Urine Metabolic Fingerprints Encode Subtypes of Kidney Diseases
Jing Yang
School of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030 P. R. China
These authors contributed equally to this work.
Search for more papers by this authorRan Wang
Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001 P. R. China
These authors contributed equally to this work.
Search for more papers by this authorMengji Zhang
School of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030 P. R. China
Search for more papers by this authorJingyang Niu
School of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030 P. R. China
Search for more papers by this authorChunde Bao
Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001 P. R. China
Search for more papers by this authorNan Shen
Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001 P. R. China
Search for more papers by this authorMin Dai
Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001 P. R. China
Search for more papers by this authorQiang Guo
Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001 P. R. China
Search for more papers by this authorProf. Qian Wang
School of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030 P. R. China
Search for more papers by this authorQin Wang
Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001 P. R. China
Search for more papers by this authorCorresponding Author
Qiong Fu
Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001 P. R. China
Search for more papers by this authorCorresponding Author
Prof. Kun Qian
School of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030 P. R. China
Search for more papers by this authorJing Yang
School of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030 P. R. China
These authors contributed equally to this work.
Search for more papers by this authorRan Wang
Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001 P. R. China
These authors contributed equally to this work.
Search for more papers by this authorMengji Zhang
School of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030 P. R. China
Search for more papers by this authorJingyang Niu
School of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030 P. R. China
Search for more papers by this authorChunde Bao
Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001 P. R. China
Search for more papers by this authorNan Shen
Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001 P. R. China
Search for more papers by this authorMin Dai
Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001 P. R. China
Search for more papers by this authorQiang Guo
Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001 P. R. China
Search for more papers by this authorProf. Qian Wang
School of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030 P. R. China
Search for more papers by this authorQin Wang
Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001 P. R. China
Search for more papers by this authorCorresponding Author
Qiong Fu
Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001 P. R. China
Search for more papers by this authorCorresponding Author
Prof. Kun Qian
School of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030 P. R. China
Search for more papers by this authorAbstract
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.
Conflict of interest
The authors declare competing financial interest. The authors have filed patents for both the technology and the use of the technology to analyze biological samples.
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