Shining a Light on Wastewater Pathogens—AI-Assisted Surface-Enhanced Raman Spectroscopy and Electrokinetics for Bacterial Identification in Wastewater

Wastewater-based epidemiology can monitor population-level infections, provide early warnings about disease outbreaks, and help control the spread at the community level [1,2]. However, broad-spectrum bacterial identification in wastewater presents outstanding challenges; notably, current culturing or fluorescence-based methods [3] to identify bacteria are unsuitable for real-time, high-throughput screening of diverse bacterial species, and may not work well in the complex wastewater matrix. Here, we harness electrokinetics and artificial intelligence (AI)-assisted Raman spectroscopy as an innovative approach that promises the identification of a wide range of pathogenic bacteria in wastewater.<br/><br/>First, we synthesize gold nanorods that can electrostatically bind to bacteria surfaces, allowing for surface-enhanced Raman spectroscopy (SERS) [4] from cell surfaces. We collect SERS from bacteria spiked into filter-sterilized wastewater, including <i>Staphylococcus aureus</i>, <i>Staphylococcus epidermidis</i>, <i>Escherichia coli</i>, and <i>Serratia marcescens</i> as model species, spanning concentrations from 10⁹ cells/mL to 10⁴ cells/mL. Spectral clustering analysis shows that bacterial signals become less distinguishable in wastewater as the concentrations decrease. To overcome this challenge, we incorporate electrokinetic effects into SERS by employing gold microelectrodes to apply electric fields, utilizing dielectrophoresis (DEP) [5] to rapidly displace and concentrate bacteria. The four types of bacteria responded to 100 kHz AC fields due to their dielectric responses, and were enriched at the microelectrode within minutes. The enrichment of bacteria is directly visualized through optical and electron microscopy, resulting in up to tenfold increases in Raman signal intensities under electrical fields at bacterial concentrations down to 10⁴ cells/mL. Such enhancement may enable the detection sensitivity to reach environmentally relevant concentrations. Next, employing machine learning models [6], we identify biologically relevant Raman fingerprint peaks characterizing proteins, nucleic acids, and lipids from bacteria surfaces, allowing for rapid identification of bacteria species in wastewater. Finally, we demonstrate that mixtures of bacteria are distinguishable from SERS under DEP effects. We discuss development of unmixing algorithms to distinguish the bacterial signatures from wastewater and identify mixtures of different bacteria species. We also discuss integrating our method with microfluidic devices to monitor complex wastewater samples. Our method can enable generalized pathogen detection and molecular recognition in complex liquid samples, such as wastewater, blood, and seawater.<br/><br/>[1] Hellmér, et al. Applied and environmental microbiology. (2014).<br/>[2] Keshaviah, et al. The Lancet Global Health. (2023).<br/>[3] Jahn, et al. Nature Microbiology. (2022).<br/>[4] Tadesse, et al., Nano Lett. (2020).<br/>[5] Pethig. John Wiley & Sons (2010).<br/>[6] Ho, et al., Nat. Comm. (2019).