Project description:Three different cell types constitute the glomerular filter: mesangial cells, endothelial cells, and podocytes. As yet, it remains unknown to what extent cellular heterogeneity exists within healthy glomerular cell populations. Here, we used nanodroplet-based, highly parallel transcriptional profiling to characterize the cellular content of purified wildtype mouse glomeruli. Unsupervised clustering of 13,000 single-cell transcriptomes identified the three known glomerular cell types. We provide a comprehensive online atlas of gene expression in glomerular cells, which can be queried and visualized using an interactive and freely available database. Novel marker genes for all glomerular cell types were identified and supported by immunohistochemistry stainings obtained from the Human Protein Atlas. Subclustering of glomerular endothelial cells revealed a subset of activated endothelium, expressing marker genes related to endothelial proliferation. Additionally, the podocyte population could be divided in three different subclusters. In conclusion, our study comprehensively characterizes gene expression in individual glomerular cells and sets the stage for the dissection of glomerular function at the single-cell level in health and disease.
Project description:To dissect the early aspects of liver haematopoiesis as well as the development of other liver cell lineages we generated a new single cell RNA-seq (scRNA-seq) atlas of the mouse E12.5 liver
Project description:Single cell RNA sequencing (scRNA-seq) has advanced the assessment of cellular heterogeneity at the single-cell resolution by identifying transcriptional similarities and differences. Data resources of scRNA-seq have been largely produced and extensively studied for the mouse retina. They serve as a powerful tool to study cellular components, transcriptome relationships, and regulatory mechanisms underlying various retinal diseases and biological processes. The large volume of mouse retinal scRNA-seq data has been released in separate repositories, limiting their widespread use in mouse retina communities. In this work, we are presenting a unified single-cell atlas for adult wild-type mouse retina using our in-house generated single-cell RNA-seq data complementing public datasets. The collected data account for over 323,000 single cells. After data integration, cell clustering, and cell type annotation, we have annotated 11 major classes and over 120 retinal cell types to form a unified single-cell reference for the mouse retina. To facilitate the public use of the reference, we have deposited it on CELLxGENE, UCSC Cell Browser, and Single Cell Portal for visualization and gene expression exploration. The unified atlas is also released to annotate new mouse retinal cells using scArches utilities. This unified reference serves an easy-to-use data resource of mouse retina communities.