Bacterial Wastewater-Based Epidemiology Using Surface-Enhanced Raman Spectroscopy and Machine Learning
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Abstract
Wastewater-based epidemiology (WBE) is a powerful tool for monitoring community disease occurrence, but current methods for bacterial detection suffer from limited scalability, the need for a priori knowledge of the target organism, and the high degree of genetic similarity between different strains of the same species. Here, we show that surface-enhanced Raman spectroscopy (SERS) can be a scalable, label-free method for detection of bacteria in wastewater. We preferentially enhance Raman signal from bacteria in wastewater using positively-charged plasmonic gold nanorods (AuNRs) that electrostatically bind to the bacterial surface. Transmission cryoelectron microscopy (cryoEM) confirms that AuNRs bind selectively to bacteria in this wastewater matrix. We spike the bacterial species Staphylococcus epidermidis, Staphylococcus aureus, Serratia marcescens , and Escerichia coli and AuNRs into filter-sterilized wastewater, varying the AuNR concentration to achieve maximum signal across all pathogens. We then collect 540 spectra from each species, and train a machine learning (ML) model to identify bacterial species in wastewater. For bacterial concentrations of 10 9 cells/mL, we achieve an accuracy exceeding 85%. We also demonstrate that this system is effective at environmentally-realistic bacterial concentrations, with a limit of bacterial detection of 10 4 cells/mL. These results are a key first step toward a label-free, high-throughput platform for bacterial WBE.
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Graph of accuracy of CNN when the test set is perturbed at various28wavenumbers. Sharp decreases can be seen with perturbation at bands associated with (i) adenine, (ii) thymine, (iii)29aryl, (iv) phosphate, and (v) carboxyl.
this is a nice way to identify which peaks contribute to the separability of the species, especially when it is not clear by eye. As an alternative, it may be possible to simply do an ANOVA across the species at every datapoint.
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Error bars indicate standard deviation of 1599 cm-1 peak, which is representative of8standard deviation for all peaks.
i understand what this is showing but it seems like an unconventional way to show this kind of data. The shading colors make it seem like there is some kind of continuous concentration sweep from 0 to 150.
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We preferentially enhance Raman signal from bacteria in19wastewater using positively-charged plasmonic gold nanorods (AuNRs) that electrostatically bind to the20bacterial surface
will this result in false positives due to other benign bacteria that may be present in the wastewarer that is still negatively charged or other non-bacterial cells?
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