Project description:Spatial transcriptomics and proteomics provide complementary information that independently transformed our understanding of complex biological processes. However, experimental integrations of these modalities are limited. To overcome this, we developed Spatial PrOtein and Transcriptome Sequencing (SPOTS) for high-throughput simultaneous integration of spatial transcriptomics and protein profiling. Compared to unimodal measurements, SPOTS substantially improves signal resolution and cell clustering and enhances the discovery power in differential gene expression analysis across tissue regions.
Project description:Integration of multiple data modalities in a spatially informed manner remains an unmet need for exploiting spatial multi-omics data. Here, we introduce SpatialGlue, a novel graph neural network with dual-attention mechanism, to decipher spatial domains by intra-omics integration of spatial location and omics measurement followed by cross-omics integration. We demonstrate that SpatialGlue can more accurately resolve spatial domains at a higher resolution across different tissue types and technology platforms, to enable biological insights into cross-modality spatial correlations.
Project description: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. We performed spatial transcriptomics to investigate the intrahepatic cell heterogeneity and the spatial distribution of transcriptionally active HBV integration events in different phases of chronic HBV infection. Our analysis revealed that transcriptionally active HBV integration occurred in chronically HBV-infected patients in different phases, including those patients with HBsAg loss, and antiviral treatment was associated with a decreased number and extent of viral integrations.
Project description:This study introduces a new imaging, spatial transcriptomics (ST), and single-cell RNA-sequencing (scRNA-seq) integration pipeline to characterize neoplastic cell state transitions during tumorigenesis. Our semi-supervised analysis pipeline was applied to examine pancreatic intraepithelial neoplasias (PanIN), the most frequent premalignant lesions that can develop into pancreatic adenocarcinoma (PDAC). Their strict diagnosis on FFPE samples has limited previous characterization of human PanINs within their microenvironment through single-cell approaches. We leverage unbiased whole transcriptome FFPE spatial profiling to enable this characterization in a rare cohort of matched low-grade and high-grade PanIN lesions to track progression and map cellular phenotypes relative to scRNA-seq data of advanced PDAC tumors. We demonstrate that cancer associated fibroblasts (CAF), including antigen-presenting CAFs (apCAF), are located close to PanINs. We further observed a transition from CAF-related inflammatory signaling to cellular proliferation during PanIN progression. We use these findings to guide panel design to perform single-cell validation with high-dimensional imaging proteomics and transcriptomics technologies. Altogether, our pipeline for spatial multi-omics characterization provides a resource for future PanIN studies. Moreover, our semi-supervised learning framework to spatial multi-omics has broad applicability across cancer types to decipher the spatiotemporal dynamics of carcinogenesis.
Project description:Emerging spatial profiling technology has enabled high-plex molecular profiling in biological tissues, preserving the spatial and morphological context of gene or protein expression. Here we describe expanded chemistry for the Digital Spatial Profiling platform to quantify whole transcriptomes in human and mouse tissues using a wide range of spatial profiling strategies and sample types. We designed multiplexed in situ hybridization probe pools targeting the protein-coding genes in the human and mouse transcriptomes, hereafter referred to as the human or mouse Whole Transcriptome Atlas (WTA). We validated the human and mouse WTA assays using cell lines to demonstrate concordance with orthogonal gene expression profiling methods in profiled region sizes ranging from ∼10-500 cells. By benchmarking against bulk RNAseq and single-molecule fluorescence in situ hybridization, we demonstrate robust transcript detection possible down to ∼100 transcripts per region. To assess the performance of WTA across tissue and sample types, we applied WTA to biological questions in cancer, molecular pathology, and developmental biology. We show that spatial profiling with WTA can detect expected spatial gene expression differences between tumor and tumor microenvironment, identify spatial disease-specific heterogeneity in gene expression in histological structures of the human kidney, and comprehensively map transcriptional programs in anatomical substructures of nine organs in the developing mouse embryo. Digital Spatial Profiling technology with the WTA assays provides a flexible method for spatial whole transcriptome profiling applicable to diverse tissue types and biological contexts.
Project description:Emerging spatial profiling technology has enabled high-plex molecular profiling in biological tissues, preserving the spatial and morphological context of gene or protein expression. Here we describe expanded chemistry for the Digital Spatial Profiling platform to quantify whole transcriptomes in human and mouse tissues using a wide range of spatial profiling strategies and sample types. We designed multiplexed in situ hybridization probe pools targeting the protein-coding genes in the human and mouse transcriptomes, hereafter referred to as the human or mouse Whole Transcriptome Atlas (WTA). We validated the human and mouse WTA assays using cell lines to demonstrate concordance with orthogonal gene expression profiling methods in profiled region sizes ranging from ∼10-500 cells. By benchmarking against bulk RNAseq and single-molecule fluorescence in situ hybridization, we demonstrate robust transcript detection possible down to ∼100 transcripts per region. To assess the performance of WTA across tissue and sample types, we applied WTA to biological questions in cancer, molecular pathology, and developmental biology. We show that spatial profiling with WTA can detect expected spatial gene expression differences between tumor and tumor microenvironment, identify spatial disease-specific heterogeneity in gene expression in histological structures of the human kidney, and comprehensively map transcriptional programs in anatomical substructures of nine organs in the developing mouse embryo. Digital Spatial Profiling technology with the WTA assays provides a flexible method for spatial whole transcriptome profiling applicable to diverse tissue types and biological contexts.
Project description:This study introduces a new imaging, spatial transcriptomics (ST), and single-cell RNA-sequencing (scRNA-seq) integration pipeline to characterize neoplastic cell state transitions during tumorigenesis. Our semi-supervised analysis pipeline was applied to examine pancreatic intraepithelial neoplasias (PanIN), the most frequent premalignant lesions that can develop into pancreatic adenocarcinoma (PDAC). Their strict diagnosis on FFPE samples has limited previous characterization of human PanINs within their microenvironment through single-cell approaches. We leverage unbiased whole transcriptome FFPE spatial profiling to enable this characterization in a rare cohort of matched low-grade and high-grade PanIN lesions to track progression and map cellular phenotypes relative to scRNA-seq data of advanced PDAC tumors. We demonstrate that cancer associated fibroblasts (CAF), including antigen-presenting CAFs (apCAF), are located close to PanINs. We further observed a transition from CAF-related inflammatory signaling to cellular proliferation during PanIN progression. We use these findings to guide panel design to perform single-cell validation with high-dimensional imaging proteomics and transcriptomics technologies. Altogether, our pipeline for spatial multi-omics characterization provides a resource for future PanIN studies. Moreover, our semi-supervised learning framework to spatial multi-omics has broad applicability across cancer types to decipher the spatiotemporal dynamics of carcinogenesis.
Project description:Emerging spatial profiling technology has enabled high-plex molecular profiling in biological tissues, preserving the spatial and morphological context of gene or protein expression. Here we describe expanded chemistry for the Digital Spatial Profiling platform to quantify whole transcriptomes in human and mouse tissues using a wide range of spatial profiling strategies and sample types. We designed multiplexed in situ hybridization probe pools targeting the protein-coding genes in the human and mouse transcriptomes, hereafter referred to as the human or mouse Whole Transcriptome Atlas (WTA). We validated the human and mouse WTA assays using cell lines to demonstrate concordance with orthogonal gene expression profiling methods in profiled region sizes ranging from ∼10-500 cells. By benchmarking against bulk RNAseq and single-molecule fluorescence in situ hybridization, we demonstrate robust transcript detection possible down to ∼100 transcripts per region. To assess the performance of WTA across tissue and sample types, we applied WTA to biological questions in cancer, molecular pathology, and developmental biology. We show that spatial profiling with WTA can detect expected spatial gene expression differences between tumor and tumor microenvironment, identify spatial disease-specific heterogeneity in gene expression in histological structures of the human kidney, and comprehensively map transcriptional programs in anatomical substructures of nine organs in the developing mouse embryo. Digital Spatial Profiling technology with the WTA assays provides a flexible method for spatial whole transcriptome profiling applicable to diverse tissue types and biological contexts.
Project description:Emerging spatial profiling technology has enabled high-plex molecular profiling in biological tissues, preserving the spatial and morphological context of gene or protein expression. Here we describe expanded chemistry for the Digital Spatial Profiling platform to quantify whole transcriptomes in human and mouse tissues using a wide range of spatial profiling strategies and sample types. We designed multiplexed in situ hybridization probe pools targeting the protein-coding genes in the human and mouse transcriptomes, hereafter referred to as the human or mouse Whole Transcriptome Atlas (WTA). We validated the human and mouse WTA assays using cell lines to demonstrate concordance with orthogonal gene expression profiling methods in profiled region sizes ranging from ∼10-500 cells. By benchmarking against bulk RNAseq and single-molecule fluorescence in situ hybridization, we demonstrate robust transcript detection possible down to ∼100 transcripts per region. To assess the performance of WTA across tissue and sample types, we applied WTA to biological questions in cancer, molecular pathology, and developmental biology. We show that spatial profiling with WTA can detect expected spatial gene expression differences between tumor and tumor microenvironment, identify spatial disease-specific heterogeneity in gene expression in histological structures of the human kidney, and comprehensively map transcriptional programs in anatomical substructures of nine organs in the developing mouse embryo. Digital Spatial Profiling technology with the WTA assays provides a flexible method for spatial whole transcriptome profiling applicable to diverse tissue types and biological contexts.