GSE232737: Genetic activation of glycolysis in osteoblasts preserves bone mass in type I diabetes [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: May 17, 2023

Summary: Type I diabetes (T1D) impairs bone accrual in patients, but the mechanism is unclear. Here in a murine monogenic model for T1D, we demonstrate that diabetes suppresses bone formation resulting in a rapid loss of both cortical and trabecular bone. Single-cell RNA sequencing uncovers metabolic dysregulation in bone marrow osteogenic cells of the diabetic mice. In vivo stable isotope tracing reveals impaired glycolysis in diabetic bone that is highly responsive to insulin stimulation. Remarkably, deletion of the insulin receptor reduces cortical but not trabecular bone. Increasing glucose uptake by overexpressing Glut1 in osteoblasts exacerbates bone defects in T1D mice. Conversely, activation of glycolysis by Pfkfb3 overexpression preserves both trabecular and cortical bone mass in the face of diabetes. The study identifies defective glucose metabolism in osteoblasts as a pathogenic mechanism for osteopenia in T1D, and furthermore implicates boosting osteoblast glycolysis as a potential anabolic therapy.

GEO Accession ID: GSE232737

PMID: No Pubmed ID

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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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