School of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China.
Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001, P. R. China.
Angew Chem Int Ed Engl. 2020 Jan 20;59(4):1703-1710. doi: 10.1002/anie.201913065. Epub 2019 Dec 12.
Metabolic fingerprints of biofluids encode diverse diseases and particularly urine detection offers complete non-invasiveness for diagnostics of the future. Present urine detection affords unsatisfactory performance and requires advanced materials to extract molecular information, due to the limited biomarkers and high sample complexity. Herein, we report plasmonic polymer@Ag for laser desorption/ionization mass spectrometry (LDI-MS) and sparse-learning-based metabolic diagnosis of kidney diseases. Using only 1 μL of urine without enrichment or purification, polymer@Ag afforded urine metabolic fingerprints (UMFs) by LDI-MS in seconds. Analysis by sparse learning discriminated lupus nephritis from various other non-lupus nephropathies and controls. We combined UMFs with urine protein levels (UPLs) and constructed a new diagnostic model to characterize subtypes of kidney diseases. Our work guides urine-based diagnosis and leads to new personalized analytical tools for other diseases.
生物流体的代谢指纹编码多种疾病，特别是尿液检测为未来的诊断提供了完全的非侵入性。由于生物标志物有限且样本复杂，目前的尿液检测性能不佳，需要先进的材料来提取分子信息。在此，我们报告了用于激光解吸/电离质谱（LDI-MS）和基于稀疏学习的肾脏疾病代谢诊断的等离子体聚合物@Ag。仅使用 1 μL 尿液，无需富集或纯化，聚合物@Ag 即可在几秒钟内通过 LDI-MS 提供尿液代谢指纹（UMF）。通过稀疏学习分析可区分狼疮肾炎与各种其他非狼疮性肾炎和对照。我们将 UMFs 与尿液蛋白水平（UPLs）相结合，并构建了一个新的诊断模型来表征肾脏疾病的亚型。我们的工作指导基于尿液的诊断，并为其他疾病提供新的个性化分析工具。
