GSE213337: Single cell analysis reveals the heterogeneity of fibroblasts in the diabetic heart

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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 14, 2022

Summary: Diabetes mellitus is one of the most chronic diseases, of which diabetic cardiomyopathy is the major cause of morbidity and involves in multiple processes such as inflammation, oxidative stress, fibrosis, extracellular collagen deposition, apoptosis, mitochondria dysfunction. However, the exact mechanisms of fibroblasts concerning type Ⅰ diabetes remain unclear. To further understand the functional roles of fibroblasts of STZ-induced diabetic mice, we lead the single cell RNA sequence. Cells were comprised of endothelial, fibroblast, cardiomyocyte, smooth muscle cells, macrophage and other type cells. Single cell sequence illustrates novel fibroblast sub-clusters and highlight the role of Lox. Real-time quantitative PCR, western blotting, immunofluorescence was used to verify the sequence data. We validate the dysfunctions of diabetic cardiomyopathy by echocardiogram. Our study supports novel insights into the pathogenesis of diabetic cardiomyopathy.

GEO Accession ID: GSE213337

PMID: 36706988

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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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