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Electrochemomics Profiling Metabolic Dynamics in Biofluids

  • Jianwu Wang
  • , Huarong Xia
  • , Chenyan Huang
  • , Qianyun Deng
  • , Ting Wang
  • , Pingqiang Cai
  • , Xiao Li
  • , Xiaopei Chi
  • , Wei Peng Goh
  • , Yahui Liu
  • , Chenyao Nie
  • , Feilong Zhang
  • , Changxian Wang
  • , Jing Yu
  • , Zhisheng Lv
  • , Xiaoshi Wang
  • , Rayner Bao Feng Ng
  • , Jinwei Cao
  • , Youquan Fu
  • , Junqi Yi
  • Xian Jun Loh, Daniel Mandler, Bing Gu, Yan Wei*, Xiaodong Chen*
*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Conventional electrochemical sensing techniques detect predefined molecular biomarkers for disease diagnosis, while compromised by the biases in the calibration process due to the complexity and volatility of peripheral biofluids. Meanwhile, abundant electrochemical information at the electrode–biofluid interfaces remains to be discovered to gain comprehensive metabolic signatures for diagnostic applications. Here, we propose an electrochemomics (EC-omics) approach to comprehensively profile the dynamics of electrochemical properties of biomolecules in peripheral biofluids during disease onset. As a proof of concept, we customized a portable electrochemical profiling platform, where the high sensitivity and low background noise of the carbon nanotube/bacterial cellulose (CNT/BC) electrodes enabled a holistic and unbiased capturing of the electrochemical features in biofluids. We applied the EC-omics platform to profile saliva for periodontitis diagnosis. The obtained saliva EC-omics database is compatible with various intelligent algorithms, which could accurately discriminate periodontitis (93%), surpassing the untargeted nuclear magnetic resonance data (89%) and significantly outperforming the periodontitis-related molecular biomarkers (70%) and peak intensity features (57%). Additionally, our study demonstrated the feasibility of EC-omics in human urine and mouse serum analysis, suggesting its potential to expand our understanding of the complex metabolic networks of biofluids and further foster a broader range of novel diagnostic tools across various sensing paradigms for decentralized healthcare.

Original languageEnglish
Pages (from-to)8264-8275
Number of pages12
JournalJournal of the American Chemical Society
Volume148
Issue number8
DOIs
StatePublished - 4 Mar 2026

Bibliographical note

Publisher Copyright:
© 2026 American Chemical Society

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