GSE213290: Characterization and reduction of non-endocrine cells accompanying islet-like endocrine cells differentiated from human iPSC

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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 13, 2022

Summary: The differentiation of pancreatic endocrine cells from human pluripotent stem cells has been thoroughly investigated for their application in cell therapy against diabetes. Although non-endocrine cells are inevitable contaminating by-products of the differentiation process, a comprehensive profile of such cells is lacking. Therefore, we characterized non-endocrine cells in iPSC-derived pancreatic islet cells (iPIC) using single-cell transcriptomic analysis. We found that non-endocrine cells consist of 1) heterogeneous proliferating cells, and 2) cells with not only pancreatic traits but also liver or intestinal traits marked by FGB or AGR2. Non-endocrine cells specifically expressed FGFR2, PLK1, and LDHB. We demonstrated that inhibition of pathways involving these genes selectively reduced the number of non-endocrine cells in the differentiation process. These findings provide useful insights into cell purification approaches and contribute to the improvement of the mass production of endocrine cells for stem cell-derived cell therapy for diabetes.

GEO Accession ID: GSE213290

PMID: 35304548

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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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