GSE128334: Niche-Specific Re-Programming of Epigenetic Landscapes Drives Myeloid Cell Diversity in NASH [scRNA-seq]

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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: Mar 14, 2019

Summary: Tissue resident macrophages and recruited monocyte-derived macrophages contribute to host defense but also play pathological roles in a diverse range of human diseases. Multiple macrophage phenotypes are often represented in a diseased tissue, but we lack a deep understanding of the mechanisms that control diversification. Here we use a combination of genetic, genomic, and imaging approaches to investigate the origins and epigenetic trajectories of hepatic myeloid cells during a diet-induced model of nonalcoholic steatohepatitis (NASH). We provide evidence that distinct micro-environments within the NASH liver induce strikingly divergent transcriptomes of resident and infiltrating cells. Myeloid cell diversification results from both remodeling open chromatin landscapes of recruited monocytes and altering activities of pre-existing enhancers of resident Kupffer cells. These findings provide evidence that niche-specific combinations of diseaseassociated environmental signals instruct resident and recruited macrophages to acquire distinct programs of gene expression and corresponding phenotypes.

GEO Accession ID: GSE128334

PMID: 32362324

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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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