GSE223832: Recruitment of stem-like T cells and resistance to exhaustion support CD4 T cell responses in autoimmunity (scRNA-Seq).

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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: Jan 26, 2023

Summary: Recent findings suggest that undifferentiated, stem-like, antigen specific T cells serve as an important long-term reservoir for autoimmune CD8 T cell responses. However, it is still unclear whether CD4 T cells exhibit a similar differentiation trajectory culminating in terminal differentiation, acquisition of an exhausted phenotype, and loss of stemness and function. We analyzed islet infiltrating T cells in 8- and 16-week old NOD mice by scRNAseq and flow cytometry and found that while CD4 T cells in autoimmune diabetes share many features of exhaustion with CD8 T cells, expression patterns of inhibitory receptors are distinct in autoimmune T cells compared to T cells in chronic LCMV infection.

GEO Accession ID: GSE223832

PMID: No Pubmed ID

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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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