Single-cell transcriptome sequencing revealed the metabolic changes and microenvironment changes of cardiomyocytes induced by diabetes

Comput Biol Chem. 2024 Oct:112:108136. doi: 10.1016/j.compbiolchem.2024.108136. Epub 2024 Jun 21.

Abstract

Purpose: Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels. This study aimed to analyze the changes underlying heterogeneities and communication properties of CMs in diabetes mellitus (DM).

Methods: GSE213337 dataset was retrieved from NCBI Gene Expression Omnibus, containing the single-cell RNA sequencing data of hearts from the control and streptozotocin-induced diabetic mice. GSEA and GSVA were used to explore the function enrichment of DEGs in CM. Cell communication analysis was carried out to study the altered signals and significant ligand-receptor interactions.

Results: Seventeen cell types were identified between DM and the controls. The increasing ratio of CM suggested the occurrence of diabetes induces potential pathological changes of CM proliferation. A total of 1144 DEGs were identified in CM. GSEA and GSVA analysis indicated the enhancing lipid metabolism involving in DM. The results of cell communication analysis suggested that high glucose activated the ability of CM receiving fibroblast and LEC, while inhibited the capacity of receiving ECC and pericyte. Furthermore, GAS and ANGPTL were significantly decreased under DM, which was consistent with the results of GSEA and GSVA. Finally, the ligand-receptor interactions such as vegfc-vegfr2, angptl1 were changes in CM.

Conclusions: The CM showed the significant heterogeneities in DM, which played an important role in myocardial fibrosis induce by hyperglycemia.

Keywords: Cardiomyocytes; Cell communication; Diabetes mellitus.

MeSH terms

  • Animals
  • Diabetes Mellitus, Experimental* / chemically induced
  • Diabetes Mellitus, Experimental* / metabolism
  • Diabetes Mellitus, Experimental* / pathology
  • Mice
  • Myocytes, Cardiac* / metabolism
  • Myocytes, Cardiac* / pathology
  • Single-Cell Analysis*
  • Transcriptome*