Electrokinetics with Surface-Enhanced Raman Spectroscopy for Bacterial Identification in Wastewater

Abstract

Bacterial presence in wastewater serves as a crucial bioindicator for monitoring population-level infections and offering early disease outbreak warnings [1,2]. However, identifying bacteria in wastewater presents outstanding challenges; notably, current culturing or fluorescence-based methods [3] to identify bacteria are slow and expensive, unsuitable for high-throughput screening of diverse bacterial species, and may not work well in the complex wastewater matrix. Here, we harness the potential of surface chemistry and electrokinetics to introduce an innovative electro-optical approach that has the promise to detect a broad spectrum of pathogenic bacteria in wastewater.We combine surface-enhanced Raman spectroscopy (SERS) [4] with alternating current electric fields, complemented by machine learning models [5], to enable rapid and amplification-free bacteria detection in filtered wastewater, at cell concentrations as low as 104 cells/mL. First, we synthesize gold nanorods that can electrostatically bind to bacteria surfaces, allowing for SERS from cell surfaces. We collect SERS from bacteria spiked into filtered wastewater, including Staphylococcus aureus, Staphylococcus epidermidis, and Escherichia coli, spanning concentrations from 109 cells/mL to 104 cells/mL. Spectral clustering analysis shows that bacteria signals become more indistinguishable from wastewater as the concentrations decrease. Further, we incorporate electrokinetic effects into SERS by employing gold microelectrodes to apply electric fields, utilizing dielectrophoresis [6] to rapidly displace and concentrate bacteria based on their dielectric responses within minutes. The enrichment of bacteria with nanorods is directly visualized through microscopy, resulting in Raman signal intensities increased by up to tenfold under electrical fields for bacterial concentrations ranging from 106 down to 104 cells/mL. Such enhancement may enable the detection sensitivity to reach environmentally-relevant concentrations. Moreover, employing data science analysis, we identify biologically-relevant “fingerprint” Raman peaks characterizing proteins, nucleic acids, and lipids from bacteria surfaces, allowing for rapid identification of bacteria species in wastewater. This novel method exhibits potential for generalized pathogen detection and molecular recognition in complex liquid samples, such as wastewater, blood, and seawater.[1] Hellmér, et al. Applied and environmental microbiology. (2014).[2] Keshaviah, et al. The Lancet Global Health. (2023).[3] Jahn, et al. Nature Microbiology. (2022).[4] Tadesse, et al., Nano Lett. (2020).[5] Ho, et al., Nat. Comm. (2019).[6] Pethig. John Wiley & Sons (2010).