Work

Detecting and classifying metabolic activity of Staphylococcus aureus by D2O-probed single-cell Raman spectroscopy and machine learning

Year: 2025

Type: article

Abstract: • Scientific question: In the context of clinical bacterial resistance surveillance, evaluation of food disinfection outcomes, and assessment of pathogen preservation efficiency, there is an urgent need for a rapid, non-destructive, and highly accurate method to enhance the detection of pathogenic microorganism viability. • Evidence before this study: Previous studies have demonstrated that stable isotopes, such as 2 H, 13 C, and 15 N, serve as practical markers when combined with Raman technology, allowing for the evaluation of metabolic capacity and pathways of various substances. Moreover, the Raman technique has been used to detect the metabolic status of Listeria innocua and the clinical drug resistance of Escherichia coli . • New findings: In this study, a rapid and non-destructive D 2 O-probed single-cell Raman spectroscopy for quantifying the C-D ratio was developed to detect the metabolic activity of Staphylococcus aureus ( S. aureus ) at the single-cell level. Using this method, combined with support vector machines (SVM) and linear discriminant analysis (LDA) models, pathogens can be identified, and their metabolic activities assessed, thereby streamlining integrated pathogen detection processes. • Significance of the study: In contrast to the traditional subculture method, this technique achieves precise detection of S. aureus metabolic activity at the single-cell level within just a few minutes using D 2 O. The integration of this method with high-throughput technology significantly improves pathogen detection capabilities across various domains, including scientific research, clinical diagnostics, and food safety, while offering promising potential for future applications. The metabolic activity of pathogens poses a substantial risk across diverse domains, including food safety, vaccine development, clinical treatment, and national biosecurity. Conventional subculturing methods typically require several days and fail to detect metabolic activity promptly, limiting their application in many areas. Consequently, there is an urgent need for a method capable of rapidly and accurately detecting this activity. This study builds upon an investigation of the effects of D 2 O on Staphylococcus aureus ( S. aureus ), utilizing D 2 O-probed single-cell Raman spectroscopy to detect the metabolic activity of S. aureus by the Carbon-Deuterium ratio (C-D ratio ). Then, it evaluates the performance of various machine learning models in classifying the metabolic states of the pathogen. Medium D 2 O concentration below 50 % has no significant impact on the growth and reproduction of S. aureus or on the classification of metabolic states of S. aureus based on the fingerprint region by machine learning models. Additionally, as the metabolic activity of S. aureus decreases, both the C-D ratio and the rate of viable cells also gradually decrease. The support vector machine model demonstrated an accuracy of 99.82 % in classifying viable and dead S. aureus , while the linear discriminant analysis model demonstrated an accuracy of 99.92 % in classifying S. aureus exhibiting distinct metabolic activities. Therefore, D 2 O-probed single-cell Raman spectroscopy, combined with high-throughput technology, can rapidly, non-destructively, and accurately detect pathogen metabolic activity, offering valuable applications across multiple fields.

Source: Biosafety and Health

DOI: 10.1016/j.bsheal.2025.03.004