GSE190726: TYK2 regulates human pancreatic beta-cell development and its responses to interferon-alpha [tyk2_scRNAseq]

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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: Dec 12, 2021

Summary: Type 1 diabetes (T1D) is an autoimmune disease that results in the destruction of insulin producing pancreatic b-cells. One of the genes associated with T1D is TYK2, which encodes a Janus kinase with critical roles in type-I interferon (IFN) mediated intracellular signalling. To study the role of TYK2 in human pancreatic b-cell development and response to IFNa, we generated TYK2 knockout human iPSCs and directed them into the pancreatic endocrine lineage. Unexpectedly, loss of TYK2 compromised the emergence of endocrine precursors by regulating KRAS expression, however, mature b-cell function was not affected. In the mature stem cell-derived islets, the loss or inhibition of TYK2 prevented IFNa-induced antigen processing and presentation, including MHC Class I expression, enhancing their survival against T-cell cytotoxicity. These results identify an unsuspected role for TYK2 on b-cell development and support TYK2 inhibition in adult b-cells as a potent therapeutic target to halt T1D progression.

GEO Accession ID: GSE190726

PMID: 36289205

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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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