Comparison of Raman spectroscopy with mass spectrometry for sequence typing of strains: a single-center study.

UNLABELLED

The rapid sequence typing (ST) of bacterial strains is crucial for effective nosocomial infection control and mitigating the spread of nosocomial pathogens, e.g., . While accurate in identifying strains, current typing methods are often impractical in clinical settings due to their time-consuming nature. This study developed a novel approach, combining surface-enhanced Raman spectroscopy (SERS) with machine-learning (ML) algorithms, to construct predictive models for sequence typing based on SERS spectra. The objective was to assess the feasibility of this integrated method for efficient sequence typing of strains. Clinically isolated strains ( = 267) were collected from a single hospital between 2013 and 2023. Based on multilocus sequence typing, 39 STs of were identified. Then, a SERS spectral database for all these strains was constructed, and predictive models based on eight ML algorithms were developed to predict SERS signals to determine their STs, among which the support vector machine (SVM) model had the best performance (fivefold cross-validation = 99.74%). The typing capacity of the SERS-SVM method was compared with that of matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) for sequence typing, confirming the superiority of SERS-SVM over MALDI-TOF mass spectrometer. This pilot study lays the groundwork for employing the SERS-ML method to rapidly identify strain types in clinical laboratories, aiding in controlling bacterial pathogen transmission. Further studies are warranted to evaluate its potential in nosocomial surveillance systems, especially for rapidly identifying outbreaks within hospitals.

IMPORTANCE

The rapid and accurate sequence typing (ST) of bacterial pathogens is pivotal in controlling transmission within healthcare settings. infection, known for its high transmissibility and drug resistance, presents a major challenge in nosocomial infection control. In this study, surface-enhanced Raman spectroscopy (SERS) was used to differentiate strains with distinct STs based on unique Raman spectral profiles. We then constructed and compared eight machine-learning models on SERS spectra to quickly identify bacterial STs. The results showed that the support vector machine model outperformed matrix-assisted laser desorption/ionization time-of-flight mass spectrometer in determining STs. This approach enables rapid identification of variants with different STs, supporting the early detection and control of nosocomial infections by this multidrug-resistant pathogen.