GSE183010: Single-cell RNA-sequencing of mouse islets exposed to proinflammatory cytokines [18hrs]

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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: Aug 30, 2021

Summary: Exposure to proinflammatory cytokines is believed to contribute to pancreatic β-cells during diabetes development. While some cytokine-mediated changes in islet gene expression are known, the heterogeneity of the response is not well-understood. Following 18 hour treatment with interleukin-1 beta (IL-1β) and interferon-gamma (IFN-γ) alone or together, mouse islets were subjected to single-cell RNA-sequencing (scRNA-seq). Inducible nitric oxide synthase (iNOS) mRNA (Nos2), antiviral genes, and immune-associated genes were induced in a subset of β-cells in response to both cytokines, while IL-1β alone activated only antiviral genes. Subsets of α- and δ-cells expressed Nos2 and exhibited similar gene expression changes as β-cells, including induction of antiviral genes and repression of identity genes. Finally, cytokine-responsiveness was inversely correlated with expression of genes encoding heat shock proteins. Our findings show that all endocrine cell types respond to cytokines, IL-1β induces the expression of protective genes in β-cells, and cellular stress gene expression is associated with an inhibition in cytokine signaling.

GEO Accession ID: GSE183010

PMID: 34927076

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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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