空间代谢组学在肾脏疾病中的研究进展

Research progress of spatial metabolomics in kidney diseases

摘要:

肾脏疾病的发病率和病死率较高，但早期一般没有明显的临床症状，通常发现时，肾脏已遭受严重损害。目前，对肾脏疾病早期诊断标志物的研究主要依赖于基因组学、蛋白质组学、转录组学和其他方法，但这些方法都不能保留时间和空间信息。代谢组学在识别潜在疾病机制、促进临床诊断和开发肾脏疾病药物治疗方面显示出越来越大的潜力。作为组学工具包的最新成员，空间代谢组学脱颖而出。该方法对新鲜组织标本进行原位质谱分析，同时能有效保存其时空信息。该文全面综述了空间代谢组学在肾脏疾病研究中的进展，包括对糖尿病肾病发生、发展的理解，对肾细胞癌和正常组织的鉴别诊断等。

Abstract:

Kidney diseases pose a major global health burden with high incidence and mortality. Early stages often lack symptoms, leading to significant damage before detection. Current research on early diagnostic biomarkers relies on genomics, proteomics, and transcriptomics, but these methods lack spatial and temporal information. Metabolomics shows increasing potential for identifying disease mechanisms, aiding diagnosis, and developing treatments for kidney diseases. Spatial metabolomics, the latest addition to the omics toolkit, enables in situ mass spectrometry analysis of fresh tissue, preserving spatial and temporal information. This review summarizes the progress of spatial metabolomics in kidney disease research, including understanding diabetic nephropathy pathogenesis and differentiating renal cell carcinoma from normal tissue.