Abstract 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. Graphical abstract [48]graphic file with name 12933_2024_2458_Figa_HTML.jpg Supplementary Information The online version contains supplementary material available at 10.1186/s12933-024-02458-x. Keywords: Diabetic macroangiopathy, Spatial multiomics, Calcification characteristics, Metabolic reprogramming, Transcriptional control Background The main manifestation of diabetic macrovascular disease is atherosclerosis of the aorta, coronary arteries, renal arteries, basilar arteries, and peripheral arterial arteries, which can lead to vascular obstruction or vascular calcification [[49]1]. At the same time, diabetic macroangiopathy can lead to stroke, myocardial infarction, heart failure and amputation, and is the main cause of death and paralysis in diabetic patients. The role of inflammation, hyperglycemia, dysglycemia, smooth muscle cell (SMC) phenotypic transformation, and other factors in diabetic macroangiopathy has been extensively documented in existing studies [[50]2–[51]4]. With the continuous development of spatial omics technology and single-cell technology, it is now possible to conduct gene expression examination with even nanoscale accuracy based on the two-dimensional spatial structure of tissue slices, thus leading to further understanding of disease pathology and physiology [[52]5]. The transformation of smooth muscle cell phenotype was considered to be an important mechanism of diabetic macrovascular disease and the initiation mechanism of vascular calcification [[53]6–[54]10]. SMCs are a very plastic cell type, which can change from physiological contraction to mesenchymal, fibroblast-like, macrophage-like, osteoblast-like or adipocyte like phenotype [[55]11, [56]12]. However, the phenotypic transformation of smooth muscle in diabetic macroangiopathy remains controversial. Glucose and fatty acid metabolism, neural metabolism, and exosome regulation have recently attracted attention simultaneously [[57]13–[58]15]. The trend of smooth muscle cell transformation in diabetics and metabolic reprogramming other than abnormal glucose and lipid metabolism remain to be further explored. To gain a more detailed and comprehensive understanding of the specific mechanisms of diabetic macrovascular disease, we performed multiomics methods of spatial transcriptome, spatial metabolome and single-cell sequencing on the lower limb blood vessels of diabetic patients, revealing the spatial characteristics of gene and metabolite expression in diabetic blood vessels from the perspective of cell resolution, and thus constructing a multi-omics map of diabetic blood vessels. By analyzing the gene and metabolite expression levels of different vascular substructures and different cells, the metabolic activation and metabolic reprogramming of diabetic great vessels were revealed. Through further definition and transcriptional drive analysis of SMCs subsets, we identified three phenotypic transformation trends of SMCs, including macrophage-like SMCs, inflammatory chemotactic SMCs, and fibroblast-like SMCs. In addition, we identified the potential transcription factor of driving smooth muscle cells phenotypic transformation and constructed transcriptional regulatory network. Based on the public database and quasi-time series analysis, the driving transcription factors corresponding to different phenotypic transformations were determined. Our study can provide a basis for further research on vascular smooth muscle lineage tracing and vascular metabolic reprogramming. The construction of multiomics atlas of diabetic great vascular diseases also provides a new direction for clinical treatment and detection. Methods Clinical samples and ethics The clinical samples and related information obtained in this study were all approved by the Research Ethics Committee of Affiliated Hospital of Jiangsu University, and all patients gave written informed consent. Included diabetic amputation patients met the following criteria: (1) Diagnosed with diabetes according to the diagnostic criteria of China’s Guidelines for the Prevention and Treatment of Type 2 Diabetes (2020); (2) Received diabetic foot major amputation according to the Guidelines for the Prevention and Treatment of Diabetic Foot in China (2019); (3) Complete relevant clinical data; (4) Computed Tomography (CT) of lower limbs showed significant calcification; (5) Sign informed consent to participate in this study. Patients meeting the following criteria were excluded: (1) Patients with chronic kidney disease, dialysis patients and other diseases that significantly affect vascular calcification; (2) Vulnerable groups such as mental cognitive-behavioral disorders; (3) With lower extremity arterial CT deficiency; (4) With severe hepatic and renal insufficiency and other multiple organ failure; (5) With Malignant tumor patient. From June 2021 to June 2023, a total of 11 patients amputated anterior tibial artery specimens were sequenced for quality control, and Table [59]S1 summarized clinical characteristics of this patient cluster. Included car accident amputation group met the following criteria: (1) Complete relevant clinical data; (2) CT of lower limbs showed no calcification; (3) Sign informed consent to participate in this study. Patients meeting the following criteria were excluded: (1) Patients with diabetes, chronic kidney disease, dialysis patients and other diseases that significantly lead vascular calcification; (2) Vulnerable groups such as mental cognitive-behavioral disorders; (3) With lower extremity arterial CT deficiency; (4) With severe hepatic and renal insufficiency and other multiple organ failure; (5) With Malignant tumor patient. Single-cell transcriptome A single-cell suspension was prepared from the tibial anterior vessels isolated from 4 diabetic patients. The samples were labeled CA1, CA2, CA3, NC1, NC2 and NC3. CA samples were the corresponding blood vessel specimens where the anterior tibial artery calcification can be seen in the lower limb CT as samples of the calcification group. NC samples were the corresponding blood vessel specimen in the lower extremity CT without calcification of anterior tibial artery as the control sample. CA1, NC1 came from the same patient and CA3, NC3 from the same patient. After the dead cells were removed, the samples were sequenced on the NovaSeq 6000 platform. Sequencing reads were processed with the Cell Ranger pipeline (version 3.0.2; 10 × Genomics) and were compared with the 10 × Genomics mm10 reference transcriptome package (version 3.0.0). Cell expression matrix was further analyzed based on seurat package (v5.0) [[60]16]. We filtered cells that have unique feature counts less than 200 or have > 5% mitochondrial counts. Use scDblFinder to evaluate the doublet identification cells [[61]17], then perform normalizing and Scaling on the data. We chose 15 Principal Component Analysis (PCA) dimensionality to find neighbors and clustered data with 0.8 resolution. Two-dimensional Uniform Manifold Approximation and Projection (UMAP) was used to represent cell clusters. The annotation of cell types and subtypes was conducted using the Blueprint and Encode databases as single references, in conjunction with marker genes based