Cardiovascular Diabetology · 2024 · 29 citations · 50 references
Life SciencesCell BiologyMolecular BiologyDiabetologyMolecular Cell BiologyMulti-omics IntegrationMulti-omics Data FusionSingle-cell Multi-omics IntegrationSingle-cell TranscriptomicsSpatial MetabolomicsSpatial Multi-omics IntegrationSpatial Single-cell AnalysisDiabetic VasculopathySingle-cell MultiomicsSingle-cell Multiomics IntegrationSingle-cell Omics AnalysisSingle-cell Omics IntegrationSingle-cell Spatial IntegrationSingle-cell Spatial ProfilingSpatial Multi-omics Analysis
BACKGROUND: Diabetic macroangiopathy has been the main cause of death and disability in diabetic patients. The mechanisms underlying smooth muscle cell transformation and metabolic reprogramming other than abnormal glucose and lipid metabolism remain to be further explored. METHOD: Single-cell transcriptome, spatial transcriptome and spatial metabolome sequencing were performed on anterior tibial artery from 11 diabetic patients with amputation. Multi-omics integration, cell communication analysis, time series analysis, network analysis, enrichment analysis, and gene expression analysis were performed to elucidate the potential molecular features. RESULT: We constructed a spatial multiomics map of diabetic blood vessels based on multiomics integration, indicating single-cell and spatial landscape of transcriptome and spatial landscape of metabolome. At the same time, the characteristics of cell composition and biological function of calcified regions were obtained by integrating spatial omics and single cell omics. On this basis, our study provides favorable evidence for the cellular fate of smooth muscle cells, which can be transformed into pro-inflammatory chemotactic smooth muscle cells, macrophage-like smooth muscle cells/foam-like smooth muscle cells, and fibroblast/chondroblast smooth muscle cells in the anterior tibial artery of diabetic patients. The smooth muscle cell phenotypic transformation is driven by transcription factors net including KDM5B, DDIT3, etc. In addition, in order to focus on metabolic reprogramming apart from abnormal glucose and lipid metabolism, we constructed a metabolic network of diabetic vascular activation, and found that HNMT and CYP27A1 participate in diabetic vascular metabolic reprogramming by combining public data. CONCLUSION: This study constructs the spatial gene-metabolism map of the whole anterior tibial artery for the first time and reveals the characteristics of vascular calcification, the phenotypic transformation trend of SMCs, and the transcriptional driving network of SMCs phenotypic transformation of diabetic macrovascular disease. In the perspective of combining the transcriptome and metabolome, the study demonstrates the activated metabolic pathways in diabetic blood vessels and the key genes involved in diabetic metabolic reprogramming.
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