GSE173193: Single cell RNA-sequencing reveals placenta cellular heterogeneity in adverse pregnancy

scRNA-seq Viewer
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: Apr 23, 2021

Summary: The placenta is a highly heterogeneous organ and is closely related to adverse pregnancy. The previous bulk sequencing of whole tissue could not show the characteristics of individual cells and the interactions between cells. Here, we select the placental tissues of the gestational diabetes group(GDM), preeclampsia group(PE), advanced age group(GL) and normal control group for single-cell sequencing in order to explain the mechanism of related diseases in more depth.nated spatial and temporal regulation of gene expression in the murine hindlimb determines the identity of mesenchymal progenitors and the development of diversity of musculoskeletal tissues they form. Hindlimb development has historically been studied with lineage tracing of individual genes selected a priori, or at the bulk tissue level, which does not allow for the determination of single cell transcriptional programs yielding mature cell types and tissues. To identify the cellular trajectories of lineage specification during limb bud development, we used single cell mRNA sequencing (scRNA-seq) to profile the developing murine hindlimb.

GEO Accession ID: GSE173193

PMID: 34025588

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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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Info Differential gene expression can be computed for a single cell type labeled group of cells vs the rest. These include wilcoxon, DESeq2, or characteristic direction.
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