and interleukin-1β (IL-1β)-expressing TAMs, a subset of macrophages elicited by a local synergy between prostaglandin E2 (PGE2) and tumour necrosis factor (TNF). Physical proximity with IL-1β+ TAMs was associated with inflammatory reprogramming and acquisition of pathogenic properties by a subset of PDAC cells. This occurrence was an early event in pancreatic tumorigenesis and led to persistent transcriptional changes associated with disease progression and poor outcomes for patients. Blocking PGE2 or IL-1β activity elicited TAM reprogramming and antagonized tumour cell-intrinsic and -extrinsic inflammation, leading to PDAC control in vivo. Targeting the PGE2-IL-1β axis may enable preventive or therapeutic strategies for reprogramming of immune dynamics in pancreatic cancer.","","True","Visium","3822","32285"
"GSE218537_GSM6751446","mouse","brain","37143153","Probing pathways by which rhynchophylline modifies sleep using spatial transcriptomics","Background: Rhynchophylline (RHY) is an alkaloid component of Uncaria, which are plants extensively used in traditional Asian medicines. Uncaria treatments increase sleep time and quality in humans, and RHY induces sleep in rats. However, like many traditional natural treatments, the mechanisms of action of RHY and Uncaria remain evasive. Moreover, it is unknown whether RHY modifies key brain oscillations during sleep. We thus aimed at defining the effects of RHY on sleep architecture and oscillations throughout a 24-h cycle, as well as identifying the underlying molecular mechanisms. Mice received systemic RHY injections at two times of the day (beginning and end of the light period), and vigilance states were studied by electrocorticographic recordings.

Results: RHY enhanced slow wave sleep (SWS) after both injections, suppressed paradoxical sleep (PS) in the light but enhanced PS in the dark period. Furthermore, RHY modified brain oscillations during both wakefulness and SWS (including delta activity dynamics) in a time-dependent manner. Interestingly, most effects were larger in females. A brain spatial transcriptomic analysis showed that RHY modifies the expression of genes linked to cell movement, apoptosis/necrosis, and transcription/translation in a brain region-independent manner, and changes those linked to sleep regulation (e.g., Hcrt, Pmch) in a brain region-specific manner (e.g., in the hypothalamus).

Conclusions: The findings provide support to the sleep-inducing effect of RHY, expose the relevance to shape wake/sleep oscillations, and highlight its effects on the transcriptome with a high spatial resolution. The exposed molecular mechanisms underlying the effect of a natural compound should benefit sleep- and brain-related medicine.","Electrocorticographic oscillations; Hypothalamus; Molecular profiling; Sex; Sleep induction; Slow wave sleep.","False","Visium","2831","32285"
"GSE218537_GSM6751447","mouse","brain","37143153","Probing pathways by which rhynchophylline modifies sleep using spatial transcriptomics","Background: Rhynchophylline (RHY) is an alkaloid component of Uncaria, which are plants extensively used in traditional Asian medicines. Uncaria treatments increase sleep time and quality in humans, and RHY induces sleep in rats. However, like many traditional natural treatments, the mechanisms of action of RHY and Uncaria remain evasive. Moreover, it is unknown whether RHY modifies key brain oscillations during sleep. We thus aimed at defining the effects of RHY on sleep architecture and oscillations throughout a 24-h cycle, as well as identifying the underlying molecular mechanisms. Mice received systemic RHY injections at two times of the day (beginning and end of the light period), and vigilance states were studied by electrocorticographic recordings.

Results: RHY enhanced slow wave sleep (SWS) after both injections, suppressed paradoxical sleep (PS) in the light but enhanced PS in the dark period. Furthermore, RHY modified brain oscillations during both wakefulness and SWS (including delta activity dynamics) in a time-dependent manner. Interestingly, most effects were larger in females. A brain spatial transcriptomic analysis showed that RHY modifies the expression of genes linked to cell movement, apoptosis/necrosis, and transcription/translation in a brain region-independent manner, and changes those linked to sleep regulation (e.g., Hcrt, Pmch) in a brain region-specific manner (e.g., in the hypothalamus).

Conclusions: The findings provide support to the sleep-inducing effect of RHY, expose the relevance to shape wake/sleep oscillations, and highlight its effects on the transcriptome with a high spatial resolution. The exposed molecular mechanisms underlying the effect of a natural compound should benefit sleep- and brain-related medicine.","Electrocorticographic oscillations; Hypothalamus; Molecular profiling; Sex; Sleep induction; Slow wave sleep.","False","Visium","2752","32285"
"GSE220218_GSM6796864","pig","heart","36602878","Insulin-like growth factor 1 reduces coronary atherosclerosis in pigs with familial hypercholesterolemia","Although murine models of coronary atherosclerotic disease have been used extensively to determine mechanisms, limited new therapeutic options have emerged. Pigs with familial hypercholesterolemia (FH pigs) develop complex coronary atheromas that are almost identical to human lesions. We reported previously that insulin-like growth factor 1 (IGF-1) reduced aortic atherosclerosis and promoted features of stable plaque in a murine model. We administered human recombinant IGF-1 or saline (control) in atherosclerotic FH pigs for 6 months. IGF-1 decreased relative coronary atheroma in vivo (intravascular ultrasound) and reduced lesion cross-sectional area (postmortem histology). IGF-1 increased plaque's fibrous cap thickness, and reduced necrotic core, macrophage content, and cell apoptosis, consistent with promotion of a stable plaque phenotype. IGF-1 reduced circulating triglycerides, markers of systemic oxidative stress, and CXCL12 chemokine levels. We used spatial transcriptomics (ST) to identify global transcriptome changes in advanced plaque compartments and to obtain mechanistic insights into IGF-1 effects. ST analysis showed that IGF-1 suppressed FOS/FOSB factors and gene expression of MMP9 and CXCL14 in plaque macrophages, suggesting possible involvement of these molecules in IGF-1's effect on atherosclerosis. Thus, IGF-1 reduced coronary plaque burden and promoted features of stable plaque in a pig model, providing support for consideration of clinical trials.","Atherosclerosis; Cardiology; Growth factors; Plaque formation; Vascular Biology.","False","Visium","2196","21303"
"GSE220218_GSM6796865","pig","heart","36602878","Insulin-like growth factor 1 reduces coronary atherosclerosis in pigs with familial hypercholesterolemia","Although murine models of coronary atherosclerotic disease have been used extensively to determine mechanisms, limited new therapeutic options have emerged. Pigs with familial hypercholesterolemia (FH pigs) develop complex coronary atheromas that are almost identical to human lesions. We reported previously that insulin-like growth factor 1 (IGF-1) reduced aortic atherosclerosis and promoted features of stable plaque in a murine model. We administered human recombinant IGF-1 or saline (control) in atherosclerotic FH pigs for 6 months. IGF-1 decreased relative coronary atheroma in vivo (intravascular ultrasound) and reduced lesion cross-sectional area (postmortem histology). IGF-1 increased plaque's fibrous cap thickness, and reduced necrotic core, macrophage content, and cell apoptosis, consistent with promotion of a stable plaque phenotype. IGF-1 reduced circulating triglycerides, markers of systemic oxidative stress, and CXCL12 chemokine levels. We used spatial transcriptomics (ST) to identify global transcriptome changes in advanced plaque compartments and to obtain mechanistic insights into IGF-1 effects. ST analysis showed that IGF-1 suppressed FOS/FOSB factors and gene expression of MMP9 and CXCL14 in plaque macrophages, suggesting possible involvement of these molecules in IGF-1's effect on atherosclerosis. Thus, IGF-1 reduced coronary plaque burden and promoted features of stable plaque in a pig model, providing support for consideration of clinical trials.","Atherosclerosis; Cardiology; Growth factors; Plaque formation; Vascular Biology.","False","Visium","1621","21303"
"GSE220218_GSM6796866","pig","heart","36602878","Insulin-like growth factor 1 reduces coronary atherosclerosis in pigs with familial hypercholesterolemia","Although murine models of coronary atherosclerotic disease have been used extensively to determine mechanisms, limited new therapeutic options have emerged. Pigs with familial hypercholesterolemia (FH pigs) develop complex coronary atheromas that are almost identical to human lesions. We reported previously that insulin-like growth factor 1 (IGF-1) reduced aortic atherosclerosis and promoted features of stable plaque in a murine model. We administered human recombinant IGF-1 or saline (control) in atherosclerotic FH pigs for 6 months. IGF-1 decreased relative coronary atheroma in vivo (intravascular ultrasound) and reduced lesion cross-sectional area (postmortem histology). IGF-1 increased plaque's fibrous cap thickness, and reduced necrotic core, macrophage content, and cell apoptosis, consistent with promotion of a stable plaque phenotype. IGF-1 reduced circulating triglycerides, markers of systemic oxidative stress, and CXCL12 chemokine levels. We used spatial transcriptomics (ST) to identify global transcriptome changes in advanced plaque compartments and to obtain mechanistic insights into IGF-1 effects. ST analysis showed that IGF-1 suppressed FOS/FOSB factors and gene expression of MMP9 and CXCL14 in plaque macrophages, suggesting possible involvement of these molecules in IGF-1's effect on atherosclerosis. Thus, IGF-1 reduced coronary plaque burden and promoted features of stable plaque in a pig model, providing support for consideration of clinical trials.","Atherosclerosis; Cardiology; Growth factors; Plaque formation; Vascular Biology.","False","Visium","1441","21303"
"GSE220218_GSM6796867","pig","heart","36602878","Insulin-like growth factor 1 reduces coronary atherosclerosis in pigs with familial hypercholesterolemia","Although murine models of coronary atherosclerotic disease have been used extensively to determine mechanisms, limited new therapeutic options have emerged. Pigs with familial hypercholesterolemia (FH pigs) develop complex coronary atheromas that are almost identical to human lesions. We reported previously that insulin-like growth factor 1 (IGF-1) reduced aortic atherosclerosis and promoted features of stable plaque in a murine model. We administered human recombinant IGF-1 or saline (control) in atherosclerotic FH pigs for 6 months. IGF-1 decreased relative coronary atheroma in vivo (intravascular ultrasound) and reduced lesion cross-sectional area (postmortem histology). IGF-1 increased plaque's fibrous cap thickness, and reduced necrotic core, macrophage content, and cell apoptosis, consistent with promotion of a stable plaque phenotype. IGF-1 reduced circulating triglycerides, markers of systemic oxidative stress, and CXCL12 chemokine levels. We used spatial transcriptomics (ST) to identify global transcriptome changes in advanced plaque compartments and to obtain mechanistic insights into IGF-1 effects. ST analysis showed that IGF-1 suppressed FOS/FOSB factors and gene expression of MMP9 and CXCL14 in plaque macrophages, suggesting possible involvement of these molecules in IGF-1's effect on atherosclerosis. Thus, IGF-1 reduced coronary plaque burden and promoted features of stable plaque in a pig model, providing support for consideration of clinical trials.","Atherosclerosis; Cardiology; Growth factors; Plaque formation; Vascular Biology.","False","Visium","963","21303"
"GSE220978_GSM6833484","human","mouth","38229179","Spatial Transcriptomic and Metabolomic Landscapes of Oral Submucous Fibrosis-Derived Oral Squamous Cell Carcinoma and its Tumor Microenvironment","In South and Southeast Asia, the habit of chewing betel nuts is prevalent, which leads to oral submucous fibrosis (OSF). OSF is a well-established precancerous lesion, and a portion of OSF cases eventually progress to oral squamous cell carcinoma (OSCC). However, the specific molecular mechanisms underlying the malignant transformation of OSCC from OSF are poorly understood. In this study, the leading-edge techniques of Spatial Transcriptomics (ST) and Spatial Metabolomics (SM) are integrated to obtain spatial location information of cancer cells, fibroblasts, and immune cells, as well as the transcriptomic and metabolomic landscapes in OSF-derived OSCC tissues. This work reveals for the first time that some OSF-derived OSCC cells undergo partial epithelial-mesenchymal transition (pEMT) within the in situ carcinoma (ISC) region, eventually acquiring fibroblast-like phenotypes and participating in collagen deposition. Complex interactions among epithelial cells, fibroblasts, and immune cells in the tumor microenvironment are demonstrated. Most importantly, significant metabolic reprogramming in OSF-derived OSCC, including abnormal polyamine metabolism, potentially playing a pivotal role in promoting tumorigenesis and immune evasion is discovered. The ST and SM data in this study shed new light on deciphering the mechanisms of OSF-derived OSCC. The work also offers invaluable clues for the prevention and treatment of OSCC.","oral squamous cell carcinoma (OSCC); oral submucous fibrosis (OSF); polyamine metabolism; spatial metabolomics; spatial transcriptomics; tumor microenvironment.","True","Visium","4199","36601"
"GSE220978_GSM6833485","human","mouth","38229179","Spatial Transcriptomic and Metabolomic Landscapes of Oral Submucous Fibrosis-Derived Oral Squamous Cell Carcinoma and its Tumor Microenvironment","In South and Southeast Asia, the habit of chewing betel nuts is prevalent, which leads to oral submucous fibrosis (OSF). OSF is a well-established precancerous lesion, and a portion of OSF cases eventually progress to oral squamous cell carcinoma (OSCC). However, the specific molecular mechanisms underlying the malignant transformation of OSCC from OSF are poorly understood. In this study, the leading-edge techniques of Spatial Transcriptomics (ST) and Spatial Metabolomics (SM) are integrated to obtain spatial location information of cancer cells, fibroblasts, and immune cells, as well as the transcriptomic and metabolomic landscapes in OSF-derived OSCC tissues. This work reveals for the first time that some OSF-derived OSCC cells undergo partial epithelial-mesenchymal transition (pEMT) within the in situ carcinoma (ISC) region, eventually acquiring fibroblast-like phenotypes and participating in collagen deposition. Complex interactions among epithelial cells, fibroblasts, and immune cells in the tumor microenvironment are demonstrated. Most importantly, significant metabolic reprogramming in OSF-derived OSCC, including abnormal polyamine metabolism, potentially playing a pivotal role in promoting tumorigenesis and immune evasion is discovered. The ST and SM data in this study shed new light on deciphering the mechanisms of OSF-derived OSCC. The work also offers invaluable clues for the prevention and treatment of OSCC.","oral squamous cell carcinoma (OSCC); oral submucous fibrosis (OSF); polyamine metabolism; spatial metabolomics; spatial transcriptomics; tumor microenvironment.","True","Visium","4290","36601"
"GSE220978_GSM6833486","human","mouth","38229179","Spatial Transcriptomic and Metabolomic Landscapes of Oral Submucous Fibrosis-Derived Oral Squamous Cell Carcinoma and its Tumor Microenvironment","In South and Southeast Asia, the habit of chewing betel nuts is prevalent, which leads to oral submucous fibrosis (OSF). OSF is a well-established precancerous lesion, and a portion of OSF cases eventually progress to oral squamous cell carcinoma (OSCC). However, the specific molecular mechanisms underlying the malignant transformation of OSCC from OSF are poorly understood. In this study, the leading-edge techniques of Spatial Transcriptomics (ST) and Spatial Metabolomics (SM) are integrated to obtain spatial location information of cancer cells, fibroblasts, and immune cells, as well as the transcriptomic and metabolomic landscapes in OSF-derived OSCC tissues. This work reveals for the first time that some OSF-derived OSCC cells undergo partial epithelial-mesenchymal transition (pEMT) within the in situ carcinoma (ISC) region, eventually acquiring fibroblast-like phenotypes and participating in collagen deposition. Complex interactions among epithelial cells, fibroblasts, and immune cells in the tumor microenvironment are demonstrated. Most importantly, significant metabolic reprogramming in OSF-derived OSCC, including abnormal polyamine metabolism, potentially playing a pivotal role in promoting tumorigenesis and immune evasion is discovered. The ST and SM data in this study shed new light on deciphering the mechanisms of OSF-derived OSCC. The work also offers invaluable clues for the prevention and treatment of OSCC.","oral squamous cell carcinoma (OSCC); oral submucous fibrosis (OSF); polyamine metabolism; spatial metabolomics; spatial transcriptomics; tumor microenvironment.","True","Visium","4514","36601"
"GSE220978_GSM6833487","human","mouth","38229179","Spatial Transcriptomic and Metabolomic Landscapes of Oral Submucous Fibrosis-Derived Oral Squamous Cell Carcinoma and its Tumor Microenvironment","In South and Southeast Asia, the habit of chewing betel nuts is prevalent, which leads to oral submucous fibrosis (OSF). OSF is a well-established precancerous lesion, and a portion of OSF cases eventually progress to oral squamous cell carcinoma (OSCC). However, the specific molecular mechanisms underlying the malignant transformation of OSCC from OSF are poorly understood. In this study, the leading-edge techniques of Spatial Transcriptomics (ST) and Spatial Metabolomics (SM) are integrated to obtain spatial location information of cancer cells, fibroblasts, and immune cells, as well as the transcriptomic and metabolomic landscapes in OSF-derived OSCC tissues. This work reveals for the first time that some OSF-derived OSCC cells undergo partial epithelial-mesenchymal transition (pEMT) within the in situ carcinoma (ISC) region, eventually acquiring fibroblast-like phenotypes and participating in collagen deposition. Complex interactions among epithelial cells, fibroblasts, and immune cells in the tumor microenvironment are demonstrated. Most importantly, significant metabolic reprogramming in OSF-derived OSCC, including abnormal polyamine metabolism, potentially playing a pivotal role in promoting tumorigenesis and immune evasion is discovered. The ST and SM data in this study shed new light on deciphering the mechanisms of OSF-derived OSCC. The work also offers invaluable clues for the prevention and treatment of OSCC.","oral squamous cell carcinoma (OSCC); oral submucous fibrosis (OSF); polyamine metabolism; spatial metabolomics; spatial transcriptomics; tumor microenvironment.","True","Visium","4316","36601"
"GSE221359_GSM6859066","mouse","liver","37863068","In vivo macrophage engineering reshapes the tumor microenvironment leading to eradication of liver metastases","Liver metastases are associated with poor response to current pharmacological treatments, including immunotherapy. We describe a lentiviral vector (LV) platform to selectively engineer liver macrophages, including Kupffer cells and tumor-associated macrophages (TAMs), to deliver type I interferon (IFNα) to liver metastases. Gene-based IFNα delivery delays the growth of colorectal and pancreatic ductal adenocarcinoma liver metastases in mice. Response to IFNα is associated with TAM immune activation, enhanced MHC-II-restricted antigen presentation and reduced exhaustion of CD8+ T cells. Conversely, increased IL-10 signaling, expansion of Eomes CD4+ T cells, a cell type displaying features of type I regulatory T (Tr1) cells, and CTLA-4 expression are associated with resistance to therapy. Targeting regulatory T cell functions by combinatorial CTLA-4 immune checkpoint blockade and IFNα LV delivery expands tumor-reactive T cells, attaining complete response in most mice. These findings support a promising therapeutic strategy with feasible translation to patients with unmet medical need.","Colorectal cancer (CRC); EOMES; Gene therapy; Immunotherapy; Interferon-alpha; Interleukin-10 (IL-10); Liver metastases; Pancreatic cancer; Tumor-associated macrophages (TAMs); Type 1 regulatory T cells (Tr1).","True","Visium","734","32287"
"GSE221359_GSM6859067","mouse","liver","37863068","In vivo macrophage engineering reshapes the tumor microenvironment leading to eradication of liver metastases","Liver metastases are associated with poor response to current pharmacological treatments, including immunotherapy. We describe a lentiviral vector (LV) platform to selectively engineer liver macrophages, including Kupffer cells and tumor-associated macrophages (TAMs), to deliver type I interferon (IFNα) to liver metastases. Gene-based IFNα delivery delays the growth of colorectal and pancreatic ductal adenocarcinoma liver metastases in mice. Response to IFNα is associated with TAM immune activation, enhanced MHC-II-restricted antigen presentation and reduced exhaustion of CD8+ T cells. Conversely, increased IL-10 signaling, expansion of Eomes CD4+ T cells, a cell type displaying features of type I regulatory T (Tr1) cells, and CTLA-4 expression are associated with resistance to therapy. Targeting regulatory T cell functions by combinatorial CTLA-4 immune checkpoint blockade and IFNα LV delivery expands tumor-reactive T cells, attaining complete response in most mice. These findings support a promising therapeutic strategy with feasible translation to patients with unmet medical need.","Colorectal cancer (CRC); EOMES; Gene therapy; Immunotherapy; Interferon-alpha; Interleukin-10 (IL-10); Liver metastases; Pancreatic cancer; Tumor-associated macrophages (TAMs); Type 1 regulatory T cells (Tr1).","True","Visium","1284","32287"
"GSE221359_GSM6859068","mouse","liver","37863068","In vivo macrophage engineering reshapes the tumor microenvironment leading to eradication of liver metastases","Liver metastases are associated with poor response to current pharmacological treatments, including immunotherapy. We describe a lentiviral vector (LV) platform to selectively engineer liver macrophages, including Kupffer cells and tumor-associated macrophages (TAMs), to deliver type I interferon (IFNα) to liver metastases. Gene-based IFNα delivery delays the growth of colorectal and pancreatic ductal adenocarcinoma liver metastases in mice. Response to IFNα is associated with TAM immune activation, enhanced MHC-II-restricted antigen presentation and reduced exhaustion of CD8+ T cells. Conversely, increased IL-10 signaling, expansion of Eomes CD4+ T cells, a cell type displaying features of type I regulatory T (Tr1) cells, and CTLA-4 expression are associated with resistance to therapy. Targeting regulatory T cell functions by combinatorial CTLA-4 immune checkpoint blockade and IFNα LV delivery expands tumor-reactive T cells, attaining complete response in most mice. These findings support a promising therapeutic strategy with feasible translation to patients with unmet medical need.","Colorectal cancer (CRC); EOMES; Gene therapy; Immunotherapy; Interferon-alpha; Interleukin-10 (IL-10); Liver metastases; Pancreatic cancer; Tumor-associated macrophages (TAMs); Type 1 regulatory T cells (Tr1).","True","Visium","1818","32287"
"GSE221359_GSM6859069","mouse","liver","37863068","In vivo macrophage engineering reshapes the tumor microenvironment leading to eradication of liver metastases","Liver metastases are associated with poor response to current pharmacological treatments, including immunotherapy. We describe a lentiviral vector (LV) platform to selectively engineer liver macrophages, including Kupffer cells and tumor-associated macrophages (TAMs), to deliver type I interferon (IFNα) to liver metastases. Gene-based IFNα delivery delays the growth of colorectal and pancreatic ductal adenocarcinoma liver metastases in mice. Response to IFNα is associated with TAM immune activation, enhanced MHC-II-restricted antigen presentation and reduced exhaustion of CD8+ T cells. Conversely, increased IL-10 signaling, expansion of Eomes CD4+ T cells, a cell type displaying features of type I regulatory T (Tr1) cells, and CTLA-4 expression are associated with resistance to therapy. Targeting regulatory T cell functions by combinatorial CTLA-4 immune checkpoint blockade and IFNα LV delivery expands tumor-reactive T cells, attaining complete response in most mice. These findings support a promising therapeutic strategy with feasible translation to patients with unmet medical need.","Colorectal cancer (CRC); EOMES; Gene therapy; Immunotherapy; Interferon-alpha; Interleukin-10 (IL-10); Liver metastases; Pancreatic cancer; Tumor-associated macrophages (TAMs); Type 1 regulatory T cells (Tr1).","True","Visium","1044","32287"
"GSE221359_GSM6859070","mouse","liver","37863068","In vivo macrophage engineering reshapes the tumor microenvironment leading to eradication of liver metastases","Liver metastases are associated with poor response to current pharmacological treatments, including immunotherapy. We describe a lentiviral vector (LV) platform to selectively engineer liver macrophages, including Kupffer cells and tumor-associated macrophages (TAMs), to deliver type I interferon (IFNα) to liver metastases. Gene-based IFNα delivery delays the growth of colorectal and pancreatic ductal adenocarcinoma liver metastases in mice. Response to IFNα is associated with TAM immune activation, enhanced MHC-II-restricted antigen presentation and reduced exhaustion of CD8+ T cells. Conversely, increased IL-10 signaling, expansion of Eomes CD4+ T cells, a cell type displaying features of type I regulatory T (Tr1) cells, and CTLA-4 expression are associated with resistance to therapy. Targeting regulatory T cell functions by combinatorial CTLA-4 immune checkpoint blockade and IFNα LV delivery expands tumor-reactive T cells, attaining complete response in most mice. These findings support a promising therapeutic strategy with feasible translation to patients with unmet medical need.","Colorectal cancer (CRC); EOMES; Gene therapy; Immunotherapy; Interferon-alpha; Interleukin-10 (IL-10); Liver metastases; Pancreatic cancer; Tumor-associated macrophages (TAMs); Type 1 regulatory T cells (Tr1).","True","Visium","1494","32287"
"GSE221359_GSM6859071","mouse","liver","37863068","In vivo macrophage engineering reshapes the tumor microenvironment leading to eradication of liver metastases","Liver metastases are associated with poor response to current pharmacological treatments, including immunotherapy. We describe a lentiviral vector (LV) platform to selectively engineer liver macrophages, including Kupffer cells and tumor-associated macrophages (TAMs), to deliver type I interferon (IFNα) to liver metastases. Gene-based IFNα delivery delays the growth of colorectal and pancreatic ductal adenocarcinoma liver metastases in mice. Response to IFNα is associated with TAM immune activation, enhanced MHC-II-restricted antigen presentation and reduced exhaustion of CD8+ T cells. Conversely, increased IL-10 signaling, expansion of Eomes CD4+ T cells, a cell type displaying features of type I regulatory T (Tr1) cells, and CTLA-4 expression are associated with resistance to therapy. Targeting regulatory T cell functions by combinatorial CTLA-4 immune checkpoint blockade and IFNα LV delivery expands tumor-reactive T cells, attaining complete response in most mice. These findings support a promising therapeutic strategy with feasible translation to patients with unmet medical need.","Colorectal cancer (CRC); EOMES; Gene therapy; Immunotherapy; Interferon-alpha; Interleukin-10 (IL-10); Liver metastases; Pancreatic cancer; Tumor-associated macrophages (TAMs); Type 1 regulatory T cells (Tr1).","True","Visium","1158","32287"
"GSE221359_GSM6859072","mouse","liver","37863068","In vivo macrophage engineering reshapes the tumor microenvironment leading to eradication of liver metastases","Liver metastases are associated with poor response to current pharmacological treatments, including immunotherapy. We describe a lentiviral vector (LV) platform to selectively engineer liver macrophages, including Kupffer cells and tumor-associated macrophages (TAMs), to deliver type I interferon (IFNα) to liver metastases. Gene-based IFNα delivery delays the growth of colorectal and pancreatic ductal adenocarcinoma liver metastases in mice. Response to IFNα is associated with TAM immune activation, enhanced MHC-II-restricted antigen presentation and reduced exhaustion of CD8+ T cells. Conversely, increased IL-10 signaling, expansion of Eomes CD4+ T cells, a cell type displaying features of type I regulatory T (Tr1) cells, and CTLA-4 expression are associated with resistance to therapy. Targeting regulatory T cell functions by combinatorial CTLA-4 immune checkpoint blockade and IFNα LV delivery expands tumor-reactive T cells, attaining complete response in most mice. These findings support a promising therapeutic strategy with feasible translation to patients with unmet medical need.","Colorectal cancer (CRC); EOMES; Gene therapy; Immunotherapy; Interferon-alpha; Interleukin-10 (IL-10); Liver metastases; Pancreatic cancer; Tumor-associated macrophages (TAMs); Type 1 regulatory T cells (Tr1).","True","Visium","1843","32287"
"GSE221359_GSM6859073","mouse","liver","37863068","In vivo macrophage engineering reshapes the tumor microenvironment leading to eradication of liver metastases","Liver metastases are associated with poor response to current pharmacological treatments, including immunotherapy. We describe a lentiviral vector (LV) platform to selectively engineer liver macrophages, including Kupffer cells and tumor-associated macrophages (TAMs), to deliver type I interferon (IFNα) to liver metastases. Gene-based IFNα delivery delays the growth of colorectal and pancreatic ductal adenocarcinoma liver metastases in mice. Response to IFNα is associated with TAM immune activation, enhanced MHC-II-restricted antigen presentation and reduced exhaustion of CD8+ T cells. Conversely, increased IL-10 signaling, expansion of Eomes CD4+ T cells, a cell type displaying features of type I regulatory T (Tr1) cells, and CTLA-4 expression are associated with resistance to therapy. Targeting regulatory T cell functions by combinatorial CTLA-4 immune checkpoint blockade and IFNα LV delivery expands tumor-reactive T cells, attaining complete response in most mice. These findings support a promising therapeutic strategy with feasible translation to patients with unmet medical need.","Colorectal cancer (CRC); EOMES; Gene therapy; Immunotherapy; Interferon-alpha; Interleukin-10 (IL-10); Liver metastases; Pancreatic cancer; Tumor-associated macrophages (TAMs); Type 1 regulatory T cells (Tr1).","True","Visium","1544","32287"
"GSE221571_GSM6886495","mouse","bone","36720873","Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples","Spatially resolved transcriptomics has enabled precise genome-wide mRNA expression profiling within tissue sections. The performance of methods targeting the polyA tails of mRNA relies on the availability of specimens with high RNA quality. Moreover, the high cost of currently available spatial resolved transcriptomics assays requires a careful sample screening process to increase the chance of obtaining high-quality data. Indeed, the upfront analysis of RNA quality can show considerable variability due to sample handling, storage, and/or intrinsic factors. We present RNA-Rescue Spatial Transcriptomics (RRST), a workflow designed to improve mRNA recovery from fresh frozen specimens with moderate to low RNA quality. First, we provide a benchmark of RRST against the standard Visium spatial gene expression protocol on high RNA quality samples represented by mouse brain and prostate cancer samples. Then, we test the RRST protocol on tissue sections collected from five challenging tissue types, including human lung, colon, small intestine, pediatric brain tumor, and mouse bone/cartilage. In total, we analyze 52 tissue sections and demonstrate that RRST is a versatile, powerful, and reproducible protocol for fresh frozen specimens of different qualities and origins.","","True","Visium","1305","19465"
"GSE221571_GSM6886496","mouse","bone","36720873","Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples","Spatially resolved transcriptomics has enabled precise genome-wide mRNA expression profiling within tissue sections. The performance of methods targeting the polyA tails of mRNA relies on the availability of specimens with high RNA quality. Moreover, the high cost of currently available spatial resolved transcriptomics assays requires a careful sample screening process to increase the chance of obtaining high-quality data. Indeed, the upfront analysis of RNA quality can show considerable variability due to sample handling, storage, and/or intrinsic factors. We present RNA-Rescue Spatial Transcriptomics (RRST), a workflow designed to improve mRNA recovery from fresh frozen specimens with moderate to low RNA quality. First, we provide a benchmark of RRST against the standard Visium spatial gene expression protocol on high RNA quality samples represented by mouse brain and prostate cancer samples. Then, we test the RRST protocol on tissue sections collected from five challenging tissue types, including human lung, colon, small intestine, pediatric brain tumor, and mouse bone/cartilage. In total, we analyze 52 tissue sections and demonstrate that RRST is a versatile, powerful, and reproducible protocol for fresh frozen specimens of different qualities and origins.","","True","Visium","1202","19465"
"GSE221571_GSM6886497","mouse","bone","36720873","Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples","Spatially resolved transcriptomics has enabled precise genome-wide mRNA expression profiling within tissue sections. The performance of methods targeting the polyA tails of mRNA relies on the availability of specimens with high RNA quality. Moreover, the high cost of currently available spatial resolved transcriptomics assays requires a careful sample screening process to increase the chance of obtaining high-quality data. Indeed, the upfront analysis of RNA quality can show considerable variability due to sample handling, storage, and/or intrinsic factors. We present RNA-Rescue Spatial Transcriptomics (RRST), a workflow designed to improve mRNA recovery from fresh frozen specimens with moderate to low RNA quality. First, we provide a benchmark of RRST against the standard Visium spatial gene expression protocol on high RNA quality samples represented by mouse brain and prostate cancer samples. Then, we test the RRST protocol on tissue sections collected from five challenging tissue types, including human lung, colon, small intestine, pediatric brain tumor, and mouse bone/cartilage. In total, we analyze 52 tissue sections and demonstrate that RRST is a versatile, powerful, and reproducible protocol for fresh frozen specimens of different qualities and origins.","","True","Visium","1298","32285"
"GSE221571_GSM6886498","mouse","bone","36720873","Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples","Spatially resolved transcriptomics has enabled precise genome-wide mRNA expression profiling within tissue sections. The performance of methods targeting the polyA tails of mRNA relies on the availability of specimens with high RNA quality. Moreover, the high cost of currently available spatial resolved transcriptomics assays requires a careful sample screening process to increase the chance of obtaining high-quality data. Indeed, the upfront analysis of RNA quality can show considerable variability due to sample handling, storage, and/or intrinsic factors. We present RNA-Rescue Spatial Transcriptomics (RRST), a workflow designed to improve mRNA recovery from fresh frozen specimens with moderate to low RNA quality. First, we provide a benchmark of RRST against the standard Visium spatial gene expression protocol on high RNA quality samples represented by mouse brain and prostate cancer samples. Then, we test the RRST protocol on tissue sections collected from five challenging tissue types, including human lung, colon, small intestine, pediatric brain tumor, and mouse bone/cartilage. In total, we analyze 52 tissue sections and demonstrate that RRST is a versatile, powerful, and reproducible protocol for fresh frozen specimens of different qualities and origins.","","True","Visium","2935","19465"
"GSE221571_GSM6886499","mouse","bone","36720873","Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples","Spatially resolved transcriptomics has enabled precise genome-wide mRNA expression profiling within tissue sections. The performance of methods targeting the polyA tails of mRNA relies on the availability of specimens with high RNA quality. Moreover, the high cost of currently available spatial resolved transcriptomics assays requires a careful sample screening process to increase the chance of obtaining high-quality data. Indeed, the upfront analysis of RNA quality can show considerable variability due to sample handling, storage, and/or intrinsic factors. We present RNA-Rescue Spatial Transcriptomics (RRST), a workflow designed to improve mRNA recovery from fresh frozen specimens with moderate to low RNA quality. First, we provide a benchmark of RRST against the standard Visium spatial gene expression protocol on high RNA quality samples represented by mouse brain and prostate cancer samples. Then, we test the RRST protocol on tissue sections collected from five challenging tissue types, including human lung, colon, small intestine, pediatric brain tumor, and mouse bone/cartilage. In total, we analyze 52 tissue sections and demonstrate that RRST is a versatile, powerful, and reproducible protocol for fresh frozen specimens of different qualities and origins.","","True","Visium","1788","19465"
"GSE221571_GSM6886500","mouse","bone","36720873","Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples","Spatially resolved transcriptomics has enabled precise genome-wide mRNA expression profiling within tissue sections. The performance of methods targeting the polyA tails of mRNA relies on the availability of specimens with high RNA quality. Moreover, the high cost of currently available spatial resolved transcriptomics assays requires a careful sample screening process to increase the chance of obtaining high-quality data. Indeed, the upfront analysis of RNA quality can show considerable variability due to sample handling, storage, and/or intrinsic factors. We present RNA-Rescue Spatial Transcriptomics (RRST), a workflow designed to improve mRNA recovery from fresh frozen specimens with moderate to low RNA quality. First, we provide a benchmark of RRST against the standard Visium spatial gene expression protocol on high RNA quality samples represented by mouse brain and prostate cancer samples. Then, we test the RRST protocol on tissue sections collected from five challenging tissue types, including human lung, colon, small intestine, pediatric brain tumor, and mouse bone/cartilage. In total, we analyze 52 tissue sections and demonstrate that RRST is a versatile, powerful, and reproducible protocol for fresh frozen specimens of different qualities and origins.","","True","Visium","1240","32285"
"GSE221571_GSM6886501","mouse","brain","36720873","Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples","Spatially resolved transcriptomics has enabled precise genome-wide mRNA expression profiling within tissue sections. The performance of methods targeting the polyA tails of mRNA relies on the availability of specimens with high RNA quality. Moreover, the high cost of currently available spatial resolved transcriptomics assays requires a careful sample screening process to increase the chance of obtaining high-quality data. Indeed, the upfront analysis of RNA quality can show considerable variability due to sample handling, storage, and/or intrinsic factors. We present RNA-Rescue Spatial Transcriptomics (RRST), a workflow designed to improve mRNA recovery from fresh frozen specimens with moderate to low RNA quality. First, we provide a benchmark of RRST against the standard Visium spatial gene expression protocol on high RNA quality samples represented by mouse brain and prostate cancer samples. Then, we test the RRST protocol on tissue sections collected from five challenging tissue types, including human lung, colon, small intestine, pediatric brain tumor, and mouse bone/cartilage. In total, we analyze 52 tissue sections and demonstrate that RRST is a versatile, powerful, and reproducible protocol for fresh frozen specimens of different qualities and origins.","","True","Visium","4426","19465"
"GSE221571_GSM6886502","mouse","brain","36720873","Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples","Spatially resolved transcriptomics has enabled precise genome-wide mRNA expression profiling within tissue sections. The performance of methods targeting the polyA tails of mRNA relies on the availability of specimens with high RNA quality. Moreover, the high cost of currently available spatial resolved transcriptomics assays requires a careful sample screening process to increase the chance of obtaining high-quality data. Indeed, the upfront analysis of RNA quality can show considerable variability due to sample handling, storage, and/or intrinsic factors. We present RNA-Rescue Spatial Transcriptomics (RRST), a workflow designed to improve mRNA recovery from fresh frozen specimens with moderate to low RNA quality. First, we provide a benchmark of RRST against the standard Visium spatial gene expression protocol on high RNA quality samples represented by mouse brain and prostate cancer samples. Then, we test the RRST protocol on tissue sections collected from five challenging tissue types, including human lung, colon, small intestine, pediatric brain tumor, and mouse bone/cartilage. In total, we analyze 52 tissue sections and demonstrate that RRST is a versatile, powerful, and reproducible protocol for fresh frozen specimens of different qualities and origins.","","True","Visium","4492","19465"
"GSE221571_GSM6886503","mouse","brain","36720873","Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples","Spatially resolved transcriptomics has enabled precise genome-wide mRNA expression profiling within tissue sections. The performance of methods targeting the polyA tails of mRNA relies on the availability of specimens with high RNA quality. Moreover, the high cost of currently available spatial resolved transcriptomics assays requires a careful sample screening process to increase the chance of obtaining high-quality data. Indeed, the upfront analysis of RNA quality can show considerable variability due to sample handling, storage, and/or intrinsic factors. We present RNA-Rescue Spatial Transcriptomics (RRST), a workflow designed to improve mRNA recovery from fresh frozen specimens with moderate to low RNA quality. First, we provide a benchmark of RRST against the standard Visium spatial gene expression protocol on high RNA quality samples represented by mouse brain and prostate cancer samples. Then, we test the RRST protocol on tissue sections collected from five challenging tissue types, including human lung, colon, small intestine, pediatric brain tumor, and mouse bone/cartilage. In total, we analyze 52 tissue sections and demonstrate that RRST is a versatile, powerful, and reproducible protocol for fresh frozen specimens of different qualities and origins.","","True","Visium","4159","19465"
"GSE221571_GSM6886504","mouse","brain","36720873","Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples","Spatially resolved transcriptomics has enabled precise genome-wide mRNA expression profiling within tissue sections. The performance of methods targeting the polyA tails of mRNA relies on the availability of specimens with high RNA quality. Moreover, the high cost of currently available spatial resolved transcriptomics assays requires a careful sample screening process to increase the chance of obtaining high-quality data. Indeed, the upfront analysis of RNA quality can show considerable variability due to sample handling, storage, and/or intrinsic factors. We present RNA-Rescue Spatial Transcriptomics (RRST), a workflow designed to improve mRNA recovery from fresh frozen specimens with moderate to low RNA quality. First, we provide a benchmark of RRST against the standard Visium spatial gene expression protocol on high RNA quality samples represented by mouse brain and prostate cancer samples. Then, we test the RRST protocol on tissue sections collected from five challenging tissue types, including human lung, colon, small intestine, pediatric brain tumor, and mouse bone/cartilage. In total, we analyze 52 tissue sections and demonstrate that RRST is a versatile, powerful, and reproducible protocol for fresh frozen specimens of different qualities and origins.","","True","Visium","4166","19465"
"GSE221571_GSM6886505","mouse","brain","36720873","Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples","Spatially resolved transcriptomics has enabled precise genome-wide mRNA expression profiling within tissue sections. The performance of methods targeting the polyA tails of mRNA relies on the availability of specimens with high RNA quality. Moreover, the high cost of currently available spatial resolved transcriptomics assays requires a careful sample screening process to increase the chance of obtaining high-quality data. Indeed, the upfront analysis of RNA quality can show considerable variability due to sample handling, storage, and/or intrinsic factors. We present RNA-Rescue Spatial Transcriptomics (RRST), a workflow designed to improve mRNA recovery from fresh frozen specimens with moderate to low RNA quality. First, we provide a benchmark of RRST against the standard Visium spatial gene expression protocol on high RNA quality samples represented by mouse brain and prostate cancer samples. Then, we test the RRST protocol on tissue sections collected from five challenging tissue types, including human lung, colon, small intestine, pediatric brain tumor, and mouse bone/cartilage. In total, we analyze 52 tissue sections and demonstrate that RRST is a versatile, powerful, and reproducible protocol for fresh frozen specimens of different qualities and origins.","","True","Visium","4514","32285"
"GSE221571_GSM6886506","mouse","brain","36720873","Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples","Spatially resolved transcriptomics has enabled precise genome-wide mRNA expression profiling within tissue sections. The performance of methods targeting the polyA tails of mRNA relies on the availability of specimens with high RNA quality. Moreover, the high cost of currently available spatial resolved transcriptomics assays requires a careful sample screening process to increase the chance of obtaining high-quality data. Indeed, the upfront analysis of RNA quality can show considerable variability due to sample handling, storage, and/or intrinsic factors. We present RNA-Rescue Spatial Transcriptomics (RRST), a workflow designed to improve mRNA recovery from fresh frozen specimens with moderate to low RNA quality. First, we provide a benchmark of RRST against the standard Visium spatial gene expression protocol on high RNA quality samples represented by mouse brain and prostate cancer samples. Then, we test the RRST protocol on tissue sections collected from five challenging tissue types, including human lung, colon, small intestine, pediatric brain tumor, and mouse bone/cartilage. In total, we analyze 52 tissue sections and demonstrate that RRST is a versatile, powerful, and reproducible protocol for fresh frozen specimens of different qualities and origins.","","True","Visium","4332","32285"
"GSE221571_GSM6886507","mouse","brain","36720873","Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples","Spatially resolved transcriptomics has enabled precise genome-wide mRNA expression profiling within tissue sections. The performance of methods targeting the polyA tails of mRNA relies on the availability of specimens with high RNA quality. Moreover, the high cost of currently available spatial resolved transcriptomics assays requires a careful sample screening process to increase the chance of obtaining high-quality data. Indeed, the upfront analysis of RNA quality can show considerable variability due to sample handling, storage, and/or intrinsic factors. We present RNA-Rescue Spatial Transcriptomics (RRST), a workflow designed to improve mRNA recovery from fresh frozen specimens with moderate to low RNA quality. First, we provide a benchmark of RRST against the standard Visium spatial gene expression protocol on high RNA quality samples represented by mouse brain and prostate cancer samples. Then, we test the RRST protocol on tissue sections collected from five challenging tissue types, including human lung, colon, small intestine, pediatric brain tumor, and mouse bone/cartilage. In total, we analyze 52 tissue sections and demonstrate that RRST is a versatile, powerful, and reproducible protocol for fresh frozen specimens of different qualities and origins.","","True","Visium","4394","32285"
"GSE221571_GSM6886508","mouse","brain","36720873","Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples","Spatially resolved transcriptomics has enabled precise genome-wide mRNA expression profiling within tissue sections. The performance of methods targeting the polyA tails of mRNA relies on the availability of specimens with high RNA quality. Moreover, the high cost of currently available spatial resolved transcriptomics assays requires a careful sample screening process to increase the chance of obtaining high-quality data. Indeed, the upfront analysis of RNA quality can show considerable variability due to sample handling, storage, and/or intrinsic factors. We present RNA-Rescue Spatial Transcriptomics (RRST), a workflow designed to improve mRNA recovery from fresh frozen specimens with moderate to low RNA quality. First, we provide a benchmark of RRST against the standard Visium spatial gene expression protocol on high RNA quality samples represented by mouse brain and prostate cancer samples. Then, we test the RRST protocol on tissue sections collected from five challenging tissue types, including human lung, colon, small intestine, pediatric brain tumor, and mouse bone/cartilage. In total, we analyze 52 tissue sections and demonstrate that RRST is a versatile, powerful, and reproducible protocol for fresh frozen specimens of different qualities and origins.","","True","Visium","4114","32285"
"GSE222410_GSM6922983","mouse","brain","37925446","Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation","Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain in male mice. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.","","False","Visium","1997","32285"
"GSE222410_GSM6922984","mouse","brain","37925446","Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation","Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain in male mice. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.","","False","Visium","2666","32285"
"GSE222410_GSM6922985","mouse","brain","37925446","Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation","Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain in male mice. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.","","False","Visium","2694","32285"
"GSE222410_GSM6922986","mouse","brain","37925446","Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation","Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain in male mice. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.","","False","Visium","2711","32285"
"GSE222410_GSM6922987","mouse","brain","37925446","Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation","Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain in male mice. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.","","False","Visium","2631","32285"
"GSE222410_GSM6922988","mouse","brain","37925446","Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation","Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain in male mice. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.","","False","Visium","2494","32285"
"GSE222410_GSM6922989","mouse","brain","37925446","Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation","Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain in male mice. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.","","False","Visium","2997","32285"
"GSE222410_GSM6922990","mouse","brain","37925446","Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation","Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain in male mice. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.","","False","Visium","2306","32285"
"GSE222410_GSM6922991","mouse","brain","37925446","Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation","Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain in male mice. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.","","False","Visium","1967","32285"
"GSE222410_GSM6922992","mouse","brain","37925446","Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation","Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain in male mice. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.","","False","Visium","2072","32285"
"GSE222410_GSM6922993","mouse","brain","37925446","Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation","Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain in male mice. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.","","False","Visium","3103","32285"
"GSE222410_GSM6922994","mouse","brain","37925446","Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation","Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain in male mice. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.","","False","Visium","2378","32285"
"GSE222410_GSM6922995","mouse","brain","37925446","Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation","Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain in male mice. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.","","False","Visium","2958","32285"
"GSE222410_GSM6922996","mouse","brain","37925446","Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation","Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain in male mice. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.","","False","Visium","2085","32285"
"GSE222410_GSM6922997","mouse","brain","37925446","Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation","Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain in male mice. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.","","False","Visium","1736","32285"
"GSE222410_GSM6922998","mouse","brain","37925446","Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation","Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain in male mice. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.","","False","Visium","2634","32285"
"GSE222981_GSM6937699","mouse","brain","36819717","Moxibustion improves hypothalamus Aqp4 polarization in APP/PS1 mice: Evidence from spatial transcriptomics","Aquaporin-4 (AQP4) is highly polarized to perivascular astrocytic endfeet. Loss of AQP4 polarization is associated with many diseases. In Alzheimer's disease (AD), AQP4 loses its normal location and thus reduces the clearance of amyloid-β plaques and tau protein. Clinical and experimental studies showed that moxibustion can improve the learning and memory abilities of AD. To explore whether moxibustion can affect the polarization of AQP4 around the blood-brain barrier (BBB), we used spatial transcriptomics (ST) to analyze the expression and polarization of Aqp4 in wild-type mice, APP/PS1 mice, and APP/PS1 mice intervened by moxibustion. The results showed that moxibustion improved the loss of abnormal polarization of AQP4 in APP/PS1 mice, especially in the hypothalamic BBB. Besides, the other 31 genes with Aqp4 as the core have similar depolarization in APP/PS1 mice, most of which are also membrane proteins. The majority of them have been reversed by moxibustion. At the same time, we employed the cerebrospinal fluid circulation gene set, which was found to be at a higher level in the group of APP/PS1 mice with moxibustion treatment. Finally, to further explore its mechanism, we analyzed the mitochondrial respiratory chain complex enzymes closely related to energy metabolism and found that moxibustion can significantly increase the expression of mitochondrial respiratory chain enzymes such as Cox6a2 in the hypothalamus, which could provide energy for mRNA transport. Our research shows that increasing the polarization of hypothalamic Aqp4 through mitochondrial energy supply may be an important target for moxibustion to improve cognitive impairment in APP/PS1 mice.","Alzheimer’s disease; aquaporin-4; hypothalamus; mitochondrial respiratory chain; moxibustion; spatial transcriptomics.","False","Visium","2903","31053"
"GSE222981_GSM6937700","mouse","brain","36819717","Moxibustion improves hypothalamus Aqp4 polarization in APP/PS1 mice: Evidence from spatial transcriptomics","Aquaporin-4 (AQP4) is highly polarized to perivascular astrocytic endfeet. Loss of AQP4 polarization is associated with many diseases. In Alzheimer's disease (AD), AQP4 loses its normal location and thus reduces the clearance of amyloid-β plaques and tau protein. Clinical and experimental studies showed that moxibustion can improve the learning and memory abilities of AD. To explore whether moxibustion can affect the polarization of AQP4 around the blood-brain barrier (BBB), we used spatial transcriptomics (ST) to analyze the expression and polarization of Aqp4 in wild-type mice, APP/PS1 mice, and APP/PS1 mice intervened by moxibustion. The results showed that moxibustion improved the loss of abnormal polarization of AQP4 in APP/PS1 mice, especially in the hypothalamic BBB. Besides, the other 31 genes with Aqp4 as the core have similar depolarization in APP/PS1 mice, most of which are also membrane proteins. The majority of them have been reversed by moxibustion. At the same time, we employed the cerebrospinal fluid circulation gene set, which was found to be at a higher level in the group of APP/PS1 mice with moxibustion treatment. Finally, to further explore its mechanism, we analyzed the mitochondrial respiratory chain complex enzymes closely related to energy metabolism and found that moxibustion can significantly increase the expression of mitochondrial respiratory chain enzymes such as Cox6a2 in the hypothalamus, which could provide energy for mRNA transport. Our research shows that increasing the polarization of hypothalamic Aqp4 through mitochondrial energy supply may be an important target for moxibustion to improve cognitive impairment in APP/PS1 mice.","Alzheimer’s disease; aquaporin-4; hypothalamus; mitochondrial respiratory chain; moxibustion; spatial transcriptomics.","False","Visium","3023","31053"
"GSE222981_GSM6937701","mouse","brain","36819717","Moxibustion improves hypothalamus Aqp4 polarization in APP/PS1 mice: Evidence from spatial transcriptomics","Aquaporin-4 (AQP4) is highly polarized to perivascular astrocytic endfeet. Loss of AQP4 polarization is associated with many diseases. In Alzheimer's disease (AD), AQP4 loses its normal location and thus reduces the clearance of amyloid-β plaques and tau protein. Clinical and experimental studies showed that moxibustion can improve the learning and memory abilities of AD. To explore whether moxibustion can affect the polarization of AQP4 around the blood-brain barrier (BBB), we used spatial transcriptomics (ST) to analyze the expression and polarization of Aqp4 in wild-type mice, APP/PS1 mice, and APP/PS1 mice intervened by moxibustion. The results showed that moxibustion improved the loss of abnormal polarization of AQP4 in APP/PS1 mice, especially in the hypothalamic BBB. Besides, the other 31 genes with Aqp4 as the core have similar depolarization in APP/PS1 mice, most of which are also membrane proteins. The majority of them have been reversed by moxibustion. At the same time, we employed the cerebrospinal fluid circulation gene set, which was found to be at a higher level in the group of APP/PS1 mice with moxibustion treatment. Finally, to further explore its mechanism, we analyzed the mitochondrial respiratory chain complex enzymes closely related to energy metabolism and found that moxibustion can significantly increase the expression of mitochondrial respiratory chain enzymes such as Cox6a2 in the hypothalamus, which could provide energy for mRNA transport. Our research shows that increasing the polarization of hypothalamic Aqp4 through mitochondrial energy supply may be an important target for moxibustion to improve cognitive impairment in APP/PS1 mice.","Alzheimer’s disease; aquaporin-4; hypothalamus; mitochondrial respiratory chain; moxibustion; spatial transcriptomics.","False","Visium","2962","31053"
"GSE223066_GSM6939154","mouse","brain","37773230","Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation","Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.","","False","Visium","2547","32285"
"GSE223066_GSM6939155","mouse","brain","37773230","Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation","Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.","","False","Visium","2906","32285"
"GSE223066_GSM6939156","mouse","brain","37773230","Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation","Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.","","False","Visium","2176","32285"
"GSE223066_GSM6939157","mouse","brain","37773230","Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation","Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.","","False","Visium","2349","32285"
"GSE223066_GSM6939158","mouse","brain","37773230","Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation","Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.","","False","Visium","2744","32285"
"GSE223066_GSM6939159","mouse","brain","37773230","Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation","Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.","","False","Visium","2593","32285"
"GSE223066_GSM6939160","mouse","brain","37773230","Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation","Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.","","False","Visium","3065","32285"
"GSE223066_GSM6939161","mouse","brain","37773230","Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation","Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.","","False","Visium","2880","32285"
"GSE223066_GSM6939162","mouse","brain","37773230","Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation","Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.","","False","Visium","2663","32285"
"GSE223066_GSM6939163","mouse","brain","37773230","Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation","Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.","","False","Visium","2550","32285"
"GSE223066_GSM6939164","mouse","brain","37773230","Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation","Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.","","False","Visium","2663","32285"
"GSE223066_GSM6939165","mouse","brain","37773230","Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation","Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.","","False","Visium","2936","32285"
"GSE223066_GSM6939166","mouse","brain","37773230","Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation","Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.","","False","Visium","2838","32285"
"GSE223066_GSM6939167","mouse","brain","37773230","Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation","Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.","","False","Visium","2778","32285"
"GSE223559_GSM6963115","human","liver","","Multimodal decoding of human liver regeneration [st_human]","","","False","Visium","2650","33538"
"GSE223559_GSM6963116","human","liver","","Multimodal decoding of human liver regeneration [st_human]","","","False","Visium","2910","33538"
"GSE223559_GSM6963117","human","liver","","Multimodal decoding of human liver regeneration [st_human]","","","False","Visium","1776","33538"
"GSE223559_GSM6963119","human","liver","","Multimodal decoding of human liver regeneration [st_human]","","","False","Visium","1969","33538"
"GSE223559_GSM6963120","human","liver","","Multimodal decoding of human liver regeneration [st_human]","","","False","Visium","4350","33538"
"GSE223559_GSM6963121","human","liver","","Multimodal decoding of human liver regeneration [st_human]","","","False","Visium","2618","33538"
"GSE223559_GSM6963122","human","liver","","Multimodal decoding of human liver regeneration [st_human]","","","False","Visium","2462","33538"
"GSE223559_GSM6963123","human","liver","","Multimodal decoding of human liver regeneration [st_human]","","","False","Visium","3645","33538"
"GSE223560_GSM6963514","mouse","liver","","Multimodal decoding of human liver regeneration [st_mouse]","","","False","Visium","1951","31053"
"GSE223560_GSM6963515","mouse","liver","","Multimodal decoding of human liver regeneration [st_mouse]","","","False","Visium","2730","31053"
"GSE223560_GSM6963517","mouse","liver","","Multimodal decoding of human liver regeneration [st_mouse]","","","False","Visium","2432","31053"
"GSE223560_GSM6963518","mouse","liver","","Multimodal decoding of human liver regeneration [st_mouse]","","","False","Visium","2521","31053"
"GSE223560_GSM6963519","mouse","liver","","Multimodal decoding of human liver regeneration [st_mouse]","","","False","Visium","1649","31053"
"GSE223560_GSM6963520","mouse","liver","","Multimodal decoding of human liver regeneration [st_mouse]","","","False","Visium","1971","31053"
"GSE223560_GSM6963521","mouse","liver","","Multimodal decoding of human liver regeneration [st_mouse]","","","False","Visium","2036","31053"
"GSE223560_GSM6963523","mouse","liver","","Multimodal decoding of human liver regeneration [st_mouse]","","","False","Visium","3478","31053"
"GSE223560_GSM6963524","mouse","liver","","Multimodal decoding of human liver regeneration [st_mouse]","","","False","Visium","1491","31053"
"GSE223560_GSM6963525","mouse","liver","","Multimodal decoding of human liver regeneration [st_mouse]","","","False","Visium","2632","31053"
"GSE223560_GSM6963526","mouse","liver","","Multimodal decoding of human liver regeneration [st_mouse]","","","False","Visium","2432","31053"
"GSE224335_GSM7019835","human","ovary","","Visium spatial gene expression analyses of ovarian clear cell carcinoma (OCCC) [visium OCCC]","","","False","Visium","1514","36601"
"GSE224335_GSM7019836","human","ovary","","Visium spatial gene expression analyses of ovarian clear cell carcinoma (OCCC) [visium OCCC]","","","False","Visium","898","36601"
"GSE224360_GSM7020543","mouse","brain","37995223","Pregnancy-responsive pools of adult neural stem cells for transient neurogenesis in mothers","Adult neural stem cells (NSCs) contribute to lifelong brain plasticity. In the adult mouse ventricular-subventricular zone, NSCs are heterogeneous and, depending on their location in the niche, give rise to different subtypes of olfactory bulb (OB) interneurons. Here, we show that multiple regionally distinct NSCs, including domains that are usually quiescent, are recruited on different gestation days during pregnancy. Synchronized activation of these adult NSC pools generates transient waves of short-lived OB interneurons, especially in layers with less neurogenesis under homeostasis. Using spatial transcriptomics, we identified molecular markers of pregnancy-associated interneurons and showed that some subsets are temporarily needed for own pup recognition. Thus, pregnancy triggers transient yet behaviorally relevant neurogenesis, highlighting the physiological relevance of adult stem cell heterogeneity.","","False","Visium","2540","32285"
"GSE224360_GSM7020544","mouse","brain","37995223","Pregnancy-responsive pools of adult neural stem cells for transient neurogenesis in mothers","Adult neural stem cells (NSCs) contribute to lifelong brain plasticity. In the adult mouse ventricular-subventricular zone, NSCs are heterogeneous and, depending on their location in the niche, give rise to different subtypes of olfactory bulb (OB) interneurons. Here, we show that multiple regionally distinct NSCs, including domains that are usually quiescent, are recruited on different gestation days during pregnancy. Synchronized activation of these adult NSC pools generates transient waves of short-lived OB interneurons, especially in layers with less neurogenesis under homeostasis. Using spatial transcriptomics, we identified molecular markers of pregnancy-associated interneurons and showed that some subsets are temporarily needed for own pup recognition. Thus, pregnancy triggers transient yet behaviorally relevant neurogenesis, highlighting the physiological relevance of adult stem cell heterogeneity.","","False","Visium","2123","32285"
"GSE224360_GSM7020545","mouse","brain","37995223","Pregnancy-responsive pools of adult neural stem cells for transient neurogenesis in mothers","Adult neural stem cells (NSCs) contribute to lifelong brain plasticity. In the adult mouse ventricular-subventricular zone, NSCs are heterogeneous and, depending on their location in the niche, give rise to different subtypes of olfactory bulb (OB) interneurons. Here, we show that multiple regionally distinct NSCs, including domains that are usually quiescent, are recruited on different gestation days during pregnancy. Synchronized activation of these adult NSC pools generates transient waves of short-lived OB interneurons, especially in layers with less neurogenesis under homeostasis. Using spatial transcriptomics, we identified molecular markers of pregnancy-associated interneurons and showed that some subsets are temporarily needed for own pup recognition. Thus, pregnancy triggers transient yet behaviorally relevant neurogenesis, highlighting the physiological relevance of adult stem cell heterogeneity.","","False","Visium","2369","32285"
"GSE224360_GSM7020546","mouse","brain","37995223","Pregnancy-responsive pools of adult neural stem cells for transient neurogenesis in mothers","Adult neural stem cells (NSCs) contribute to lifelong brain plasticity. In the adult mouse ventricular-subventricular zone, NSCs are heterogeneous and, depending on their location in the niche, give rise to different subtypes of olfactory bulb (OB) interneurons. Here, we show that multiple regionally distinct NSCs, including domains that are usually quiescent, are recruited on different gestation days during pregnancy. Synchronized activation of these adult NSC pools generates transient waves of short-lived OB interneurons, especially in layers with less neurogenesis under homeostasis. Using spatial transcriptomics, we identified molecular markers of pregnancy-associated interneurons and showed that some subsets are temporarily needed for own pup recognition. Thus, pregnancy triggers transient yet behaviorally relevant neurogenesis, highlighting the physiological relevance of adult stem cell heterogeneity.","","False","Visium","2648","32285"
"GSE224411_GSM7021870","human","liver","37080163,37591207","Title 1: Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces. Title 2: Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces.","Abstract 1: Recent advances in spatial transcriptomics (STs) enable gene expression measurements from a tissue sample while retaining its spatial context. This technology enables unprecedented in situ resolution of the regulatory pathways that underlie the heterogeneity in the tumor as well as the tumor microenvironment (TME). The direct characterization of cellular co-localization with spatial technologies facilities quantification of the molecular changes resulting from direct cell-cell interaction, as it occurs in tumor-immune interactions. We present SpaceMarkers, a bioinformatics algorithm to infer molecular changes from cell-cell interactions from latent space analysis of ST data. We apply this approach to infer the molecular changes from tumor-immune interactions in Visium spatial transcriptomics data of metastasis, invasive and precursor lesions, and immunotherapy treatment. Further transfer learning in matched scRNA-seq data enabled further quantification of the specific cell types in which SpaceMarkers are enriched. Altogether, SpaceMarkers can identify the location and context-specific molecular interactions within the TME from ST data. Abstract 2:","Keywords 1: cell-cell interactions; latent space factorization; single-cell transcriptomics; spatial analysis; spatial transcriptomics; transfer learning; tumor microenvironment. Keywords 2:","True","Visium","3006","36601"
"GSE224411_GSM7021871","human","pancreas","37080163,37591207","Title 1: Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces. Title 2: Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces.","Abstract 1: Recent advances in spatial transcriptomics (STs) enable gene expression measurements from a tissue sample while retaining its spatial context. This technology enables unprecedented in situ resolution of the regulatory pathways that underlie the heterogeneity in the tumor as well as the tumor microenvironment (TME). The direct characterization of cellular co-localization with spatial technologies facilities quantification of the molecular changes resulting from direct cell-cell interaction, as it occurs in tumor-immune interactions. We present SpaceMarkers, a bioinformatics algorithm to infer molecular changes from cell-cell interactions from latent space analysis of ST data. We apply this approach to infer the molecular changes from tumor-immune interactions in Visium spatial transcriptomics data of metastasis, invasive and precursor lesions, and immunotherapy treatment. Further transfer learning in matched scRNA-seq data enabled further quantification of the specific cell types in which SpaceMarkers are enriched. Altogether, SpaceMarkers can identify the location and context-specific molecular interactions within the TME from ST data. Abstract 2:","Keywords 1: cell-cell interactions; latent space factorization; single-cell transcriptomics; spatial analysis; spatial transcriptomics; transfer learning; tumor microenvironment. Keywords 2:","True","Visium","1872","17943"
"GSE224411_GSM7021872","human","pancreas","37080163,37591207","Title 1: Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces. Title 2: Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces.","Abstract 1: Recent advances in spatial transcriptomics (STs) enable gene expression measurements from a tissue sample while retaining its spatial context. This technology enables unprecedented in situ resolution of the regulatory pathways that underlie the heterogeneity in the tumor as well as the tumor microenvironment (TME). The direct characterization of cellular co-localization with spatial technologies facilities quantification of the molecular changes resulting from direct cell-cell interaction, as it occurs in tumor-immune interactions. We present SpaceMarkers, a bioinformatics algorithm to infer molecular changes from cell-cell interactions from latent space analysis of ST data. We apply this approach to infer the molecular changes from tumor-immune interactions in Visium spatial transcriptomics data of metastasis, invasive and precursor lesions, and immunotherapy treatment. Further transfer learning in matched scRNA-seq data enabled further quantification of the specific cell types in which SpaceMarkers are enriched. Altogether, SpaceMarkers can identify the location and context-specific molecular interactions within the TME from ST data. Abstract 2:","Keywords 1: cell-cell interactions; latent space factorization; single-cell transcriptomics; spatial analysis; spatial transcriptomics; transfer learning; tumor microenvironment. Keywords 2:","True","Visium","1351","36601"
"GSE225766_GSM7055901","mouse","gastrocnemius","37410813","A cellular and molecular spatial atlas of dystrophic muscle","Asynchronous skeletal muscle degeneration/regeneration is a hallmark feature of Duchenne muscular dystrophy (DMD); however, traditional -omics technologies that lack spatial context make it difficult to study the biological mechanisms of how asynchronous regeneration contributes to disease progression. Here, using the severely dystrophic D2-mdx mouse model, we generated a high-resolution cellular and molecular spatial atlas of dystrophic muscle by integrating spatial transcriptomics and single-cell RNAseq datasets. Unbiased clustering revealed nonuniform distribution of unique cell populations throughout D2-mdx muscle that were associated with multiple regenerative timepoints, demonstrating that this model faithfully recapitulates the asynchronous regeneration observed in human DMD muscle. By probing spatiotemporal gene expression signatures, we found that propagation of inflammatory and fibrotic signals from locally damaged areas contributes to widespread pathology and that querying expression signatures within discrete microenvironments can identify targetable pathways for DMD therapy. Overall, this spatial atlas of dystrophic muscle provides a valuable resource for studying DMD disease biology and therapeutic target discovery.","Duchenne muscular dystrophy; asynchronous regeneration; skeletal muscle; spatial transcriptomics.","False","Visium","1493","31053"
"GSE225766_GSM7055902","mouse","gastrocnemius","37410813","A cellular and molecular spatial atlas of dystrophic muscle","Asynchronous skeletal muscle degeneration/regeneration is a hallmark feature of Duchenne muscular dystrophy (DMD); however, traditional -omics technologies that lack spatial context make it difficult to study the biological mechanisms of how asynchronous regeneration contributes to disease progression. Here, using the severely dystrophic D2-mdx mouse model, we generated a high-resolution cellular and molecular spatial atlas of dystrophic muscle by integrating spatial transcriptomics and single-cell RNAseq datasets. Unbiased clustering revealed nonuniform distribution of unique cell populations throughout D2-mdx muscle that were associated with multiple regenerative timepoints, demonstrating that this model faithfully recapitulates the asynchronous regeneration observed in human DMD muscle. By probing spatiotemporal gene expression signatures, we found that propagation of inflammatory and fibrotic signals from locally damaged areas contributes to widespread pathology and that querying expression signatures within discrete microenvironments can identify targetable pathways for DMD therapy. Overall, this spatial atlas of dystrophic muscle provides a valuable resource for studying DMD disease biology and therapeutic target discovery.","Duchenne muscular dystrophy; asynchronous regeneration; skeletal muscle; spatial transcriptomics.","False","Visium","1438","31053"
"GSE225766_GSM7055903","mouse","gastrocnemius","37410813","A cellular and molecular spatial atlas of dystrophic muscle","Asynchronous skeletal muscle degeneration/regeneration is a hallmark feature of Duchenne muscular dystrophy (DMD); however, traditional -omics technologies that lack spatial context make it difficult to study the biological mechanisms of how asynchronous regeneration contributes to disease progression. Here, using the severely dystrophic D2-mdx mouse model, we generated a high-resolution cellular and molecular spatial atlas of dystrophic muscle by integrating spatial transcriptomics and single-cell RNAseq datasets. Unbiased clustering revealed nonuniform distribution of unique cell populations throughout D2-mdx muscle that were associated with multiple regenerative timepoints, demonstrating that this model faithfully recapitulates the asynchronous regeneration observed in human DMD muscle. By probing spatiotemporal gene expression signatures, we found that propagation of inflammatory and fibrotic signals from locally damaged areas contributes to widespread pathology and that querying expression signatures within discrete microenvironments can identify targetable pathways for DMD therapy. Overall, this spatial atlas of dystrophic muscle provides a valuable resource for studying DMD disease biology and therapeutic target discovery.","Duchenne muscular dystrophy; asynchronous regeneration; skeletal muscle; spatial transcriptomics.","False","Visium","1064","31053"
"GSE225766_GSM7055904","mouse","gastrocnemius","37410813","A cellular and molecular spatial atlas of dystrophic muscle","Asynchronous skeletal muscle degeneration/regeneration is a hallmark feature of Duchenne muscular dystrophy (DMD); however, traditional -omics technologies that lack spatial context make it difficult to study the biological mechanisms of how asynchronous regeneration contributes to disease progression. Here, using the severely dystrophic D2-mdx mouse model, we generated a high-resolution cellular and molecular spatial atlas of dystrophic muscle by integrating spatial transcriptomics and single-cell RNAseq datasets. Unbiased clustering revealed nonuniform distribution of unique cell populations throughout D2-mdx muscle that were associated with multiple regenerative timepoints, demonstrating that this model faithfully recapitulates the asynchronous regeneration observed in human DMD muscle. By probing spatiotemporal gene expression signatures, we found that propagation of inflammatory and fibrotic signals from locally damaged areas contributes to widespread pathology and that querying expression signatures within discrete microenvironments can identify targetable pathways for DMD therapy. Overall, this spatial atlas of dystrophic muscle provides a valuable resource for studying DMD disease biology and therapeutic target discovery.","Duchenne muscular dystrophy; asynchronous regeneration; skeletal muscle; spatial transcriptomics.","False","Visium","959","31053"
"GSE225766_GSM7055905","mouse","gastrocnemius","37410813","A cellular and molecular spatial atlas of dystrophic muscle","Asynchronous skeletal muscle degeneration/regeneration is a hallmark feature of Duchenne muscular dystrophy (DMD); however, traditional -omics technologies that lack spatial context make it difficult to study the biological mechanisms of how asynchronous regeneration contributes to disease progression. Here, using the severely dystrophic D2-mdx mouse model, we generated a high-resolution cellular and molecular spatial atlas of dystrophic muscle by integrating spatial transcriptomics and single-cell RNAseq datasets. Unbiased clustering revealed nonuniform distribution of unique cell populations throughout D2-mdx muscle that were associated with multiple regenerative timepoints, demonstrating that this model faithfully recapitulates the asynchronous regeneration observed in human DMD muscle. By probing spatiotemporal gene expression signatures, we found that propagation of inflammatory and fibrotic signals from locally damaged areas contributes to widespread pathology and that querying expression signatures within discrete microenvironments can identify targetable pathways for DMD therapy. Overall, this spatial atlas of dystrophic muscle provides a valuable resource for studying DMD disease biology and therapeutic target discovery.","Duchenne muscular dystrophy; asynchronous regeneration; skeletal muscle; spatial transcriptomics.","False","Visium","1497","31053"
"GSE225766_GSM7055906","mouse","gastrocnemius","37410813","A cellular and molecular spatial atlas of dystrophic muscle","Asynchronous skeletal muscle degeneration/regeneration is a hallmark feature of Duchenne muscular dystrophy (DMD); however, traditional -omics technologies that lack spatial context make it difficult to study the biological mechanisms of how asynchronous regeneration contributes to disease progression. Here, using the severely dystrophic D2-mdx mouse model, we generated a high-resolution cellular and molecular spatial atlas of dystrophic muscle by integrating spatial transcriptomics and single-cell RNAseq datasets. Unbiased clustering revealed nonuniform distribution of unique cell populations throughout D2-mdx muscle that were associated with multiple regenerative timepoints, demonstrating that this model faithfully recapitulates the asynchronous regeneration observed in human DMD muscle. By probing spatiotemporal gene expression signatures, we found that propagation of inflammatory and fibrotic signals from locally damaged areas contributes to widespread pathology and that querying expression signatures within discrete microenvironments can identify targetable pathways for DMD therapy. Overall, this spatial atlas of dystrophic muscle provides a valuable resource for studying DMD disease biology and therapeutic target discovery.","Duchenne muscular dystrophy; asynchronous regeneration; skeletal muscle; spatial transcriptomics.","False","Visium","826","31053"
"GSE225766_GSM7055907","mouse","gastrocnemius","37410813","A cellular and molecular spatial atlas of dystrophic muscle","Asynchronous skeletal muscle degeneration/regeneration is a hallmark feature of Duchenne muscular dystrophy (DMD); however, traditional -omics technologies that lack spatial context make it difficult to study the biological mechanisms of how asynchronous regeneration contributes to disease progression. Here, using the severely dystrophic D2-mdx mouse model, we generated a high-resolution cellular and molecular spatial atlas of dystrophic muscle by integrating spatial transcriptomics and single-cell RNAseq datasets. Unbiased clustering revealed nonuniform distribution of unique cell populations throughout D2-mdx muscle that were associated with multiple regenerative timepoints, demonstrating that this model faithfully recapitulates the asynchronous regeneration observed in human DMD muscle. By probing spatiotemporal gene expression signatures, we found that propagation of inflammatory and fibrotic signals from locally damaged areas contributes to widespread pathology and that querying expression signatures within discrete microenvironments can identify targetable pathways for DMD therapy. Overall, this spatial atlas of dystrophic muscle provides a valuable resource for studying DMD disease biology and therapeutic target discovery.","Duchenne muscular dystrophy; asynchronous regeneration; skeletal muscle; spatial transcriptomics.","False","Visium","1155","31053"
"GSE225766_GSM7055908","mouse","gastrocnemius","37410813","A cellular and molecular spatial atlas of dystrophic muscle","Asynchronous skeletal muscle degeneration/regeneration is a hallmark feature of Duchenne muscular dystrophy (DMD); however, traditional -omics technologies that lack spatial context make it difficult to study the biological mechanisms of how asynchronous regeneration contributes to disease progression. Here, using the severely dystrophic D2-mdx mouse model, we generated a high-resolution cellular and molecular spatial atlas of dystrophic muscle by integrating spatial transcriptomics and single-cell RNAseq datasets. Unbiased clustering revealed nonuniform distribution of unique cell populations throughout D2-mdx muscle that were associated with multiple regenerative timepoints, demonstrating that this model faithfully recapitulates the asynchronous regeneration observed in human DMD muscle. By probing spatiotemporal gene expression signatures, we found that propagation of inflammatory and fibrotic signals from locally damaged areas contributes to widespread pathology and that querying expression signatures within discrete microenvironments can identify targetable pathways for DMD therapy. Overall, this spatial atlas of dystrophic muscle provides a valuable resource for studying DMD disease biology and therapeutic target discovery.","Duchenne muscular dystrophy; asynchronous regeneration; skeletal muscle; spatial transcriptomics.","False","Visium","1136","31053"
"GSE225766_GSM7230943","mouse","gastrocnemius","37410813","A cellular and molecular spatial atlas of dystrophic muscle","Asynchronous skeletal muscle degeneration/regeneration is a hallmark feature of Duchenne muscular dystrophy (DMD); however, traditional -omics technologies that lack spatial context make it difficult to study the biological mechanisms of how asynchronous regeneration contributes to disease progression. Here, using the severely dystrophic D2-mdx mouse model, we generated a high-resolution cellular and molecular spatial atlas of dystrophic muscle by integrating spatial transcriptomics and single-cell RNAseq datasets. Unbiased clustering revealed nonuniform distribution of unique cell populations throughout D2-mdx muscle that were associated with multiple regenerative timepoints, demonstrating that this model faithfully recapitulates the asynchronous regeneration observed in human DMD muscle. By probing spatiotemporal gene expression signatures, we found that propagation of inflammatory and fibrotic signals from locally damaged areas contributes to widespread pathology and that querying expression signatures within discrete microenvironments can identify targetable pathways for DMD therapy. Overall, this spatial atlas of dystrophic muscle provides a valuable resource for studying DMD disease biology and therapeutic target discovery.","Duchenne muscular dystrophy; asynchronous regeneration; skeletal muscle; spatial transcriptomics.","False","Visium","2516","31053"
"GSE225766_GSM7230944","mouse","gastrocnemius","37410813","A cellular and molecular spatial atlas of dystrophic muscle","Asynchronous skeletal muscle degeneration/regeneration is a hallmark feature of Duchenne muscular dystrophy (DMD); however, traditional -omics technologies that lack spatial context make it difficult to study the biological mechanisms of how asynchronous regeneration contributes to disease progression. Here, using the severely dystrophic D2-mdx mouse model, we generated a high-resolution cellular and molecular spatial atlas of dystrophic muscle by integrating spatial transcriptomics and single-cell RNAseq datasets. Unbiased clustering revealed nonuniform distribution of unique cell populations throughout D2-mdx muscle that were associated with multiple regenerative timepoints, demonstrating that this model faithfully recapitulates the asynchronous regeneration observed in human DMD muscle. By probing spatiotemporal gene expression signatures, we found that propagation of inflammatory and fibrotic signals from locally damaged areas contributes to widespread pathology and that querying expression signatures within discrete microenvironments can identify targetable pathways for DMD therapy. Overall, this spatial atlas of dystrophic muscle provides a valuable resource for studying DMD disease biology and therapeutic target discovery.","Duchenne muscular dystrophy; asynchronous regeneration; skeletal muscle; spatial transcriptomics.","False","Visium","1854","31053"
"GSE225766_GSM7230945","mouse","gastrocnemius","37410813","A cellular and molecular spatial atlas of dystrophic muscle","Asynchronous skeletal muscle degeneration/regeneration is a hallmark feature of Duchenne muscular dystrophy (DMD); however, traditional -omics technologies that lack spatial context make it difficult to study the biological mechanisms of how asynchronous regeneration contributes to disease progression. Here, using the severely dystrophic D2-mdx mouse model, we generated a high-resolution cellular and molecular spatial atlas of dystrophic muscle by integrating spatial transcriptomics and single-cell RNAseq datasets. Unbiased clustering revealed nonuniform distribution of unique cell populations throughout D2-mdx muscle that were associated with multiple regenerative timepoints, demonstrating that this model faithfully recapitulates the asynchronous regeneration observed in human DMD muscle. By probing spatiotemporal gene expression signatures, we found that propagation of inflammatory and fibrotic signals from locally damaged areas contributes to widespread pathology and that querying expression signatures within discrete microenvironments can identify targetable pathways for DMD therapy. Overall, this spatial atlas of dystrophic muscle provides a valuable resource for studying DMD disease biology and therapeutic target discovery.","Duchenne muscular dystrophy; asynchronous regeneration; skeletal muscle; spatial transcriptomics.","False","Visium","2285","31053"
"GSE226208_GSM7068162","mouse","brain","","Shared inflammatory glial cell signature after brain injury, revealed by spatial, temporal and cell-type-specific profiling of the murine cerebral cortex [spatial transcriptomics]","","","False","Visium","1991","27998"
"GSE226208_GSM7068163","mouse","brain","","Shared inflammatory glial cell signature after brain injury, revealed by spatial, temporal and cell-type-specific profiling of the murine cerebral cortex [spatial transcriptomics]","","","False","Visium","1740","27998"
"GSE226376_GSM7267862","mouse","skin","37328193","Tripterygium wilfordii derivative celastrol, a YAP inhibitor, has antifibrotic effects in systemic sclerosis","Objectives: Systemic sclerosis (SSc) is characterised by extensive tissue fibrosis maintained by mechanotranductive/proadhesive signalling. Drugs targeting this pathway are therefore of likely therapeutic benefit. The mechanosensitive transcriptional co-activator, yes activated protein-1 (YAP1), is activated in SSc fibroblasts. The terpenoid celastrol is a YAP1 inhibitor; however, if celastrol can alleviate SSc fibrosis is unknown. Moreover, the cell niches required for skin fibrosis are unknown.

Methods: Human dermal fibroblasts from healthy individuals and patients with diffuse cutaneous SSc were treated with or without transforming growth factor β1 (TGFβ1), with or without celastrol. Mice were subjected to the bleomycin-induced model of skin SSc, in the presence or absence of celastrol. Fibrosis was assessed using RNA Sequencing, real-time PCR, spatial transcriptomic analyses, Western blot, ELISA and histological analyses.

Results: In dermal fibroblasts, celastrol impaired the ability of TGFβ1 to induce an SSc-like pattern of gene expression, including that of cellular communication network factor 2, collagen I and TGFβ1. Celastrol alleviated the persistent fibrotic phenotype of dermal fibroblasts cultured from lesions of SSc patients. In the bleomycin-induced model of skin SSc, increased expression of genes associated with reticular fibroblast and hippo/YAP clusters was observed; conversely, celastrol inhibited these bleomycin-induced changes and blocked nuclear localisation of YAP.

Conclusions: Our data clarify niches within the skin activated in fibrosis and suggest that compounds, such as celastrol, that antagonise the YAP pathway may be potential treatments for SSc skin fibrosis.","Arthritis, Experimental; Fibroblasts; Scleroderma, Systemic; Therapeutics.","False","Visium","395","19465"
"GSE226376_GSM7267863","mouse","skin","37328193","Tripterygium wilfordii derivative celastrol, a YAP inhibitor, has antifibrotic effects in systemic sclerosis","Objectives: Systemic sclerosis (SSc) is characterised by extensive tissue fibrosis maintained by mechanotranductive/proadhesive signalling. Drugs targeting this pathway are therefore of likely therapeutic benefit. The mechanosensitive transcriptional co-activator, yes activated protein-1 (YAP1), is activated in SSc fibroblasts. The terpenoid celastrol is a YAP1 inhibitor; however, if celastrol can alleviate SSc fibrosis is unknown. Moreover, the cell niches required for skin fibrosis are unknown.

Methods: Human dermal fibroblasts from healthy individuals and patients with diffuse cutaneous SSc were treated with or without transforming growth factor β1 (TGFβ1), with or without celastrol. Mice were subjected to the bleomycin-induced model of skin SSc, in the presence or absence of celastrol. Fibrosis was assessed using RNA Sequencing, real-time PCR, spatial transcriptomic analyses, Western blot, ELISA and histological analyses.

Results: In dermal fibroblasts, celastrol impaired the ability of TGFβ1 to induce an SSc-like pattern of gene expression, including that of cellular communication network factor 2, collagen I and TGFβ1. Celastrol alleviated the persistent fibrotic phenotype of dermal fibroblasts cultured from lesions of SSc patients. In the bleomycin-induced model of skin SSc, increased expression of genes associated with reticular fibroblast and hippo/YAP clusters was observed; conversely, celastrol inhibited these bleomycin-induced changes and blocked nuclear localisation of YAP.

Conclusions: Our data clarify niches within the skin activated in fibrosis and suggest that compounds, such as celastrol, that antagonise the YAP pathway may be potential treatments for SSc skin fibrosis.","Arthritis, Experimental; Fibroblasts; Scleroderma, Systemic; Therapeutics.","False","Visium","598","19465"
"GSE226376_GSM7267864","mouse","skin","37328193","Tripterygium wilfordii derivative celastrol, a YAP inhibitor, has antifibrotic effects in systemic sclerosis","Objectives: Systemic sclerosis (SSc) is characterised by extensive tissue fibrosis maintained by mechanotranductive/proadhesive signalling. Drugs targeting this pathway are therefore of likely therapeutic benefit. The mechanosensitive transcriptional co-activator, yes activated protein-1 (YAP1), is activated in SSc fibroblasts. The terpenoid celastrol is a YAP1 inhibitor; however, if celastrol can alleviate SSc fibrosis is unknown. Moreover, the cell niches required for skin fibrosis are unknown.

Methods: Human dermal fibroblasts from healthy individuals and patients with diffuse cutaneous SSc were treated with or without transforming growth factor β1 (TGFβ1), with or without celastrol. Mice were subjected to the bleomycin-induced model of skin SSc, in the presence or absence of celastrol. Fibrosis was assessed using RNA Sequencing, real-time PCR, spatial transcriptomic analyses, Western blot, ELISA and histological analyses.

Results: In dermal fibroblasts, celastrol impaired the ability of TGFβ1 to induce an SSc-like pattern of gene expression, including that of cellular communication network factor 2, collagen I and TGFβ1. Celastrol alleviated the persistent fibrotic phenotype of dermal fibroblasts cultured from lesions of SSc patients. In the bleomycin-induced model of skin SSc, increased expression of genes associated with reticular fibroblast and hippo/YAP clusters was observed; conversely, celastrol inhibited these bleomycin-induced changes and blocked nuclear localisation of YAP.

Conclusions: Our data clarify niches within the skin activated in fibrosis and suggest that compounds, such as celastrol, that antagonise the YAP pathway may be potential treatments for SSc skin fibrosis.","Arthritis, Experimental; Fibroblasts; Scleroderma, Systemic; Therapeutics.","False","Visium","354","19465"
"GSE226376_GSM7267865","mouse","skin","37328193","Tripterygium wilfordii derivative celastrol, a YAP inhibitor, has antifibrotic effects in systemic sclerosis","Objectives: Systemic sclerosis (SSc) is characterised by extensive tissue fibrosis maintained by mechanotranductive/proadhesive signalling. Drugs targeting this pathway are therefore of likely therapeutic benefit. The mechanosensitive transcriptional co-activator, yes activated protein-1 (YAP1), is activated in SSc fibroblasts. The terpenoid celastrol is a YAP1 inhibitor; however, if celastrol can alleviate SSc fibrosis is unknown. Moreover, the cell niches required for skin fibrosis are unknown.

Methods: Human dermal fibroblasts from healthy individuals and patients with diffuse cutaneous SSc were treated with or without transforming growth factor β1 (TGFβ1), with or without celastrol. Mice were subjected to the bleomycin-induced model of skin SSc, in the presence or absence of celastrol. Fibrosis was assessed using RNA Sequencing, real-time PCR, spatial transcriptomic analyses, Western blot, ELISA and histological analyses.

Results: In dermal fibroblasts, celastrol impaired the ability of TGFβ1 to induce an SSc-like pattern of gene expression, including that of cellular communication network factor 2, collagen I and TGFβ1. Celastrol alleviated the persistent fibrotic phenotype of dermal fibroblasts cultured from lesions of SSc patients. In the bleomycin-induced model of skin SSc, increased expression of genes associated with reticular fibroblast and hippo/YAP clusters was observed; conversely, celastrol inhibited these bleomycin-induced changes and blocked nuclear localisation of YAP.

Conclusions: Our data clarify niches within the skin activated in fibrosis and suggest that compounds, such as celastrol, that antagonise the YAP pathway may be potential treatments for SSc skin fibrosis.","Arthritis, Experimental; Fibroblasts; Scleroderma, Systemic; Therapeutics.","False","Visium","406","19465"
"GSE226376_GSM7267866","mouse","skin","37328193","Tripterygium wilfordii derivative celastrol, a YAP inhibitor, has antifibrotic effects in systemic sclerosis","Objectives: Systemic sclerosis (SSc) is characterised by extensive tissue fibrosis maintained by mechanotranductive/proadhesive signalling. Drugs targeting this pathway are therefore of likely therapeutic benefit. The mechanosensitive transcriptional co-activator, yes activated protein-1 (YAP1), is activated in SSc fibroblasts. The terpenoid celastrol is a YAP1 inhibitor; however, if celastrol can alleviate SSc fibrosis is unknown. Moreover, the cell niches required for skin fibrosis are unknown.

Methods: Human dermal fibroblasts from healthy individuals and patients with diffuse cutaneous SSc were treated with or without transforming growth factor β1 (TGFβ1), with or without celastrol. Mice were subjected to the bleomycin-induced model of skin SSc, in the presence or absence of celastrol. Fibrosis was assessed using RNA Sequencing, real-time PCR, spatial transcriptomic analyses, Western blot, ELISA and histological analyses.

Results: In dermal fibroblasts, celastrol impaired the ability of TGFβ1 to induce an SSc-like pattern of gene expression, including that of cellular communication network factor 2, collagen I and TGFβ1. Celastrol alleviated the persistent fibrotic phenotype of dermal fibroblasts cultured from lesions of SSc patients. In the bleomycin-induced model of skin SSc, increased expression of genes associated with reticular fibroblast and hippo/YAP clusters was observed; conversely, celastrol inhibited these bleomycin-induced changes and blocked nuclear localisation of YAP.

Conclusions: Our data clarify niches within the skin activated in fibrosis and suggest that compounds, such as celastrol, that antagonise the YAP pathway may be potential treatments for SSc skin fibrosis.","Arthritis, Experimental; Fibroblasts; Scleroderma, Systemic; Therapeutics.","False","Visium","663","19465"
"GSE226376_GSM7267867","mouse","skin","37328193","Tripterygium wilfordii derivative celastrol, a YAP inhibitor, has antifibrotic effects in systemic sclerosis","Objectives: Systemic sclerosis (SSc) is characterised by extensive tissue fibrosis maintained by mechanotranductive/proadhesive signalling. Drugs targeting this pathway are therefore of likely therapeutic benefit. The mechanosensitive transcriptional co-activator, yes activated protein-1 (YAP1), is activated in SSc fibroblasts. The terpenoid celastrol is a YAP1 inhibitor; however, if celastrol can alleviate SSc fibrosis is unknown. Moreover, the cell niches required for skin fibrosis are unknown.

Methods: Human dermal fibroblasts from healthy individuals and patients with diffuse cutaneous SSc were treated with or without transforming growth factor β1 (TGFβ1), with or without celastrol. Mice were subjected to the bleomycin-induced model of skin SSc, in the presence or absence of celastrol. Fibrosis was assessed using RNA Sequencing, real-time PCR, spatial transcriptomic analyses, Western blot, ELISA and histological analyses.

Results: In dermal fibroblasts, celastrol impaired the ability of TGFβ1 to induce an SSc-like pattern of gene expression, including that of cellular communication network factor 2, collagen I and TGFβ1. Celastrol alleviated the persistent fibrotic phenotype of dermal fibroblasts cultured from lesions of SSc patients. In the bleomycin-induced model of skin SSc, increased expression of genes associated with reticular fibroblast and hippo/YAP clusters was observed; conversely, celastrol inhibited these bleomycin-induced changes and blocked nuclear localisation of YAP.

Conclusions: Our data clarify niches within the skin activated in fibrosis and suggest that compounds, such as celastrol, that antagonise the YAP pathway may be potential treatments for SSc skin fibrosis.","Arthritis, Experimental; Fibroblasts; Scleroderma, Systemic; Therapeutics.","False","Visium","552","19465"
"GSE226376_GSM7267869","mouse","skin","37328193","Tripterygium wilfordii derivative celastrol, a YAP inhibitor, has antifibrotic effects in systemic sclerosis","Objectives: Systemic sclerosis (SSc) is characterised by extensive tissue fibrosis maintained by mechanotranductive/proadhesive signalling. Drugs targeting this pathway are therefore of likely therapeutic benefit. The mechanosensitive transcriptional co-activator, yes activated protein-1 (YAP1), is activated in SSc fibroblasts. The terpenoid celastrol is a YAP1 inhibitor; however, if celastrol can alleviate SSc fibrosis is unknown. Moreover, the cell niches required for skin fibrosis are unknown.

Methods: Human dermal fibroblasts from healthy individuals and patients with diffuse cutaneous SSc were treated with or without transforming growth factor β1 (TGFβ1), with or without celastrol. Mice were subjected to the bleomycin-induced model of skin SSc, in the presence or absence of celastrol. Fibrosis was assessed using RNA Sequencing, real-time PCR, spatial transcriptomic analyses, Western blot, ELISA and histological analyses.

Results: In dermal fibroblasts, celastrol impaired the ability of TGFβ1 to induce an SSc-like pattern of gene expression, including that of cellular communication network factor 2, collagen I and TGFβ1. Celastrol alleviated the persistent fibrotic phenotype of dermal fibroblasts cultured from lesions of SSc patients. In the bleomycin-induced model of skin SSc, increased expression of genes associated with reticular fibroblast and hippo/YAP clusters was observed; conversely, celastrol inhibited these bleomycin-induced changes and blocked nuclear localisation of YAP.

Conclusions: Our data clarify niches within the skin activated in fibrosis and suggest that compounds, such as celastrol, that antagonise the YAP pathway may be potential treatments for SSc skin fibrosis.","Arthritis, Experimental; Fibroblasts; Scleroderma, Systemic; Therapeutics.","False","Visium","526","19465"
"GSE226376_GSM7267870","mouse","skin","37328193","Tripterygium wilfordii derivative celastrol, a YAP inhibitor, has antifibrotic effects in systemic sclerosis","Objectives: Systemic sclerosis (SSc) is characterised by extensive tissue fibrosis maintained by mechanotranductive/proadhesive signalling. Drugs targeting this pathway are therefore of likely therapeutic benefit. The mechanosensitive transcriptional co-activator, yes activated protein-1 (YAP1), is activated in SSc fibroblasts. The terpenoid celastrol is a YAP1 inhibitor; however, if celastrol can alleviate SSc fibrosis is unknown. Moreover, the cell niches required for skin fibrosis are unknown.

Methods: Human dermal fibroblasts from healthy individuals and patients with diffuse cutaneous SSc were treated with or without transforming growth factor β1 (TGFβ1), with or without celastrol. Mice were subjected to the bleomycin-induced model of skin SSc, in the presence or absence of celastrol. Fibrosis was assessed using RNA Sequencing, real-time PCR, spatial transcriptomic analyses, Western blot, ELISA and histological analyses.

Results: In dermal fibroblasts, celastrol impaired the ability of TGFβ1 to induce an SSc-like pattern of gene expression, including that of cellular communication network factor 2, collagen I and TGFβ1. Celastrol alleviated the persistent fibrotic phenotype of dermal fibroblasts cultured from lesions of SSc patients. In the bleomycin-induced model of skin SSc, increased expression of genes associated with reticular fibroblast and hippo/YAP clusters was observed; conversely, celastrol inhibited these bleomycin-induced changes and blocked nuclear localisation of YAP.

Conclusions: Our data clarify niches within the skin activated in fibrosis and suggest that compounds, such as celastrol, that antagonise the YAP pathway may be potential treatments for SSc skin fibrosis.","Arthritis, Experimental; Fibroblasts; Scleroderma, Systemic; Therapeutics.","False","Visium","624","19465"
"GSE226533_GSM7078497","human","kidney","","Human and Murine Kidneys Contain Transcriptionally and Spatially Orthologous Resident Macrophage Subpopulations in Acute Kidney Injury","","","False","Visium","4237","36601"
"GSE226533_GSM7078498","human","kidney","","Human and Murine Kidneys Contain Transcriptionally and Spatially Orthologous Resident Macrophage Subpopulations in Acute Kidney Injury","","","False","Visium","4059","36601"
"GSE226997_GSM7089855","human","colon","","Visium Spatial transcriptomics analysis of human primary colorectal cancer","","","True","Visium","4148","17944"
"GSE226997_GSM7089856","human","colon","","Visium Spatial transcriptomics analysis of human primary colorectal cancer","","","True","Visium","3737","17944"
"GSE226997_GSM7089857","human","colon","","Visium Spatial transcriptomics analysis of human primary colorectal cancer","","","True","Visium","4676","19144"
"GSE226997_GSM7089858","human","colon","","Visium Spatial transcriptomics analysis of human primary colorectal cancer","","","True","Visium","3535","17944"
"GSE227019_GSM7090083","human","ovary","36882687","Comparison of the Illumina NextSeq 2000 and GeneMind Genolab M sequencing platforms for spatial transcriptomics","Background: The Illumina sequencing systems demonstrate high efficiency and power and remain the most popular platforms. Platforms with similar throughput and quality profiles but lower costs are under intensive development. In this study, we compared two platforms Illumina NextSeq 2000 and GeneMind Genolab M for 10x Genomics Visium spatial transcriptomics.

Results: The performed comparison demonstrates that GeneMind Genolab M sequencing platform produces highly consistent with Illumina NextSeq 2000 sequencing results. Both platforms have similar performance in terms of sequencing quality and detection of UMI, spatial barcode, and probe sequence. Raw read mapping and following read counting produced highly comparable results that is confirmed by quality control metrics and strong correlation between expression profiles in the same tissue spots. Downstream analysis including dimension reduction and clustering demonstrated similar results, and differential gene expression analysis predominantly detected the same genes for both platforms.

Conclusions: GeneMind Genolab M instrument is similar to Illumina sequencing efficacy and is suitable for 10x Genomics Visium spatial transcriptomics.","10x Genomics Visium; GeneMind Genolab M; Illumina NextSeq 2000; Sequencing; Spatial transcriptomics.","False","Visium","2770","17943"
"GSE227019_GSM7090084","human","ovary","36882687","Comparison of the Illumina NextSeq 2000 and GeneMind Genolab M sequencing platforms for spatial transcriptomics","Background: The Illumina sequencing systems demonstrate high efficiency and power and remain the most popular platforms. Platforms with similar throughput and quality profiles but lower costs are under intensive development. In this study, we compared two platforms Illumina NextSeq 2000 and GeneMind Genolab M for 10x Genomics Visium spatial transcriptomics.

Results: The performed comparison demonstrates that GeneMind Genolab M sequencing platform produces highly consistent with Illumina NextSeq 2000 sequencing results. Both platforms have similar performance in terms of sequencing quality and detection of UMI, spatial barcode, and probe sequence. Raw read mapping and following read counting produced highly comparable results that is confirmed by quality control metrics and strong correlation between expression profiles in the same tissue spots. Downstream analysis including dimension reduction and clustering demonstrated similar results, and differential gene expression analysis predominantly detected the same genes for both platforms.

Conclusions: GeneMind Genolab M instrument is similar to Illumina sequencing efficacy and is suitable for 10x Genomics Visium spatial transcriptomics.","10x Genomics Visium; GeneMind Genolab M; Illumina NextSeq 2000; Sequencing; Spatial transcriptomics.","False","Visium","2558","17943"
"GSE227019_GSM7090085","human","ovary","36882687","Comparison of the Illumina NextSeq 2000 and GeneMind Genolab M sequencing platforms for spatial transcriptomics","Background: The Illumina sequencing systems demonstrate high efficiency and power and remain the most popular platforms. Platforms with similar throughput and quality profiles but lower costs are under intensive development. In this study, we compared two platforms Illumina NextSeq 2000 and GeneMind Genolab M for 10x Genomics Visium spatial transcriptomics.

Results: The performed comparison demonstrates that GeneMind Genolab M sequencing platform produces highly consistent with Illumina NextSeq 2000 sequencing results. Both platforms have similar performance in terms of sequencing quality and detection of UMI, spatial barcode, and probe sequence. Raw read mapping and following read counting produced highly comparable results that is confirmed by quality control metrics and strong correlation between expression profiles in the same tissue spots. Downstream analysis including dimension reduction and clustering demonstrated similar results, and differential gene expression analysis predominantly detected the same genes for both platforms.

Conclusions: GeneMind Genolab M instrument is similar to Illumina sequencing efficacy and is suitable for 10x Genomics Visium spatial transcriptomics.","10x Genomics Visium; GeneMind Genolab M; Illumina NextSeq 2000; Sequencing; Spatial transcriptomics.","False","Visium","1979","17943"
"GSE227019_GSM7090086","human","ovary","36882687","Comparison of the Illumina NextSeq 2000 and GeneMind Genolab M sequencing platforms for spatial transcriptomics","Background: The Illumina sequencing systems demonstrate high efficiency and power and remain the most popular platforms. Platforms with similar throughput and quality profiles but lower costs are under intensive development. In this study, we compared two platforms Illumina NextSeq 2000 and GeneMind Genolab M for 10x Genomics Visium spatial transcriptomics.

Results: The performed comparison demonstrates that GeneMind Genolab M sequencing platform produces highly consistent with Illumina NextSeq 2000 sequencing results. Both platforms have similar performance in terms of sequencing quality and detection of UMI, spatial barcode, and probe sequence. Raw read mapping and following read counting produced highly comparable results that is confirmed by quality control metrics and strong correlation between expression profiles in the same tissue spots. Downstream analysis including dimension reduction and clustering demonstrated similar results, and differential gene expression analysis predominantly detected the same genes for both platforms.

Conclusions: GeneMind Genolab M instrument is similar to Illumina sequencing efficacy and is suitable for 10x Genomics Visium spatial transcriptomics.","10x Genomics Visium; GeneMind Genolab M; Illumina NextSeq 2000; Sequencing; Spatial transcriptomics.","False","Visium","2584","17943"
"GSE227019_GSM7090087","human","ovary","36882687","Comparison of the Illumina NextSeq 2000 and GeneMind Genolab M sequencing platforms for spatial transcriptomics","Background: The Illumina sequencing systems demonstrate high efficiency and power and remain the most popular platforms. Platforms with similar throughput and quality profiles but lower costs are under intensive development. In this study, we compared two platforms Illumina NextSeq 2000 and GeneMind Genolab M for 10x Genomics Visium spatial transcriptomics.

Results: The performed comparison demonstrates that GeneMind Genolab M sequencing platform produces highly consistent with Illumina NextSeq 2000 sequencing results. Both platforms have similar performance in terms of sequencing quality and detection of UMI, spatial barcode, and probe sequence. Raw read mapping and following read counting produced highly comparable results that is confirmed by quality control metrics and strong correlation between expression profiles in the same tissue spots. Downstream analysis including dimension reduction and clustering demonstrated similar results, and differential gene expression analysis predominantly detected the same genes for both platforms.

Conclusions: GeneMind Genolab M instrument is similar to Illumina sequencing efficacy and is suitable for 10x Genomics Visium spatial transcriptomics.","10x Genomics Visium; GeneMind Genolab M; Illumina NextSeq 2000; Sequencing; Spatial transcriptomics.","False","Visium","2567","17943"
"GSE227019_GSM7090088","human","ovary","36882687","Comparison of the Illumina NextSeq 2000 and GeneMind Genolab M sequencing platforms for spatial transcriptomics","Background: The Illumina sequencing systems demonstrate high efficiency and power and remain the most popular platforms. Platforms with similar throughput and quality profiles but lower costs are under intensive development. In this study, we compared two platforms Illumina NextSeq 2000 and GeneMind Genolab M for 10x Genomics Visium spatial transcriptomics.

Results: The performed comparison demonstrates that GeneMind Genolab M sequencing platform produces highly consistent with Illumina NextSeq 2000 sequencing results. Both platforms have similar performance in terms of sequencing quality and detection of UMI, spatial barcode, and probe sequence. Raw read mapping and following read counting produced highly comparable results that is confirmed by quality control metrics and strong correlation between expression profiles in the same tissue spots. Downstream analysis including dimension reduction and clustering demonstrated similar results, and differential gene expression analysis predominantly detected the same genes for both platforms.

Conclusions: GeneMind Genolab M instrument is similar to Illumina sequencing efficacy and is suitable for 10x Genomics Visium spatial transcriptomics.","10x Genomics Visium; GeneMind Genolab M; Illumina NextSeq 2000; Sequencing; Spatial transcriptomics.","False","Visium","1977","17943"
"GSE227045_GSM7090416","mouse","kidney","38360882","High resolution spatial profiling of kidney injury and repair using RNA hybridization-based in situ sequencing","Emerging spatially resolved transcriptomics technologies allow for the measurement of gene expression in situ at cellular resolution. We apply direct RNA hybridization-based in situ sequencing (dRNA HybISS, Cartana part of 10xGenomics) to compare male and female healthy mouse kidneys and the male kidney injury and repair timecourse. A pre-selected panel of 200 genes is used to identify cell state dynamics patterns during injury and repair. We develop a new computational pipeline, CellScopes, for the rapid analysis, multi-omic integration and visualization of spatially resolved transcriptomic datasets. The resulting dataset allows us to resolve 13 kidney cell types within distinct kidney niches, dynamic alterations in cell state over the course of injury and repair and cell-cell interactions between leukocytes and kidney parenchyma. At late timepoints after injury, C3+ leukocytes are enriched near pro-inflammatory, failed-repair proximal tubule cells. Integration of snRNA-seq dataset from the same injury and repair samples also allows us to impute the spatial localization of genes not directly measured by dRNA HybISS.","","False","Visium","1815","32285"
"GSE228685_GSM7134911","human","lacrimal gland","37370820","Spatial Transcriptomics Identifies Expression Signatures Specific to Lacrimal Gland Adenoid Cystic Carcinoma Cells","Although primary tumors of the lacrimal gland are rare, adenoid cystic carcinoma (ACC) is the most common and lethal epithelial lacrimal gland malignancy. Traditional management of lacrimal gland adenoid cystic carcinoma (LGACC) involves the removal of the eye and surrounding socket contents, followed by chemoradiation. Even with this radical treatment, the 10-year survival rate for LGACC is 20% given the propensity for recurrence and metastasis. Due to the rarity of LGACC, its pathobiology is not well-understood, leading to difficulties in diagnosis, treatment, and effective management. Here, we integrate bulk RNA sequencing (RNA-seq) and spatial transcriptomics to identify a specific LGACC gene signature that can inform novel targeted therapies. Of the 3499 differentially expressed genes identified by bulk RNA-seq, the results of our spatial transcriptomic analysis reveal 15 upregulated and 12 downregulated genes that specifically arise from LGACC cells, whereas fibroblasts, reactive fibrotic tissue, and nervous and skeletal muscle account for the remaining bulk RNA-seq signature. In light of the analysis, we identified a transitional state cell or stem cell cluster. The results of the pathway analysis identified the upregulation of PI3K-Akt signaling, IL-17 signaling, and multiple other cancer pathways. This study provides insights into the molecular and cellular landscape of LGACC, which can inform new, targeted therapies to improve patient outcomes.","lacrimal gland adenoid cystic carcinoma; rare cancer; spatial transcriptomics; transcriptomic signature.","True","Visium","3584","17943"
"GSE228972_GSM7146857","human","joint","38396288","CD200+ fibroblasts form a pro-resolving mesenchymal network in arthritis","Fibroblasts are important regulators of inflammation, but whether fibroblasts change phenotype during resolution of inflammation is not clear. Here we use positron emission tomography to detect fibroblast activation protein (FAP) as a means to visualize fibroblast activation in vivo during inflammation in humans. While tracer accumulation is high in active arthritis, it decreases after tumor necrosis factor and interleukin-17A inhibition. Biopsy-based single-cell RNA-sequencing analyses in experimental arthritis show that FAP signal reduction reflects a phenotypic switch from pro-inflammatory MMP3+/IL6+ fibroblasts (high FAP internalization) to pro-resolving CD200+DKK3+ fibroblasts (low FAP internalization). Spatial transcriptomics of human joints indicates that pro-resolving niches of CD200+DKK3+ fibroblasts cluster with type 2 innate lymphoid cells, whereas MMP3+/IL6+ fibroblasts colocalize with inflammatory immune cells. CD200+DKK3+ fibroblasts stabilized the type 2 innate lymphoid cell phenotype and induced resolution of arthritis via CD200-CD200R1 signaling. Taken together, these data suggest a dynamic molecular regulation of the mesenchymal compartment during resolution of inflammation.","","True","Visium","1389","36601"
"GSE228972_GSM7146858","human","joint","38396288","CD200+ fibroblasts form a pro-resolving mesenchymal network in arthritis","Fibroblasts are important regulators of inflammation, but whether fibroblasts change phenotype during resolution of inflammation is not clear. Here we use positron emission tomography to detect fibroblast activation protein (FAP) as a means to visualize fibroblast activation in vivo during inflammation in humans. While tracer accumulation is high in active arthritis, it decreases after tumor necrosis factor and interleukin-17A inhibition. Biopsy-based single-cell RNA-sequencing analyses in experimental arthritis show that FAP signal reduction reflects a phenotypic switch from pro-inflammatory MMP3+/IL6+ fibroblasts (high FAP internalization) to pro-resolving CD200+DKK3+ fibroblasts (low FAP internalization). Spatial transcriptomics of human joints indicates that pro-resolving niches of CD200+DKK3+ fibroblasts cluster with type 2 innate lymphoid cells, whereas MMP3+/IL6+ fibroblasts colocalize with inflammatory immune cells. CD200+DKK3+ fibroblasts stabilized the type 2 innate lymphoid cell phenotype and induced resolution of arthritis via CD200-CD200R1 signaling. Taken together, these data suggest a dynamic molecular regulation of the mesenchymal compartment during resolution of inflammation.","","True","Visium","1368","36601"
"GSE228972_GSM7146859","human","joint","38396288","CD200+ fibroblasts form a pro-resolving mesenchymal network in arthritis","Fibroblasts are important regulators of inflammation, but whether fibroblasts change phenotype during resolution of inflammation is not clear. Here we use positron emission tomography to detect fibroblast activation protein (FAP) as a means to visualize fibroblast activation in vivo during inflammation in humans. While tracer accumulation is high in active arthritis, it decreases after tumor necrosis factor and interleukin-17A inhibition. Biopsy-based single-cell RNA-sequencing analyses in experimental arthritis show that FAP signal reduction reflects a phenotypic switch from pro-inflammatory MMP3+/IL6+ fibroblasts (high FAP internalization) to pro-resolving CD200+DKK3+ fibroblasts (low FAP internalization). Spatial transcriptomics of human joints indicates that pro-resolving niches of CD200+DKK3+ fibroblasts cluster with type 2 innate lymphoid cells, whereas MMP3+/IL6+ fibroblasts colocalize with inflammatory immune cells. CD200+DKK3+ fibroblasts stabilized the type 2 innate lymphoid cell phenotype and induced resolution of arthritis via CD200-CD200R1 signaling. Taken together, these data suggest a dynamic molecular regulation of the mesenchymal compartment during resolution of inflammation.","","True","Visium","927","36601"
"GSE228972_GSM7146860","human","joint","38396288","CD200+ fibroblasts form a pro-resolving mesenchymal network in arthritis","Fibroblasts are important regulators of inflammation, but whether fibroblasts change phenotype during resolution of inflammation is not clear. Here we use positron emission tomography to detect fibroblast activation protein (FAP) as a means to visualize fibroblast activation in vivo during inflammation in humans. While tracer accumulation is high in active arthritis, it decreases after tumor necrosis factor and interleukin-17A inhibition. Biopsy-based single-cell RNA-sequencing analyses in experimental arthritis show that FAP signal reduction reflects a phenotypic switch from pro-inflammatory MMP3+/IL6+ fibroblasts (high FAP internalization) to pro-resolving CD200+DKK3+ fibroblasts (low FAP internalization). Spatial transcriptomics of human joints indicates that pro-resolving niches of CD200+DKK3+ fibroblasts cluster with type 2 innate lymphoid cells, whereas MMP3+/IL6+ fibroblasts colocalize with inflammatory immune cells. CD200+DKK3+ fibroblasts stabilized the type 2 innate lymphoid cell phenotype and induced resolution of arthritis via CD200-CD200R1 signaling. Taken together, these data suggest a dynamic molecular regulation of the mesenchymal compartment during resolution of inflammation.","","True","Visium","899","36601"
"GSE229635_GSM7169587","fish","the main electric organ","","Spatial transcriptome of muscle sample from the main electric organ of electric eel","","","False","Visium","2677","23221"
"GSE229635_GSM7169588","fish","the main electric organ","","Spatial transcriptome of muscle sample from the main electric organ of electric eel","","","False","Visium","2862","23221"
"GSE229635_GSM7169589","fish","the main electric organ","","Spatial transcriptome of muscle sample from the main electric organ of electric eel","","","False","Visium","2778","23221"
"GSE232910_GSM7392310","mouse","brain","","Spatial Multimodal Analysis: MALDI-MSI and Spatial Transcriptomics within the same tissue section","","","False","Visium","2856","32285"
"GSE232910_GSM7392311","mouse","brain","","Spatial Multimodal Analysis: MALDI-MSI and Spatial Transcriptomics within the same tissue section","","","False","Visium","3002","32285"
"GSE232910_GSM7392312","mouse","brain","","Spatial Multimodal Analysis: MALDI-MSI and Spatial Transcriptomics within the same tissue section","","","False","Visium","3163","32285"
"GSE232910_GSM7392313","mouse","brain","","Spatial Multimodal Analysis: MALDI-MSI and Spatial Transcriptomics within the same tissue section","","","False","Visium","2913","32285"
"GSE232910_GSM7392314","mouse","brain","","Spatial Multimodal Analysis: MALDI-MSI and Spatial Transcriptomics within the same tissue section","","","False","Visium","2675","32285"
"GSE232910_GSM7392315","mouse","brain","","Spatial Multimodal Analysis: MALDI-MSI and Spatial Transcriptomics within the same tissue section","","","False","Visium","3120","32285"
"GSE232910_GSM7392316","mouse","brain","","Spatial Multimodal Analysis: MALDI-MSI and Spatial Transcriptomics within the same tissue section","","","False","Visium","2918","32285"
"GSE232910_GSM7392317","mouse","brain","","Spatial Multimodal Analysis: MALDI-MSI and Spatial Transcriptomics within the same tissue section","","","False","Visium","3017","32285"
"GSE232910_GSM7392318","mouse","brain","","Spatial Multimodal Analysis: MALDI-MSI and Spatial Transcriptomics within the same tissue section","","","False","Visium","2964","32285"
"GSE232910_GSM7392319","mouse","brain","","Spatial Multimodal Analysis: MALDI-MSI and Spatial Transcriptomics within the same tissue section","","","False","Visium","3357","32285"
"GSE232910_GSM7392320","mouse","brain","","Spatial Multimodal Analysis: MALDI-MSI and Spatial Transcriptomics within the same tissue section","","","False","Visium","3601","32285"
"GSE232910_GSM7392321","mouse","brain","","Spatial Multimodal Analysis: MALDI-MSI and Spatial Transcriptomics within the same tissue section","","","False","Visium","3504","32285"
"GSE232910_GSM7392322","mouse","brain","","Spatial Multimodal Analysis: MALDI-MSI and Spatial Transcriptomics within the same tissue section","","","False","Visium","3617","32285"
"GSE232910_GSM7392323","mouse","brain","","Spatial Multimodal Analysis: MALDI-MSI and Spatial Transcriptomics within the same tissue section","","","False","Visium","3116","32285"
"GSE232910_GSM7392324","human","brain","","Spatial Multimodal Analysis: MALDI-MSI and Spatial Transcriptomics within the same tissue section","","","False","Visium","3900","36601"
"GSE232910_GSM7392325","human","brain","","Spatial Multimodal Analysis: MALDI-MSI and Spatial Transcriptomics within the same tissue section","","","False","Visium","4968","17943"
"GSE232910_GSM7392326","human","brain","","Spatial Multimodal Analysis: MALDI-MSI and Spatial Transcriptomics within the same tissue section","","","False","Visium","4984","17943"
"GSE232910_GSM7392327","human","brain","","Spatial Multimodal Analysis: MALDI-MSI and Spatial Transcriptomics within the same tissue section","","","False","Visium","4868","17943"
"GSE232910_GSM7392328","human","brain","","Spatial Multimodal Analysis: MALDI-MSI and Spatial Transcriptomics within the same tissue section","","","False","Visium","4633","17943"
"GSE233254_GSM7421780","human","pancreas","37285225","Spatial Transcriptomics of Intraductal Papillary Mucinous Neoplasms of the Pancreas Identifies NKX6-2 as a Driver of Gastric Differentiation and Indolent Biological Potential","Intraductal papillary mucinous neoplasms (IPMN) of the pancreas are bona fide precursor lesions of pancreatic ductal adenocarcinoma (PDAC). The most common subtype of IPMNs harbors a gastric foveolar-type epithelium, and these low-grade mucinous neoplasms are harbingers of IPMNs with high-grade dysplasia and cancer. The molecular underpinning of gastric differentiation in IPMNs is unknown, although identifying drivers of this indolent phenotype might enable opportunities for intercepting progression to high-grade IPMN and cancer. We conducted spatial transcriptomics on a cohort of IPMNs, followed by orthogonal and cross-species validation studies, which established the transcription factor NKX6-2 as a key determinant of gastric cell identity in low-grade IPMNs. Loss of NKX6-2 expression is a consistent feature of IPMN progression, while reexpression of Nkx6-2 in murine IPMN lines recapitulates the aforementioned gastric transcriptional program and glandular morphology. Our study identifies NKX6-2 as a previously unknown transcription factor driving indolent gastric differentiation in IPMN pathogenesis.

Significance: Identification of the molecular features driving IPMN development and differentiation is critical to prevent cancer progression and enhance risk stratification. We used spatial profiling to characterize the epithelium and microenvironment of IPMN, which revealed a previously unknown link between NKX6-2 and gastric differentiation, the latter associated with indolent biological potential. See related commentary by Ben-Shmuel and Scherz-Shouval, p. 1768. This article is highlighted in the In This Issue feature, p. 1749.","","True","Visium","782","36601"
"GSE233254_GSM7421781","human","pancreas","37285225","Spatial Transcriptomics of Intraductal Papillary Mucinous Neoplasms of the Pancreas Identifies NKX6-2 as a Driver of Gastric Differentiation and Indolent Biological Potential","Intraductal papillary mucinous neoplasms (IPMN) of the pancreas are bona fide precursor lesions of pancreatic ductal adenocarcinoma (PDAC). The most common subtype of IPMNs harbors a gastric foveolar-type epithelium, and these low-grade mucinous neoplasms are harbingers of IPMNs with high-grade dysplasia and cancer. The molecular underpinning of gastric differentiation in IPMNs is unknown, although identifying drivers of this indolent phenotype might enable opportunities for intercepting progression to high-grade IPMN and cancer. We conducted spatial transcriptomics on a cohort of IPMNs, followed by orthogonal and cross-species validation studies, which established the transcription factor NKX6-2 as a key determinant of gastric cell identity in low-grade IPMNs. Loss of NKX6-2 expression is a consistent feature of IPMN progression, while reexpression of Nkx6-2 in murine IPMN lines recapitulates the aforementioned gastric transcriptional program and glandular morphology. Our study identifies NKX6-2 as a previously unknown transcription factor driving indolent gastric differentiation in IPMN pathogenesis.

Significance: Identification of the molecular features driving IPMN development and differentiation is critical to prevent cancer progression and enhance risk stratification. We used spatial profiling to characterize the epithelium and microenvironment of IPMN, which revealed a previously unknown link between NKX6-2 and gastric differentiation, the latter associated with indolent biological potential. See related commentary by Ben-Shmuel and Scherz-Shouval, p. 1768. This article is highlighted in the In This Issue feature, p. 1749.","","True","Visium","1371","36601"
"GSE233254_GSM7421782","human","pancreas","37285225","Spatial Transcriptomics of Intraductal Papillary Mucinous Neoplasms of the Pancreas Identifies NKX6-2 as a Driver of Gastric Differentiation and Indolent Biological Potential","Intraductal papillary mucinous neoplasms (IPMN) of the pancreas are bona fide precursor lesions of pancreatic ductal adenocarcinoma (PDAC). The most common subtype of IPMNs harbors a gastric foveolar-type epithelium, and these low-grade mucinous neoplasms are harbingers of IPMNs with high-grade dysplasia and cancer. The molecular underpinning of gastric differentiation in IPMNs is unknown, although identifying drivers of this indolent phenotype might enable opportunities for intercepting progression to high-grade IPMN and cancer. We conducted spatial transcriptomics on a cohort of IPMNs, followed by orthogonal and cross-species validation studies, which established the transcription factor NKX6-2 as a key determinant of gastric cell identity in low-grade IPMNs. Loss of NKX6-2 expression is a consistent feature of IPMN progression, while reexpression of Nkx6-2 in murine IPMN lines recapitulates the aforementioned gastric transcriptional program and glandular morphology. Our study identifies NKX6-2 as a previously unknown transcription factor driving indolent gastric differentiation in IPMN pathogenesis.

Significance: Identification of the molecular features driving IPMN development and differentiation is critical to prevent cancer progression and enhance risk stratification. We used spatial profiling to characterize the epithelium and microenvironment of IPMN, which revealed a previously unknown link between NKX6-2 and gastric differentiation, the latter associated with indolent biological potential. See related commentary by Ben-Shmuel and Scherz-Shouval, p. 1768. This article is highlighted in the In This Issue feature, p. 1749.","","True","Visium","778","36601"
"GSE233254_GSM7421783","human","pancreas","37285225","Spatial Transcriptomics of Intraductal Papillary Mucinous Neoplasms of the Pancreas Identifies NKX6-2 as a Driver of Gastric Differentiation and Indolent Biological Potential","Intraductal papillary mucinous neoplasms (IPMN) of the pancreas are bona fide precursor lesions of pancreatic ductal adenocarcinoma (PDAC). The most common subtype of IPMNs harbors a gastric foveolar-type epithelium, and these low-grade mucinous neoplasms are harbingers of IPMNs with high-grade dysplasia and cancer. The molecular underpinning of gastric differentiation in IPMNs is unknown, although identifying drivers of this indolent phenotype might enable opportunities for intercepting progression to high-grade IPMN and cancer. We conducted spatial transcriptomics on a cohort of IPMNs, followed by orthogonal and cross-species validation studies, which established the transcription factor NKX6-2 as a key determinant of gastric cell identity in low-grade IPMNs. Loss of NKX6-2 expression is a consistent feature of IPMN progression, while reexpression of Nkx6-2 in murine IPMN lines recapitulates the aforementioned gastric transcriptional program and glandular morphology. Our study identifies NKX6-2 as a previously unknown transcription factor driving indolent gastric differentiation in IPMN pathogenesis.

Significance: Identification of the molecular features driving IPMN development and differentiation is critical to prevent cancer progression and enhance risk stratification. We used spatial profiling to characterize the epithelium and microenvironment of IPMN, which revealed a previously unknown link between NKX6-2 and gastric differentiation, the latter associated with indolent biological potential. See related commentary by Ben-Shmuel and Scherz-Shouval, p. 1768. This article is highlighted in the In This Issue feature, p. 1749.","","True","Visium","839","36601"
"GSE233254_GSM7421784","human","pancreas","37285225","Spatial Transcriptomics of Intraductal Papillary Mucinous Neoplasms of the Pancreas Identifies NKX6-2 as a Driver of Gastric Differentiation and Indolent Biological Potential","Intraductal papillary mucinous neoplasms (IPMN) of the pancreas are bona fide precursor lesions of pancreatic ductal adenocarcinoma (PDAC). The most common subtype of IPMNs harbors a gastric foveolar-type epithelium, and these low-grade mucinous neoplasms are harbingers of IPMNs with high-grade dysplasia and cancer. The molecular underpinning of gastric differentiation in IPMNs is unknown, although identifying drivers of this indolent phenotype might enable opportunities for intercepting progression to high-grade IPMN and cancer. We conducted spatial transcriptomics on a cohort of IPMNs, followed by orthogonal and cross-species validation studies, which established the transcription factor NKX6-2 as a key determinant of gastric cell identity in low-grade IPMNs. Loss of NKX6-2 expression is a consistent feature of IPMN progression, while reexpression of Nkx6-2 in murine IPMN lines recapitulates the aforementioned gastric transcriptional program and glandular morphology. Our study identifies NKX6-2 as a previously unknown transcription factor driving indolent gastric differentiation in IPMN pathogenesis.

Significance: Identification of the molecular features driving IPMN development and differentiation is critical to prevent cancer progression and enhance risk stratification. We used spatial profiling to characterize the epithelium and microenvironment of IPMN, which revealed a previously unknown link between NKX6-2 and gastric differentiation, the latter associated with indolent biological potential. See related commentary by Ben-Shmuel and Scherz-Shouval, p. 1768. This article is highlighted in the In This Issue feature, p. 1749.","","True","Visium","700","36601"
"GSE233254_GSM7421785","human","pancreas","37285225","Spatial Transcriptomics of Intraductal Papillary Mucinous Neoplasms of the Pancreas Identifies NKX6-2 as a Driver of Gastric Differentiation and Indolent Biological Potential","Intraductal papillary mucinous neoplasms (IPMN) of the pancreas are bona fide precursor lesions of pancreatic ductal adenocarcinoma (PDAC). The most common subtype of IPMNs harbors a gastric foveolar-type epithelium, and these low-grade mucinous neoplasms are harbingers of IPMNs with high-grade dysplasia and cancer. The molecular underpinning of gastric differentiation in IPMNs is unknown, although identifying drivers of this indolent phenotype might enable opportunities for intercepting progression to high-grade IPMN and cancer. We conducted spatial transcriptomics on a cohort of IPMNs, followed by orthogonal and cross-species validation studies, which established the transcription factor NKX6-2 as a key determinant of gastric cell identity in low-grade IPMNs. Loss of NKX6-2 expression is a consistent feature of IPMN progression, while reexpression of Nkx6-2 in murine IPMN lines recapitulates the aforementioned gastric transcriptional program and glandular morphology. Our study identifies NKX6-2 as a previously unknown transcription factor driving indolent gastric differentiation in IPMN pathogenesis.

Significance: Identification of the molecular features driving IPMN development and differentiation is critical to prevent cancer progression and enhance risk stratification. We used spatial profiling to characterize the epithelium and microenvironment of IPMN, which revealed a previously unknown link between NKX6-2 and gastric differentiation, the latter associated with indolent biological potential. See related commentary by Ben-Shmuel and Scherz-Shouval, p. 1768. This article is highlighted in the In This Issue feature, p. 1749.","","True","Visium","885","36601"
"GSE233254_GSM7421786","human","pancreas","37285225","Spatial Transcriptomics of Intraductal Papillary Mucinous Neoplasms of the Pancreas Identifies NKX6-2 as a Driver of Gastric Differentiation and Indolent Biological Potential","Intraductal papillary mucinous neoplasms (IPMN) of the pancreas are bona fide precursor lesions of pancreatic ductal adenocarcinoma (PDAC). The most common subtype of IPMNs harbors a gastric foveolar-type epithelium, and these low-grade mucinous neoplasms are harbingers of IPMNs with high-grade dysplasia and cancer. The molecular underpinning of gastric differentiation in IPMNs is unknown, although identifying drivers of this indolent phenotype might enable opportunities for intercepting progression to high-grade IPMN and cancer. We conducted spatial transcriptomics on a cohort of IPMNs, followed by orthogonal and cross-species validation studies, which established the transcription factor NKX6-2 as a key determinant of gastric cell identity in low-grade IPMNs. Loss of NKX6-2 expression is a consistent feature of IPMN progression, while reexpression of Nkx6-2 in murine IPMN lines recapitulates the aforementioned gastric transcriptional program and glandular morphology. Our study identifies NKX6-2 as a previously unknown transcription factor driving indolent gastric differentiation in IPMN pathogenesis.

Significance: Identification of the molecular features driving IPMN development and differentiation is critical to prevent cancer progression and enhance risk stratification. We used spatial profiling to characterize the epithelium and microenvironment of IPMN, which revealed a previously unknown link between NKX6-2 and gastric differentiation, the latter associated with indolent biological potential. See related commentary by Ben-Shmuel and Scherz-Shouval, p. 1768. This article is highlighted in the In This Issue feature, p. 1749.","","True","Visium","862","36601"
"GSE233254_GSM7421787","human","pancreas","37285225","Spatial Transcriptomics of Intraductal Papillary Mucinous Neoplasms of the Pancreas Identifies NKX6-2 as a Driver of Gastric Differentiation and Indolent Biological Potential","Intraductal papillary mucinous neoplasms (IPMN) of the pancreas are bona fide precursor lesions of pancreatic ductal adenocarcinoma (PDAC). The most common subtype of IPMNs harbors a gastric foveolar-type epithelium, and these low-grade mucinous neoplasms are harbingers of IPMNs with high-grade dysplasia and cancer. The molecular underpinning of gastric differentiation in IPMNs is unknown, although identifying drivers of this indolent phenotype might enable opportunities for intercepting progression to high-grade IPMN and cancer. We conducted spatial transcriptomics on a cohort of IPMNs, followed by orthogonal and cross-species validation studies, which established the transcription factor NKX6-2 as a key determinant of gastric cell identity in low-grade IPMNs. Loss of NKX6-2 expression is a consistent feature of IPMN progression, while reexpression of Nkx6-2 in murine IPMN lines recapitulates the aforementioned gastric transcriptional program and glandular morphology. Our study identifies NKX6-2 as a previously unknown transcription factor driving indolent gastric differentiation in IPMN pathogenesis.

Significance: Identification of the molecular features driving IPMN development and differentiation is critical to prevent cancer progression and enhance risk stratification. We used spatial profiling to characterize the epithelium and microenvironment of IPMN, which revealed a previously unknown link between NKX6-2 and gastric differentiation, the latter associated with indolent biological potential. See related commentary by Ben-Shmuel and Scherz-Shouval, p. 1768. This article is highlighted in the In This Issue feature, p. 1749.","","True","Visium","1002","36601"
"GSE233254_GSM7421788","human","pancreas","37285225","Spatial Transcriptomics of Intraductal Papillary Mucinous Neoplasms of the Pancreas Identifies NKX6-2 as a Driver of Gastric Differentiation and Indolent Biological Potential","Intraductal papillary mucinous neoplasms (IPMN) of the pancreas are bona fide precursor lesions of pancreatic ductal adenocarcinoma (PDAC). The most common subtype of IPMNs harbors a gastric foveolar-type epithelium, and these low-grade mucinous neoplasms are harbingers of IPMNs with high-grade dysplasia and cancer. The molecular underpinning of gastric differentiation in IPMNs is unknown, although identifying drivers of this indolent phenotype might enable opportunities for intercepting progression to high-grade IPMN and cancer. We conducted spatial transcriptomics on a cohort of IPMNs, followed by orthogonal and cross-species validation studies, which established the transcription factor NKX6-2 as a key determinant of gastric cell identity in low-grade IPMNs. Loss of NKX6-2 expression is a consistent feature of IPMN progression, while reexpression of Nkx6-2 in murine IPMN lines recapitulates the aforementioned gastric transcriptional program and glandular morphology. Our study identifies NKX6-2 as a previously unknown transcription factor driving indolent gastric differentiation in IPMN pathogenesis.

Significance: Identification of the molecular features driving IPMN development and differentiation is critical to prevent cancer progression and enhance risk stratification. We used spatial profiling to characterize the epithelium and microenvironment of IPMN, which revealed a previously unknown link between NKX6-2 and gastric differentiation, the latter associated with indolent biological potential. See related commentary by Ben-Shmuel and Scherz-Shouval, p. 1768. This article is highlighted in the In This Issue feature, p. 1749.","","True","Visium","475","36601"
"GSE233254_GSM7421789","human","pancreas","37285225","Spatial Transcriptomics of Intraductal Papillary Mucinous Neoplasms of the Pancreas Identifies NKX6-2 as a Driver of Gastric Differentiation and Indolent Biological Potential","Intraductal papillary mucinous neoplasms (IPMN) of the pancreas are bona fide precursor lesions of pancreatic ductal adenocarcinoma (PDAC). The most common subtype of IPMNs harbors a gastric foveolar-type epithelium, and these low-grade mucinous neoplasms are harbingers of IPMNs with high-grade dysplasia and cancer. The molecular underpinning of gastric differentiation in IPMNs is unknown, although identifying drivers of this indolent phenotype might enable opportunities for intercepting progression to high-grade IPMN and cancer. We conducted spatial transcriptomics on a cohort of IPMNs, followed by orthogonal and cross-species validation studies, which established the transcription factor NKX6-2 as a key determinant of gastric cell identity in low-grade IPMNs. Loss of NKX6-2 expression is a consistent feature of IPMN progression, while reexpression of Nkx6-2 in murine IPMN lines recapitulates the aforementioned gastric transcriptional program and glandular morphology. Our study identifies NKX6-2 as a previously unknown transcription factor driving indolent gastric differentiation in IPMN pathogenesis.

Significance: Identification of the molecular features driving IPMN development and differentiation is critical to prevent cancer progression and enhance risk stratification. We used spatial profiling to characterize the epithelium and microenvironment of IPMN, which revealed a previously unknown link between NKX6-2 and gastric differentiation, the latter associated with indolent biological potential. See related commentary by Ben-Shmuel and Scherz-Shouval, p. 1768. This article is highlighted in the In This Issue feature, p. 1749.","","True","Visium","992","36601"
"GSE233254_GSM7421790","human","pancreas","37285225","Spatial Transcriptomics of Intraductal Papillary Mucinous Neoplasms of the Pancreas Identifies NKX6-2 as a Driver of Gastric Differentiation and Indolent Biological Potential","Intraductal papillary mucinous neoplasms (IPMN) of the pancreas are bona fide precursor lesions of pancreatic ductal adenocarcinoma (PDAC). The most common subtype of IPMNs harbors a gastric foveolar-type epithelium, and these low-grade mucinous neoplasms are harbingers of IPMNs with high-grade dysplasia and cancer. The molecular underpinning of gastric differentiation in IPMNs is unknown, although identifying drivers of this indolent phenotype might enable opportunities for intercepting progression to high-grade IPMN and cancer. We conducted spatial transcriptomics on a cohort of IPMNs, followed by orthogonal and cross-species validation studies, which established the transcription factor NKX6-2 as a key determinant of gastric cell identity in low-grade IPMNs. Loss of NKX6-2 expression is a consistent feature of IPMN progression, while reexpression of Nkx6-2 in murine IPMN lines recapitulates the aforementioned gastric transcriptional program and glandular morphology. Our study identifies NKX6-2 as a previously unknown transcription factor driving indolent gastric differentiation in IPMN pathogenesis.

Significance: Identification of the molecular features driving IPMN development and differentiation is critical to prevent cancer progression and enhance risk stratification. We used spatial profiling to characterize the epithelium and microenvironment of IPMN, which revealed a previously unknown link between NKX6-2 and gastric differentiation, the latter associated with indolent biological potential. See related commentary by Ben-Shmuel and Scherz-Shouval, p. 1768. This article is highlighted in the In This Issue feature, p. 1749.","","True","Visium","1684","36601"
"GSE233254_GSM7421791","human","pancreas","37285225","Spatial Transcriptomics of Intraductal Papillary Mucinous Neoplasms of the Pancreas Identifies NKX6-2 as a Driver of Gastric Differentiation and Indolent Biological Potential","Intraductal papillary mucinous neoplasms (IPMN) of the pancreas are bona fide precursor lesions of pancreatic ductal adenocarcinoma (PDAC). The most common subtype of IPMNs harbors a gastric foveolar-type epithelium, and these low-grade mucinous neoplasms are harbingers of IPMNs with high-grade dysplasia and cancer. The molecular underpinning of gastric differentiation in IPMNs is unknown, although identifying drivers of this indolent phenotype might enable opportunities for intercepting progression to high-grade IPMN and cancer. We conducted spatial transcriptomics on a cohort of IPMNs, followed by orthogonal and cross-species validation studies, which established the transcription factor NKX6-2 as a key determinant of gastric cell identity in low-grade IPMNs. Loss of NKX6-2 expression is a consistent feature of IPMN progression, while reexpression of Nkx6-2 in murine IPMN lines recapitulates the aforementioned gastric transcriptional program and glandular morphology. Our study identifies NKX6-2 as a previously unknown transcription factor driving indolent gastric differentiation in IPMN pathogenesis.

Significance: Identification of the molecular features driving IPMN development and differentiation is critical to prevent cancer progression and enhance risk stratification. We used spatial profiling to characterize the epithelium and microenvironment of IPMN, which revealed a previously unknown link between NKX6-2 and gastric differentiation, the latter associated with indolent biological potential. See related commentary by Ben-Shmuel and Scherz-Shouval, p. 1768. This article is highlighted in the In This Issue feature, p. 1749.","","True","Visium","879","36601"
"GSE233254_GSM7421792","human","pancreas","37285225","Spatial Transcriptomics of Intraductal Papillary Mucinous Neoplasms of the Pancreas Identifies NKX6-2 as a Driver of Gastric Differentiation and Indolent Biological Potential","Intraductal papillary mucinous neoplasms (IPMN) of the pancreas are bona fide precursor lesions of pancreatic ductal adenocarcinoma (PDAC). The most common subtype of IPMNs harbors a gastric foveolar-type epithelium, and these low-grade mucinous neoplasms are harbingers of IPMNs with high-grade dysplasia and cancer. The molecular underpinning of gastric differentiation in IPMNs is unknown, although identifying drivers of this indolent phenotype might enable opportunities for intercepting progression to high-grade IPMN and cancer. We conducted spatial transcriptomics on a cohort of IPMNs, followed by orthogonal and cross-species validation studies, which established the transcription factor NKX6-2 as a key determinant of gastric cell identity in low-grade IPMNs. Loss of NKX6-2 expression is a consistent feature of IPMN progression, while reexpression of Nkx6-2 in murine IPMN lines recapitulates the aforementioned gastric transcriptional program and glandular morphology. Our study identifies NKX6-2 as a previously unknown transcription factor driving indolent gastric differentiation in IPMN pathogenesis.

Significance: Identification of the molecular features driving IPMN development and differentiation is critical to prevent cancer progression and enhance risk stratification. We used spatial profiling to characterize the epithelium and microenvironment of IPMN, which revealed a previously unknown link between NKX6-2 and gastric differentiation, the latter associated with indolent biological potential. See related commentary by Ben-Shmuel and Scherz-Shouval, p. 1768. This article is highlighted in the In This Issue feature, p. 1749.","","True","Visium","996","36601"
"GSE233254_GSM7422460","mouse","pancreas","37285225","Spatial Transcriptomics of Intraductal Papillary Mucinous Neoplasms of the Pancreas Identifies NKX6-2 as a Driver of Gastric Differentiation and Indolent Biological Potential","Intraductal papillary mucinous neoplasms (IPMN) of the pancreas are bona fide precursor lesions of pancreatic ductal adenocarcinoma (PDAC). The most common subtype of IPMNs harbors a gastric foveolar-type epithelium, and these low-grade mucinous neoplasms are harbingers of IPMNs with high-grade dysplasia and cancer. The molecular underpinning of gastric differentiation in IPMNs is unknown, although identifying drivers of this indolent phenotype might enable opportunities for intercepting progression to high-grade IPMN and cancer. We conducted spatial transcriptomics on a cohort of IPMNs, followed by orthogonal and cross-species validation studies, which established the transcription factor NKX6-2 as a key determinant of gastric cell identity in low-grade IPMNs. Loss of NKX6-2 expression is a consistent feature of IPMN progression, while reexpression of Nkx6-2 in murine IPMN lines recapitulates the aforementioned gastric transcriptional program and glandular morphology. Our study identifies NKX6-2 as a previously unknown transcription factor driving indolent gastric differentiation in IPMN pathogenesis.

Significance: Identification of the molecular features driving IPMN development and differentiation is critical to prevent cancer progression and enhance risk stratification. We used spatial profiling to characterize the epithelium and microenvironment of IPMN, which revealed a previously unknown link between NKX6-2 and gastric differentiation, the latter associated with indolent biological potential. See related commentary by Ben-Shmuel and Scherz-Shouval, p. 1768. This article is highlighted in the In This Issue feature, p. 1749.","","True","Visium","1692","32285"
"GSE233254_GSM7422461","mouse","pancreas","37285225","Spatial Transcriptomics of Intraductal Papillary Mucinous Neoplasms of the Pancreas Identifies NKX6-2 as a Driver of Gastric Differentiation and Indolent Biological Potential","Intraductal papillary mucinous neoplasms (IPMN) of the pancreas are bona fide precursor lesions of pancreatic ductal adenocarcinoma (PDAC). The most common subtype of IPMNs harbors a gastric foveolar-type epithelium, and these low-grade mucinous neoplasms are harbingers of IPMNs with high-grade dysplasia and cancer. The molecular underpinning of gastric differentiation in IPMNs is unknown, although identifying drivers of this indolent phenotype might enable opportunities for intercepting progression to high-grade IPMN and cancer. We conducted spatial transcriptomics on a cohort of IPMNs, followed by orthogonal and cross-species validation studies, which established the transcription factor NKX6-2 as a key determinant of gastric cell identity in low-grade IPMNs. Loss of NKX6-2 expression is a consistent feature of IPMN progression, while reexpression of Nkx6-2 in murine IPMN lines recapitulates the aforementioned gastric transcriptional program and glandular morphology. Our study identifies NKX6-2 as a previously unknown transcription factor driving indolent gastric differentiation in IPMN pathogenesis.

Significance: Identification of the molecular features driving IPMN development and differentiation is critical to prevent cancer progression and enhance risk stratification. We used spatial profiling to characterize the epithelium and microenvironment of IPMN, which revealed a previously unknown link between NKX6-2 and gastric differentiation, the latter associated with indolent biological potential. See related commentary by Ben-Shmuel and Scherz-Shouval, p. 1768. This article is highlighted in the In This Issue feature, p. 1749.","","True","Visium","2942","32285"
"GSE233512_GSM7429786","mouse","kidney","38017015","Single cell and spatial transcriptomics analysis of kidney double negative T lymphocytes in normal and ischemic mouse kidneys","T cells are important in the pathogenesis of acute kidney injury (AKI), and TCR+CD4-CD8- (double negative-DN) are T cells that have regulatory properties. However, there is limited information on DN T cells compared to traditional CD4+ and CD8+ cells. To elucidate the molecular signature and spatial dynamics of DN T cells during AKI, we performed single-cell RNA sequencing (scRNA-seq) on sorted murine DN, CD4+, and CD8+ cells combined with spatial transcriptomic profiling of normal and post AKI mouse kidneys. scRNA-seq revealed distinct transcriptional profiles for DN, CD4+, and CD8+ T cells of mouse kidneys with enrichment of Kcnq5, Klrb1c, Fcer1g, and Klre1 expression in DN T cells compared to CD4+ and CD8+ T cells in normal kidney tissue. We validated the expression of these four genes in mouse kidney DN, CD4+ and CD8+ T cells using RT-PCR and Kcnq5, Klrb1, and Fcer1g genes with the NIH human kidney precision medicine project (KPMP). Spatial transcriptomics in normal and ischemic mouse kidney tissue showed a localized cluster of T cells in the outer medulla expressing DN T cell genes including Fcer1g. These results provide a template for future studies in DN T as well as CD4+ and CD8+ cells in normal and diseased kidneys.","","False","Visium","2616","32285"
"GSE233512_GSM7429787","mouse","kidney","38017015","Single cell and spatial transcriptomics analysis of kidney double negative T lymphocytes in normal and ischemic mouse kidneys","T cells are important in the pathogenesis of acute kidney injury (AKI), and TCR+CD4-CD8- (double negative-DN) are T cells that have regulatory properties. However, there is limited information on DN T cells compared to traditional CD4+ and CD8+ cells. To elucidate the molecular signature and spatial dynamics of DN T cells during AKI, we performed single-cell RNA sequencing (scRNA-seq) on sorted murine DN, CD4+, and CD8+ cells combined with spatial transcriptomic profiling of normal and post AKI mouse kidneys. scRNA-seq revealed distinct transcriptional profiles for DN, CD4+, and CD8+ T cells of mouse kidneys with enrichment of Kcnq5, Klrb1c, Fcer1g, and Klre1 expression in DN T cells compared to CD4+ and CD8+ T cells in normal kidney tissue. We validated the expression of these four genes in mouse kidney DN, CD4+ and CD8+ T cells using RT-PCR and Kcnq5, Klrb1, and Fcer1g genes with the NIH human kidney precision medicine project (KPMP). Spatial transcriptomics in normal and ischemic mouse kidney tissue showed a localized cluster of T cells in the outer medulla expressing DN T cell genes including Fcer1g. These results provide a template for future studies in DN T as well as CD4+ and CD8+ cells in normal and diseased kidneys.","","False","Visium","2682","32285"
"GSE233512_GSM7429788","mouse","kidney","38017015","Single cell and spatial transcriptomics analysis of kidney double negative T lymphocytes in normal and ischemic mouse kidneys","T cells are important in the pathogenesis of acute kidney injury (AKI), and TCR+CD4-CD8- (double negative-DN) are T cells that have regulatory properties. However, there is limited information on DN T cells compared to traditional CD4+ and CD8+ cells. To elucidate the molecular signature and spatial dynamics of DN T cells during AKI, we performed single-cell RNA sequencing (scRNA-seq) on sorted murine DN, CD4+, and CD8+ cells combined with spatial transcriptomic profiling of normal and post AKI mouse kidneys. scRNA-seq revealed distinct transcriptional profiles for DN, CD4+, and CD8+ T cells of mouse kidneys with enrichment of Kcnq5, Klrb1c, Fcer1g, and Klre1 expression in DN T cells compared to CD4+ and CD8+ T cells in normal kidney tissue. We validated the expression of these four genes in mouse kidney DN, CD4+ and CD8+ T cells using RT-PCR and Kcnq5, Klrb1, and Fcer1g genes with the NIH human kidney precision medicine project (KPMP). Spatial transcriptomics in normal and ischemic mouse kidney tissue showed a localized cluster of T cells in the outer medulla expressing DN T cell genes including Fcer1g. These results provide a template for future studies in DN T as well as CD4+ and CD8+ cells in normal and diseased kidneys.","","False","Visium","2401","32285"
"GSE233512_GSM7429789","mouse","kidney","38017015","Single cell and spatial transcriptomics analysis of kidney double negative T lymphocytes in normal and ischemic mouse kidneys","T cells are important in the pathogenesis of acute kidney injury (AKI), and TCR+CD4-CD8- (double negative-DN) are T cells that have regulatory properties. However, there is limited information on DN T cells compared to traditional CD4+ and CD8+ cells. To elucidate the molecular signature and spatial dynamics of DN T cells during AKI, we performed single-cell RNA sequencing (scRNA-seq) on sorted murine DN, CD4+, and CD8+ cells combined with spatial transcriptomic profiling of normal and post AKI mouse kidneys. scRNA-seq revealed distinct transcriptional profiles for DN, CD4+, and CD8+ T cells of mouse kidneys with enrichment of Kcnq5, Klrb1c, Fcer1g, and Klre1 expression in DN T cells compared to CD4+ and CD8+ T cells in normal kidney tissue. We validated the expression of these four genes in mouse kidney DN, CD4+ and CD8+ T cells using RT-PCR and Kcnq5, Klrb1, and Fcer1g genes with the NIH human kidney precision medicine project (KPMP). Spatial transcriptomics in normal and ischemic mouse kidney tissue showed a localized cluster of T cells in the outer medulla expressing DN T cell genes including Fcer1g. These results provide a template for future studies in DN T as well as CD4+ and CD8+ cells in normal and diseased kidneys.","","False","Visium","2613","32285"
"GSE235672_GSM7507311","human","brain","37460871","Siglec-9 acts as an immune-checkpoint molecule on macrophages in glioblastoma, restricting T-cell priming and immunotherapy response","Neoadjuvant immune-checkpoint blockade therapy only benefits a limited fraction of patients with glioblastoma multiforme (GBM). Thus, targeting other immunomodulators on myeloid cells is an attractive therapeutic option. Here, we performed single-cell RNA sequencing and spatial transcriptomics of patients with GBM treated with neoadjuvant anti-PD-1 therapy. We identified unique monocyte-derived tumor-associated macrophage subpopulations with functional plasticity that highly expressed the immunosuppressive SIGLEC9 gene and preferentially accumulated in the nonresponders to anti-PD-1 treatment. Deletion of Siglece (murine homolog) resulted in dramatically restrained tumor development and prolonged survival in mouse models. Mechanistically, targeting Siglece directly activated both CD4+ T cells and CD8+ T cells through antigen presentation, secreted chemokines and co-stimulatory factor interactions. Furthermore, Siglece deletion synergized with anti-PD-1/PD-L1 treatment to improve antitumor efficacy. Our data demonstrated that Siglec-9 is an immune-checkpoint molecule on macrophages that can be targeted to enhance anti-PD-1/PD-L1 therapeutic efficacy for GBM treatment.","","True","Visium","2587","33538"
"GSE235672_GSM7507312","human","brain","37460871","Siglec-9 acts as an immune-checkpoint molecule on macrophages in glioblastoma, restricting T-cell priming and immunotherapy response","Neoadjuvant immune-checkpoint blockade therapy only benefits a limited fraction of patients with glioblastoma multiforme (GBM). Thus, targeting other immunomodulators on myeloid cells is an attractive therapeutic option. Here, we performed single-cell RNA sequencing and spatial transcriptomics of patients with GBM treated with neoadjuvant anti-PD-1 therapy. We identified unique monocyte-derived tumor-associated macrophage subpopulations with functional plasticity that highly expressed the immunosuppressive SIGLEC9 gene and preferentially accumulated in the nonresponders to anti-PD-1 treatment. Deletion of Siglece (murine homolog) resulted in dramatically restrained tumor development and prolonged survival in mouse models. Mechanistically, targeting Siglece directly activated both CD4+ T cells and CD8+ T cells through antigen presentation, secreted chemokines and co-stimulatory factor interactions. Furthermore, Siglece deletion synergized with anti-PD-1/PD-L1 treatment to improve antitumor efficacy. Our data demonstrated that Siglec-9 is an immune-checkpoint molecule on macrophages that can be targeted to enhance anti-PD-1/PD-L1 therapeutic efficacy for GBM treatment.","","True","Visium","1883","33538"
"GSE235672_GSM7507313","human","brain","37460871","Siglec-9 acts as an immune-checkpoint molecule on macrophages in glioblastoma, restricting T-cell priming and immunotherapy response","Neoadjuvant immune-checkpoint blockade therapy only benefits a limited fraction of patients with glioblastoma multiforme (GBM). Thus, targeting other immunomodulators on myeloid cells is an attractive therapeutic option. Here, we performed single-cell RNA sequencing and spatial transcriptomics of patients with GBM treated with neoadjuvant anti-PD-1 therapy. We identified unique monocyte-derived tumor-associated macrophage subpopulations with functional plasticity that highly expressed the immunosuppressive SIGLEC9 gene and preferentially accumulated in the nonresponders to anti-PD-1 treatment. Deletion of Siglece (murine homolog) resulted in dramatically restrained tumor development and prolonged survival in mouse models. Mechanistically, targeting Siglece directly activated both CD4+ T cells and CD8+ T cells through antigen presentation, secreted chemokines and co-stimulatory factor interactions. Furthermore, Siglece deletion synergized with anti-PD-1/PD-L1 treatment to improve antitumor efficacy. Our data demonstrated that Siglec-9 is an immune-checkpoint molecule on macrophages that can be targeted to enhance anti-PD-1/PD-L1 therapeutic efficacy for GBM treatment.","","True","Visium","1876","33538"
"GSE235672_GSM7507314","human","brain","37460871","Siglec-9 acts as an immune-checkpoint molecule on macrophages in glioblastoma, restricting T-cell priming and immunotherapy response","Neoadjuvant immune-checkpoint blockade therapy only benefits a limited fraction of patients with glioblastoma multiforme (GBM). Thus, targeting other immunomodulators on myeloid cells is an attractive therapeutic option. Here, we performed single-cell RNA sequencing and spatial transcriptomics of patients with GBM treated with neoadjuvant anti-PD-1 therapy. We identified unique monocyte-derived tumor-associated macrophage subpopulations with functional plasticity that highly expressed the immunosuppressive SIGLEC9 gene and preferentially accumulated in the nonresponders to anti-PD-1 treatment. Deletion of Siglece (murine homolog) resulted in dramatically restrained tumor development and prolonged survival in mouse models. Mechanistically, targeting Siglece directly activated both CD4+ T cells and CD8+ T cells through antigen presentation, secreted chemokines and co-stimulatory factor interactions. Furthermore, Siglece deletion synergized with anti-PD-1/PD-L1 treatment to improve antitumor efficacy. Our data demonstrated that Siglec-9 is an immune-checkpoint molecule on macrophages that can be targeted to enhance anti-PD-1/PD-L1 therapeutic efficacy for GBM treatment.","","True","Visium","2963","33538"
"GSE235672_GSM7507315","human","brain","37460871","Siglec-9 acts as an immune-checkpoint molecule on macrophages in glioblastoma, restricting T-cell priming and immunotherapy response","Neoadjuvant immune-checkpoint blockade therapy only benefits a limited fraction of patients with glioblastoma multiforme (GBM). Thus, targeting other immunomodulators on myeloid cells is an attractive therapeutic option. Here, we performed single-cell RNA sequencing and spatial transcriptomics of patients with GBM treated with neoadjuvant anti-PD-1 therapy. We identified unique monocyte-derived tumor-associated macrophage subpopulations with functional plasticity that highly expressed the immunosuppressive SIGLEC9 gene and preferentially accumulated in the nonresponders to anti-PD-1 treatment. Deletion of Siglece (murine homolog) resulted in dramatically restrained tumor development and prolonged survival in mouse models. Mechanistically, targeting Siglece directly activated both CD4+ T cells and CD8+ T cells through antigen presentation, secreted chemokines and co-stimulatory factor interactions. Furthermore, Siglece deletion synergized with anti-PD-1/PD-L1 treatment to improve antitumor efficacy. Our data demonstrated that Siglec-9 is an immune-checkpoint molecule on macrophages that can be targeted to enhance anti-PD-1/PD-L1 therapeutic efficacy for GBM treatment.","","True","Visium","1858","36601"
"GSE235672_GSM7507316","human","brain","37460871","Siglec-9 acts as an immune-checkpoint molecule on macrophages in glioblastoma, restricting T-cell priming and immunotherapy response","Neoadjuvant immune-checkpoint blockade therapy only benefits a limited fraction of patients with glioblastoma multiforme (GBM). Thus, targeting other immunomodulators on myeloid cells is an attractive therapeutic option. Here, we performed single-cell RNA sequencing and spatial transcriptomics of patients with GBM treated with neoadjuvant anti-PD-1 therapy. We identified unique monocyte-derived tumor-associated macrophage subpopulations with functional plasticity that highly expressed the immunosuppressive SIGLEC9 gene and preferentially accumulated in the nonresponders to anti-PD-1 treatment. Deletion of Siglece (murine homolog) resulted in dramatically restrained tumor development and prolonged survival in mouse models. Mechanistically, targeting Siglece directly activated both CD4+ T cells and CD8+ T cells through antigen presentation, secreted chemokines and co-stimulatory factor interactions. Furthermore, Siglece deletion synergized with anti-PD-1/PD-L1 treatment to improve antitumor efficacy. Our data demonstrated that Siglec-9 is an immune-checkpoint molecule on macrophages that can be targeted to enhance anti-PD-1/PD-L1 therapeutic efficacy for GBM treatment.","","True","Visium","2560","33538"
"GSE235672_GSM7507317","human","brain","37460871","Siglec-9 acts as an immune-checkpoint molecule on macrophages in glioblastoma, restricting T-cell priming and immunotherapy response","Neoadjuvant immune-checkpoint blockade therapy only benefits a limited fraction of patients with glioblastoma multiforme (GBM). Thus, targeting other immunomodulators on myeloid cells is an attractive therapeutic option. Here, we performed single-cell RNA sequencing and spatial transcriptomics of patients with GBM treated with neoadjuvant anti-PD-1 therapy. We identified unique monocyte-derived tumor-associated macrophage subpopulations with functional plasticity that highly expressed the immunosuppressive SIGLEC9 gene and preferentially accumulated in the nonresponders to anti-PD-1 treatment. Deletion of Siglece (murine homolog) resulted in dramatically restrained tumor development and prolonged survival in mouse models. Mechanistically, targeting Siglece directly activated both CD4+ T cells and CD8+ T cells through antigen presentation, secreted chemokines and co-stimulatory factor interactions. Furthermore, Siglece deletion synergized with anti-PD-1/PD-L1 treatment to improve antitumor efficacy. Our data demonstrated that Siglec-9 is an immune-checkpoint molecule on macrophages that can be targeted to enhance anti-PD-1/PD-L1 therapeutic efficacy for GBM treatment.","","True","Visium","2027","33538"
"GSE235672_GSM7507318","human","brain","37460871","Siglec-9 acts as an immune-checkpoint molecule on macrophages in glioblastoma, restricting T-cell priming and immunotherapy response","Neoadjuvant immune-checkpoint blockade therapy only benefits a limited fraction of patients with glioblastoma multiforme (GBM). Thus, targeting other immunomodulators on myeloid cells is an attractive therapeutic option. Here, we performed single-cell RNA sequencing and spatial transcriptomics of patients with GBM treated with neoadjuvant anti-PD-1 therapy. We identified unique monocyte-derived tumor-associated macrophage subpopulations with functional plasticity that highly expressed the immunosuppressive SIGLEC9 gene and preferentially accumulated in the nonresponders to anti-PD-1 treatment. Deletion of Siglece (murine homolog) resulted in dramatically restrained tumor development and prolonged survival in mouse models. Mechanistically, targeting Siglece directly activated both CD4+ T cells and CD8+ T cells through antigen presentation, secreted chemokines and co-stimulatory factor interactions. Furthermore, Siglece deletion synergized with anti-PD-1/PD-L1 treatment to improve antitumor efficacy. Our data demonstrated that Siglec-9 is an immune-checkpoint molecule on macrophages that can be targeted to enhance anti-PD-1/PD-L1 therapeutic efficacy for GBM treatment.","","True","Visium","1555","36601"
"GSE235672_GSM7507319","human","brain","37460871","Siglec-9 acts as an immune-checkpoint molecule on macrophages in glioblastoma, restricting T-cell priming and immunotherapy response","Neoadjuvant immune-checkpoint blockade therapy only benefits a limited fraction of patients with glioblastoma multiforme (GBM). Thus, targeting other immunomodulators on myeloid cells is an attractive therapeutic option. Here, we performed single-cell RNA sequencing and spatial transcriptomics of patients with GBM treated with neoadjuvant anti-PD-1 therapy. We identified unique monocyte-derived tumor-associated macrophage subpopulations with functional plasticity that highly expressed the immunosuppressive SIGLEC9 gene and preferentially accumulated in the nonresponders to anti-PD-1 treatment. Deletion of Siglece (murine homolog) resulted in dramatically restrained tumor development and prolonged survival in mouse models. Mechanistically, targeting Siglece directly activated both CD4+ T cells and CD8+ T cells through antigen presentation, secreted chemokines and co-stimulatory factor interactions. Furthermore, Siglece deletion synergized with anti-PD-1/PD-L1 treatment to improve antitumor efficacy. Our data demonstrated that Siglec-9 is an immune-checkpoint molecule on macrophages that can be targeted to enhance anti-PD-1/PD-L1 therapeutic efficacy for GBM treatment.","","True","Visium","1733","36601"
"GSE235672_GSM7507320","human","brain","37460871","Siglec-9 acts as an immune-checkpoint molecule on macrophages in glioblastoma, restricting T-cell priming and immunotherapy response","Neoadjuvant immune-checkpoint blockade therapy only benefits a limited fraction of patients with glioblastoma multiforme (GBM). Thus, targeting other immunomodulators on myeloid cells is an attractive therapeutic option. Here, we performed single-cell RNA sequencing and spatial transcriptomics of patients with GBM treated with neoadjuvant anti-PD-1 therapy. We identified unique monocyte-derived tumor-associated macrophage subpopulations with functional plasticity that highly expressed the immunosuppressive SIGLEC9 gene and preferentially accumulated in the nonresponders to anti-PD-1 treatment. Deletion of Siglece (murine homolog) resulted in dramatically restrained tumor development and prolonged survival in mouse models. Mechanistically, targeting Siglece directly activated both CD4+ T cells and CD8+ T cells through antigen presentation, secreted chemokines and co-stimulatory factor interactions. Furthermore, Siglece deletion synergized with anti-PD-1/PD-L1 treatment to improve antitumor efficacy. Our data demonstrated that Siglec-9 is an immune-checkpoint molecule on macrophages that can be targeted to enhance anti-PD-1/PD-L1 therapeutic efficacy for GBM treatment.","","True","Visium","2253","36601"
"GSE235672_GSM7507321","human","brain","37460871","Siglec-9 acts as an immune-checkpoint molecule on macrophages in glioblastoma, restricting T-cell priming and immunotherapy response","Neoadjuvant immune-checkpoint blockade therapy only benefits a limited fraction of patients with glioblastoma multiforme (GBM). Thus, targeting other immunomodulators on myeloid cells is an attractive therapeutic option. Here, we performed single-cell RNA sequencing and spatial transcriptomics of patients with GBM treated with neoadjuvant anti-PD-1 therapy. We identified unique monocyte-derived tumor-associated macrophage subpopulations with functional plasticity that highly expressed the immunosuppressive SIGLEC9 gene and preferentially accumulated in the nonresponders to anti-PD-1 treatment. Deletion of Siglece (murine homolog) resulted in dramatically restrained tumor development and prolonged survival in mouse models. Mechanistically, targeting Siglece directly activated both CD4+ T cells and CD8+ T cells through antigen presentation, secreted chemokines and co-stimulatory factor interactions. Furthermore, Siglece deletion synergized with anti-PD-1/PD-L1 treatment to improve antitumor efficacy. Our data demonstrated that Siglec-9 is an immune-checkpoint molecule on macrophages that can be targeted to enhance anti-PD-1/PD-L1 therapeutic efficacy for GBM treatment.","","True","Visium","2176","33538"
"GSE235672_GSM7507322","human","brain","37460871","Siglec-9 acts as an immune-checkpoint molecule on macrophages in glioblastoma, restricting T-cell priming and immunotherapy response","Neoadjuvant immune-checkpoint blockade therapy only benefits a limited fraction of patients with glioblastoma multiforme (GBM). Thus, targeting other immunomodulators on myeloid cells is an attractive therapeutic option. Here, we performed single-cell RNA sequencing and spatial transcriptomics of patients with GBM treated with neoadjuvant anti-PD-1 therapy. We identified unique monocyte-derived tumor-associated macrophage subpopulations with functional plasticity that highly expressed the immunosuppressive SIGLEC9 gene and preferentially accumulated in the nonresponders to anti-PD-1 treatment. Deletion of Siglece (murine homolog) resulted in dramatically restrained tumor development and prolonged survival in mouse models. Mechanistically, targeting Siglece directly activated both CD4+ T cells and CD8+ T cells through antigen presentation, secreted chemokines and co-stimulatory factor interactions. Furthermore, Siglece deletion synergized with anti-PD-1/PD-L1 treatment to improve antitumor efficacy. Our data demonstrated that Siglec-9 is an immune-checkpoint molecule on macrophages that can be targeted to enhance anti-PD-1/PD-L1 therapeutic efficacy for GBM treatment.","","True","Visium","2494","33538"
"GSE235672_GSM7507323","human","brain","37460871","Siglec-9 acts as an immune-checkpoint molecule on macrophages in glioblastoma, restricting T-cell priming and immunotherapy response","Neoadjuvant immune-checkpoint blockade therapy only benefits a limited fraction of patients with glioblastoma multiforme (GBM). Thus, targeting other immunomodulators on myeloid cells is an attractive therapeutic option. Here, we performed single-cell RNA sequencing and spatial transcriptomics of patients with GBM treated with neoadjuvant anti-PD-1 therapy. We identified unique monocyte-derived tumor-associated macrophage subpopulations with functional plasticity that highly expressed the immunosuppressive SIGLEC9 gene and preferentially accumulated in the nonresponders to anti-PD-1 treatment. Deletion of Siglece (murine homolog) resulted in dramatically restrained tumor development and prolonged survival in mouse models. Mechanistically, targeting Siglece directly activated both CD4+ T cells and CD8+ T cells through antigen presentation, secreted chemokines and co-stimulatory factor interactions. Furthermore, Siglece deletion synergized with anti-PD-1/PD-L1 treatment to improve antitumor efficacy. Our data demonstrated that Siglec-9 is an immune-checkpoint molecule on macrophages that can be targeted to enhance anti-PD-1/PD-L1 therapeutic efficacy for GBM treatment.","","True","Visium","2499","33538"
"GSE235672_GSM7507324","human","brain","37460871","Siglec-9 acts as an immune-checkpoint molecule on macrophages in glioblastoma, restricting T-cell priming and immunotherapy response","Neoadjuvant immune-checkpoint blockade therapy only benefits a limited fraction of patients with glioblastoma multiforme (GBM). Thus, targeting other immunomodulators on myeloid cells is an attractive therapeutic option. Here, we performed single-cell RNA sequencing and spatial transcriptomics of patients with GBM treated with neoadjuvant anti-PD-1 therapy. We identified unique monocyte-derived tumor-associated macrophage subpopulations with functional plasticity that highly expressed the immunosuppressive SIGLEC9 gene and preferentially accumulated in the nonresponders to anti-PD-1 treatment. Deletion of Siglece (murine homolog) resulted in dramatically restrained tumor development and prolonged survival in mouse models. Mechanistically, targeting Siglece directly activated both CD4+ T cells and CD8+ T cells through antigen presentation, secreted chemokines and co-stimulatory factor interactions. Furthermore, Siglece deletion synergized with anti-PD-1/PD-L1 treatment to improve antitumor efficacy. Our data demonstrated that Siglec-9 is an immune-checkpoint molecule on macrophages that can be targeted to enhance anti-PD-1/PD-L1 therapeutic efficacy for GBM treatment.","","True","Visium","1887","33538"
"GSE235672_GSM7507325","human","brain","37460871","Siglec-9 acts as an immune-checkpoint molecule on macrophages in glioblastoma, restricting T-cell priming and immunotherapy response","Neoadjuvant immune-checkpoint blockade therapy only benefits a limited fraction of patients with glioblastoma multiforme (GBM). Thus, targeting other immunomodulators on myeloid cells is an attractive therapeutic option. Here, we performed single-cell RNA sequencing and spatial transcriptomics of patients with GBM treated with neoadjuvant anti-PD-1 therapy. We identified unique monocyte-derived tumor-associated macrophage subpopulations with functional plasticity that highly expressed the immunosuppressive SIGLEC9 gene and preferentially accumulated in the nonresponders to anti-PD-1 treatment. Deletion of Siglece (murine homolog) resulted in dramatically restrained tumor development and prolonged survival in mouse models. Mechanistically, targeting Siglece directly activated both CD4+ T cells and CD8+ T cells through antigen presentation, secreted chemokines and co-stimulatory factor interactions. Furthermore, Siglece deletion synergized with anti-PD-1/PD-L1 treatment to improve antitumor efficacy. Our data demonstrated that Siglec-9 is an immune-checkpoint molecule on macrophages that can be targeted to enhance anti-PD-1/PD-L1 therapeutic efficacy for GBM treatment.","","True","Visium","2578","33538"
"GSE235672_GSM7507326","human","brain","37460871","Siglec-9 acts as an immune-checkpoint molecule on macrophages in glioblastoma, restricting T-cell priming and immunotherapy response","Neoadjuvant immune-checkpoint blockade therapy only benefits a limited fraction of patients with glioblastoma multiforme (GBM). Thus, targeting other immunomodulators on myeloid cells is an attractive therapeutic option. Here, we performed single-cell RNA sequencing and spatial transcriptomics of patients with GBM treated with neoadjuvant anti-PD-1 therapy. We identified unique monocyte-derived tumor-associated macrophage subpopulations with functional plasticity that highly expressed the immunosuppressive SIGLEC9 gene and preferentially accumulated in the nonresponders to anti-PD-1 treatment. Deletion of Siglece (murine homolog) resulted in dramatically restrained tumor development and prolonged survival in mouse models. Mechanistically, targeting Siglece directly activated both CD4+ T cells and CD8+ T cells through antigen presentation, secreted chemokines and co-stimulatory factor interactions. Furthermore, Siglece deletion synergized with anti-PD-1/PD-L1 treatment to improve antitumor efficacy. Our data demonstrated that Siglec-9 is an immune-checkpoint molecule on macrophages that can be targeted to enhance anti-PD-1/PD-L1 therapeutic efficacy for GBM treatment.","","True","Visium","1948","33538"
"GSE235742_GSM7508783","mouse","colon","37878672","Genetic vulnerability to Crohn's disease reveals a spatially resolved epithelial restitution program","Effective tissue repair requires coordinated intercellular communication to sense damage, remodel the tissue, and restore function. Here, we dissected the healing response in the intestinal mucosa by mapping intercellular communication at single-cell resolution and integrating with spatial transcriptomics. We demonstrated that a risk variant for Crohn's disease, hepatocyte growth factor activator (HGFAC) Arg509His (R509H), disrupted a damage-sensing pathway connecting the coagulation cascade to growth factors that drive the differentiation of wound-associated epithelial (WAE) cells and production of a localized retinoic acid (RA) gradient to promote fibroblast-mediated tissue remodeling. Specifically, we showed that HGFAC R509H was activated by thrombin protease activity but exhibited impaired proteolytic activation of the growth factor macrophage-stimulating protein (MSP). In Hgfac R509H mice, reduced MSP activation in response to wounding of the colon resulted in impaired WAE cell induction and delayed healing. Through integration of single-cell transcriptomics and spatial transcriptomics, we demonstrated that WAE cells generated RA in a spatially restricted region of the wound site and that mucosal fibroblasts responded to this signal by producing extracellular matrix and growth factors. We further dissected this WAE cell-fibroblast signaling circuit in vitro using a genetically tractable organoid coculture model. Collectively, these studies exploited a genetic perturbation associated with human disease to disrupt a fundamental biological process and then reconstructed a spatially resolved mechanistic model of tissue healing.","","False","Visium","502","32285"
"GSE235742_GSM7508784","mouse","colon","37878672","Genetic vulnerability to Crohn's disease reveals a spatially resolved epithelial restitution program","Effective tissue repair requires coordinated intercellular communication to sense damage, remodel the tissue, and restore function. Here, we dissected the healing response in the intestinal mucosa by mapping intercellular communication at single-cell resolution and integrating with spatial transcriptomics. We demonstrated that a risk variant for Crohn's disease, hepatocyte growth factor activator (HGFAC) Arg509His (R509H), disrupted a damage-sensing pathway connecting the coagulation cascade to growth factors that drive the differentiation of wound-associated epithelial (WAE) cells and production of a localized retinoic acid (RA) gradient to promote fibroblast-mediated tissue remodeling. Specifically, we showed that HGFAC R509H was activated by thrombin protease activity but exhibited impaired proteolytic activation of the growth factor macrophage-stimulating protein (MSP). In Hgfac R509H mice, reduced MSP activation in response to wounding of the colon resulted in impaired WAE cell induction and delayed healing. Through integration of single-cell transcriptomics and spatial transcriptomics, we demonstrated that WAE cells generated RA in a spatially restricted region of the wound site and that mucosal fibroblasts responded to this signal by producing extracellular matrix and growth factors. We further dissected this WAE cell-fibroblast signaling circuit in vitro using a genetically tractable organoid coculture model. Collectively, these studies exploited a genetic perturbation associated with human disease to disrupt a fundamental biological process and then reconstructed a spatially resolved mechanistic model of tissue healing.","","False","Visium","640","32285"
"GSE235742_GSM7508785","mouse","colon","37878672","Genetic vulnerability to Crohn's disease reveals a spatially resolved epithelial restitution program","Effective tissue repair requires coordinated intercellular communication to sense damage, remodel the tissue, and restore function. Here, we dissected the healing response in the intestinal mucosa by mapping intercellular communication at single-cell resolution and integrating with spatial transcriptomics. We demonstrated that a risk variant for Crohn's disease, hepatocyte growth factor activator (HGFAC) Arg509His (R509H), disrupted a damage-sensing pathway connecting the coagulation cascade to growth factors that drive the differentiation of wound-associated epithelial (WAE) cells and production of a localized retinoic acid (RA) gradient to promote fibroblast-mediated tissue remodeling. Specifically, we showed that HGFAC R509H was activated by thrombin protease activity but exhibited impaired proteolytic activation of the growth factor macrophage-stimulating protein (MSP). In Hgfac R509H mice, reduced MSP activation in response to wounding of the colon resulted in impaired WAE cell induction and delayed healing. Through integration of single-cell transcriptomics and spatial transcriptomics, we demonstrated that WAE cells generated RA in a spatially restricted region of the wound site and that mucosal fibroblasts responded to this signal by producing extracellular matrix and growth factors. We further dissected this WAE cell-fibroblast signaling circuit in vitro using a genetically tractable organoid coculture model. Collectively, these studies exploited a genetic perturbation associated with human disease to disrupt a fundamental biological process and then reconstructed a spatially resolved mechanistic model of tissue healing.","","False","Visium","572","32285"
"GSE236171_GSM7519181","mouse","brain","38007580","Robust mapping of spatiotemporal trajectories and cell-cell interactions in healthy and diseased tissues","Spatial transcriptomics (ST) technologies generate multiple data types from biological samples, namely gene expression, physical distance between data points, and/or tissue morphology. Here we developed three computational-statistical algorithms that integrate all three data types to advance understanding of cellular processes. First, we present a spatial graph-based method, pseudo-time-space (PSTS), to model and uncover relationships between transcriptional states of cells across tissues undergoing dynamic change (e.g. neurodevelopment, brain injury and/or microglia activation, and cancer progression). We further developed a spatially-constrained two-level permutation (SCTP) test to study cell-cell interaction, finding highly interactive tissue regions across thousands of ligand-receptor pairs with markedly reduced false discovery rates. Finally, we present a spatial graph-based imputation method with neural network (stSME), to correct for technical noise/dropout and increase ST data coverage. Together, the algorithms that we developed, implemented in the comprehensive and fast stLearn software, allow for robust interrogation of biological processes within healthy and diseased tissues.","","True","Visium","2968","31053"
"GSE236171_GSM7519182","mouse","brain","38007580","Robust mapping of spatiotemporal trajectories and cell-cell interactions in healthy and diseased tissues","Spatial transcriptomics (ST) technologies generate multiple data types from biological samples, namely gene expression, physical distance between data points, and/or tissue morphology. Here we developed three computational-statistical algorithms that integrate all three data types to advance understanding of cellular processes. First, we present a spatial graph-based method, pseudo-time-space (PSTS), to model and uncover relationships between transcriptional states of cells across tissues undergoing dynamic change (e.g. neurodevelopment, brain injury and/or microglia activation, and cancer progression). We further developed a spatially-constrained two-level permutation (SCTP) test to study cell-cell interaction, finding highly interactive tissue regions across thousands of ligand-receptor pairs with markedly reduced false discovery rates. Finally, we present a spatial graph-based imputation method with neural network (stSME), to correct for technical noise/dropout and increase ST data coverage. Together, the algorithms that we developed, implemented in the comprehensive and fast stLearn software, allow for robust interrogation of biological processes within healthy and diseased tissues.","","True","Visium","2442","31053"
"GSE236424_GSM7536059","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","1291","32287"
"GSE236424_GSM7536060","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","1390","32287"
"GSE236424_GSM7536061","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","970","32287"
"GSE236424_GSM7536062","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","1033","32287"
"GSE236424_GSM7536063","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","984","32287"
"GSE236424_GSM7536065","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","1250","32287"
"GSE236424_GSM7536066","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","897","32287"
"GSE236424_GSM7536067","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","1973","32287"
"GSE236424_GSM7536068","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","2281","32287"
"GSE236424_GSM7536070","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","1908","32287"
"GSE236424_GSM7536071","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","1988","32287"
"GSE236424_GSM7536072","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","2066","32287"
"GSE236424_GSM7536073","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","1258","32287"
"GSE236424_GSM7536075","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","1397","32287"
"GSE236424_GSM7536076","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","1636","32287"
"GSE236424_GSM7536077","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","1168","32287"
"GSE236424_GSM7536078","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","1196","32287"
"GSE236424_GSM7536079","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","1170","32287"
"GSE236424_GSM7536080","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","1259","32287"
"GSE236424_GSM7536081","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","1635","32287"
"GSE236424_GSM7536082","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","1338","32287"
"GSE236424_GSM7536084","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","1147","32287"
"GSE236424_GSM7536085","mouse","liver","","Spatio-temporal dynamics of the mouse liver shapes hepatocytes heterogeneity and impacts in vivo gene transfer and gene editing [Visium Spatial Transcriptomics]","","","False","Visium","1532","32287"
"GSE237183_GSM7596587","human","glioblastoma","","Integrative spatial analysis reveals a multi-layered organization of glioblastoma","","","False","Visium","2397","33538"
"GSE237183_GSM7596588","human","glioblastoma","","Integrative spatial analysis reveals a multi-layered organization of glioblastoma","","","False","Visium","1387","33538"
"GSE237183_GSM7596589","human","glioblastoma","","Integrative spatial analysis reveals a multi-layered organization of glioblastoma","","","False","Visium","2792","33538"
"GSE237183_GSM7596590","human","glioblastoma","","Integrative spatial analysis reveals a multi-layered organization of glioblastoma","","","False","Visium","2199","33538"
"GSE237183_GSM7596591","human","glioblastoma","","Integrative spatial analysis reveals a multi-layered organization of glioblastoma","","","False","Visium","1280","33538"
"GSE237183_GSM7596592","human","glioblastoma","","Integrative spatial analysis reveals a multi-layered organization of glioblastoma","","","False","Visium","2780","33538"
"GSE237183_GSM7596593","human","glioblastoma","","Integrative spatial analysis reveals a multi-layered organization of glioblastoma","","","False","Visium","1436","33538"
"GSE237183_GSM7596594","human","glioblastoma","","Integrative spatial analysis reveals a multi-layered organization of glioblastoma","","","False","Visium","955","33538"
"GSE237183_GSM7596595","human","glioblastoma","","Integrative spatial analysis reveals a multi-layered organization of glioblastoma","","","False","Visium","1726","33538"
"GSE237183_GSM7596596","human","glioblastoma","","Integrative spatial analysis reveals a multi-layered organization of glioblastoma","","","False","Visium","1112","33538"
"GSE237183_GSM7596597","human","glioblastoma","","Integrative spatial analysis reveals a multi-layered organization of glioblastoma","","","False","Visium","2486","33538"
"GSE237183_GSM7596598","human","glioblastoma","","Integrative spatial analysis reveals a multi-layered organization of glioblastoma","","","False","Visium","2595","33538"
"GSE237183_GSM7596599","human","glioblastoma","","Integrative spatial analysis reveals a multi-layered organization of glioblastoma","","","False","Visium","2343","33538"
"GSE237183_GSM7596600","human","glioma","","Integrative spatial analysis reveals a multi-layered organization of glioblastoma","","","False","Visium","965","33538"
"GSE237183_GSM7596601","human","glioma","","Integrative spatial analysis reveals a multi-layered organization of glioblastoma","","","False","Visium","3105","33538"
"GSE237183_GSM7596602","human","glioma","","Integrative spatial analysis reveals a multi-layered organization of glioblastoma","","","False","Visium","2607","33538"
"GSE237183_GSM7596603","human","glioma","","Integrative spatial analysis reveals a multi-layered organization of glioblastoma","","","False","Visium","2637","33538"
"GSE237183_GSM7596604","human","glioma","","Integrative spatial analysis reveals a multi-layered organization of glioblastoma","","","False","Visium","1300","33538"
"GSE237183_GSM7596605","human","glioma","","Integrative spatial analysis reveals a multi-layered organization of glioblastoma","","","False","Visium","3464","33538"
"GSE237771_GSM7648697","human","skin","","Epithelial-Immune Metabolic Codependency Fuels Inflammatory Disease [Spatial Transcriptomics]","","","False","Visium","230","36601"
"GSE237771_GSM7648698","human","skin","","Epithelial-Immune Metabolic Codependency Fuels Inflammatory Disease [Spatial Transcriptomics]","","","False","Visium","662","36601"
"GSE237771_GSM7648699","human","skin","","Epithelial-Immune Metabolic Codependency Fuels Inflammatory Disease [Spatial Transcriptomics]","","","False","Visium","378","36601"
"GSE237771_GSM7648700","human","skin","","Epithelial-Immune Metabolic Codependency Fuels Inflammatory Disease [Spatial Transcriptomics]","","","False","Visium","276","36601"
"GSE240102_GSM7681546","plant","root","","Spatial Co-transcriptomics Reveals Discrete Stages of the Arbuscular Mycorrhizal Symbiosis II","","","False","Visium","2087","49822"
"GSE240102_GSM7681547","plant","root","","Spatial Co-transcriptomics Reveals Discrete Stages of the Arbuscular Mycorrhizal Symbiosis II","","","False","Visium","2054","49822"
"GSE240102_GSM7681548","plant","root","","Spatial Co-transcriptomics Reveals Discrete Stages of the Arbuscular Mycorrhizal Symbiosis II","","","False","Visium","1932","49822"
"GSE240102_GSM7681549","plant","root","","Spatial Co-transcriptomics Reveals Discrete Stages of the Arbuscular Mycorrhizal Symbiosis II","","","False","Visium","2506","94448"
"GSE240102_GSM7681550","plant","root","","Spatial Co-transcriptomics Reveals Discrete Stages of the Arbuscular Mycorrhizal Symbiosis II","","","False","Visium","2657","94448"
"GSE240102_GSM7681551","plant","root","","Spatial Co-transcriptomics Reveals Discrete Stages of the Arbuscular Mycorrhizal Symbiosis II","","","False","Visium","2844","94448"
"GSE240102_GSM7681552","plant","root","","Spatial Co-transcriptomics Reveals Discrete Stages of the Arbuscular Mycorrhizal Symbiosis II","","","False","Visium","2360","94448"
"GSE240102_GSM7681553","plant","root","","Spatial Co-transcriptomics Reveals Discrete Stages of the Arbuscular Mycorrhizal Symbiosis II","","","False","Visium","1733","94448"
"GSE240102_GSM7681554","plant","root","","Spatial Co-transcriptomics Reveals Discrete Stages of the Arbuscular Mycorrhizal Symbiosis II","","","False","Visium","1512","94448"
"GSE240102_GSM7681555","plant","root","","Spatial Co-transcriptomics Reveals Discrete Stages of the Arbuscular Mycorrhizal Symbiosis II","","","False","Visium","1782","94448"
"GSE240102_GSM7681556","plant","root","","Spatial Co-transcriptomics Reveals Discrete Stages of the Arbuscular Mycorrhizal Symbiosis II","","","False","Visium","1808","94448"
"GSE240102_GSM7681557","plant","root","","Spatial Co-transcriptomics Reveals Discrete Stages of the Arbuscular Mycorrhizal Symbiosis II","","","False","Visium","1920","94448"
"GSE240102_GSM7681558","plant","root","","Spatial Co-transcriptomics Reveals Discrete Stages of the Arbuscular Mycorrhizal Symbiosis II","","","False","Visium","2217","94448"
"GSE240102_GSM7681559","plant","root","","Spatial Co-transcriptomics Reveals Discrete Stages of the Arbuscular Mycorrhizal Symbiosis II","","","False","Visium","1836","94448"
"GSE240102_GSM7681560","plant","root","","Spatial Co-transcriptomics Reveals Discrete Stages of the Arbuscular Mycorrhizal Symbiosis II","","","False","Visium","1948","94448"
"GSE240102_GSM7681561","plant","root","","Spatial Co-transcriptomics Reveals Discrete Stages of the Arbuscular Mycorrhizal Symbiosis II","","","False","Visium","1655","94448"
"GSE240102_GSM7681562","plant","root","","Spatial Co-transcriptomics Reveals Discrete Stages of the Arbuscular Mycorrhizal Symbiosis II","","","False","Visium","1559","94448"
"GSE240102_GSM7681563","plant","root","","Spatial Co-transcriptomics Reveals Discrete Stages of the Arbuscular Mycorrhizal Symbiosis II","","","False","Visium","1453","94448"
"GSE240102_GSM7681564","plant","root","","Spatial Co-transcriptomics Reveals Discrete Stages of the Arbuscular Mycorrhizal Symbiosis II","","","False","Visium","1400","94448"
"GSE240429_GSM7697868","human","liver","38199298","Single-cell, single-nucleus, and spatial transcriptomics characterization of the immunological landscape in the healthy and PSC human liver","Background & aims: Primary sclerosing cholangitis (PSC) is an immune-mediated cholestatic liver disease for which there is an unmet need to understand the cellular composition of the affected liver and how it underlies disease pathogenesis. We aimed to generate a comprehensive atlas of the PSC liver using multi-omic modalities and protein-based functional validation.

Methods: We employed single-cell and single-nucleus RNA sequencing (47,156 cells and 23,000 nuclei) and spatial transcriptomics (one sample by 10x Visium and five samples with Nanostring GeoMx DSP) to profile the cellular ecosystem in 10 PSC livers. Transcriptomic profiles were compared to 24 neurologically deceased donor livers (107,542 cells) and spatial transcriptomics controls, as well as 18,240 cells and 20,202 nuclei from three PBC livers. Flow cytometry was performed to validate PSC-specific differences in immune cell phenotype and function.

Results: PSC explants with parenchymal cirrhosis and prominent periductal fibrosis contained a population of cholangiocyte-like hepatocytes that were surrounded by diverse immune cell populations. PSC-associated biliary, mesenchymal, and endothelial populations expressed chemokine and cytokine transcripts involved in immune cell recruitment. Additionally, expanded CD4+ T cells and recruited myeloid populations in the PSC liver expressed the corresponding receptors to these chemokines and cytokines, suggesting potential recruitment. Tissue-resident macrophages, by contrast, were reduced in number and exhibited a dysfunctional and downregulated inflammatory response to lipopolysaccharide and interferon-γ stimulation.

Conclusions: We present a comprehensive atlas of the PSC liver and demonstrate an exhaustion-like phenotype of myeloid cells and markers of chronic cytokine expression in late-stage PSC lesions. This atlas expands our understanding of the cellular complexity of PSC and has potential to guide the development of novel treatments.

Impact and implications: Primary sclerosing cholangitis (PSC) is a rare liver disease characterized by chronic inflammation and irreparable damage to the bile ducts, which eventually results in liver failure. Due to a limited understanding of the underlying pathogenesis of disease, treatment options are limited. To address this, we sequenced healthy and diseased livers to compare the activity, interactions, and localization of immune and non-immune cells. This revealed that hepatocytes lining PSC scar regions co-express cholangiocyte markers, whereas immune cells infiltrate the scar lesions. Of these cells, macrophages, which typically contribute to tissue repair, were enriched in immunoregulatory genes and demonstrated a lack of responsiveness to stimulation. These cells may be involved in maintaining hepatic inflammation and could be a target for novel therapies.","Liver; Myeloid Dysfunction; Primary Sclerosing Cholangitis; Single Cell RNA sequencing; Spatial Transcriptomics.","False","Visium","2378","36601"
"GSE240429_GSM7697869","human","liver","38199298","Single-cell, single-nucleus, and spatial transcriptomics characterization of the immunological landscape in the healthy and PSC human liver","Background & aims: Primary sclerosing cholangitis (PSC) is an immune-mediated cholestatic liver disease for which there is an unmet need to understand the cellular composition of the affected liver and how it underlies disease pathogenesis. We aimed to generate a comprehensive atlas of the PSC liver using multi-omic modalities and protein-based functional validation.

Methods: We employed single-cell and single-nucleus RNA sequencing (47,156 cells and 23,000 nuclei) and spatial transcriptomics (one sample by 10x Visium and five samples with Nanostring GeoMx DSP) to profile the cellular ecosystem in 10 PSC livers. Transcriptomic profiles were compared to 24 neurologically deceased donor livers (107,542 cells) and spatial transcriptomics controls, as well as 18,240 cells and 20,202 nuclei from three PBC livers. Flow cytometry was performed to validate PSC-specific differences in immune cell phenotype and function.

Results: PSC explants with parenchymal cirrhosis and prominent periductal fibrosis contained a population of cholangiocyte-like hepatocytes that were surrounded by diverse immune cell populations. PSC-associated biliary, mesenchymal, and endothelial populations expressed chemokine and cytokine transcripts involved in immune cell recruitment. Additionally, expanded CD4+ T cells and recruited myeloid populations in the PSC liver expressed the corresponding receptors to these chemokines and cytokines, suggesting potential recruitment. Tissue-resident macrophages, by contrast, were reduced in number and exhibited a dysfunctional and downregulated inflammatory response to lipopolysaccharide and interferon-γ stimulation.

Conclusions: We present a comprehensive atlas of the PSC liver and demonstrate an exhaustion-like phenotype of myeloid cells and markers of chronic cytokine expression in late-stage PSC lesions. This atlas expands our understanding of the cellular complexity of PSC and has potential to guide the development of novel treatments.

Impact and implications: Primary sclerosing cholangitis (PSC) is a rare liver disease characterized by chronic inflammation and irreparable damage to the bile ducts, which eventually results in liver failure. Due to a limited understanding of the underlying pathogenesis of disease, treatment options are limited. To address this, we sequenced healthy and diseased livers to compare the activity, interactions, and localization of immune and non-immune cells. This revealed that hepatocytes lining PSC scar regions co-express cholangiocyte markers, whereas immune cells infiltrate the scar lesions. Of these cells, macrophages, which typically contribute to tissue repair, were enriched in immunoregulatory genes and demonstrated a lack of responsiveness to stimulation. These cells may be involved in maintaining hepatic inflammation and could be a target for novel therapies.","Liver; Myeloid Dysfunction; Primary Sclerosing Cholangitis; Single Cell RNA sequencing; Spatial Transcriptomics.","False","Visium","2349","36601"
"GSE240429_GSM7697870","human","liver","38199298","Single-cell, single-nucleus, and spatial transcriptomics characterization of the immunological landscape in the healthy and PSC human liver","Background & aims: Primary sclerosing cholangitis (PSC) is an immune-mediated cholestatic liver disease for which there is an unmet need to understand the cellular composition of the affected liver and how it underlies disease pathogenesis. We aimed to generate a comprehensive atlas of the PSC liver using multi-omic modalities and protein-based functional validation.

Methods: We employed single-cell and single-nucleus RNA sequencing (47,156 cells and 23,000 nuclei) and spatial transcriptomics (one sample by 10x Visium and five samples with Nanostring GeoMx DSP) to profile the cellular ecosystem in 10 PSC livers. Transcriptomic profiles were compared to 24 neurologically deceased donor livers (107,542 cells) and spatial transcriptomics controls, as well as 18,240 cells and 20,202 nuclei from three PBC livers. Flow cytometry was performed to validate PSC-specific differences in immune cell phenotype and function.

Results: PSC explants with parenchymal cirrhosis and prominent periductal fibrosis contained a population of cholangiocyte-like hepatocytes that were surrounded by diverse immune cell populations. PSC-associated biliary, mesenchymal, and endothelial populations expressed chemokine and cytokine transcripts involved in immune cell recruitment. Additionally, expanded CD4+ T cells and recruited myeloid populations in the PSC liver expressed the corresponding receptors to these chemokines and cytokines, suggesting potential recruitment. Tissue-resident macrophages, by contrast, were reduced in number and exhibited a dysfunctional and downregulated inflammatory response to lipopolysaccharide and interferon-γ stimulation.

Conclusions: We present a comprehensive atlas of the PSC liver and demonstrate an exhaustion-like phenotype of myeloid cells and markers of chronic cytokine expression in late-stage PSC lesions. This atlas expands our understanding of the cellular complexity of PSC and has potential to guide the development of novel treatments.

Impact and implications: Primary sclerosing cholangitis (PSC) is a rare liver disease characterized by chronic inflammation and irreparable damage to the bile ducts, which eventually results in liver failure. Due to a limited understanding of the underlying pathogenesis of disease, treatment options are limited. To address this, we sequenced healthy and diseased livers to compare the activity, interactions, and localization of immune and non-immune cells. This revealed that hepatocytes lining PSC scar regions co-express cholangiocyte markers, whereas immune cells infiltrate the scar lesions. Of these cells, macrophages, which typically contribute to tissue repair, were enriched in immunoregulatory genes and demonstrated a lack of responsiveness to stimulation. These cells may be involved in maintaining hepatic inflammation and could be a target for novel therapies.","Liver; Myeloid Dysfunction; Primary Sclerosing Cholangitis; Single Cell RNA sequencing; Spatial Transcriptomics.","False","Visium","2277","36601"
"GSE240429_GSM7697871","human","liver","38199298","Single-cell, single-nucleus, and spatial transcriptomics characterization of the immunological landscape in the healthy and PSC human liver","Background & aims: Primary sclerosing cholangitis (PSC) is an immune-mediated cholestatic liver disease for which there is an unmet need to understand the cellular composition of the affected liver and how it underlies disease pathogenesis. We aimed to generate a comprehensive atlas of the PSC liver using multi-omic modalities and protein-based functional validation.

Methods: We employed single-cell and single-nucleus RNA sequencing (47,156 cells and 23,000 nuclei) and spatial transcriptomics (one sample by 10x Visium and five samples with Nanostring GeoMx DSP) to profile the cellular ecosystem in 10 PSC livers. Transcriptomic profiles were compared to 24 neurologically deceased donor livers (107,542 cells) and spatial transcriptomics controls, as well as 18,240 cells and 20,202 nuclei from three PBC livers. Flow cytometry was performed to validate PSC-specific differences in immune cell phenotype and function.

Results: PSC explants with parenchymal cirrhosis and prominent periductal fibrosis contained a population of cholangiocyte-like hepatocytes that were surrounded by diverse immune cell populations. PSC-associated biliary, mesenchymal, and endothelial populations expressed chemokine and cytokine transcripts involved in immune cell recruitment. Additionally, expanded CD4+ T cells and recruited myeloid populations in the PSC liver expressed the corresponding receptors to these chemokines and cytokines, suggesting potential recruitment. Tissue-resident macrophages, by contrast, were reduced in number and exhibited a dysfunctional and downregulated inflammatory response to lipopolysaccharide and interferon-γ stimulation.

Conclusions: We present a comprehensive atlas of the PSC liver and demonstrate an exhaustion-like phenotype of myeloid cells and markers of chronic cytokine expression in late-stage PSC lesions. This atlas expands our understanding of the cellular complexity of PSC and has potential to guide the development of novel treatments.

Impact and implications: Primary sclerosing cholangitis (PSC) is a rare liver disease characterized by chronic inflammation and irreparable damage to the bile ducts, which eventually results in liver failure. Due to a limited understanding of the underlying pathogenesis of disease, treatment options are limited. To address this, we sequenced healthy and diseased livers to compare the activity, interactions, and localization of immune and non-immune cells. This revealed that hepatocytes lining PSC scar regions co-express cholangiocyte markers, whereas immune cells infiltrate the scar lesions. Of these cells, macrophages, which typically contribute to tissue repair, were enriched in immunoregulatory genes and demonstrated a lack of responsiveness to stimulation. These cells may be involved in maintaining hepatic inflammation and could be a target for novel therapies.","Liver; Myeloid Dysfunction; Primary Sclerosing Cholangitis; Single Cell RNA sequencing; Spatial Transcriptomics.","False","Visium","2265","36601"
"GSE240715_GSM7708218","mouse","brain","37855382","Spatial transcriptomics map of the embryonic mouse brain - a tool to explore neurogenesis","The developing brain has a well-organized anatomical structure comprising different types of neural and non-neural cells. Stem cells, progenitors and newborn neurons tightly interact with their neighbouring cells and tissue microenvironment, and this intricate interplay ultimately shapes the output of neurogenesis. Given the relevance of spatial cues during brain development, we acknowledge the necessity for a spatial transcriptomics map accessible to the neurodevelopmental community. To fulfil this need, we generated spatially resolved RNA sequencing (RNAseq) data from embryonic day 13.5 mouse brain sections immunostained for mitotic active neural and vascular cells. Unsupervised clustering defined specific cell type populations of diverse lineages and differentiation states. Differential expression analysis revealed unique transcriptional signatures across specific brain areas, uncovering novel features inherent to particular anatomical domains. Finally, we integrated existing single-cell RNAseq datasets into our spatial transcriptomics map, adding tissue context to single-cell RNAseq data. In summary, we provide a valuable tool that enables the exploration and discovery of unforeseen molecular players involved in neurogenesis, particularly in the crosstalk between different cell types.","Gene expression atlas; Mouse telencephalon; Neurodevelopment; Single-cell and spatial transcriptomics data integration.","False","Visium","1196","32285"
"GSE240715_GSM7708219","mouse","brain","37855382","Spatial transcriptomics map of the embryonic mouse brain - a tool to explore neurogenesis","The developing brain has a well-organized anatomical structure comprising different types of neural and non-neural cells. Stem cells, progenitors and newborn neurons tightly interact with their neighbouring cells and tissue microenvironment, and this intricate interplay ultimately shapes the output of neurogenesis. Given the relevance of spatial cues during brain development, we acknowledge the necessity for a spatial transcriptomics map accessible to the neurodevelopmental community. To fulfil this need, we generated spatially resolved RNA sequencing (RNAseq) data from embryonic day 13.5 mouse brain sections immunostained for mitotic active neural and vascular cells. Unsupervised clustering defined specific cell type populations of diverse lineages and differentiation states. Differential expression analysis revealed unique transcriptional signatures across specific brain areas, uncovering novel features inherent to particular anatomical domains. Finally, we integrated existing single-cell RNAseq datasets into our spatial transcriptomics map, adding tissue context to single-cell RNAseq data. In summary, we provide a valuable tool that enables the exploration and discovery of unforeseen molecular players involved in neurogenesis, particularly in the crosstalk between different cell types.","Gene expression atlas; Mouse telencephalon; Neurodevelopment; Single-cell and spatial transcriptomics data integration.","False","Visium","1152","32285"
"GSE240715_GSM7708220","mouse","brain","37855382","Spatial transcriptomics map of the embryonic mouse brain - a tool to explore neurogenesis","The developing brain has a well-organized anatomical structure comprising different types of neural and non-neural cells. Stem cells, progenitors and newborn neurons tightly interact with their neighbouring cells and tissue microenvironment, and this intricate interplay ultimately shapes the output of neurogenesis. Given the relevance of spatial cues during brain development, we acknowledge the necessity for a spatial transcriptomics map accessible to the neurodevelopmental community. To fulfil this need, we generated spatially resolved RNA sequencing (RNAseq) data from embryonic day 13.5 mouse brain sections immunostained for mitotic active neural and vascular cells. Unsupervised clustering defined specific cell type populations of diverse lineages and differentiation states. Differential expression analysis revealed unique transcriptional signatures across specific brain areas, uncovering novel features inherent to particular anatomical domains. Finally, we integrated existing single-cell RNAseq datasets into our spatial transcriptomics map, adding tissue context to single-cell RNAseq data. In summary, we provide a valuable tool that enables the exploration and discovery of unforeseen molecular players involved in neurogenesis, particularly in the crosstalk between different cell types.","Gene expression atlas; Mouse telencephalon; Neurodevelopment; Single-cell and spatial transcriptomics data integration.","False","Visium","984","32285"
"GSE240715_GSM7708221","mouse","brain","37855382","Spatial transcriptomics map of the embryonic mouse brain - a tool to explore neurogenesis","The developing brain has a well-organized anatomical structure comprising different types of neural and non-neural cells. Stem cells, progenitors and newborn neurons tightly interact with their neighbouring cells and tissue microenvironment, and this intricate interplay ultimately shapes the output of neurogenesis. Given the relevance of spatial cues during brain development, we acknowledge the necessity for a spatial transcriptomics map accessible to the neurodevelopmental community. To fulfil this need, we generated spatially resolved RNA sequencing (RNAseq) data from embryonic day 13.5 mouse brain sections immunostained for mitotic active neural and vascular cells. Unsupervised clustering defined specific cell type populations of diverse lineages and differentiation states. Differential expression analysis revealed unique transcriptional signatures across specific brain areas, uncovering novel features inherent to particular anatomical domains. Finally, we integrated existing single-cell RNAseq datasets into our spatial transcriptomics map, adding tissue context to single-cell RNAseq data. In summary, we provide a valuable tool that enables the exploration and discovery of unforeseen molecular players involved in neurogenesis, particularly in the crosstalk between different cell types.","Gene expression atlas; Mouse telencephalon; Neurodevelopment; Single-cell and spatial transcriptomics data integration.","False","Visium","1266","32285"
"GSE242270_GSM7757568","human","kidney","","Spatiotemporal immune atlas of a clinical-grade gene-edited pig-to-human kidney xenotransplant","","","False","Visium","3555","47963"
"GSE242270_GSM7757569","pig","kidney","","Spatiotemporal immune atlas of a clinical-grade gene-edited pig-to-human kidney xenotransplant","","","False","Visium","4443","41918"
"GSE242311_GSM7757970","human","breast","","Spatial heterogeneity of integrins and their ligands in primary breast tumors","","","True","Visium","2830","17943"
"GSE242311_GSM7757971","human","breast","","Spatial heterogeneity of integrins and their ligands in primary breast tumors","","","True","Visium","2185","17943"
"GSE242311_GSM7757972","human","breast","","Spatial heterogeneity of integrins and their ligands in primary breast tumors","","","True","Visium","1505","17943"
"GSE242311_GSM7757973","human","breast","","Spatial heterogeneity of integrins and their ligands in primary breast tumors","","","True","Visium","2420","17943"
"GSE242311_GSM7757974","human","breast","","Spatial heterogeneity of integrins and their ligands in primary breast tumors","","","True","Visium","2276","17943"
"GSE242311_GSM7757975","human","breast","","Spatial heterogeneity of integrins and their ligands in primary breast tumors","","","True","Visium","3210","17943"
"GSE242311_GSM7757976","human","breast","","Spatial heterogeneity of integrins and their ligands in primary breast tumors","","","True","Visium","2622","17943"
"GSE242311_GSM7757977","human","breast","","Spatial heterogeneity of integrins and their ligands in primary breast tumors","","","True","Visium","2707","17943"
"GSE242311_GSM7757978","human","breast","","Spatial heterogeneity of integrins and their ligands in primary breast tumors","","","True","Visium","2753","17943"
"GSE242311_GSM7757979","human","breast","","Spatial heterogeneity of integrins and their ligands in primary breast tumors","","","True","Visium","2166","17943"
"GSE242311_GSM7757980","human","breast","","Spatial heterogeneity of integrins and their ligands in primary breast tumors","","","True","Visium","2305","17943"
"GSE242311_GSM7757981","human","breast","","Spatial heterogeneity of integrins and their ligands in primary breast tumors","","","True","Visium","3127","17943"
"GSE242311_GSM7757982","human","breast","","Spatial heterogeneity of integrins and their ligands in primary breast tumors","","","True","Visium","2440","17943"
"GSE242311_GSM7757983","human","breast","","Spatial heterogeneity of integrins and their ligands in primary breast tumors","","","True","Visium","2670","17943"
"GSE242311_GSM7757984","human","breast","","Spatial heterogeneity of integrins and their ligands in primary breast tumors","","","True","Visium","2885","17943"
"GSE242311_GSM7757985","human","breast","","Spatial heterogeneity of integrins and their ligands in primary breast tumors","","","True","Visium","3275","17943"
"GSE243179_GSM7780296","human","heart","37881937","Clonal Proliferation Within Smooth Muscle Cells in Unstable Human Atherosclerotic Lesions","Background: Studies in humans and mice using the expression of an X-linked gene or lineage tracing, respectively, have suggested that clones of smooth muscle cells (SMCs) exist in human atherosclerotic lesions but are limited by either spatial resolution or translatability of the model.

Methods: Phenotypic clonality can be detected by X-chromosome inactivation patterns. We investigated whether clones of SMCs exist in unstable human atheroma using RNA in situ hybridization (BaseScope) to identify a naturally occurring 24-nucleotide deletion in the 3'UTR of the X-linked BGN (biglycan) gene, a proteoglycan highly expressed by SMCs. BGN-specific BaseScope probes were designed to target the wild-type or deletion mRNA. Three different coronary artery plaque types (erosion, rupture, and adaptive intimal thickening) were selected from heterozygous females for the deletion BGN. Hybridization of target RNA-specific probes was used to visualize the spatial distribution of mutants. A clonality index was calculated from the percentage of each probe in each region of interest. Spatial transcriptomics were used to identify differentially expressed transcripts within clonal and nonclonal regions.

Results: Less than one-half of regions of interest in the intimal plaque were considered clonal with the mean percent regions of interest with clonality higher in the intimal plaque than in the media. This was consistent for all plaque types. The relationship of the dominant clone in the intimal plaque and media showed significant concordance. In comparison with the nonclonal lesions, the regions with SMC clonality had lower expression of genes encoding cell growth suppressors such as CD74, SERF-2 (small EDRK-rich factor 2), CTSB (cathepsin B), and HLA-DPA1 (major histocompatibility complex, class II, DP alpha 1), among others.

Conclusions: Our novel approach to examine clonality suggests atherosclerosis is primarily a disease of polyclonally and to a lesser extent clonally expanded SMCs and may have implications for the development of antiatherosclerotic therapies.","atherosclerosis; biglycan; coronary artery disease; gene expression; pathology.","False","Visium","2615","17943"
"GSE243179_GSM7780297","human","heart","37881937","Clonal Proliferation Within Smooth Muscle Cells in Unstable Human Atherosclerotic Lesions","Background: Studies in humans and mice using the expression of an X-linked gene or lineage tracing, respectively, have suggested that clones of smooth muscle cells (SMCs) exist in human atherosclerotic lesions but are limited by either spatial resolution or translatability of the model.

Methods: Phenotypic clonality can be detected by X-chromosome inactivation patterns. We investigated whether clones of SMCs exist in unstable human atheroma using RNA in situ hybridization (BaseScope) to identify a naturally occurring 24-nucleotide deletion in the 3'UTR of the X-linked BGN (biglycan) gene, a proteoglycan highly expressed by SMCs. BGN-specific BaseScope probes were designed to target the wild-type or deletion mRNA. Three different coronary artery plaque types (erosion, rupture, and adaptive intimal thickening) were selected from heterozygous females for the deletion BGN. Hybridization of target RNA-specific probes was used to visualize the spatial distribution of mutants. A clonality index was calculated from the percentage of each probe in each region of interest. Spatial transcriptomics were used to identify differentially expressed transcripts within clonal and nonclonal regions.

Results: Less than one-half of regions of interest in the intimal plaque were considered clonal with the mean percent regions of interest with clonality higher in the intimal plaque than in the media. This was consistent for all plaque types. The relationship of the dominant clone in the intimal plaque and media showed significant concordance. In comparison with the nonclonal lesions, the regions with SMC clonality had lower expression of genes encoding cell growth suppressors such as CD74, SERF-2 (small EDRK-rich factor 2), CTSB (cathepsin B), and HLA-DPA1 (major histocompatibility complex, class II, DP alpha 1), among others.

Conclusions: Our novel approach to examine clonality suggests atherosclerosis is primarily a disease of polyclonally and to a lesser extent clonally expanded SMCs and may have implications for the development of antiatherosclerotic therapies.","atherosclerosis; biglycan; coronary artery disease; gene expression; pathology.","False","Visium","1283","17943"
"GSE243225_GSM7781044","ambystoma mexicanum","arm","37816738","Multi-species atlas resolves an axolotl limb development and regeneration paradox","Humans and other tetrapods are considered to require apical-ectodermal-ridge (AER) cells for limb development, and AER-like cells are suggested to be re-formed to initiate limb regeneration. Paradoxically, the presence of AER in the axolotl, a primary model organism for regeneration, remains controversial. Here, by leveraging a single-cell transcriptomics-based multi-species atlas, composed of axolotl, human, mouse, chicken, and frog cells, we first establish that axolotls contain cells with AER characteristics. Further analyses and spatial transcriptomics reveal that axolotl limbs do not fully re-form AER cells during regeneration. Moreover, the axolotl mesoderm displays part of the AER machinery, revealing a program for limb (re)growth. These results clarify the debate about the axolotl AER and the extent to which the limb developmental program is recapitulated during regeneration.","","False","Visium","1089","99088"
"GSE243291_GSM7782914","human","muscle","38067175","Spatial Transcriptomics Reveals Signatures of Histopathological Changes in Muscular Sarcoidosis","Sarcoidosis is a multisystemic disease characterized by non-caseating granuloma infiltrating various organs. The form with symptomatic muscular involvement is called muscular sarcoidosis. The impact of immune cells composing the granuloma on the skeletal muscle is misunderstood. Here, we investigated the granuloma-skeletal muscle interactions through spatial transcriptomics on two patients affected by muscular sarcoidosis. Five major transcriptomic clusters corresponding to perigranuloma, granuloma, and three successive muscle tissue areas (proximal, intermediate, and distal) around the granuloma were identified. Analyses revealed upregulated pathways in the granuloma corresponding to the activation of T-lymphocytes and monocytes/macrophages cytokines, the upregulation of extracellular matrix signatures, and the induction of the TGF-β signaling in the perigranuloma. A comparison between the proximal and distal muscles to the granuloma revealed an inverse correlation between the distance to the granuloma and the upregulation of cellular response to interferon-γ/α, TNF-α, IL-1,4,6, fibroblast proliferation, epithelial to mesenchymal cell transition, and the downregulation of muscle gene expression. These data shed light on the intercommunications between granulomas and the muscle tissue and provide pathophysiological mechanisms by showing that granuloma immune cells have a direct impact on proximal muscle tissue by promoting its progressive replacement by fibrosis via the expression of pro-inflammatory and profibrosing signatures. These data could possibly explain the evolution towards a state of disability for some patients.","Visium; fibrosis; granuloma; muscular sarcoidosis; skeletal muscle; spatial transcriptomic.","False","Visium","2299","17943"
"GSE243291_GSM7782915","human","muscle","38067175","Spatial Transcriptomics Reveals Signatures of Histopathological Changes in Muscular Sarcoidosis","Sarcoidosis is a multisystemic disease characterized by non-caseating granuloma infiltrating various organs. The form with symptomatic muscular involvement is called muscular sarcoidosis. The impact of immune cells composing the granuloma on the skeletal muscle is misunderstood. Here, we investigated the granuloma-skeletal muscle interactions through spatial transcriptomics on two patients affected by muscular sarcoidosis. Five major transcriptomic clusters corresponding to perigranuloma, granuloma, and three successive muscle tissue areas (proximal, intermediate, and distal) around the granuloma were identified. Analyses revealed upregulated pathways in the granuloma corresponding to the activation of T-lymphocytes and monocytes/macrophages cytokines, the upregulation of extracellular matrix signatures, and the induction of the TGF-β signaling in the perigranuloma. A comparison between the proximal and distal muscles to the granuloma revealed an inverse correlation between the distance to the granuloma and the upregulation of cellular response to interferon-γ/α, TNF-α, IL-1,4,6, fibroblast proliferation, epithelial to mesenchymal cell transition, and the downregulation of muscle gene expression. These data shed light on the intercommunications between granulomas and the muscle tissue and provide pathophysiological mechanisms by showing that granuloma immune cells have a direct impact on proximal muscle tissue by promoting its progressive replacement by fibrosis via the expression of pro-inflammatory and profibrosing signatures. These data could possibly explain the evolution towards a state of disability for some patients.","Visium; fibrosis; granuloma; muscular sarcoidosis; skeletal muscle; spatial transcriptomic.","False","Visium","2723","17943"
"GSE243367_GSM7784328","human","liver","37968095","Spatial transcriptomics reveals a low extent of transcriptionally active hepatitis B virus integration in patients with HBsAg loss","Objective: Hepatitis B virus (HBV) can integrate into the chromosomes of infected hepatocytes, contributing to the production of hepatitis B surface antigen (HBsAg) and to hepatocarcinogenesis. In this study, we aimed to explore whether transcriptionally active HBV integration events spread throughout the liver tissue in different phases of chronic HBV infection, especially in patients with HBsAg loss.

Design: We constructed high-resolution spatial transcriptomes of liver biopsies containing 13 059 tissue spots from 18 patients with chronic HBV infection to analyse the occurrence and relative distribution of transcriptionally active viral integration events. Immunohistochemistry was performed to evaluate the expression of HBsAg and HBV core antigen. Intrahepatic covalently closed circular DNA (cccDNA) levels were quantified by real-time qPCR.

Results: Spatial transcriptome sequencing identified the presence of 13 154 virus-host chimeric reads in 7.86% (1026 of 13 059) of liver tissue spots in all patients, including three patients with HBsAg loss. These HBV integration sites were randomly distributed on chromosomes and can localise in host genes involved in hepatocarcinogenesis, such as ALB, CLU and APOB. Patients who were receiving or had received antiviral treatment had a significantly lower percentage of viral integration-containing spots and significantly fewer chimeric reads than treatment-naïve patients. Intrahepatic cccDNA levels correlated well with viral integration events.

Conclusion: Transcriptionally active HBV integration occurred in chronically HBV-infected patients at different phases, including in patients with HBsAg loss. Antiviral treatment was associated with a decreased number and extent of transcriptionally active viral integrations, implying that early treatment intervention may further reduce the number of viral integration events.","chronic viral hepatitis; hepatitis B; liver biopsy.","False","Visium","1158","36601"
"GSE243367_GSM7784329","human","liver","37968095","Spatial transcriptomics reveals a low extent of transcriptionally active hepatitis B virus integration in patients with HBsAg loss","Objective: Hepatitis B virus (HBV) can integrate into the chromosomes of infected hepatocytes, contributing to the production of hepatitis B surface antigen (HBsAg) and to hepatocarcinogenesis. In this study, we aimed to explore whether transcriptionally active HBV integration events spread throughout the liver tissue in different phases of chronic HBV infection, especially in patients with HBsAg loss.

Design: We constructed high-resolution spatial transcriptomes of liver biopsies containing 13 059 tissue spots from 18 patients with chronic HBV infection to analyse the occurrence and relative distribution of transcriptionally active viral integration events. Immunohistochemistry was performed to evaluate the expression of HBsAg and HBV core antigen. Intrahepatic covalently closed circular DNA (cccDNA) levels were quantified by real-time qPCR.

Results: Spatial transcriptome sequencing identified the presence of 13 154 virus-host chimeric reads in 7.86% (1026 of 13 059) of liver tissue spots in all patients, including three patients with HBsAg loss. These HBV integration sites were randomly distributed on chromosomes and can localise in host genes involved in hepatocarcinogenesis, such as ALB, CLU and APOB. Patients who were receiving or had received antiviral treatment had a significantly lower percentage of viral integration-containing spots and significantly fewer chimeric reads than treatment-naïve patients. Intrahepatic cccDNA levels correlated well with viral integration events.

Conclusion: Transcriptionally active HBV integration occurred in chronically HBV-infected patients at different phases, including in patients with HBsAg loss. Antiviral treatment was associated with a decreased number and extent of transcriptionally active viral integrations, implying that early treatment intervention may further reduce the number of viral integration events.","chronic viral hepatitis; hepatitis B; liver biopsy.","False","Visium","1928","36601"
"GSE243367_GSM7784330","human","liver","37968095","Spatial transcriptomics reveals a low extent of transcriptionally active hepatitis B virus integration in patients with HBsAg loss","Objective: Hepatitis B virus (HBV) can integrate into the chromosomes of infected hepatocytes, contributing to the production of hepatitis B surface antigen (HBsAg) and to hepatocarcinogenesis. In this study, we aimed to explore whether transcriptionally active HBV integration events spread throughout the liver tissue in different phases of chronic HBV infection, especially in patients with HBsAg loss.

Design: We constructed high-resolution spatial transcriptomes of liver biopsies containing 13 059 tissue spots from 18 patients with chronic HBV infection to analyse the occurrence and relative distribution of transcriptionally active viral integration events. Immunohistochemistry was performed to evaluate the expression of HBsAg and HBV core antigen. Intrahepatic covalently closed circular DNA (cccDNA) levels were quantified by real-time qPCR.

Results: Spatial transcriptome sequencing identified the presence of 13 154 virus-host chimeric reads in 7.86% (1026 of 13 059) of liver tissue spots in all patients, including three patients with HBsAg loss. These HBV integration sites were randomly distributed on chromosomes and can localise in host genes involved in hepatocarcinogenesis, such as ALB, CLU and APOB. Patients who were receiving or had received antiviral treatment had a significantly lower percentage of viral integration-containing spots and significantly fewer chimeric reads than treatment-naïve patients. Intrahepatic cccDNA levels correlated well with viral integration events.

Conclusion: Transcriptionally active HBV integration occurred in chronically HBV-infected patients at different phases, including in patients with HBsAg loss. Antiviral treatment was associated with a decreased number and extent of transcriptionally active viral integrations, implying that early treatment intervention may further reduce the number of viral integration events.","chronic viral hepatitis; hepatitis B; liver biopsy.","False","Visium","1471","36601"
"GSE243367_GSM7784331","human","liver","37968095","Spatial transcriptomics reveals a low extent of transcriptionally active hepatitis B virus integration in patients with HBsAg loss","Objective: Hepatitis B virus (HBV) can integrate into the chromosomes of infected hepatocytes, contributing to the production of hepatitis B surface antigen (HBsAg) and to hepatocarcinogenesis. In this study, we aimed to explore whether transcriptionally active HBV integration events spread throughout the liver tissue in different phases of chronic HBV infection, especially in patients with HBsAg loss.

Design: We constructed high-resolution spatial transcriptomes of liver biopsies containing 13 059 tissue spots from 18 patients with chronic HBV infection to analyse the occurrence and relative distribution of transcriptionally active viral integration events. Immunohistochemistry was performed to evaluate the expression of HBsAg and HBV core antigen. Intrahepatic covalently closed circular DNA (cccDNA) levels were quantified by real-time qPCR.

Results: Spatial transcriptome sequencing identified the presence of 13 154 virus-host chimeric reads in 7.86% (1026 of 13 059) of liver tissue spots in all patients, including three patients with HBsAg loss. These HBV integration sites were randomly distributed on chromosomes and can localise in host genes involved in hepatocarcinogenesis, such as ALB, CLU and APOB. Patients who were receiving or had received antiviral treatment had a significantly lower percentage of viral integration-containing spots and significantly fewer chimeric reads than treatment-naïve patients. Intrahepatic cccDNA levels correlated well with viral integration events.

Conclusion: Transcriptionally active HBV integration occurred in chronically HBV-infected patients at different phases, including in patients with HBsAg loss. Antiviral treatment was associated with a decreased number and extent of transcriptionally active viral integrations, implying that early treatment intervention may further reduce the number of viral integration events.","chronic viral hepatitis; hepatitis B; liver biopsy.","False","Visium","1742","36601"
"GSE243367_GSM7784332","human","liver","37968095","Spatial transcriptomics reveals a low extent of transcriptionally active hepatitis B virus integration in patients with HBsAg loss","Objective: Hepatitis B virus (HBV) can integrate into the chromosomes of infected hepatocytes, contributing to the production of hepatitis B surface antigen (HBsAg) and to hepatocarcinogenesis. In this study, we aimed to explore whether transcriptionally active HBV integration events spread throughout the liver tissue in different phases of chronic HBV infection, especially in patients with HBsAg loss.

Design: We constructed high-resolution spatial transcriptomes of liver biopsies containing 13 059 tissue spots from 18 patients with chronic HBV infection to analyse the occurrence and relative distribution of transcriptionally active viral integration events. Immunohistochemistry was performed to evaluate the expression of HBsAg and HBV core antigen. Intrahepatic covalently closed circular DNA (cccDNA) levels were quantified by real-time qPCR.

Results: Spatial transcriptome sequencing identified the presence of 13 154 virus-host chimeric reads in 7.86% (1026 of 13 059) of liver tissue spots in all patients, including three patients with HBsAg loss. These HBV integration sites were randomly distributed on chromosomes and can localise in host genes involved in hepatocarcinogenesis, such as ALB, CLU and APOB. Patients who were receiving or had received antiviral treatment had a significantly lower percentage of viral integration-containing spots and significantly fewer chimeric reads than treatment-naïve patients. Intrahepatic cccDNA levels correlated well with viral integration events.

Conclusion: Transcriptionally active HBV integration occurred in chronically HBV-infected patients at different phases, including in patients with HBsAg loss. Antiviral treatment was associated with a decreased number and extent of transcriptionally active viral integrations, implying that early treatment intervention may further reduce the number of viral integration events.","chronic viral hepatitis; hepatitis B; liver biopsy.","False","Visium","1274","36601"
"GSE243367_GSM7784333","human","liver","37968095","Spatial transcriptomics reveals a low extent of transcriptionally active hepatitis B virus integration in patients with HBsAg loss","Objective: Hepatitis B virus (HBV) can integrate into the chromosomes of infected hepatocytes, contributing to the production of hepatitis B surface antigen (HBsAg) and to hepatocarcinogenesis. In this study, we aimed to explore whether transcriptionally active HBV integration events spread throughout the liver tissue in different phases of chronic HBV infection, especially in patients with HBsAg loss.

Design: We constructed high-resolution spatial transcriptomes of liver biopsies containing 13 059 tissue spots from 18 patients with chronic HBV infection to analyse the occurrence and relative distribution of transcriptionally active viral integration events. Immunohistochemistry was performed to evaluate the expression of HBsAg and HBV core antigen. Intrahepatic covalently closed circular DNA (cccDNA) levels were quantified by real-time qPCR.

Results: Spatial transcriptome sequencing identified the presence of 13 154 virus-host chimeric reads in 7.86% (1026 of 13 059) of liver tissue spots in all patients, including three patients with HBsAg loss. These HBV integration sites were randomly distributed on chromosomes and can localise in host genes involved in hepatocarcinogenesis, such as ALB, CLU and APOB. Patients who were receiving or had received antiviral treatment had a significantly lower percentage of viral integration-containing spots and significantly fewer chimeric reads than treatment-naïve patients. Intrahepatic cccDNA levels correlated well with viral integration events.

Conclusion: Transcriptionally active HBV integration occurred in chronically HBV-infected patients at different phases, including in patients with HBsAg loss. Antiviral treatment was associated with a decreased number and extent of transcriptionally active viral integrations, implying that early treatment intervention may further reduce the number of viral integration events.","chronic viral hepatitis; hepatitis B; liver biopsy.","False","Visium","2091","36601"
"GSE243367_GSM7784334","human","liver","37968095","Spatial transcriptomics reveals a low extent of transcriptionally active hepatitis B virus integration in patients with HBsAg loss","Objective: Hepatitis B virus (HBV) can integrate into the chromosomes of infected hepatocytes, contributing to the production of hepatitis B surface antigen (HBsAg) and to hepatocarcinogenesis. In this study, we aimed to explore whether transcriptionally active HBV integration events spread throughout the liver tissue in different phases of chronic HBV infection, especially in patients with HBsAg loss.

Design: We constructed high-resolution spatial transcriptomes of liver biopsies containing 13 059 tissue spots from 18 patients with chronic HBV infection to analyse the occurrence and relative distribution of transcriptionally active viral integration events. Immunohistochemistry was performed to evaluate the expression of HBsAg and HBV core antigen. Intrahepatic covalently closed circular DNA (cccDNA) levels were quantified by real-time qPCR.

Results: Spatial transcriptome sequencing identified the presence of 13 154 virus-host chimeric reads in 7.86% (1026 of 13 059) of liver tissue spots in all patients, including three patients with HBsAg loss. These HBV integration sites were randomly distributed on chromosomes and can localise in host genes involved in hepatocarcinogenesis, such as ALB, CLU and APOB. Patients who were receiving or had received antiviral treatment had a significantly lower percentage of viral integration-containing spots and significantly fewer chimeric reads than treatment-naïve patients. Intrahepatic cccDNA levels correlated well with viral integration events.

Conclusion: Transcriptionally active HBV integration occurred in chronically HBV-infected patients at different phases, including in patients with HBsAg loss. Antiviral treatment was associated with a decreased number and extent of transcriptionally active viral integrations, implying that early treatment intervention may further reduce the number of viral integration events.","chronic viral hepatitis; hepatitis B; liver biopsy.","False","Visium","1213","36601"
"GSE243367_GSM7784335","human","liver","37968095","Spatial transcriptomics reveals a low extent of transcriptionally active hepatitis B virus integration in patients with HBsAg loss","Objective: Hepatitis B virus (HBV) can integrate into the chromosomes of infected hepatocytes, contributing to the production of hepatitis B surface antigen (HBsAg) and to hepatocarcinogenesis. In this study, we aimed to explore whether transcriptionally active HBV integration events spread throughout the liver tissue in different phases of chronic HBV infection, especially in patients with HBsAg loss.

Design: We constructed high-resolution spatial transcriptomes of liver biopsies containing 13 059 tissue spots from 18 patients with chronic HBV infection to analyse the occurrence and relative distribution of transcriptionally active viral integration events. Immunohistochemistry was performed to evaluate the expression of HBsAg and HBV core antigen. Intrahepatic covalently closed circular DNA (cccDNA) levels were quantified by real-time qPCR.

Results: Spatial transcriptome sequencing identified the presence of 13 154 virus-host chimeric reads in 7.86% (1026 of 13 059) of liver tissue spots in all patients, including three patients with HBsAg loss. These HBV integration sites were randomly distributed on chromosomes and can localise in host genes involved in hepatocarcinogenesis, such as ALB, CLU and APOB. Patients who were receiving or had received antiviral treatment had a significantly lower percentage of viral integration-containing spots and significantly fewer chimeric reads than treatment-naïve patients. Intrahepatic cccDNA levels correlated well with viral integration events.

Conclusion: Transcriptionally active HBV integration occurred in chronically HBV-infected patients at different phases, including in patients with HBsAg loss. Antiviral treatment was associated with a decreased number and extent of transcriptionally active viral integrations, implying that early treatment intervention may further reduce the number of viral integration events.","chronic viral hepatitis; hepatitis B; liver biopsy.","False","Visium","1872","36601"
"GSE243367_GSM7784336","human","liver","37968095","Spatial transcriptomics reveals a low extent of transcriptionally active hepatitis B virus integration in patients with HBsAg loss","Objective: Hepatitis B virus (HBV) can integrate into the chromosomes of infected hepatocytes, contributing to the production of hepatitis B surface antigen (HBsAg) and to hepatocarcinogenesis. In this study, we aimed to explore whether transcriptionally active HBV integration events spread throughout the liver tissue in different phases of chronic HBV infection, especially in patients with HBsAg loss.

Design: We constructed high-resolution spatial transcriptomes of liver biopsies containing 13 059 tissue spots from 18 patients with chronic HBV infection to analyse the occurrence and relative distribution of transcriptionally active viral integration events. Immunohistochemistry was performed to evaluate the expression of HBsAg and HBV core antigen. Intrahepatic covalently closed circular DNA (cccDNA) levels were quantified by real-time qPCR.

Results: Spatial transcriptome sequencing identified the presence of 13 154 virus-host chimeric reads in 7.86% (1026 of 13 059) of liver tissue spots in all patients, including three patients with HBsAg loss. These HBV integration sites were randomly distributed on chromosomes and can localise in host genes involved in hepatocarcinogenesis, such as ALB, CLU and APOB. Patients who were receiving or had received antiviral treatment had a significantly lower percentage of viral integration-containing spots and significantly fewer chimeric reads than treatment-naïve patients. Intrahepatic cccDNA levels correlated well with viral integration events.

Conclusion: Transcriptionally active HBV integration occurred in chronically HBV-infected patients at different phases, including in patients with HBsAg loss. Antiviral treatment was associated with a decreased number and extent of transcriptionally active viral integrations, implying that early treatment intervention may further reduce the number of viral integration events.","chronic viral hepatitis; hepatitis B; liver biopsy.","False","Visium","1574","36601"
"GSE243981_GSM7845914","human","liver","38199298","Single-cell, single-nucleus, and spatial transcriptomics characterization of the immunological landscape in the healthy and PSC human liver","Background & aims: Primary sclerosing cholangitis (PSC) is an immune-mediated cholestatic liver disease for which there is an unmet need to understand the cellular composition of the affected liver and how it underlies disease pathogenesis. We aimed to generate a comprehensive atlas of the PSC liver using multi-omic modalities and protein-based functional validation.

Methods: We employed single-cell and single-nucleus RNA sequencing (47,156 cells and 23,000 nuclei) and spatial transcriptomics (one sample by 10x Visium and five samples with Nanostring GeoMx DSP) to profile the cellular ecosystem in 10 PSC livers. Transcriptomic profiles were compared to 24 neurologically deceased donor livers (107,542 cells) and spatial transcriptomics controls, as well as 18,240 cells and 20,202 nuclei from three PBC livers. Flow cytometry was performed to validate PSC-specific differences in immune cell phenotype and function.

Results: PSC explants with parenchymal cirrhosis and prominent periductal fibrosis contained a population of cholangiocyte-like hepatocytes that were surrounded by diverse immune cell populations. PSC-associated biliary, mesenchymal, and endothelial populations expressed chemokine and cytokine transcripts involved in immune cell recruitment. Additionally, expanded CD4+ T cells and recruited myeloid populations in the PSC liver expressed the corresponding receptors to these chemokines and cytokines, suggesting potential recruitment. Tissue-resident macrophages, by contrast, were reduced in number and exhibited a dysfunctional and downregulated inflammatory response to lipopolysaccharide and interferon-γ stimulation.

Conclusions: We present a comprehensive atlas of the PSC liver and demonstrate an exhaustion-like phenotype of myeloid cells and markers of chronic cytokine expression in late-stage PSC lesions. This atlas expands our understanding of the cellular complexity of PSC and has potential to guide the development of novel treatments.

Impact and implications: Primary sclerosing cholangitis (PSC) is a rare liver disease characterized by chronic inflammation and irreparable damage to the bile ducts, which eventually results in liver failure. Due to a limited understanding of the underlying pathogenesis of disease, treatment options are limited. To address this, we sequenced healthy and diseased livers to compare the activity, interactions, and localization of immune and non-immune cells. This revealed that hepatocytes lining PSC scar regions co-express cholangiocyte markers, whereas immune cells infiltrate the scar lesions. Of these cells, macrophages, which typically contribute to tissue repair, were enriched in immunoregulatory genes and demonstrated a lack of responsiveness to stimulation. These cells may be involved in maintaining hepatic inflammation and could be a target for novel therapies.","Liver; Myeloid Dysfunction; Primary Sclerosing Cholangitis; Single Cell RNA sequencing; Spatial Transcriptomics.","False","Visium","3118","36601"
"GSE243981_GSM7845915","human","liver","38199298","Single-cell, single-nucleus, and spatial transcriptomics characterization of the immunological landscape in the healthy and PSC human liver","Background & aims: Primary sclerosing cholangitis (PSC) is an immune-mediated cholestatic liver disease for which there is an unmet need to understand the cellular composition of the affected liver and how it underlies disease pathogenesis. We aimed to generate a comprehensive atlas of the PSC liver using multi-omic modalities and protein-based functional validation.

Methods: We employed single-cell and single-nucleus RNA sequencing (47,156 cells and 23,000 nuclei) and spatial transcriptomics (one sample by 10x Visium and five samples with Nanostring GeoMx DSP) to profile the cellular ecosystem in 10 PSC livers. Transcriptomic profiles were compared to 24 neurologically deceased donor livers (107,542 cells) and spatial transcriptomics controls, as well as 18,240 cells and 20,202 nuclei from three PBC livers. Flow cytometry was performed to validate PSC-specific differences in immune cell phenotype and function.

Results: PSC explants with parenchymal cirrhosis and prominent periductal fibrosis contained a population of cholangiocyte-like hepatocytes that were surrounded by diverse immune cell populations. PSC-associated biliary, mesenchymal, and endothelial populations expressed chemokine and cytokine transcripts involved in immune cell recruitment. Additionally, expanded CD4+ T cells and recruited myeloid populations in the PSC liver expressed the corresponding receptors to these chemokines and cytokines, suggesting potential recruitment. Tissue-resident macrophages, by contrast, were reduced in number and exhibited a dysfunctional and downregulated inflammatory response to lipopolysaccharide and interferon-γ stimulation.

Conclusions: We present a comprehensive atlas of the PSC liver and demonstrate an exhaustion-like phenotype of myeloid cells and markers of chronic cytokine expression in late-stage PSC lesions. This atlas expands our understanding of the cellular complexity of PSC and has potential to guide the development of novel treatments.

Impact and implications: Primary sclerosing cholangitis (PSC) is a rare liver disease characterized by chronic inflammation and irreparable damage to the bile ducts, which eventually results in liver failure. Due to a limited understanding of the underlying pathogenesis of disease, treatment options are limited. To address this, we sequenced healthy and diseased livers to compare the activity, interactions, and localization of immune and non-immune cells. This revealed that hepatocytes lining PSC scar regions co-express cholangiocyte markers, whereas immune cells infiltrate the scar lesions. Of these cells, macrophages, which typically contribute to tissue repair, were enriched in immunoregulatory genes and demonstrated a lack of responsiveness to stimulation. These cells may be involved in maintaining hepatic inflammation and could be a target for novel therapies.","Liver; Myeloid Dysfunction; Primary Sclerosing Cholangitis; Single Cell RNA sequencing; Spatial Transcriptomics.","False","Visium","2670","36601"
"GSE243981_GSM7845916","human","liver","38199298","Single-cell, single-nucleus, and spatial transcriptomics characterization of the immunological landscape in the healthy and PSC human liver","Background & aims: Primary sclerosing cholangitis (PSC) is an immune-mediated cholestatic liver disease for which there is an unmet need to understand the cellular composition of the affected liver and how it underlies disease pathogenesis. We aimed to generate a comprehensive atlas of the PSC liver using multi-omic modalities and protein-based functional validation.

Methods: We employed single-cell and single-nucleus RNA sequencing (47,156 cells and 23,000 nuclei) and spatial transcriptomics (one sample by 10x Visium and five samples with Nanostring GeoMx DSP) to profile the cellular ecosystem in 10 PSC livers. Transcriptomic profiles were compared to 24 neurologically deceased donor livers (107,542 cells) and spatial transcriptomics controls, as well as 18,240 cells and 20,202 nuclei from three PBC livers. Flow cytometry was performed to validate PSC-specific differences in immune cell phenotype and function.

Results: PSC explants with parenchymal cirrhosis and prominent periductal fibrosis contained a population of cholangiocyte-like hepatocytes that were surrounded by diverse immune cell populations. PSC-associated biliary, mesenchymal, and endothelial populations expressed chemokine and cytokine transcripts involved in immune cell recruitment. Additionally, expanded CD4+ T cells and recruited myeloid populations in the PSC liver expressed the corresponding receptors to these chemokines and cytokines, suggesting potential recruitment. Tissue-resident macrophages, by contrast, were reduced in number and exhibited a dysfunctional and downregulated inflammatory response to lipopolysaccharide and interferon-γ stimulation.

Conclusions: We present a comprehensive atlas of the PSC liver and demonstrate an exhaustion-like phenotype of myeloid cells and markers of chronic cytokine expression in late-stage PSC lesions. This atlas expands our understanding of the cellular complexity of PSC and has potential to guide the development of novel treatments.

Impact and implications: Primary sclerosing cholangitis (PSC) is a rare liver disease characterized by chronic inflammation and irreparable damage to the bile ducts, which eventually results in liver failure. Due to a limited understanding of the underlying pathogenesis of disease, treatment options are limited. To address this, we sequenced healthy and diseased livers to compare the activity, interactions, and localization of immune and non-immune cells. This revealed that hepatocytes lining PSC scar regions co-express cholangiocyte markers, whereas immune cells infiltrate the scar lesions. Of these cells, macrophages, which typically contribute to tissue repair, were enriched in immunoregulatory genes and demonstrated a lack of responsiveness to stimulation. These cells may be involved in maintaining hepatic inflammation and could be a target for novel therapies.","Liver; Myeloid Dysfunction; Primary Sclerosing Cholangitis; Single Cell RNA sequencing; Spatial Transcriptomics.","False","Visium","3322","36601"
"GSE243981_GSM7845917","human","liver","38199298","Single-cell, single-nucleus, and spatial transcriptomics characterization of the immunological landscape in the healthy and PSC human liver","Background & aims: Primary sclerosing cholangitis (PSC) is an immune-mediated cholestatic liver disease for which there is an unmet need to understand the cellular composition of the affected liver and how it underlies disease pathogenesis. We aimed to generate a comprehensive atlas of the PSC liver using multi-omic modalities and protein-based functional validation.

Methods: We employed single-cell and single-nucleus RNA sequencing (47,156 cells and 23,000 nuclei) and spatial transcriptomics (one sample by 10x Visium and five samples with Nanostring GeoMx DSP) to profile the cellular ecosystem in 10 PSC livers. Transcriptomic profiles were compared to 24 neurologically deceased donor livers (107,542 cells) and spatial transcriptomics controls, as well as 18,240 cells and 20,202 nuclei from three PBC livers. Flow cytometry was performed to validate PSC-specific differences in immune cell phenotype and function.

Results: PSC explants with parenchymal cirrhosis and prominent periductal fibrosis contained a population of cholangiocyte-like hepatocytes that were surrounded by diverse immune cell populations. PSC-associated biliary, mesenchymal, and endothelial populations expressed chemokine and cytokine transcripts involved in immune cell recruitment. Additionally, expanded CD4+ T cells and recruited myeloid populations in the PSC liver expressed the corresponding receptors to these chemokines and cytokines, suggesting potential recruitment. Tissue-resident macrophages, by contrast, were reduced in number and exhibited a dysfunctional and downregulated inflammatory response to lipopolysaccharide and interferon-γ stimulation.

Conclusions: We present a comprehensive atlas of the PSC liver and demonstrate an exhaustion-like phenotype of myeloid cells and markers of chronic cytokine expression in late-stage PSC lesions. This atlas expands our understanding of the cellular complexity of PSC and has potential to guide the development of novel treatments.

Impact and implications: Primary sclerosing cholangitis (PSC) is a rare liver disease characterized by chronic inflammation and irreparable damage to the bile ducts, which eventually results in liver failure. Due to a limited understanding of the underlying pathogenesis of disease, treatment options are limited. To address this, we sequenced healthy and diseased livers to compare the activity, interactions, and localization of immune and non-immune cells. This revealed that hepatocytes lining PSC scar regions co-express cholangiocyte markers, whereas immune cells infiltrate the scar lesions. Of these cells, macrophages, which typically contribute to tissue repair, were enriched in immunoregulatory genes and demonstrated a lack of responsiveness to stimulation. These cells may be involved in maintaining hepatic inflammation and could be a target for novel therapies.","Liver; Myeloid Dysfunction; Primary Sclerosing Cholangitis; Single Cell RNA sequencing; Spatial Transcriptomics.","False","Visium","3174","36601"
"GSE244534_GSM7818789","mouse","pancreas","38207030","Deterministic reprogramming of neutrophils within tumors","Neutrophils are increasingly recognized as key players in the tumor immune response and are associated with poor clinical outcomes. Despite recent advances characterizing the diversity of neutrophil states in cancer, common trajectories and mechanisms governing the ontogeny and relationship between these neutrophil states remain undefined. Here, we demonstrate that immature and mature neutrophils that enter tumors undergo irreversible epigenetic, transcriptional, and proteomic modifications to converge into a distinct, terminally differentiated dcTRAIL-R1+ state. Reprogrammed dcTRAIL-R1+ neutrophils predominantly localize to a glycolytic and hypoxic niche at the tumor core and exert pro-angiogenic function that favors tumor growth. We found similar trajectories in neutrophils across multiple tumor types and in humans, suggesting that targeting this program may provide a means of enhancing certain cancer immunotherapies.","","True","Visium","4410","32285"
"GSE244534_GSM7818790","mouse","pancreas","38207030","Deterministic reprogramming of neutrophils within tumors","Neutrophils are increasingly recognized as key players in the tumor immune response and are associated with poor clinical outcomes. Despite recent advances characterizing the diversity of neutrophil states in cancer, common trajectories and mechanisms governing the ontogeny and relationship between these neutrophil states remain undefined. Here, we demonstrate that immature and mature neutrophils that enter tumors undergo irreversible epigenetic, transcriptional, and proteomic modifications to converge into a distinct, terminally differentiated dcTRAIL-R1+ state. Reprogrammed dcTRAIL-R1+ neutrophils predominantly localize to a glycolytic and hypoxic niche at the tumor core and exert pro-angiogenic function that favors tumor growth. We found similar trajectories in neutrophils across multiple tumor types and in humans, suggesting that targeting this program may provide a means of enhancing certain cancer immunotherapies.","","True","Visium","4653","32285"
"GSE244534_GSM7818791","mouse","pancreas","38207030","Deterministic reprogramming of neutrophils within tumors","Neutrophils are increasingly recognized as key players in the tumor immune response and are associated with poor clinical outcomes. Despite recent advances characterizing the diversity of neutrophil states in cancer, common trajectories and mechanisms governing the ontogeny and relationship between these neutrophil states remain undefined. Here, we demonstrate that immature and mature neutrophils that enter tumors undergo irreversible epigenetic, transcriptional, and proteomic modifications to converge into a distinct, terminally differentiated dcTRAIL-R1+ state. Reprogrammed dcTRAIL-R1+ neutrophils predominantly localize to a glycolytic and hypoxic niche at the tumor core and exert pro-angiogenic function that favors tumor growth. We found similar trajectories in neutrophils across multiple tumor types and in humans, suggesting that targeting this program may provide a means of enhancing certain cancer immunotherapies.","","True","Visium","3402","32285"
"GSE244534_GSM7818792","mouse","pancreas","38207030","Deterministic reprogramming of neutrophils within tumors","Neutrophils are increasingly recognized as key players in the tumor immune response and are associated with poor clinical outcomes. Despite recent advances characterizing the diversity of neutrophil states in cancer, common trajectories and mechanisms governing the ontogeny and relationship between these neutrophil states remain undefined. Here, we demonstrate that immature and mature neutrophils that enter tumors undergo irreversible epigenetic, transcriptional, and proteomic modifications to converge into a distinct, terminally differentiated dcTRAIL-R1+ state. Reprogrammed dcTRAIL-R1+ neutrophils predominantly localize to a glycolytic and hypoxic niche at the tumor core and exert pro-angiogenic function that favors tumor growth. We found similar trajectories in neutrophils across multiple tumor types and in humans, suggesting that targeting this program may provide a means of enhancing certain cancer immunotherapies.","","True","Visium","4225","32285"
"GSE245097_GSM7836297","mouse","tendon","38117901","TrkA-mediated sensory innervation of injured mouse tendon supports tendon sheath progenitor cell expansion and tendon repair","Peripheral neurons terminate at the surface of tendons partly to relay nociceptive pain signals; however, the role of peripheral nerves in tendon injury and repair remains unclear. Here, we show that after Achilles tendon injury in mice, there is new nerve growth near tendon cells that express nerve growth factor (NGF). Conditional deletion of the Ngf gene in either myeloid or mesenchymal mouse cells limited both innervation and tendon repair. Similarly, inhibition of the NGF receptor tropomyosin receptor kinase A (TrkA) abrogated tendon healing in mouse tendon injury. Sural nerve transection blocked the postinjury increase in tendon sensory innervation and the expansion of tendon sheath progenitor cells (TSPCs) expressing tubulin polymerization promoting protein family member 3. Single cell and spatial transcriptomics revealed that disruption of sensory innervation resulted in dysregulated inflammatory signaling and transforming growth factor-β (TGFβ) signaling in injured mouse tendon. Culture of mouse TSPCs with conditioned medium from dorsal root ganglia neuron further supported a role for neuronal mediators and TGFβ signaling in TSPC proliferation. Transcriptomic and histologic analyses of injured human tendon biopsy samples supported a role for innervation and TGFβ signaling in human tendon regeneration. Last, treating mice after tendon injury systemically with a small-molecule partial agonist of TrkA increased neurovascular response, TGFβ signaling, TSPC expansion, and tendon tissue repair. Although further studies should investigate the potential effects of denervation on mechanical loading of tendon, our results suggest that peripheral innervation is critical for the regenerative response after acute tendon injury.","","False","Visium","3047","32285"
"GSE245097_GSM7836298","mouse","tendon","38117901","TrkA-mediated sensory innervation of injured mouse tendon supports tendon sheath progenitor cell expansion and tendon repair","Peripheral neurons terminate at the surface of tendons partly to relay nociceptive pain signals; however, the role of peripheral nerves in tendon injury and repair remains unclear. Here, we show that after Achilles tendon injury in mice, there is new nerve growth near tendon cells that express nerve growth factor (NGF). Conditional deletion of the Ngf gene in either myeloid or mesenchymal mouse cells limited both innervation and tendon repair. Similarly, inhibition of the NGF receptor tropomyosin receptor kinase A (TrkA) abrogated tendon healing in mouse tendon injury. Sural nerve transection blocked the postinjury increase in tendon sensory innervation and the expansion of tendon sheath progenitor cells (TSPCs) expressing tubulin polymerization promoting protein family member 3. Single cell and spatial transcriptomics revealed that disruption of sensory innervation resulted in dysregulated inflammatory signaling and transforming growth factor-β (TGFβ) signaling in injured mouse tendon. Culture of mouse TSPCs with conditioned medium from dorsal root ganglia neuron further supported a role for neuronal mediators and TGFβ signaling in TSPC proliferation. Transcriptomic and histologic analyses of injured human tendon biopsy samples supported a role for innervation and TGFβ signaling in human tendon regeneration. Last, treating mice after tendon injury systemically with a small-molecule partial agonist of TrkA increased neurovascular response, TGFβ signaling, TSPC expansion, and tendon tissue repair. Although further studies should investigate the potential effects of denervation on mechanical loading of tendon, our results suggest that peripheral innervation is critical for the regenerative response after acute tendon injury.","","False","Visium","2027","32285"
"GSE245097_GSM7836299","mouse","tendon","38117901","TrkA-mediated sensory innervation of injured mouse tendon supports tendon sheath progenitor cell expansion and tendon repair","Peripheral neurons terminate at the surface of tendons partly to relay nociceptive pain signals; however, the role of peripheral nerves in tendon injury and repair remains unclear. Here, we show that after Achilles tendon injury in mice, there is new nerve growth near tendon cells that express nerve growth factor (NGF). Conditional deletion of the Ngf gene in either myeloid or mesenchymal mouse cells limited both innervation and tendon repair. Similarly, inhibition of the NGF receptor tropomyosin receptor kinase A (TrkA) abrogated tendon healing in mouse tendon injury. Sural nerve transection blocked the postinjury increase in tendon sensory innervation and the expansion of tendon sheath progenitor cells (TSPCs) expressing tubulin polymerization promoting protein family member 3. Single cell and spatial transcriptomics revealed that disruption of sensory innervation resulted in dysregulated inflammatory signaling and transforming growth factor-β (TGFβ) signaling in injured mouse tendon. Culture of mouse TSPCs with conditioned medium from dorsal root ganglia neuron further supported a role for neuronal mediators and TGFβ signaling in TSPC proliferation. Transcriptomic and histologic analyses of injured human tendon biopsy samples supported a role for innervation and TGFβ signaling in human tendon regeneration. Last, treating mice after tendon injury systemically with a small-molecule partial agonist of TrkA increased neurovascular response, TGFβ signaling, TSPC expansion, and tendon tissue repair. Although further studies should investigate the potential effects of denervation on mechanical loading of tendon, our results suggest that peripheral innervation is critical for the regenerative response after acute tendon injury.","","False","Visium","2837","32285"
"GSE245097_GSM7836300","mouse","tendon","38117901","TrkA-mediated sensory innervation of injured mouse tendon supports tendon sheath progenitor cell expansion and tendon repair","Peripheral neurons terminate at the surface of tendons partly to relay nociceptive pain signals; however, the role of peripheral nerves in tendon injury and repair remains unclear. Here, we show that after Achilles tendon injury in mice, there is new nerve growth near tendon cells that express nerve growth factor (NGF). Conditional deletion of the Ngf gene in either myeloid or mesenchymal mouse cells limited both innervation and tendon repair. Similarly, inhibition of the NGF receptor tropomyosin receptor kinase A (TrkA) abrogated tendon healing in mouse tendon injury. Sural nerve transection blocked the postinjury increase in tendon sensory innervation and the expansion of tendon sheath progenitor cells (TSPCs) expressing tubulin polymerization promoting protein family member 3. Single cell and spatial transcriptomics revealed that disruption of sensory innervation resulted in dysregulated inflammatory signaling and transforming growth factor-β (TGFβ) signaling in injured mouse tendon. Culture of mouse TSPCs with conditioned medium from dorsal root ganglia neuron further supported a role for neuronal mediators and TGFβ signaling in TSPC proliferation. Transcriptomic and histologic analyses of injured human tendon biopsy samples supported a role for innervation and TGFβ signaling in human tendon regeneration. Last, treating mice after tendon injury systemically with a small-molecule partial agonist of TrkA increased neurovascular response, TGFβ signaling, TSPC expansion, and tendon tissue repair. Although further studies should investigate the potential effects of denervation on mechanical loading of tendon, our results suggest that peripheral innervation is critical for the regenerative response after acute tendon injury.","","False","Visium","2751","32285"
"GSE245110_GSM7836925","mouse","lymph node","38374265","SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains","Modern multiomic technologies can generate deep multiscale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make analyses and integration of high-dimensional omic datasets challenging. Here we present Significant Latent Factor Interaction Discovery and Exploration (SLIDE), a first-in-class interpretable machine learning technique for identifying significant interacting latent factors underlying outcomes of interest from high-dimensional omic datasets. SLIDE makes no assumptions regarding data-generating mechanisms, comes with theoretical guarantees regarding identifiability of the latent factors/corresponding inference, and has rigorous false discovery rate control. Using SLIDE on single-cell and spatial omic datasets, we uncovered significant interacting latent factors underlying a range of molecular, cellular and organismal phenotypes. SLIDE outperforms/performs at least as well as a wide range of state-of-the-art approaches, including other latent factor approaches. More importantly, it provides biological inference beyond prediction that other methods do not afford. Thus, SLIDE is a versatile engine for biological discovery from modern multiomic datasets.","","False","Visium","657","32285"
"GSE245110_GSM7836926","mouse","lymph node","38374265","SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains","Modern multiomic technologies can generate deep multiscale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make analyses and integration of high-dimensional omic datasets challenging. Here we present Significant Latent Factor Interaction Discovery and Exploration (SLIDE), a first-in-class interpretable machine learning technique for identifying significant interacting latent factors underlying outcomes of interest from high-dimensional omic datasets. SLIDE makes no assumptions regarding data-generating mechanisms, comes with theoretical guarantees regarding identifiability of the latent factors/corresponding inference, and has rigorous false discovery rate control. Using SLIDE on single-cell and spatial omic datasets, we uncovered significant interacting latent factors underlying a range of molecular, cellular and organismal phenotypes. SLIDE outperforms/performs at least as well as a wide range of state-of-the-art approaches, including other latent factor approaches. More importantly, it provides biological inference beyond prediction that other methods do not afford. Thus, SLIDE is a versatile engine for biological discovery from modern multiomic datasets.","","False","Visium","582","32285"
"GSE245110_GSM7836927","mouse","lymph node","38374265","SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains","Modern multiomic technologies can generate deep multiscale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make analyses and integration of high-dimensional omic datasets challenging. Here we present Significant Latent Factor Interaction Discovery and Exploration (SLIDE), a first-in-class interpretable machine learning technique for identifying significant interacting latent factors underlying outcomes of interest from high-dimensional omic datasets. SLIDE makes no assumptions regarding data-generating mechanisms, comes with theoretical guarantees regarding identifiability of the latent factors/corresponding inference, and has rigorous false discovery rate control. Using SLIDE on single-cell and spatial omic datasets, we uncovered significant interacting latent factors underlying a range of molecular, cellular and organismal phenotypes. SLIDE outperforms/performs at least as well as a wide range of state-of-the-art approaches, including other latent factor approaches. More importantly, it provides biological inference beyond prediction that other methods do not afford. Thus, SLIDE is a versatile engine for biological discovery from modern multiomic datasets.","","False","Visium","621","32285"
"GSE245110_GSM7836928","mouse","lymph node","38374265","SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains","Modern multiomic technologies can generate deep multiscale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make analyses and integration of high-dimensional omic datasets challenging. Here we present Significant Latent Factor Interaction Discovery and Exploration (SLIDE), a first-in-class interpretable machine learning technique for identifying significant interacting latent factors underlying outcomes of interest from high-dimensional omic datasets. SLIDE makes no assumptions regarding data-generating mechanisms, comes with theoretical guarantees regarding identifiability of the latent factors/corresponding inference, and has rigorous false discovery rate control. Using SLIDE on single-cell and spatial omic datasets, we uncovered significant interacting latent factors underlying a range of molecular, cellular and organismal phenotypes. SLIDE outperforms/performs at least as well as a wide range of state-of-the-art approaches, including other latent factor approaches. More importantly, it provides biological inference beyond prediction that other methods do not afford. Thus, SLIDE is a versatile engine for biological discovery from modern multiomic datasets.","","False","Visium","885","32285"
"GSE245263_GSM7839621","mouse","brain","","Spatial transcriptomics of brains from tumor-bearing C57BL/6 mice","","","True","Visium","2158","32272"
"GSE245313_GSM7840724","frog","tail","","Characterization of regeneration initiating cells during Xenopus laevis tail regeneration [spatial transcriptomics]","","","False","Visium","1805","44478"
"GSE245313_GSM7840725","frog","tail","","Characterization of regeneration initiating cells during Xenopus laevis tail regeneration [spatial transcriptomics]","","","False","Visium","1267","44478"
"GSE245313_GSM7840726","frog","tail","","Characterization of regeneration initiating cells during Xenopus laevis tail regeneration [spatial transcriptomics]","","","False","Visium","2123","44478"
"GSE245388_GSM7841727","human","prostate","","Spatial transcriptomics reveals unexpected histological heterogeneity in primary prostate","","","False","Visium","64","36601"
"GSE245388_GSM7841728","human","prostate","","Spatial transcriptomics reveals unexpected histological heterogeneity in primary prostate","","","False","Visium","130","36601"
"GSE245388_GSM7841729","human","prostate","","Spatial transcriptomics reveals unexpected histological heterogeneity in primary prostate","","","False","Visium","235","36601"
"GSE245388_GSM7841730","human","prostate","","Spatial transcriptomics reveals unexpected histological heterogeneity in primary prostate","","","False","Visium","149","36601"
"GSE245388_GSM7841731","human","prostate","","Spatial transcriptomics reveals unexpected histological heterogeneity in primary prostate","","","False","Visium","444","36601"
"GSE245388_GSM7841732","human","prostate","","Spatial transcriptomics reveals unexpected histological heterogeneity in primary prostate","","","False","Visium","99","36601"
"GSE245388_GSM7841733","human","prostate","","Spatial transcriptomics reveals unexpected histological heterogeneity in primary prostate","","","False","Visium","498","36601"
"GSE245388_GSM7841734","human","prostate","","Spatial transcriptomics reveals unexpected histological heterogeneity in primary prostate","","","False","Visium","302","36601"
"GSE245908_GSM7850822","human","liver","","Identification of TREM1+CD163+ myeloid cells as a deleterious immune subset in HCC [Spatial Transcriptomics]","","","False","Visium","2261","36601"
"GSE245908_GSM7850823","human","liver","","Identification of TREM1+CD163+ myeloid cells as a deleterious immune subset in HCC [Spatial Transcriptomics]","","","False","Visium","1987","36601"
"GSE248356_GSM7911966","mouse","melanoma","","Spatial transcriptomics reveals the pharmacological effects on tumors and TME structural heterogeneity under MASK vaccine treatment","","","True","Visium","4798","32589"
"GSE248356_GSM7911967","mouse","melanoma","","Spatial transcriptomics reveals the pharmacological effects on tumors and TME structural heterogeneity under MASK vaccine treatment","","","True","Visium","4898","32589"
"GSE248356_GSM7911968","mouse","melanoma","","Spatial transcriptomics reveals the pharmacological effects on tumors and TME structural heterogeneity under MASK vaccine treatment","","","True","Visium","4663","32589"
"GSE248646_GSM7918345","rattus norvegicus","tendon","","Spatial Gene Expression in the Adult Rat Patellar Tendon","","","False","Visium","2179","25399"
"GSE248646_GSM7918346","rattus norvegicus","tendon","","Spatial Gene Expression in the Adult Rat Patellar Tendon","","","False","Visium","1624","25399"
"GSE249729_GSM7962127","human","skin","38113104","Tertiary lymphoid structures sustain cutaneous B cell activity in hidradenitis suppurativa","Hidradenitis suppurativa (HS) is a chronic skin condition affecting approximately 1% of the US population. HS skin lesions are highly inflammatory and characterized by a large immune infiltrate. While B cells and plasma cells comprise a major component of this immune milieu, the biology and the contribution of these cells in HS pathogenesis are unclear. We aimed to investigate the dynamics and microenvironmental interactions of B cells within cutaneous HS lesions. Combining histological analysis, single-cell RNA sequencing, and spatial transcriptomics profiling of HS lesions, we defined the tissue microenvironment relative to B cell activity within this disease. Our findings identified tertiary lymphoid structures (TLSs) within HS lesions and described organized interactions among T cells, B cells, antigen-presenting cells, and skin stroma. We found evidence that B cells within HS TLSs actively underwent maturation, including participation in germinal center reactions and class switch recombination. Moreover, skin stroma and accumulating T cells were primed to support the formation of TLSs and facilitate B cell recruitment during HS. Our data definitively demonstrated the presence of TLSs in lesional HS skin and point to ongoing cutaneous B cell maturation through class switch recombination and affinity maturation during disease progression in this inflamed nonlymphoid tissue.","Adaptive immunity; Dermatology; Immunology; Skin.","False","Visium","3766","17943"
"GSE249729_GSM7962128","human","skin","38113104","Tertiary lymphoid structures sustain cutaneous B cell activity in hidradenitis suppurativa","Hidradenitis suppurativa (HS) is a chronic skin condition affecting approximately 1% of the US population. HS skin lesions are highly inflammatory and characterized by a large immune infiltrate. While B cells and plasma cells comprise a major component of this immune milieu, the biology and the contribution of these cells in HS pathogenesis are unclear. We aimed to investigate the dynamics and microenvironmental interactions of B cells within cutaneous HS lesions. Combining histological analysis, single-cell RNA sequencing, and spatial transcriptomics profiling of HS lesions, we defined the tissue microenvironment relative to B cell activity within this disease. Our findings identified tertiary lymphoid structures (TLSs) within HS lesions and described organized interactions among T cells, B cells, antigen-presenting cells, and skin stroma. We found evidence that B cells within HS TLSs actively underwent maturation, including participation in germinal center reactions and class switch recombination. Moreover, skin stroma and accumulating T cells were primed to support the formation of TLSs and facilitate B cell recruitment during HS. Our data definitively demonstrated the presence of TLSs in lesional HS skin and point to ongoing cutaneous B cell maturation through class switch recombination and affinity maturation during disease progression in this inflamed nonlymphoid tissue.","Adaptive immunity; Dermatology; Immunology; Skin.","False","Visium","4437","17943"
"GSE249729_GSM7962129","human","skin","38113104","Tertiary lymphoid structures sustain cutaneous B cell activity in hidradenitis suppurativa","Hidradenitis suppurativa (HS) is a chronic skin condition affecting approximately 1% of the US population. HS skin lesions are highly inflammatory and characterized by a large immune infiltrate. While B cells and plasma cells comprise a major component of this immune milieu, the biology and the contribution of these cells in HS pathogenesis are unclear. We aimed to investigate the dynamics and microenvironmental interactions of B cells within cutaneous HS lesions. Combining histological analysis, single-cell RNA sequencing, and spatial transcriptomics profiling of HS lesions, we defined the tissue microenvironment relative to B cell activity within this disease. Our findings identified tertiary lymphoid structures (TLSs) within HS lesions and described organized interactions among T cells, B cells, antigen-presenting cells, and skin stroma. We found evidence that B cells within HS TLSs actively underwent maturation, including participation in germinal center reactions and class switch recombination. Moreover, skin stroma and accumulating T cells were primed to support the formation of TLSs and facilitate B cell recruitment during HS. Our data definitively demonstrated the presence of TLSs in lesional HS skin and point to ongoing cutaneous B cell maturation through class switch recombination and affinity maturation during disease progression in this inflamed nonlymphoid tissue.","Adaptive immunity; Dermatology; Immunology; Skin.","False","Visium","1815","17943"
"GSE251693_GSM7986081","human","colon","","Double-negative B cells and DNASE1L3 colocalise with microbiota in gut-associated lymphoid tissue","","","False","Visium","3408","17943"
"GSE251693_GSM7986082","human","colon","","Double-negative B cells and DNASE1L3 colocalise with microbiota in gut-associated lymphoid tissue","","","False","Visium","2351","17943"
"GSE251950_GSM7990473","human","stomach","","Spatial transcriptomics of gastric cancers (Visium 10X platform)","","","True","Visium","3834","36601"
"GSE251950_GSM7990474","human","stomach","","Spatial transcriptomics of gastric cancers (Visium 10X platform)","","","True","Visium","4274","36601"
"GSE251950_GSM7990475","human","stomach","","Spatial transcriptomics of gastric cancers (Visium 10X platform)","","","True","Visium","3043","36601"
"GSE251950_GSM7990476","human","stomach","","Spatial transcriptomics of gastric cancers (Visium 10X platform)","","","True","Visium","4244","36601"
"GSE251950_GSM7990477","human","stomach","","Spatial transcriptomics of gastric cancers (Visium 10X platform)","","","True","Visium","3577","36601"
"GSE251950_GSM7990478","human","stomach","","Spatial transcriptomics of gastric cancers (Visium 10X platform)","","","True","Visium","2426","36601"
"GSE251950_GSM7990479","human","stomach","","Spatial transcriptomics of gastric cancers (Visium 10X platform)","","","True","Visium","2483","36601"
"GSE251950_GSM7990480","human","stomach","","Spatial transcriptomics of gastric cancers (Visium 10X platform)","","","True","Visium","1882","36601"
"GSE251950_GSM7990481","human","stomach","","Spatial transcriptomics of gastric cancers (Visium 10X platform)","","","True","Visium","3037","36601"
"GSE251950_GSM7990482","human","stomach","","Spatial transcriptomics of gastric cancers (Visium 10X platform)","","","True","Visium","3491","36601"
"GSE254364_GSM8041061","human","liver","36652202","High-Resolution Analysis of Mononuclear Phagocytes Reveals GPNMB as a Prognostic Marker in Human Colorectal Liver Metastasis","Patients with colorectal liver metastasis (CLM) present with heterogenous clinical outcomes and improved classification is needed to ameliorate the therapeutic output. Macrophages (Mϕ) hold promise as prognostic classifiers and therapeutic targets. Here, stemming from a single-cell analysis of mononuclear phagocytes infiltrating human CLM, we identified two Mϕ markers associated with distinct populations with opposite clinical relevance. The invasive margin of CLM was enriched in pro-inflammatory monocyte-derived Mϕ (MoMϕ) expressing the monocytic marker SERPINB2, and a more differentiated population, tumor-associated Mϕ (TAM), expressing glycoprotein nonmetastatic melanoma protein B (GPNMB). SERPINB2+ MoMϕ had an early inflammatory profile, whereas GPNMB+ TAMs were enriched in pathways of matrix degradation, angiogenesis, and lipid metabolism and were found closer to the tumor margin, as confirmed by spatial transcriptomics on CLM specimens. In a cohort of patients, a high infiltration of SERPINB2+ cells independently associated with longer disease-free survival (DFS; P = 0.033), whereas a high density of GPNMB+ cells correlated with shorter DFS (P = 0.012) and overall survival (P = 0.002). Cell-cell interaction analysis defined opposing roles for MoMϕ and TAMs, suggesting that SERPINB2+ and GPNMB+ cells are discrete populations of Mϕ and may be exploited for further translation to an immune-based stratification tool. This study provides evidence of how multi-omics approaches can identify nonredundant, clinically relevant markers for further translation to immune-based patient stratification tools and therapeutic targets. GPNMB has been shown to set Mϕ in an immunosuppressive mode. Our high dimensional analyses provide further evidence that GPNMB is a negative prognostic indicator and a potential player in the protumor function of Mϕ populations.","","True","Visium","4160","17943"
"GSE254364_GSM8041062","human","liver","36652202","High-Resolution Analysis of Mononuclear Phagocytes Reveals GPNMB as a Prognostic Marker in Human Colorectal Liver Metastasis","Patients with colorectal liver metastasis (CLM) present with heterogenous clinical outcomes and improved classification is needed to ameliorate the therapeutic output. Macrophages (Mϕ) hold promise as prognostic classifiers and therapeutic targets. Here, stemming from a single-cell analysis of mononuclear phagocytes infiltrating human CLM, we identified two Mϕ markers associated with distinct populations with opposite clinical relevance. The invasive margin of CLM was enriched in pro-inflammatory monocyte-derived Mϕ (MoMϕ) expressing the monocytic marker SERPINB2, and a more differentiated population, tumor-associated Mϕ (TAM), expressing glycoprotein nonmetastatic melanoma protein B (GPNMB). SERPINB2+ MoMϕ had an early inflammatory profile, whereas GPNMB+ TAMs were enriched in pathways of matrix degradation, angiogenesis, and lipid metabolism and were found closer to the tumor margin, as confirmed by spatial transcriptomics on CLM specimens. In a cohort of patients, a high infiltration of SERPINB2+ cells independently associated with longer disease-free survival (DFS; P = 0.033), whereas a high density of GPNMB+ cells correlated with shorter DFS (P = 0.012) and overall survival (P = 0.002). Cell-cell interaction analysis defined opposing roles for MoMϕ and TAMs, suggesting that SERPINB2+ and GPNMB+ cells are discrete populations of Mϕ and may be exploited for further translation to an immune-based stratification tool. This study provides evidence of how multi-omics approaches can identify nonredundant, clinically relevant markers for further translation to immune-based patient stratification tools and therapeutic targets. GPNMB has been shown to set Mϕ in an immunosuppressive mode. Our high dimensional analyses provide further evidence that GPNMB is a negative prognostic indicator and a potential player in the protumor function of Mϕ populations.","","True","Visium","3070","17943"
"GSE254652_GSM8047864","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","2207","32285"
"GSE254652_GSM8047865","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","1948","32285"
"GSE254652_GSM8047866","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","1220","32285"
"GSE254652_GSM8047867","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","2238","32285"
"GSE254652_GSM8047868","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","592","19465"
"GSE254652_GSM8047869","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","637","19465"
"GSE254652_GSM8047870","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","833","19465"
"GSE254652_GSM8047871","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","3224","32285"
"GSE254652_GSM8047872","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","2048","32285"
"GSE254652_GSM8047873","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","1877","32285"
"GSE254652_GSM8047874","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","2222","32285"
"GSE254652_GSM8047875","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","2213","32285"
"GSE254652_GSM8047876","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","2708","32285"
"GSE254652_GSM8047877","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","2244","32285"
"GSE254652_GSM8047878","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","2528","32285"
"GSE254652_GSM8047879","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","2034","19465"
"GSE254652_GSM8047880","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","1910","19465"
"GSE254652_GSM8047881","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","1922","19465"
"GSE254652_GSM8047882","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","1640","19465"
"GSE254652_GSM8047883","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","2553","19465"
"GSE254652_GSM8047884","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","2498","19465"
"GSE254652_GSM8047885","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","2002","19465"
"GSE254652_GSM8047886","mouse","spleen","","Comparing 10x Visium spatial transcriptomic technologies","","","False","Visium","1711","19465"
"GSE254829_GSM8058242","human","pancreas","","PanIN and CAF Transitions in Pancreatic Carcinogenesis Revealed with Spatial Data Integration","","","False","Visium","2291","17943"
"GSE254829_GSM8058243","human","pancreas","","PanIN and CAF Transitions in Pancreatic Carcinogenesis Revealed with Spatial Data Integration","","","False","Visium","2172","17943"
"GSE254829_GSM8058244","human","pancreas","","PanIN and CAF Transitions in Pancreatic Carcinogenesis Revealed with Spatial Data Integration","","","False","Visium","2118","17943"
"GSE254829_GSM8058245","human","pancreas","","PanIN and CAF Transitions in Pancreatic Carcinogenesis Revealed with Spatial Data Integration","","","False","Visium","2635","17943"
"GSE254829_GSM8058246","human","pancreas","","PanIN and CAF Transitions in Pancreatic Carcinogenesis Revealed with Spatial Data Integration","","","False","Visium","2682","17943"
"GSE254829_GSM8058247","human","pancreas","","PanIN and CAF Transitions in Pancreatic Carcinogenesis Revealed with Spatial Data Integration","","","False","Visium","1872","17943"
"GSE254829_GSM8058248","human","pancreas","","PanIN and CAF Transitions in Pancreatic Carcinogenesis Revealed with Spatial Data Integration","","","False","Visium","2392","17943"
"GSE254829_GSM8058249","human","pancreas","","PanIN and CAF Transitions in Pancreatic Carcinogenesis Revealed with Spatial Data Integration","","","False","Visium","2119","17943"
"GSE254829_GSM8058250","human","pancreas","","PanIN and CAF Transitions in Pancreatic Carcinogenesis Revealed with Spatial Data Integration","","","False","Visium","2689","17943"
"GSE254829_GSM8058251","human","pancreas","","PanIN and CAF Transitions in Pancreatic Carcinogenesis Revealed with Spatial Data Integration","","","False","Visium","2864","17943"
"GSE254829_GSM8058252","human","pancreas","","PanIN and CAF Transitions in Pancreatic Carcinogenesis Revealed with Spatial Data Integration","","","False","Visium","2238","17943"
"GSE254829_GSM8058253","human","pancreas","","PanIN and CAF Transitions in Pancreatic Carcinogenesis Revealed with Spatial Data Integration","","","False","Visium","1484","17943"
"GSE254829_GSM8058254","human","pancreas","","PanIN and CAF Transitions in Pancreatic Carcinogenesis Revealed with Spatial Data Integration","","","False","Visium","1893","17943"
"GSE254829_GSM8058255","human","pancreas","","PanIN and CAF Transitions in Pancreatic Carcinogenesis Revealed with Spatial Data Integration","","","False","Visium","2573","17943"
"GSE259363_GSM8114827","mouse","liver","","Glycolysis in hepatic stellate cells coordinates fibrogenic extracellular vesicle release in a spatial manner to amplify liver fibrosis","","","False","Visium","3064","19465"
"GSE259363_GSM8114828","mouse","liver","","Glycolysis in hepatic stellate cells coordinates fibrogenic extracellular vesicle release in a spatial manner to amplify liver fibrosis","","","False","Visium","4134","19465"
"GSE259363_GSM8114829","mouse","liver","","Glycolysis in hepatic stellate cells coordinates fibrogenic extracellular vesicle release in a spatial manner to amplify liver fibrosis","","","False","Visium","3865","19465"
"GSE259363_GSM8114830","mouse","liver","","Glycolysis in hepatic stellate cells coordinates fibrogenic extracellular vesicle release in a spatial manner to amplify liver fibrosis","","","False","Visium","2577","19465"
"Human_Brain+Kidney_10X_02212023_Visium","human","brain+kidney","","","","","False","Visium","2805","19465"
"Human_Brain_10X_02132023_Visium","human","brain","","","","","False","Visium","10878","18085"
"Human_Brain_10X_10272020_Visium_Cerebellum_WholeTranscriptome","human","brain","","","","","False","Visium","4992","36601"
"Human_Brain_10X_10272020_Visium_WholeTranscriptome","human","brain","","","","","False","Visium","3468","36601"
"Human_Brain_Kwon_10162023_Visium_V10A27004_A1_Br3874","human","brain","","Influence of Alzheimer's Disease Related Neuropathology on Local Microenvironment Gene Expression in the Human Inferior Temporal Cortex","Neuropathological lesions in the brains of individuals affected with neurodegenerative disorders are hypothesized to trigger molecular and cellular processes that disturb the homeostasis of local microenvironments. Here, we applied the 10x Genomics Visium Spatial Proteogenomics (Visium-SPG) platform, which couples spatial gene expression with immunofluorescence (IF) protein co-detection, to evaluate its ability to quantify changes in spatial gene expression with respect to amyloid-beta (Aβ) and hyperphosphorylated tau (pTau) pathology in post-mortem human brain tissue from individuals with Alzheimer's disease (AD). We identified transcriptomic signatures associated with proximity to Aβ in the human inferior temporal cortex during late-stage AD, which we further investigated at cellular resolution with combined IF and single-molecule fluorescent in situ hybridization (smFISH). The study provides a data analysis workflow for Visium-SPG, and the data represent a proof-of-principle for the power of multi-omic profiling in identifying changes in molecular dynamics that are spatially associated with pathology in the human brain. We provide the scientific community with web-based, interactive resources to access the datasets of the spatially resolved AD-related transcriptomes.","","False","Visium","4910","36601"
"Human_Brain_Kwon_10162023_Visium_V10A27004_D1_Br3880","human","brain","","Influence of Alzheimer's Disease Related Neuropathology on Local Microenvironment Gene Expression in the Human Inferior Temporal Cortex","Neuropathological lesions in the brains of individuals affected with neurodegenerative disorders are hypothesized to trigger molecular and cellular processes that disturb the homeostasis of local microenvironments. Here, we applied the 10x Genomics Visium Spatial Proteogenomics (Visium-SPG) platform, which couples spatial gene expression with immunofluorescence (IF) protein co-detection, to evaluate its ability to quantify changes in spatial gene expression with respect to amyloid-beta (Aβ) and hyperphosphorylated tau (pTau) pathology in post-mortem human brain tissue from individuals with Alzheimer's disease (AD). We identified transcriptomic signatures associated with proximity to Aβ in the human inferior temporal cortex during late-stage AD, which we further investigated at cellular resolution with combined IF and single-molecule fluorescent in situ hybridization (smFISH). The study provides a data analysis workflow for Visium-SPG, and the data represent a proof-of-principle for the power of multi-omic profiling in identifying changes in molecular dynamics that are spatially associated with pathology in the human brain. We provide the scientific community with web-based, interactive resources to access the datasets of the spatially resolved AD-related transcriptomes.","","False","Visium","4610","36601"
"Human_Brain_Kwon_10162023_Visium_V10A27106_A1_Br3874","human","brain","","Influence of Alzheimer's Disease Related Neuropathology on Local Microenvironment Gene Expression in the Human Inferior Temporal Cortex","Neuropathological lesions in the brains of individuals affected with neurodegenerative disorders are hypothesized to trigger molecular and cellular processes that disturb the homeostasis of local microenvironments. Here, we applied the 10x Genomics Visium Spatial Proteogenomics (Visium-SPG) platform, which couples spatial gene expression with immunofluorescence (IF) protein co-detection, to evaluate its ability to quantify changes in spatial gene expression with respect to amyloid-beta (Aβ) and hyperphosphorylated tau (pTau) pathology in post-mortem human brain tissue from individuals with Alzheimer's disease (AD). We identified transcriptomic signatures associated with proximity to Aβ in the human inferior temporal cortex during late-stage AD, which we further investigated at cellular resolution with combined IF and single-molecule fluorescent in situ hybridization (smFISH). The study provides a data analysis workflow for Visium-SPG, and the data represent a proof-of-principle for the power of multi-omic profiling in identifying changes in molecular dynamics that are spatially associated with pathology in the human brain. We provide the scientific community with web-based, interactive resources to access the datasets of the spatially resolved AD-related transcriptomes.","","False","Visium","4187","36601"
"Human_Brain_Kwon_10162023_Visium_V10A27106_B1_Br3854","human","brain","","Influence of Alzheimer's Disease Related Neuropathology on Local Microenvironment Gene Expression in the Human Inferior Temporal Cortex","Neuropathological lesions in the brains of individuals affected with neurodegenerative disorders are hypothesized to trigger molecular and cellular processes that disturb the homeostasis of local microenvironments. Here, we applied the 10x Genomics Visium Spatial Proteogenomics (Visium-SPG) platform, which couples spatial gene expression with immunofluorescence (IF) protein co-detection, to evaluate its ability to quantify changes in spatial gene expression with respect to amyloid-beta (Aβ) and hyperphosphorylated tau (pTau) pathology in post-mortem human brain tissue from individuals with Alzheimer's disease (AD). We identified transcriptomic signatures associated with proximity to Aβ in the human inferior temporal cortex during late-stage AD, which we further investigated at cellular resolution with combined IF and single-molecule fluorescent in situ hybridization (smFISH). The study provides a data analysis workflow for Visium-SPG, and the data represent a proof-of-principle for the power of multi-omic profiling in identifying changes in molecular dynamics that are spatially associated with pathology in the human brain. We provide the scientific community with web-based, interactive resources to access the datasets of the spatially resolved AD-related transcriptomes.","","False","Visium","2686","36601"
"Human_Brain_Kwon_10162023_Visium_V10A27106_C1_Br3873","human","brain","","Influence of Alzheimer's Disease Related Neuropathology on Local Microenvironment Gene Expression in the Human Inferior Temporal Cortex","Neuropathological lesions in the brains of individuals affected with neurodegenerative disorders are hypothesized to trigger molecular and cellular processes that disturb the homeostasis of local microenvironments. Here, we applied the 10x Genomics Visium Spatial Proteogenomics (Visium-SPG) platform, which couples spatial gene expression with immunofluorescence (IF) protein co-detection, to evaluate its ability to quantify changes in spatial gene expression with respect to amyloid-beta (Aβ) and hyperphosphorylated tau (pTau) pathology in post-mortem human brain tissue from individuals with Alzheimer's disease (AD). We identified transcriptomic signatures associated with proximity to Aβ in the human inferior temporal cortex during late-stage AD, which we further investigated at cellular resolution with combined IF and single-molecule fluorescent in situ hybridization (smFISH). The study provides a data analysis workflow for Visium-SPG, and the data represent a proof-of-principle for the power of multi-omic profiling in identifying changes in molecular dynamics that are spatially associated with pathology in the human brain. We provide the scientific community with web-based, interactive resources to access the datasets of the spatially resolved AD-related transcriptomes.","","False","Visium","2975","36601"
"Human_Brain_Kwon_10162023_Visium_V10A27106_D1_Br3880","human","brain","","Influence of Alzheimer's Disease Related Neuropathology on Local Microenvironment Gene Expression in the Human Inferior Temporal Cortex","Neuropathological lesions in the brains of individuals affected with neurodegenerative disorders are hypothesized to trigger molecular and cellular processes that disturb the homeostasis of local microenvironments. Here, we applied the 10x Genomics Visium Spatial Proteogenomics (Visium-SPG) platform, which couples spatial gene expression with immunofluorescence (IF) protein co-detection, to evaluate its ability to quantify changes in spatial gene expression with respect to amyloid-beta (Aβ) and hyperphosphorylated tau (pTau) pathology in post-mortem human brain tissue from individuals with Alzheimer's disease (AD). We identified transcriptomic signatures associated with proximity to Aβ in the human inferior temporal cortex during late-stage AD, which we further investigated at cellular resolution with combined IF and single-molecule fluorescent in situ hybridization (smFISH). The study provides a data analysis workflow for Visium-SPG, and the data represent a proof-of-principle for the power of multi-omic profiling in identifying changes in molecular dynamics that are spatially associated with pathology in the human brain. We provide the scientific community with web-based, interactive resources to access the datasets of the spatially resolved AD-related transcriptomes.","","False","Visium","4552","36601"
"Human_Brain_Kwon_10162023_Visium_V10T31036_A1_Br3874","human","brain","","Influence of Alzheimer's Disease Related Neuropathology on Local Microenvironment Gene Expression in the Human Inferior Temporal Cortex","Neuropathological lesions in the brains of individuals affected with neurodegenerative disorders are hypothesized to trigger molecular and cellular processes that disturb the homeostasis of local microenvironments. Here, we applied the 10x Genomics Visium Spatial Proteogenomics (Visium-SPG) platform, which couples spatial gene expression with immunofluorescence (IF) protein co-detection, to evaluate its ability to quantify changes in spatial gene expression with respect to amyloid-beta (Aβ) and hyperphosphorylated tau (pTau) pathology in post-mortem human brain tissue from individuals with Alzheimer's disease (AD). We identified transcriptomic signatures associated with proximity to Aβ in the human inferior temporal cortex during late-stage AD, which we further investigated at cellular resolution with combined IF and single-molecule fluorescent in situ hybridization (smFISH). The study provides a data analysis workflow for Visium-SPG, and the data represent a proof-of-principle for the power of multi-omic profiling in identifying changes in molecular dynamics that are spatially associated with pathology in the human brain. We provide the scientific community with web-based, interactive resources to access the datasets of the spatially resolved AD-related transcriptomes.","","False","Visium","4300","36601"
"Human_Brain_Kwon_10162023_Visium_V10T31036_B1_Br3854","human","brain","","Influence of Alzheimer's Disease Related Neuropathology on Local Microenvironment Gene Expression in the Human Inferior Temporal Cortex","Neuropathological lesions in the brains of individuals affected with neurodegenerative disorders are hypothesized to trigger molecular and cellular processes that disturb the homeostasis of local microenvironments. Here, we applied the 10x Genomics Visium Spatial Proteogenomics (Visium-SPG) platform, which couples spatial gene expression with immunofluorescence (IF) protein co-detection, to evaluate its ability to quantify changes in spatial gene expression with respect to amyloid-beta (Aβ) and hyperphosphorylated tau (pTau) pathology in post-mortem human brain tissue from individuals with Alzheimer's disease (AD). We identified transcriptomic signatures associated with proximity to Aβ in the human inferior temporal cortex during late-stage AD, which we further investigated at cellular resolution with combined IF and single-molecule fluorescent in situ hybridization (smFISH). The study provides a data analysis workflow for Visium-SPG, and the data represent a proof-of-principle for the power of multi-omic profiling in identifying changes in molecular dynamics that are spatially associated with pathology in the human brain. We provide the scientific community with web-based, interactive resources to access the datasets of the spatially resolved AD-related transcriptomes.","","False","Visium","3524","36601"
"Human_Brain_Kwon_10162023_Visium_V10T31036_C1_Br3873","human","brain","","Influence of Alzheimer's Disease Related Neuropathology on Local Microenvironment Gene Expression in the Human Inferior Temporal Cortex","Neuropathological lesions in the brains of individuals affected with neurodegenerative disorders are hypothesized to trigger molecular and cellular processes that disturb the homeostasis of local microenvironments. Here, we applied the 10x Genomics Visium Spatial Proteogenomics (Visium-SPG) platform, which couples spatial gene expression with immunofluorescence (IF) protein co-detection, to evaluate its ability to quantify changes in spatial gene expression with respect to amyloid-beta (Aβ) and hyperphosphorylated tau (pTau) pathology in post-mortem human brain tissue from individuals with Alzheimer's disease (AD). We identified transcriptomic signatures associated with proximity to Aβ in the human inferior temporal cortex during late-stage AD, which we further investigated at cellular resolution with combined IF and single-molecule fluorescent in situ hybridization (smFISH). The study provides a data analysis workflow for Visium-SPG, and the data represent a proof-of-principle for the power of multi-omic profiling in identifying changes in molecular dynamics that are spatially associated with pathology in the human brain. We provide the scientific community with web-based, interactive resources to access the datasets of the spatially resolved AD-related transcriptomes.","","False","Visium","2878","36601"
"Human_Brain_Kwon_10162023_Visium_V10T31036_D1_Br3880","human","brain","","Influence of Alzheimer's Disease Related Neuropathology on Local Microenvironment Gene Expression in the Human Inferior Temporal Cortex","Neuropathological lesions in the brains of individuals affected with neurodegenerative disorders are hypothesized to trigger molecular and cellular processes that disturb the homeostasis of local microenvironments. Here, we applied the 10x Genomics Visium Spatial Proteogenomics (Visium-SPG) platform, which couples spatial gene expression with immunofluorescence (IF) protein co-detection, to evaluate its ability to quantify changes in spatial gene expression with respect to amyloid-beta (Aβ) and hyperphosphorylated tau (pTau) pathology in post-mortem human brain tissue from individuals with Alzheimer's disease (AD). We identified transcriptomic signatures associated with proximity to Aβ in the human inferior temporal cortex during late-stage AD, which we further investigated at cellular resolution with combined IF and single-molecule fluorescent in situ hybridization (smFISH). The study provides a data analysis workflow for Visium-SPG, and the data represent a proof-of-principle for the power of multi-omic profiling in identifying changes in molecular dynamics that are spatially associated with pathology in the human brain. We provide the scientific community with web-based, interactive resources to access the datasets of the spatially resolved AD-related transcriptomes.","","False","Visium","4590","36601"
"Human_Brain_Louise_02152023_Visium_Br2720_ant","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","3167","36601"
"Human_Brain_Louise_02152023_Visium_Br2720_mid","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","1825","36601"
"Human_Brain_Louise_02152023_Visium_Br2720_post","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","4684","36601"
"Human_Brain_Louise_02152023_Visium_Br2743_ant","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","4144","36601"
"Human_Brain_Louise_02152023_Visium_Br2743_mid","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","4246","36601"
"Human_Brain_Louise_02152023_Visium_Br2743_post","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","3869","36601"
"Human_Brain_Louise_02152023_Visium_Br3942_ant","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","4041","36601"
"Human_Brain_Louise_02152023_Visium_Br3942_mid","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","3928","36601"
"Human_Brain_Louise_02152023_Visium_Br3942_post","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","4400","36601"
"Human_Brain_Louise_02152023_Visium_Br6423_ant","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","3906","36601"
"Human_Brain_Louise_02152023_Visium_Br6423_mid","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","3984","36601"
"Human_Brain_Louise_02152023_Visium_Br6423_post","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","3851","36601"
"Human_Brain_Louise_02152023_Visium_Br6432_ant","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","3984","36601"
"Human_Brain_Louise_02152023_Visium_Br6432_mid","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","3511","36601"
"Human_Brain_Louise_02152023_Visium_Br6432_post","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","2887","36601"
"Human_Brain_Louise_02152023_Visium_Br6471_ant","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","3187","36601"
"Human_Brain_Louise_02152023_Visium_Br6471_mid","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","4541","36601"
"Human_Brain_Louise_02152023_Visium_Br6471_post","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","4416","36601"
"Human_Brain_Louise_02152023_Visium_Br6522_ant","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","4298","36601"
"Human_Brain_Louise_02152023_Visium_Br6522_mid","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","3767","36601"
"Human_Brain_Louise_02152023_Visium_Br6522_post","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","3910","36601"
"Human_Brain_Louise_02152023_Visium_Br8325_ant","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","3529","36601"
"Human_Brain_Louise_02152023_Visium_Br8325_mid","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","4160","36601"
"Human_Brain_Louise_02152023_Visium_Br8325_post","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","4399","36601"
"Human_Brain_Louise_02152023_Visium_Br8492_ant","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","4793","36601"
"Human_Brain_Louise_02152023_Visium_Br8492_mid","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","4435","36601"
"Human_Brain_Louise_02152023_Visium_Br8492_post","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","4618","36601"
"Human_Brain_Louise_02152023_Visium_Br8667_ant","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","3658","36601"
"Human_Brain_Louise_02152023_Visium_Br8667_mid","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","4265","36601"
"Human_Brain_Louise_02152023_Visium_Br8667_post","human","brain","36824961","Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex","Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.","","False","Visium","4397","36601"
"Human_Brain_Maynard_02082021_Visium_151507","human","brain","33558695","Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex","We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex. We identified extensive layer-enriched expression signatures and refined associations to previous laminar markers. We overlaid our laminar expression signatures on large-scale single nucleus RNA-sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions in which morphological architecture is not as well defined as cortical laminae. Last, we created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research ( http://research.libd.org/spatialLIBD ).","","False","Visium","4226","33538"
"Human_Brain_Maynard_02082021_Visium_151508","human","brain","33558695","Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex","We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex. We identified extensive layer-enriched expression signatures and refined associations to previous laminar markers. We overlaid our laminar expression signatures on large-scale single nucleus RNA-sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions in which morphological architecture is not as well defined as cortical laminae. Last, we created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research ( http://research.libd.org/spatialLIBD ).","","False","Visium","4384","33538"
"Human_Brain_Maynard_02082021_Visium_151509","human","brain","33558695","Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex","We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex. We identified extensive layer-enriched expression signatures and refined associations to previous laminar markers. We overlaid our laminar expression signatures on large-scale single nucleus RNA-sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions in which morphological architecture is not as well defined as cortical laminae. Last, we created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research ( http://research.libd.org/spatialLIBD ).","","False","Visium","4789","33538"
"Human_Brain_Maynard_02082021_Visium_151510","human","brain","33558695","Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex","We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex. We identified extensive layer-enriched expression signatures and refined associations to previous laminar markers. We overlaid our laminar expression signatures on large-scale single nucleus RNA-sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions in which morphological architecture is not as well defined as cortical laminae. Last, we created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research ( http://research.libd.org/spatialLIBD ).","","False","Visium","4634","33538"
"Human_Brain_Maynard_02082021_Visium_151669","human","brain","33558695","Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex","We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex. We identified extensive layer-enriched expression signatures and refined associations to previous laminar markers. We overlaid our laminar expression signatures on large-scale single nucleus RNA-sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions in which morphological architecture is not as well defined as cortical laminae. Last, we created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research ( http://research.libd.org/spatialLIBD ).","","False","Visium","3661","33538"
"Human_Brain_Maynard_02082021_Visium_151670","human","brain","33558695","Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex","We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex. We identified extensive layer-enriched expression signatures and refined associations to previous laminar markers. We overlaid our laminar expression signatures on large-scale single nucleus RNA-sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions in which morphological architecture is not as well defined as cortical laminae. Last, we created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research ( http://research.libd.org/spatialLIBD ).","","False","Visium","3498","33538"
"Human_Brain_Maynard_02082021_Visium_151671","human","brain","33558695","Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex","We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex. We identified extensive layer-enriched expression signatures and refined associations to previous laminar markers. We overlaid our laminar expression signatures on large-scale single nucleus RNA-sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions in which morphological architecture is not as well defined as cortical laminae. Last, we created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research ( http://research.libd.org/spatialLIBD ).","","False","Visium","4110","33538"
"Human_Brain_Maynard_02082021_Visium_151672","human","brain","33558695","Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex","We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex. We identified extensive layer-enriched expression signatures and refined associations to previous laminar markers. We overlaid our laminar expression signatures on large-scale single nucleus RNA-sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions in which morphological architecture is not as well defined as cortical laminae. Last, we created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research ( http://research.libd.org/spatialLIBD ).","","False","Visium","4015","33538"
"Human_Brain_Maynard_02082021_Visium_151673","human","brain","33558695","Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex","We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex. We identified extensive layer-enriched expression signatures and refined associations to previous laminar markers. We overlaid our laminar expression signatures on large-scale single nucleus RNA-sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions in which morphological architecture is not as well defined as cortical laminae. Last, we created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research ( http://research.libd.org/spatialLIBD ).","","False","Visium","3639","33538"
"Human_Brain_Maynard_02082021_Visium_151674","human","brain","33558695","Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex","We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex. We identified extensive layer-enriched expression signatures and refined associations to previous laminar markers. We overlaid our laminar expression signatures on large-scale single nucleus RNA-sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions in which morphological architecture is not as well defined as cortical laminae. Last, we created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research ( http://research.libd.org/spatialLIBD ).","","False","Visium","3673","33538"
"Human_Brain_Maynard_02082021_Visium_151675","human","brain","33558695","Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex","We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex. We identified extensive layer-enriched expression signatures and refined associations to previous laminar markers. We overlaid our laminar expression signatures on large-scale single nucleus RNA-sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions in which morphological architecture is not as well defined as cortical laminae. Last, we created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research ( http://research.libd.org/spatialLIBD ).","","False","Visium","3592","33538"
"Human_Brain_Maynard_02082021_Visium_151676","human","brain","33558695","Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex","We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex. We identified extensive layer-enriched expression signatures and refined associations to previous laminar markers. We overlaid our laminar expression signatures on large-scale single nucleus RNA-sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions in which morphological architecture is not as well defined as cortical laminae. Last, we created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research ( http://research.libd.org/spatialLIBD ).","","False","Visium","3460","33538"
"Human_Breast_10X_02132023_Visium","human","breast","","","","","False","Visium","1657","18085"
"Human_Breast_10X_06092021_Visium","human","breast","","","","","False","Visium","2518","17943"
"Human_Breast_10X_06232020_Visium_Block_A_Section_1","human","breast","","","","","False","Visium","3798","36601"
"Human_Breast_10X_06232020_Visium_Block_A_Section_2","human","breast","","","","","False","Visium","3987","36601"
"Human_Breast_10X_07012022_Visium","human","breast","","","","","False","Visium","4898","36601"
"Human_Breast_10X_10272020_Visium_WholeTranscriptome","human","breast","","","","","False","Visium","4325","36601"
"Human_Breast_Wu_06052021_Visium_1142243F","human","breast","34493872","A single-cell and spatially resolved atlas of human breast cancers","Breast cancers are complex cellular ecosystems where heterotypic interactions play central roles in disease progression and response to therapy. However, our knowledge of their cellular composition and organization is limited. Here we present a single-cell and spatially resolved transcriptomics analysis of human breast cancers. We developed a single-cell method of intrinsic subtype classification (SCSubtype) to reveal recurrent neoplastic cell heterogeneity. Immunophenotyping using cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) provides high-resolution immune profiles, including new PD-L1/PD-L2+ macrophage populations associated with clinical outcome. Mesenchymal cells displayed diverse functions and cell-surface protein expression through differentiation within three major lineages. Stromal-immune niches were spatially organized in tumors, offering insights into antitumor immune regulation. Using single-cell signatures, we deconvoluted large breast cancer cohorts to stratify them into nine clusters, termed 'ecotypes', with unique cellular compositions and clinical outcomes. This study provides a comprehensive transcriptional atlas of the cellular architecture of breast cancer.","","True","Visium","4784","28402"
"Human_Breast_Wu_06052021_Visium_1160920F","human","breast","34493872","A single-cell and spatially resolved atlas of human breast cancers","Breast cancers are complex cellular ecosystems where heterotypic interactions play central roles in disease progression and response to therapy. However, our knowledge of their cellular composition and organization is limited. Here we present a single-cell and spatially resolved transcriptomics analysis of human breast cancers. We developed a single-cell method of intrinsic subtype classification (SCSubtype) to reveal recurrent neoplastic cell heterogeneity. Immunophenotyping using cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) provides high-resolution immune profiles, including new PD-L1/PD-L2+ macrophage populations associated with clinical outcome. Mesenchymal cells displayed diverse functions and cell-surface protein expression through differentiation within three major lineages. Stromal-immune niches were spatially organized in tumors, offering insights into antitumor immune regulation. Using single-cell signatures, we deconvoluted large breast cancer cohorts to stratify them into nine clusters, termed 'ecotypes', with unique cellular compositions and clinical outcomes. This study provides a comprehensive transcriptional atlas of the cellular architecture of breast cancer.","","True","Visium","4895","28402"
"Human_Breast_Wu_06052021_Visium_CID4465","human","breast","34493872","A single-cell and spatially resolved atlas of human breast cancers","Breast cancers are complex cellular ecosystems where heterotypic interactions play central roles in disease progression and response to therapy. However, our knowledge of their cellular composition and organization is limited. Here we present a single-cell and spatially resolved transcriptomics analysis of human breast cancers. We developed a single-cell method of intrinsic subtype classification (SCSubtype) to reveal recurrent neoplastic cell heterogeneity. Immunophenotyping using cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) provides high-resolution immune profiles, including new PD-L1/PD-L2+ macrophage populations associated with clinical outcome. Mesenchymal cells displayed diverse functions and cell-surface protein expression through differentiation within three major lineages. Stromal-immune niches were spatially organized in tumors, offering insights into antitumor immune regulation. Using single-cell signatures, we deconvoluted large breast cancer cohorts to stratify them into nine clusters, termed 'ecotypes', with unique cellular compositions and clinical outcomes. This study provides a comprehensive transcriptional atlas of the cellular architecture of breast cancer.","","True","Visium","1211","19237"
"Human_Breast_Wu_06052021_Visium_CID44971","human","breast","34493872","A single-cell and spatially resolved atlas of human breast cancers","Breast cancers are complex cellular ecosystems where heterotypic interactions play central roles in disease progression and response to therapy. However, our knowledge of their cellular composition and organization is limited. Here we present a single-cell and spatially resolved transcriptomics analysis of human breast cancers. We developed a single-cell method of intrinsic subtype classification (SCSubtype) to reveal recurrent neoplastic cell heterogeneity. Immunophenotyping using cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) provides high-resolution immune profiles, including new PD-L1/PD-L2+ macrophage populations associated with clinical outcome. Mesenchymal cells displayed diverse functions and cell-surface protein expression through differentiation within three major lineages. Stromal-immune niches were spatially organized in tumors, offering insights into antitumor immune regulation. Using single-cell signatures, we deconvoluted large breast cancer cohorts to stratify them into nine clusters, termed 'ecotypes', with unique cellular compositions and clinical outcomes. This study provides a comprehensive transcriptional atlas of the cellular architecture of breast cancer.","","True","Visium","1160","19237"
"Human_Breast_Wu_06052021_Visium_CID4535","human","breast","34493872","A single-cell and spatially resolved atlas of human breast cancers","Breast cancers are complex cellular ecosystems where heterotypic interactions play central roles in disease progression and response to therapy. However, our knowledge of their cellular composition and organization is limited. Here we present a single-cell and spatially resolved transcriptomics analysis of human breast cancers. We developed a single-cell method of intrinsic subtype classification (SCSubtype) to reveal recurrent neoplastic cell heterogeneity. Immunophenotyping using cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) provides high-resolution immune profiles, including new PD-L1/PD-L2+ macrophage populations associated with clinical outcome. Mesenchymal cells displayed diverse functions and cell-surface protein expression through differentiation within three major lineages. Stromal-immune niches were spatially organized in tumors, offering insights into antitumor immune regulation. Using single-cell signatures, we deconvoluted large breast cancer cohorts to stratify them into nine clusters, termed 'ecotypes', with unique cellular compositions and clinical outcomes. This study provides a comprehensive transcriptional atlas of the cellular architecture of breast cancer.","","True","Visium","1125","19237"
"Human_Cerebellum_10X_07122022_Visium","human","cerebellum","","","","","False","Visium","4992","1186"
"Human_Cervix_10X_03282022_Visium","human","cervix","","","","","False","Visium","2781","17943"
"Human_Colon_10X_10052023_Visium_control_rep1","human","colon","","","","","False","Visium","6487","18085"
"Human_Colon_10X_10052023_Visium_control_rep2","human","colon","","","","","False","Visium","6414","18085"
"Human_Colon_10X_10052023_Visium_post_xenium_rep1","human","colon","","","","","False","Visium","6518","18085"
"Human_Colon_10X_10052023_Visium_post_xenium_rep2","human","colon","","","","","False","Visium","6352","18085"
"Human_Colorectal_10X_02132023_Visium","human","colorectal","","","","","False","Visium","9080","18085"
"Human_Heart_10X_06232020_Visium","human","heart","","","","","False","Visium","4247","36601"
"Human_Heart_Kuppe_10082022_Visium_AKK001_157785","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","3086","36601"
"Human_Heart_Kuppe_10082022_Visium_AKK002_157779","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","3778","36601"
"Human_Heart_Kuppe_10082022_Visium_AKK002_157781","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","4662","36601"
"Human_Heart_Kuppe_10082022_Visium_AKK002_157782","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","2995","36601"
"Human_Heart_Kuppe_10082022_Visium_AKK003_157775","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","4278","36601"
"Human_Heart_Kuppe_10082022_Visium_AKK003_157777","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","3377","36601"
"Human_Heart_Kuppe_10082022_Visium_AKK004_157772","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","4534","36601"
"Human_Heart_Kuppe_10082022_Visium_AKK006_157771","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","4279","36601"
"Human_Heart_Kuppe_10082022_Visium_Visium_10_CK288","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","3420","36601"
"Human_Heart_Kuppe_10082022_Visium_Visium_11_CK289","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","2960","36601"
"Human_Heart_Kuppe_10082022_Visium_Visium_12_CK290","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","3193","36601"
"Human_Heart_Kuppe_10082022_Visium_Visium_13_CK291","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","2803","36601"
"Human_Heart_Kuppe_10082022_Visium_Visium_14_CK292","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","3242","36601"
"Human_Heart_Kuppe_10082022_Visium_Visium_15_CK293","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","4365","36601"
"Human_Heart_Kuppe_10082022_Visium_Visium_16_CK294","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","4262","36601"
"Human_Heart_Kuppe_10082022_Visium_Visium_17_CK295","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","3535","36601"
"Human_Heart_Kuppe_10082022_Visium_Visium_18_CK296","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","3580","36601"
"Human_Heart_Kuppe_10082022_Visium_Visium_19_CK297","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","4281","36601"
"Human_Heart_Kuppe_10082022_Visium_Visium_1_CK279","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","2049","36601"
"Human_Heart_Kuppe_10082022_Visium_Visium_20_CK298","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","3120","36601"
"Human_Heart_Kuppe_10082022_Visium_Visium_2_CK280","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","2822","36601"
"Human_Heart_Kuppe_10082022_Visium_Visium_3_CK281","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","2936","36601"
"Human_Heart_Kuppe_10082022_Visium_Visium_4_CK282","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","2461","36601"
"Human_Heart_Kuppe_10082022_Visium_Visium_5_CK283","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","3562","36601"
"Human_Heart_Kuppe_10082022_Visium_Visium_6_CK284","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","3642","36601"
"Human_Heart_Kuppe_10082022_Visium_Visium_7_CK285","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","3656","36601"
"Human_Heart_Kuppe_10082022_Visium_Visium_8_CK286","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","2029","36601"
"Human_Heart_Kuppe_10082022_Visium_Visium_9_CK287","human","heart","35948637","Spatial multi-omic map of human myocardial infarction","Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.","","False","Visium","4451","36601"
"Human_Kidney_10X_02132023_Visium","human","kidney","","","","","False","Visium","5936","18085"
"Human_LargeIntestine_10X_03282022_Visium","human","largeintestine","","","","","False","Visium","2660","17943"
"Human_LargeIntestine_10X_10272020_Visium_WholeTranscriptome","human","largeintestine","","","","","False","Visium","3138","36601"
"Human_Lung_10X_02132023_Visium","human","lung","","","","","False","Visium","6195","18085"
"Human_Lung_10X_07132022_Visium","human","lung","","","","","False","Visium","3858","18085"
"Human_LymphNode_10X_06232020_Visium","human","lymphnode","","","","","False","Visium","4035","36601"
"Human_Ovary_10X_03282022_Visium","human","ovary","","","","","False","Visium","3455","17943"
"Human_Ovary_10X_07142022_Visium","human","ovary","","","","","False","Visium","4674","18085"
"Human_Prostate_10X_06092021_Visium_cancer","human","prostate","","","","","False","Visium","4371","17943"
"Human_Prostate_10X_06092021_Visium_normal","human","prostate","","","","","False","Visium","2543","17943"
"Human_Prostate_10X_07122022_Visium","human","prostate","","","","","False","Visium","3460","17943"
"Human_Prostate_10X_10082021_Visium","human","prostate","","","","","False","Visium","3043","17943"
"Human_Prostate_Erickson_08102022_Visium_Patient_1_H1_2","human","prostate","35948708","Spatially resolved clonal copy number alterations in benign and malignant tissue","Defining the transition from benign to malignant tissue is fundamental to improving early diagnosis of cancer1. Here we use a systematic approach to study spatial genome integrity in situ and describe previously unidentified clonal relationships. We used spatially resolved transcriptomics2 to infer spatial copy number variations in >120,000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue using an organ-wide approach focused on the prostate. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of capturing the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.","","True","Visium","2775","33538"
"Human_Prostate_Erickson_08102022_Visium_Patient_1_H1_4","human","prostate","35948708","Spatially resolved clonal copy number alterations in benign and malignant tissue","Defining the transition from benign to malignant tissue is fundamental to improving early diagnosis of cancer1. Here we use a systematic approach to study spatial genome integrity in situ and describe previously unidentified clonal relationships. We used spatially resolved transcriptomics2 to infer spatial copy number variations in >120,000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue using an organ-wide approach focused on the prostate. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of capturing the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.","","True","Visium","4079","33538"
"Human_Prostate_Erickson_08102022_Visium_Patient_1_H1_5","human","prostate","35948708","Spatially resolved clonal copy number alterations in benign and malignant tissue","Defining the transition from benign to malignant tissue is fundamental to improving early diagnosis of cancer1. Here we use a systematic approach to study spatial genome integrity in situ and describe previously unidentified clonal relationships. We used spatially resolved transcriptomics2 to infer spatial copy number variations in >120,000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue using an organ-wide approach focused on the prostate. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of capturing the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.","","True","Visium","3856","33538"
"Human_Prostate_Erickson_08102022_Visium_Patient_1_H2_1","human","prostate","35948708","Spatially resolved clonal copy number alterations in benign and malignant tissue","Defining the transition from benign to malignant tissue is fundamental to improving early diagnosis of cancer1. Here we use a systematic approach to study spatial genome integrity in situ and describe previously unidentified clonal relationships. We used spatially resolved transcriptomics2 to infer spatial copy number variations in >120,000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue using an organ-wide approach focused on the prostate. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of capturing the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.","","True","Visium","3092","33538"
"Human_Prostate_Erickson_08102022_Visium_Patient_1_H2_2","human","prostate","35948708","Spatially resolved clonal copy number alterations in benign and malignant tissue","Defining the transition from benign to malignant tissue is fundamental to improving early diagnosis of cancer1. Here we use a systematic approach to study spatial genome integrity in situ and describe previously unidentified clonal relationships. We used spatially resolved transcriptomics2 to infer spatial copy number variations in >120,000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue using an organ-wide approach focused on the prostate. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of capturing the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.","","True","Visium","3190","33538"
"Human_Prostate_Erickson_08102022_Visium_Patient_1_H2_5","human","prostate","35948708","Spatially resolved clonal copy number alterations in benign and malignant tissue","Defining the transition from benign to malignant tissue is fundamental to improving early diagnosis of cancer1. Here we use a systematic approach to study spatial genome integrity in situ and describe previously unidentified clonal relationships. We used spatially resolved transcriptomics2 to infer spatial copy number variations in >120,000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue using an organ-wide approach focused on the prostate. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of capturing the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.","","True","Visium","3554","33538"
"Human_Prostate_Erickson_08102022_Visium_Patient_1_V1_2","human","prostate","35948708","Spatially resolved clonal copy number alterations in benign and malignant tissue","Defining the transition from benign to malignant tissue is fundamental to improving early diagnosis of cancer1. Here we use a systematic approach to study spatial genome integrity in situ and describe previously unidentified clonal relationships. We used spatially resolved transcriptomics2 to infer spatial copy number variations in >120,000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue using an organ-wide approach focused on the prostate. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of capturing the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.","","True","Visium","2736","33538"
"Mouse_Brain+Lung_10X_02212023_Visium_1mm_edge","mouse","brain+lung","","","","","False","Visium","4679","19465"
"Mouse_Brain+Lung_10X_02212023_Visium_2mm_edge","mouse","brain+lung","","","","","False","Visium","4228","19465"
"Mouse_Brain_10X_01062023_Visium","mouse","brain","","","","","False","Visium","4298","19465"
"Mouse_Brain_10X_03292022_Visium","mouse","brain","","","","","False","Visium","2702","32285"
"Mouse_Brain_10X_05252023_Visium","mouse","brain","","","","","False","Visium","2702","32285"
"Mouse_Brain_10X_06082023_Visium_FFPE_Sagittal_Mouse_Brain","mouse","brain","","","","","False","Visium","8470","19465"
"Mouse_Brain_10X_06082023_Visium_Fixed_Frozen_Sagittal_Mouse_Brain","mouse","brain","","","","","False","Visium","5134","19465"
"Mouse_Brain_10X_06082023_Visium_Fresh_Frozen_Sagittal_Mouse_Brain","mouse","brain","","","","","False","Visium","6965","19465"
"Mouse_Brain_10X_06232020_Visium_Coronal","mouse","brain","","","","","False","Visium","2702","32285"
"Mouse_Brain_10X_06232020_Visium_Sagittal_Anterior_Section_1","mouse","brain","","","","","False","Visium","2695","32285"
"Mouse_Brain_10X_06232020_Visium_Sagittal_Anterior_Section_2","mouse","brain","","","","","False","Visium","2825","32285"
"Mouse_Brain_10X_06232020_Visium_Sagittal_Posterior_Section_1","mouse","brain","","","","","False","Visium","3355","32285"
"Mouse_Brain_10X_06232020_Visium_Sagittal_Posterior_Section_2","mouse","brain","","","","","False","Visium","3289","32285"
"Mouse_Brain_10X_07122022_Visium_Sagittal_Anterior_Section_2","mouse","brain","","","","","False","Visium","2823","32285"
"Mouse_Brain_10X_07122022_Visium_Sagittal_Posterior_Section_2","mouse","brain","","","","","False","Visium","3289","32285"
"Mouse_Brain_10X_07132022_Visium_coronal_sec1","mouse","brain","","","","","False","Visium","2310","19465"
"Mouse_Brain_10X_07132022_Visium_coronal_sec2","mouse","brain","","","","","False","Visium","2235","19465"
"Mouse_Brain_10X_08162021_Visium","mouse","brain","","","","","False","Visium","2264","19465"
"Mouse_Brain_10X_10052023_Visium_control_rep1","mouse","brain","","","","","False","Visium","5254","32589"
"Mouse_Brain_10X_10052023_Visium_control_rep2","mouse","brain","","","","","False","Visium","5324","32589"
"Mouse_Brain_10X_10052023_Visium_post_xenium_rep1","mouse","brain","","","","","False","Visium","5303","19465"
"Mouse_Brain_10X_10052023_Visium_post_xenium_rep2","mouse","brain","","","","","False","Visium","5221","19465"
"Mouse_Brain_10X_10182022_Visium","mouse","brain","","","","","False","Visium","2797","32285"
"Mouse_Embryo_10X_05252023_Visium","mouse","embryo","","","","","False","Visium","6434","19465"
"Mouse_Embryo_10X_07142022_Visium","mouse","embryo","","","","","False","Visium","6443","19465"
"Mouse_Kidney_10X_06232020_Visium","mouse","kidney","","","","","False","Visium","1438","32285"
"Mouse_Kidney_10X_08162021_Visium","mouse","kidney","","","","","False","Visium","3124","19465"
"Mouse_OlfactoryBulb_10X_03242022_Visium","mouse","olfactory bulb","","","","","False","Visium","1185","32285"
"Human_Colon_10X_03252024_VisiumHD","human","colon","","","","","False","VisiumHD","545913","18085"
"Human_Lung_10X_03292024_VisiumHD","human","lung","","","","","False","VisiumHD","605471","18085"
"Mouse_Brain_10X_03292024_VIsiumHD","mouse","brain","","","","","False","VisiumHD","393543","19059"
"Mouse_Intenstine_10X_03252024_VisiumHD","mouse","intestine","","","","","False","VisiumHD","351817","19059"