GSE145228: Targeting OCA-B/Pou2af1 blocks type-1 diabetes and reduces infiltration of activated, islet-reactive T cells

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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: Feb 13, 2020

Summary: The transcriptional coregulator OCA-B is induced in stimulated naïve CD4+ T cells, where docks with transcription factor Oct1 to regulate genes such as Il2 and Ifng. OCA-B regulates its targets in cases of repeated antigen exposure, a necessary feature of autoimmunity. Polymorphisms in binding sites for Oct1, and by extension OCA-B, as associated with multiple forms of autoimmunity including autoimmune (type-1) diabetes. We hypothesized that T cell-specific OCA-B deletion would protect mice from type-1 diabetes, and that pharmacologic OCA-B inhibition would provide similar protection. We developed an Ocab (Pou2af1) conditional allele and backcrossed it onto a diabetes-prone NOD/ShiLtJ strain background. T cell-specific OCA-B loss protected mice from spontaneous T1D. To clarify the mechanism, we profiled leukocytes from prediabetic islets by single-cell RNA sequencing and T cell receptor clonotype analysis.

GEO Accession ID: GSE145228

PMID: 33295943

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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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