GSE218316: Single-cell transcriptomics of primary human pancreatic islet cells exposed to stress conditions associated with beta-cell failure.

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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: Nov 18, 2022

Summary: Diabetes mellitus (DM) is a chronic disease associated with elevated blood glucose level and resulting from a loss of functional beta-cell mass. The goal of this study is to investigate the response of human pancreatic cells to pathophysiological conditions associated with beta-cell dysfunction, ultimately to identify molecular mechanisms contributing to the development of diabetes. Isolated primary human islets from three non-diabetic donors were exposed in vitro to pro-inflammatory, oxidative, metabolic and endoplasmic reticulum stress for up to 3 days. Subsequently the cells were processed for single-cell RNA sequencing (scRNA-seq). Analysis of the dataset revealed both common and specific molecular response of each pancreatic cell type to the various stress conditions.

GEO Accession ID: GSE218316

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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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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