Project description:Using Multiome and previously published sc/snRNA-seq data, we studied eight anatomical regions of the human heart including left and right ventricular free walls (LV and RV), left and right atria (LA and RA), left ventricular apex (AX), interventricular septum (SP), sino-atrial node (SAN) and atrioventricular node (AVN). For the first time, we profile the cells of the human cardiac conduction system, revealing their distinctive repertoire of ion channels, G-protein coupled receptors and cell-cell interactions. We map the identified cells to spatial transcriptomic data to discover cellular niches within the eight regions of the heart.
Project description:Using Multiome and previously published sc/snRNA-seq data, we studied eight anatomical regions of the human heart including left and right ventricular free walls (LV and RV), left and right atria (LA and RA), left ventricular apex (AX), interventricular septum (SP), sino-atrial node (SAN) and atrioventricular node (AVN). For the first time, we profile the cells of the human cardiac conduction system, revealing their distinctive repertoire of ion channels, G-protein coupled receptors and cell-cell interactions. We map the identified cells to spatial transcriptomic data to discover cellular niches within the eight regions of the heart.
Project description:Using Multiome and previously published sc/snRNA-seq data, we studied eight anatomical regions of the human heart including left and right ventricular free walls (LV and RV), left and right atria (LA and RA), left ventricular apex (AX), interventricular septum (SP), sino-atrial node (SAN) and atrioventricular node (AVN). For the first time, we profile the cells of the human cardiac conduction system, revealing their distinctive repertoire of ion channels, G-protein coupled receptors and cell-cell interactions. We map the identified cells to spatial transcriptomic data to discover cellular niches within the eight regions of the heart.
Project description:The function of a cell is defined by its intrinsic characteristics and its niche: the tissue microenvironment in which it dwells. Here we combine single-cell and spatial transcriptomics data to discover cellular niches within eight regions of the human heart. We map cells to microanatomical locations and integrate knowledge-based and unsupervised structural annotations. We also profile the cells of the human cardiac conduction system1. The results revealed their distinctive repertoire of ion channels, G-protein-coupled receptors (GPCRs) and regulatory networks, and implicated FOXP2 in the pacemaker phenotype. We show that the sinoatrial node is compartmentalized, with a core of pacemaker cells, fibroblasts and glial cells supporting glutamatergic signalling. Using a custom CellPhoneDB.org module, we identify trans-synaptic pacemaker cell interactions with glia. We introduce a druggable target prediction tool, drug2cell, which leverages single-cell profiles and drug-target interactions to provide mechanistic insights into the chronotropic effects of drugs, including GLP-1 analogues. In the epicardium, we show enrichment of both IgG+ and IgA+ plasma cells forming immune niches that may contribute to infection defence. Overall, we provide new clarity to cardiac electro-anatomy and immunology, and our suite of computational approaches can be applied to other tissues and organs.
Project description:These samples are part of a study to provide a spatially resolved single-cell multiomics map of human trophoblast differentiation in early pregnancy. Here we profiled with 10x Visium Spatial transcriptomics of the entire maternal-fetal interface including the myometrium, allowing us to resolve the full trajectory of trophoblast differentiation.
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.