GSE221936: Osteoblast-intrinsic defect in glucose metabolism impairs bone formation in type II diabetic male mice.

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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 30, 2022

Summary: Skeletal fragility is associated with type 2 diabetes mellitus (T2D), but the underlying mechanism is not well understood. Here, in a mouse model for youth-onset T2D, we show that both trabecular and cortical bone mass are reduced due to diminished osteoblast activity. Stable isotope tracing in vivo with 13C-glucose demonstrates that both glycolysis and glucose fueling of the TCA cycle are impaired in diabetic bones. Similarly, Seahorse assays show suppression of both glycolysis and oxidative phosphorylation by diabetes in bone marrow mesenchymal cells as a whole, whereas single-cell RNA sequencing reveals distinct modes of metabolic dysregulation among the subpopulations. Metformin not only promotes glycolysis and osteoblast differentiation in vitro, but also improves bone mass in diabetic mice. Finally, osteoblast-specific overexpression of either Hif1a, a general inducer of glycolysis, or Pfkfb3 which stimulates a specific step in glycolysis, averts bone loss in T2D mice. The study identifies osteoblast-intrinsic defects in glucose metabolism as an underlying cause of diabetic osteopenia, which may be targeted therapeutically.

GEO Accession ID: GSE221936

PMID: 37144869

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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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