Genotypic and Phenotypic Characterisation of Staphylococcus aureus Enterotoxins Using Single-Cell Raman Spectroscopy and Metabolomics

The discrepancy between the genotypic and phenotypic expression of enterotoxins in <i>S. aureus</i> had long been a significant challenge in toxin detection. However, the accurate and rapid application of Raman spectroscopy for the genotypic and phenotypic characterisation of <i>S. aureus</i> enterotoxins remains problematic. To address this, the present study utilised a single-cell Raman spectra database from 31 <i>S. aureus</i> isolates, acquired via a Raman laser tweezer system. When combined with convolutional neural network analysis, this approach achieved an average accuracy of 99.71% for identifying single-gene toxin types and 99.44% for multi-gene toxin types, with an average phenotypic identification accuracy of 98.71%. Notably, the phenotypic identification accuracy for the three strains carrying the <i>sea</i> and <i>seb</i> genes reached 100%, and the validation accuracy using unknown genotypes and phenotypes exceeded 85%. Furthermore, the CNN analysis identified characteristic spectral peaks for <i>S. aureus</i> enterotoxin genotypes at 1663-1665 cm^-1, 1570 cm^-1, and 1117-1119 cm^-1, corresponding to protein α-helices, guanine, and nucleic acid backbones respectively. Representative peaks for the phenotype were found at 1302-1314 cm^-1 and 912-923 cm^-1, corresponding to proteins/lipids and polysaccharides, respectively. Representative peaks for different virulence phenotypes carrying multiple enterotoxin genes were located at 1074-1076 cm^-1, 1253-1255 cm^-1, 1326 cm^-1, and 1327 cm^-1, corresponding to proteins, nucleic acids, and lipids, respectively. Furthermore, metabolomic analysis of three <i>S. aureus</i> strains (<i>sea</i>+<i>seb</i>+, <i>sea</i>+<i>seb</i>-, <i>sea</i>-<i>seb</i>+) revealed metabolic differences in fatty acids, purines, phenylalanine, and aspartic acid, consistent with the corresponding distinct Raman spectral peaks (1458, 1179, 1406-1409 cm^-1). Thus, this study employed <i>S. aureus</i> as a proof-of-concept, establishing for the first time a method combining Raman laser tweezers with convolutional neural networks for identifying <i>S. aureus</i> enterotoxin genotypes and phenotypes. It clarified the Raman spectral differential peaks and their corresponding biomarkers among five classical enterotoxin genotypes and phenotypic strains, providing a novel approach for accurate toxin typing and virulence characterisation.