GSE212009: Heterogeneity of islet-infiltrating interleukin-21+ CD4 T cells in a mouse model of type 1 diabetes

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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: Aug 25, 2022

Summary: Interleukin (IL)-21 is essential for type 1 diabetes (T1D) development in the NOD mouse model. IL-21-expressing CD4 T cells are present in pancreatic islets where they contribute to disease progression. However, little is known about their phenotype and differentiation states. To fill this gap, we generated a novel IL-21 reporter NOD strain to further characterize IL-21+ CD4 T cells in T1D. IL-21+ CD4 T cells accumulate in pancreatic islets and recognize β-cell antigens. Single-cell RNA sequencing revealed that most CD4 T effector cells in islets actively express IL-21 and they are highly diabetogenic despite expressing multiple inhibitory molecules, including PD-1 and LAG3. Islet IL-21+ CD4 T cells segregate into four phenotypically and transcriptionally distinct differentiation states, less differentiated early effectors, Tfh-like cells, and two Th1 subsets. Trajectory analysis predicts that early effectors differentiate into both Tfh-like and terminal Th1 cells. We further demonstrated that intrinsic IL-27 signaling controls the differentiation of islet IL-21+ CD4 T cells, contributing to their helper function. Collectively, our study reveals the heterogeneity of islet-infiltrating IL-21+ CD4 T cells and indicates that both Tfh-like and Th1 subsets continuously produce IL-21 throughout their differentiation process, highlighting the important sources of IL-21 in T1D pathogenesis.

GEO Accession ID: GSE212009

PMID: 36762954

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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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