scRNA-seq Viewer
Info Studies were incorporated in a barcode, matrix, feature format. This format can be found on the 10x Genomics website for processing of single cell studies to obtain the gene expression matrices. For each study, the metadata incorporated in GEO were manually curated into profiles and the samples were separated based on applicable groups and conditions. The expression data from the cells for the samples within the same profile and condition were aggregated into an expression matrix with the cell barcodes having the sample name appended to it to ensure unique cell names.

Description (from GEO)

Submission Date: Aug 03, 2020

Summary: In type 1 diabetes (T1D) autoreactive CD8 T cells infiltrate pancreatic islets and destroy insulin-producing β cells. Progression to T1D onset is a chronic process, which suggests that the effector activity of β-cell autoreactive CD8 T cells needs to be maintained throughout the course of disease development. The mechanism that sustains diabetogenic CD8 T cell effectors during the course of T1D progression has not been completely defined. Here we used single-cell RNA sequencing to gain further insight into the phenotypic complexity of islet-infiltrating CD8 T cells in NOD mice. We identified two functionally distinct subsets of activated CD8 T cells, CD44highTCF1+CXCR6- and CD44highTCF1-CXCR6+, in islets of prediabetic NOD mice. Compared to CD44highTCF1+CXCR6- CD8 T cells, the CD44highTCF1-CXCR6+ subset expressed higher levels of inhibitory and cytotoxic molecules and was more prone to apoptosis. Adoptive cell transfer experiments revealed that CD44highTCF1+CXCR6- CD8 T cells, through continuous generation of the CD44highTCF1-CXCR6+ subset, were more capable than the latter population to promote insulitis and the development of T1D. We further showed that direct interleukin-27 (IL-27) signaling in CD8 T cells promoted the generation of terminal effectors from the CD44highTCF1+CXCR6- population. These results indicate that islet CD44highTCF1+CXCR6- CD8 T cells are a progenitor-like subset with self-renewing capacity and under an IL-27 controlled mechanism they differentiate into the CD44highTCF1-CXCR6+ terminal effector population. Our study provides new insight into the sustainability of the CD8 T cell response in the pathogenesis of T1D.

GEO Accession ID: GSE155593

PMID: 34507949

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Info Preprocessing and downstream analysis were computed using the scanpy Python library and the steps of processing followed the Seurat vignette. Cells and genes with no expression or very low expression were removed from the dataset based on a predefined threshold. The data was then normalized across the expression within the cells and log normalized. The top 2000 highly variable genes were extracted to be used for downstream analysis. For each of these aggregated data matrices, the clusters were computed using the leiden algorithm. Scanpy was then used to compute the PCA, t-SNE, and UMAPs. The points in the plots are labelled by their corresponding cell type labels. The cell type labels were computed using the wilcoxon method as the differential gene expression method. The top 250 genes were then used for enrichment analysis against the CellMarker library in order to determine the most appropriate cell type label with the lowest p-value.
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Info Differential gene expression can be computed for a single cell type labeled group of cells vs the rest. These include wilcoxon, DESeq2, or characteristic direction.
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