Advancing Whole-Cell Biosensors: Kinetics-Dependent Metabolic SERS Analytics for Pollutant Differentiation and...

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.