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

Area of Science:

• Environmental Science and Engineering
• Biosensing and Nanotechnology

Background:

• Whole-cell biosensors (WCBs) are vital for environmental monitoring but often struggle with differentiating multiple analytes due to low-dimensional signal outputs.
• Existing WCBs using fluorescence lack the multiplexing capability required for complex environmental samples.
• A need exists for advanced WCBs capable of simultaneous detection and differentiation of diverse pollutants.

Purpose of the Study:

• To develop a novel surface-enhanced Raman scattering (SERS)-based WCB strategy for detecting and differentiating multiple environmental pollutants.
• To utilize kinetics-dependent metabolic responses of bacteria to analytes for enhanced biosensing capabilities.
• To establish a facile and efficient platform for pollutant classification and quantification in complex environments.

Main Methods:

• Employed wild-type *Escherichia coli* (*E. coli*) as the sensing bacterium without genetic modification.
• Utilized ultrathin gold shell coated silver nanoparticles (Ag@AuNPs) as sensitive SERS enhancement substrates.
• Combined SERS spectra from pollutant-exposed *E. coli* at various incubation times with machine learning (ML) algorithms for predictive analytics.

Main Results:

• Successfully differentiated 8 heavy metals (100% accuracy) and 5 per- and polyfluoroalkyl substances (PFASs) (≥92% accuracy).
• Demonstrated high sensitivity and biocompatibility of Ag@AuNPs, enabling detection of trace metabolites and minimizing cellular toxicity.
• Achieved precise classification and quantification of representative pollutants, including successful detection in seawater samples.

Conclusions:

• The developed SERS-based WCB platform offers a powerful method for differentiating and quantifying multiple pollutants based on metabolic responses.
• The integration of ML algorithms with SERS spectra provides robust predictive analytics for complex environmental monitoring.
• This facile and efficient WCB strategy shows significant potential for real-world environmental pollution detection and management.