Spatial metabolomics: A new tool for unravelling the metabolic disorders and heterogeneity in diabetic kidney disease (Review)
Year: 2026
Type: article
Abstract: Diabetic kidney disease (DKD) is a microvascular complication of diabetes, characterized by region-specific metabolic reprogramming that disrupts kidney function and markedly impairs patient prognosis.By enabling in situ visualization and analysis of metabolite distribution within kidney tissue, spatial metabolomics offers a unique advantage in detecting spatial heterogeneity in metabolic alterations, which is inaccessible through conventional metabolomics.This approach not only enhances the understanding of DKD pathophysiology but also provides a solid foundation for the development of precision nephrology strategies informed by spatial metabolite data.The present review discusses the fundamental workflows and spatial resolution capabilities of spatial metabolomics, summarizing the key metabolites involved in regional metabolic disruptions in multiple DKD animal models.Moreover, it highlights notable metabolites, including glucose, succinate, phosphatidylserine, lysophosphatidylglycerol, phosphatidylglycerol, sphingomyelin, phosphatidylcholine, phosphatidylethanolamine, taurine, glutamate, L-carnitine, choline, adenosine monophosphate and guanosine monophosphate.The continued advancement of imaging technologies and data analysis methodologies is expected to further refine the spatial resolution and precision of spatial metabolomics, thereby facilitating its broader application in clinical practice.Contents 1. Introduction 2. Basic principles of spatial metabolomics technology 3. Comparison of major spatial metabolomics technologies 4. Functional specialization and metabolic disturbances in DKD 5. Spatial metabolomics reveals region-specific disturbances in glucose metabolism and the TCA cycle in DKD 6. Lipid metabolism in DKD 7. Amino acid metabolism in DKD 8. Nucleic acid metabolism in DKD 9. Discussion and future perspectives 10.Conclusion
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W7126097353