Liu Li, Feng Bing, Song Yang, Zhan Taijie, Liu Dongxin, Ding Jia, Song Xiaohui, Xu Jian, Wang Duochun, Wei Qiang
National Pathogen Resource Center, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Qingdao Single-Cell Biotechnology Co., Ltd., Qingdao 266100, China.
Biosaf Health. 2025 Mar 27;7(2):94-102. doi: 10.1016/j.bsheal.2025.03.004. eCollection 2025 Apr.
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 DO on (), utilizing DO-probed single-cell Raman spectroscopy to detect the metabolic activity of by the Carbon-Deuterium ratio (C-D). Then, it evaluates the performance of various machine learning models in classifying the metabolic states of the pathogen. Medium DO concentration below 50 % has no significant impact on the growth and reproduction of or on the classification of metabolic states of based on the fingerprint region by machine learning models. Additionally, as the metabolic activity of decreases, both the C-D 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 , while the linear discriminant analysis model demonstrated an accuracy of 99.92 % in classifying exhibiting distinct metabolic activities. Therefore, DO-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.
病原体的代谢活性在食品安全、疫苗研发、临床治疗和国家生物安全等多个领域构成了重大风险。传统的传代培养方法通常需要数天时间，且无法及时检测到代谢活性，限制了它们在许多领域的应用。因此，迫切需要一种能够快速、准确地检测这种活性的方法。本研究基于对溶解氧（DO）对（）影响的调查，利用DO探测的单细胞拉曼光谱通过碳 - 氘比（C - D）检测（）的代谢活性。然后，评估各种机器学习模型在对病原体代谢状态进行分类方面的性能。低于50%的中等DO浓度对（）的生长繁殖或基于机器学习模型指纹区域的（）代谢状态分类没有显著影响。此外，随着（）代谢活性的降低，C - D和活细胞率也逐渐降低。支持向量机模型在区分活的和死的（）时准确率为99.82%，而线性判别分析模型在区分表现出不同代谢活性的（）时准确率为99.92%。因此，DO探测的单细胞拉曼光谱与高通量技术相结合，可以快速、无损且准确地检测病原体代谢活性，在多个领域具有重要应用价值。
