中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 烟台海岸带研究所
  3. 中国科学院烟台海岸带研究所
  4. 中科院海岸带环境过程与生态修复重点实验室
Advancing Whole-Cell Biosensors: Kinetics-Dependent Metabolic SERS Analytics for Pollutant Differentiation and Quantification

文献类型：期刊论文

作者Zhou, Tianyu3,5; Zhang, Zhiyang4,5; Chen, Jiadong1; Wang, Qiaoning5; Chen, Yan3,5; Wu, Yanzhou5; Choo, Jaebum1; Chen, Lingxin2,5
刊名ANALYTICAL CHEMISTRY
出版日期2025-08-26
卷号97期号:33页码:18392-18403
ISSN号0003-2700
DOI10.1021/acs.analchem.5c04008
通讯作者Zhang, Zhiyang(zyzhang@yic.ac.cn) ; Choo, Jaebum(jbchoo@cau.ac.kr) ; Chen, Lingxin(lxchen@yic.ac.cn)
英文摘要Whole-cell biosensors (WCBs), which detect targeting analytes through cellular responses, have become powerful tools for environmental monitoring. However, existing WCBs often rely on the single-channel low-dimension signal outputs (e.g., fluorescence), hindering the detection and differentiation of multiple analytes. Herein, we demonstrated a surface enhanced Raman scattering (SERS)-based WCB strategy via detecting kinetics-dependent metabolic responses between multiple pollutants and bacteria, enabling differentiation of 8 heavy metals and 5 perfluorinated compounds (PFASs). In this strategy, the wild-type Escherichia coli (E. coli) without gene editing is used as the sensing bacterium, and ultrathin gold shell coated silver nanoparticles (Ag@AuNPs) are used as SERS enhancement substrates. The Ag@AuNPs exhibit high sensitivity and biocompatibility, enabling the determination of trace bacterial metabolites and preventing signal interference from cellular toxicity responses to silver-based nanoparticles. By combining the SERS spectra of the pollutant-exposed E. coli at different bacteria-nanoparticle coincubation time points, we constructed joint SERS spectra for predictive analytics using machine learning (ML) algorithms. We have successfully achieved the precise classification of various pollutants with high prediction accuracy, including different types and forms of heavy metals (100%) and different PFASs (>= 92%), as well as the quantification of representative pollutants. The successful detection of different heavy metal ions and PFASs in seawater demonstrates its potential for detecting and distinguishing harmful pollutants in complex real-world environments. This work demonstrates a facile and efficient WCB platform for pollutant classification and quantification, providing an effective analytical method for environmental monitoring.
WOS研究方向Chemistry
语种英语
WOS记录号WOS:001550167100001
资助机构National Natural Science Foundation of China ; Key Deployment Project of Centre for Ocean Mega-Research of Science, Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Research Foundation of Korea ; Technology Innovation Program ; Ministry of Trade, Industry, and Energy (MOTIE, Korea) ; Taishan Scholars Program ; Natural Science Foundation of Shandong Province ; Special Fund for the Scholar Program of Yantai
源URL[http://ir.yic.ac.cn/handle/133337/41031]  
专题烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室
通讯作者Zhang, Zhiyang; Choo, Jaebum; Chen, Lingxin
作者单位1.Chung Ang Univ, Dept Chem, Seoul 06974, South Korea
2.Qingdao Marine Sci & Technol Ctr, Lab Marine Biol & Biotechnol, Qingdao 266237, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao 266071, Peoples R China
5.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Res Ctr Coastal Environm Engn & Technol, CAS Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Peoples R China
推荐引用方式
GB/T 7714		 Zhou, Tianyu,Zhang, Zhiyang,Chen, Jiadong,et al. Advancing Whole-Cell Biosensors: Kinetics-Dependent Metabolic SERS Analytics for Pollutant Differentiation and Quantification[J]. ANALYTICAL CHEMISTRY,2025,97(33):18392-18403.
APA Zhou, Tianyu.,Zhang, Zhiyang.,Chen, Jiadong.,Wang, Qiaoning.,Chen, Yan.,...&Chen, Lingxin.(2025).Advancing Whole-Cell Biosensors: Kinetics-Dependent Metabolic SERS Analytics for Pollutant Differentiation and Quantification.ANALYTICAL CHEMISTRY,97(33),18392-18403.
MLA Zhou, Tianyu,et al."Advancing Whole-Cell Biosensors: Kinetics-Dependent Metabolic SERS Analytics for Pollutant Differentiation and Quantification".ANALYTICAL CHEMISTRY 97.33(2025):18392-18403.

入库方式： OAI收割

来源：烟台海岸带研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。