GSE153834: Self-Renewing Tri-Potent Stem/Progenitor-like Cells from Adult Human Pancreas

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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: Jul 06, 2020

Summary: Stem and progenitor cells in the adult human pancreas provide an under-explored resource for regenerative medicine. Using micro-manipulation and methylcellulose-containing colony/organoid assays, we identified cells within the human cadaveric exocrine pancreas that fulfill the definition of a stem cell: able to self-renew and differentiate. Exocrine tissues were collected after the isolation of endocrine cells, dissociated into single cells, and plated into a 3-dimensional semisolid medium. We found that some pancreatic ductal cells gave rise to cystic colonies/organoids containing pancreatic duct, acinar, and endocrine lineage cells. These cells self-renewed and expanded approximately 300-fold over 9 weeks. When transplanted into diabetic mice, colonies/organoids lowered blood glucose levels and gave rise to insulin-expressing endocrine cells. Thus, stem/progenitor-like cells capable of self-renewal and differentiation either preexist in the adult human pancreas or readily adapt in culture. These human cells have implications for therapy in diabetes patients.

GEO Accession ID: GSE153834

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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