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Urine Metabolic Fingerprints Encode Subtypes of Kidney Diseases
Author(s) -
Yang Jing,
Wang Ran,
Huang Lin,
Zhang Mengji,
Niu Jingyang,
Bao Chunde,
Shen Nan,
Dai Min,
Guo Qiang,
Wang Qian,
Wang Qin,
Fu Qiong,
Qian Kun
Publication year - 2020
Publication title -
angewandte chemie international edition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.831
H-Index - 550
eISSN - 1521-3773
pISSN - 1433-7851
DOI - 10.1002/anie.201913065
Subject(s) - urine , mass spectrometry , computational biology , kidney , lupus nephritis , biomarker discovery , proteomics , chromatography , chemistry , biology , medicine , biochemistry , gene , disease
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.
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