GSE185036: Human Pluripotent Stem Cell-derived Islets Ameliorate Diabetes in Nonhuman Primates [human_singlecell]

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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: Sep 29, 2021

Summary: Pluripotent stem cell-derived islets (hPSC-islets) are a promising cell resource for diabetes treatment. Here, we demonstrate that transplantation of pluripotent stem cell-derived islets into diabetic nonprimates effectively restored endogenous insulin secretion and improved glycemic control. Single-cell RNA sequencing analysis of S6D2 clusters confirmed the existence of the three major pancreatic endocrine cell populations (β cells, α-like cells and δ-like cells) and their proportions, which altogether accounted for 80%. Importantly, hierarchical clustering of S6D2 hCiPSC-islets, 10 wpt kidney grafts and primary islets showed that the hCiPSC differentiated pancreatic endocrine cells shared similar global gene expression profiles to their native counterparts in primary islets. Single-cell RNA sequencing analysis on PBMCs revealed the potential immune response of recipient macaque to hCiPSC-islets.

GEO Accession ID: GSE185036

PMID: 35115708

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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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