Bacterial Wastewater-Based Epidemiology Using Surface-Enhanced Raman Spectroscopy and Machine Learning DOI Creative Commons
Liam Herndon, Yirui Zhang, Fareeha Safir

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: July 23, 2024

Abstract Wastewater-based epidemiology (WBE) is a powerful tool for monitoring community disease occurrence, but current methods bacterial detection suffer from limited scalability, the need priori knowledge of target organism, and high degree genetic similarity between different strains same species. Here, we show that surface-enhanced Raman spectroscopy (SERS) can be scalable, label-free method bacteria in wastewater. We preferentially enhance signal wastewater using positively-charged plasmonic gold nanorods (AuNRs) electrostatically bind to surface. Transmission cryoelectron microscopy (cryoEM) confirms AuNRs selectively this matrix. spike species Staphylococcus epidermidis, aureus, Serratia marcescens , Escerichia coli into filter-sterilized wastewater, varying AuNR concentration achieve maximum across all pathogens. then collect 540 spectra each species, train machine learning (ML) model identify For concentrations 10 9 cells/mL, an accuracy exceeding 85%. also demonstrate system effective at environmentally-realistic concentrations, with limit 4 cells/mL. These results are key first step toward label-free, high-throughput platform WBE.

Language: Английский

Bacterial Wastewater-Based Epidemiology Using Surface-Enhanced Raman Spectroscopy and Machine Learning DOI
Liam Herndon, Yirui Zhang, Fareeha Safir

et al.

Nano Letters, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 17, 2025

Although wastewater-based epidemiology has been used extensively for the surveillance of viral diseases, it not to a similar extent bacterial diseases. This is in part owing difficulties distinguishing pathogenic from nonpathogenic bacteria using PCR methods. Here, we show that surface-enhanced Raman spectroscopy (SERS) can be scalable, label-free method detection wastewater. We enhance signal wastewater plasmonic gold nanorods (AuNRs) electrostatically bind surface and confirm this binding cryoelectron microscopy. spike four clinically relevant species AuNRs into filtered wastewater, varying AuNR concentration maximize signal. then collect 540 spectra each at 109 cells/mL train machine learning model identify them with more than 87% accuracy. also demonstrate an environmentally realistic limit 104 cells/mL. These results are key step toward SERS platform WBE.

Language: Английский

Citations

0
Temporal, Spatial, and Methodological Considerations in Wastewater-Based Epidemiology for Sexually Transmitted Infections DOI
William Chen, Kyle Bibby

ACS ES&T Water, Journal Year: 2025, Volume and Issue: unknown

Published: April 28, 2025

Language: Английский

Citations

0
Bacterial Wastewater-Based Epidemiology Using Surface-Enhanced Raman Spectroscopy and Machine Learning DOI Creative Commons
Liam Herndon, Yirui Zhang, Fareeha Safir

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: July 23, 2024

Abstract Wastewater-based epidemiology (WBE) is a powerful tool for monitoring community disease occurrence, but current methods bacterial detection suffer from limited scalability, the need priori knowledge of target organism, and high degree genetic similarity between different strains same species. Here, we show that surface-enhanced Raman spectroscopy (SERS) can be scalable, label-free method bacteria in wastewater. We preferentially enhance signal wastewater using positively-charged plasmonic gold nanorods (AuNRs) electrostatically bind to surface. Transmission cryoelectron microscopy (cryoEM) confirms AuNRs selectively this matrix. spike species Staphylococcus epidermidis, aureus, Serratia marcescens , Escerichia coli into filter-sterilized wastewater, varying AuNR concentration achieve maximum across all pathogens. then collect 540 spectra each species, train machine learning (ML) model identify For concentrations 10 9 cells/mL, an accuracy exceeding 85%. also demonstrate system effective at environmentally-realistic concentrations, with limit 4 cells/mL. These results are key first step toward label-free, high-throughput platform WBE.

Language: Английский

Citations

0