High resolution spatially resolved transcriptomic atlas of kidney injury and repair by RNA hybridization-based in situ sequencing [10X Visium]
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ABSTRACT: High resolution spatially resolved transcriptomic atlas of kidney injury and repair by RNA hybridization-based in situ sequencing [10X Visium]
Project description:Spatially resolved transcriptomics technologies allow for the measurement of gene expression in situ. We applied direct RNA hybridization-based in situ sequencing (ISS, Cartana) to compare male and female healthy mouse kidneys and the male kidneys injury and repair timecourse of ischemic reperfusion injury (IRI). A pre-selected panel of 200 genes were used to identify the dynamics of cell states and their spatial distributions during injury and repair. We developed a new computational pipeline, CellScopes, for the rapid analysis, multi-omic integration and visualization of spatially resolved transcriptomic datasets. The resulting atlas allowed us to resolve distinct kidney niches, dynamic alterations in cell state over the course of injury and repair and cell-cell interactions between leukocytes and kidney parenchyma. Projection of snRNA-seq dataset from the same injury and repair samples allowed us to impute the spatial localization of genes not directly measured by Cartana.
Project description:Spatially resolved transcriptomics technologies allow for the measurement of gene expression in situ. We applied direct RNA hybridization-based in situ sequencing (ISS, Cartana) to compare male and female healthy mouse kidneys and the male kidneys injury and repair timecourse of ischemic reperfusion injury (IRI). A pre-selected panel of 200 genes were used to identify the dynamics of cell states and their spatial distributions during injury and repair. We developed a new computational pipeline, CellScopes, for the rapid analysis, multi-omic integration and visualization of spatially resolved transcriptomic datasets. The resulting atlas allowed us to resolve distinct kidney niches, dynamic alterations in cell state over the course of injury and repair and cell-cell interactions between leukocytes and kidney parenchyma. Projection of snRNA-seq dataset from the same injury and repair samples allowed us to impute the spatial localization of genes not directly measured by Cartana.
Project description:<p>Recent developments in spatially resolved -omics have enabled the joint study of gene expression, metabolite levels and tissue morphology, offering greater insights into biological pathways. Integrating these modalities from matched tissue sections to probe spatially-coordinated processes, however, remains challenging. Here we introduce MAGPIE, a framework for co-registering spatially resolved transcriptomics, metabolomics, and tissue morphology from the same or consecutive sections. We show MAGPIE’s generalisability and scalability on spatial multi-omics data from multiple tissues, combining Visium with MALDI and DESI mass spectrometry imaging. MAGPIE was also applied to new multimodal datasets generated with a specialised sampling strategy to characterise the metabolic and transcriptomic landscape in an in vivo model of drug-induced pulmonary fibrosis and to link small-molecule co-detection with endogenous lung responses. MAGPIE demonstrates the refined resolution and enhanced interpretability that spatial multimodal analyses provide for studying tissue injury especially in pharmacological contexts, and delivers a modular, accessible workflow for data integration</p>
Project description:Spatially resolved gene expression was prepard by dissociated hman prostate tissue to single cells, and collected & prepped for RNA-seq using the Visium Spatial Gene Expression kit. 5000 cells were collected and sequenced at a depth of 50k cells/gene on a 2X150nt lane in a NovaSeq 6000. SpaceRanger alignment was performed to produce the RAW files
Project description:We performed Visium CytAssist (10X), GeoMx DSP (Nanostring) and Chromium Flex (10X Genomics) full transcriptome profiling on Breast Cancer (BC), Lung Cancer (LC) and diffuse large B cell lymphoma (DLBCL) samples from archival FFPE blocks. We explore the data quality across blocks with different storage times and DV200 values for all the three methods. We compared the cell type signature purity between ST methods Visium and GeoMx by utilising pathology annotations and scRNAseq. For the Visium and Chromium methods with a large number of data points we explored the heterogeneity between tissues. Finally, we demonstrate the discovery of patient-specific tumor-TME interactions across all three methods.