Rodriguez Axell, Purvinsh Yana, Zhang Junjie, Rogovskyy Artem S, Kurouski Dmitry
Department of Biochemistry and Biophysics, Texas A&M University, College Station, Texas 77843, United States.
Department of Pathobiology and Diagnostic Investigation, Michigan State University, East Lansing, Michigan 48824, United States.
Anal Chem. 2025 May 6;97(17):9535-9539. doi: 10.1021/acs.analchem.5c01677. Epub 2025 Apr 21.
Every year, bacterial infections are responsible for over 7 million deaths globally. Timely detection and identification of these pathogens enable timely administration of antimicrobial agents, which can save thousands of lives. Most of the currently known approaches that can address these needs are time- and labor consuming. In this study, we examine the potential of innovative nano-infrared spectroscopy, also known as atomic force microscopy infrared (AFM-IR) spectroscopy, and machine learning in the identification of different bacteria. We demonstrate that a single bacteria cell is sufficient to identify , , and two strains of with 100% accuracy. The identification is based on the vibrational bands that originate from the components of the cell wall as well as the interior biomolecules of the bacterial cell. These results indicate that nano-IR spectroscopy can be used for the nondestructive, confirmatory, and label-free identification of pathogenic microorganisms at the single-cell level.
每年，细菌感染在全球导致超过700万人死亡。及时检测和鉴定这些病原体能够及时施用抗菌药物，从而挽救数千人的生命。目前已知的能够满足这些需求的大多数方法都耗时且费力。在本研究中，我们考察了创新的纳米红外光谱技术（也称为原子力显微镜红外光谱技术，即AFM-IR光谱技术）以及机器学习在鉴定不同细菌方面的潜力。我们证明，单个细菌细胞就足以100%准确地鉴定出 、 、 以及两种 菌株。这种鉴定基于源自细胞壁成分以及细菌细胞内部生物分子的振动谱带。这些结果表明，纳米红外光谱技术可用于在单细胞水平上对致病微生物进行无损、确证且无需标记的鉴定。
