Spatial-resolved metabolomics reveals tissue-specific metabolic reprogramming in diabetic nephropathy by using mass spectrometry imaging

• DOI: 10.1016/j.apsb.2021.05.013
• Publication Date: 2021-05-20T14:47:01Z
• Authors: Zhonghua Wang; Wenqing Fu; Meiling Huo; Bingshu He; Yaqi Liu; Lu Tian; Wanfang Li; Zhi Zhou; Baili Wang; Jianzhen Xia; Yanhua Chen; Jinfeng Wei; Zeper Abliz
• Keywords: Mass spectrometry imaging; 0303 health sciences; 03 medical and health sciences; Spatial-resolved metabolomics; Metabolic reprogramming; Original Article; Diabetic nephropathy; Therapeutics. Pharmacology; RM1-950; Astragaloside IV; 3. Good health

Detailed knowledge on tissue-specific metabolic reprogramming in diabetic nephropathy (DN) is vital for more accurate understanding the molecular pathological signature and developing novel therapeutic strategies. In the present study, a spatial-resolved metabolomics approach based on air flow-assisted desorption electrospray ionization (AFADESI) and matrix-assisted laser desorption ionization (MALDI) integrated mass spectrometry imaging (MSI) was proposed to investigate tissue-specific metabolic alterations in the kidneys of high-fat diet-fed and streptozotocin (STZ)-treated DN rats and the therapeutic effect of astragaloside IV, a potential anti-diabetic drug, against DN. As a result, a wide range of functional metabolites including sugars, amino acids, nucleotides and their derivatives, fatty acids, phospholipids, sphingolipids, glycerides, carnitine and its derivatives, vitamins, peptides, and metal ions associated with DN were identified and their unique distribution patterns in the rat kidney were visualized with high chemical specificity and high spatial resolution. These region-specific metabolic disturbances were ameliorated by repeated oral administration of astragaloside IV (100 mg/kg) for 12 weeks. This study provided more comprehensive and detailed information about the tissue-specific metabolic reprogramming and molecular pathological signature in the kidney of diabetic rats. These findings highlighted the promising potential of AFADESI and MALDI integrated MSI based metabolomics approach for application in metabolic kidney diseases.