Machine learning-based impedance system for real-time recognition of antibiotic-susceptible bacteria with parallel cytometry

• Published in: Sensors and actuators. B, Chemical Chemical, 2023-01, Vol.374, p.132698, Article 132698
• Main Authors: Tang, Tao, Liu, Xun, Yuan, Yapeng, Kiya, Ryota, Zhang, Tianlong, Yang, Yang, Suetsugu, Shiro, Yamazaki, Yoichi, Ota, Nobutoshi, Yamamoto, Koki, Kamikubo, Hironari, Tanaka, Yo, Li, Ming, Hosokawa, Yoichiroh, Yalikun, Yaxiaer
• Format: Article
• Language: English
• Subjects: Antibiotic susceptibility test; Antibiotics; Bacteria; Cytometry; Dielectric properties; E coli; Impedance; Impedance cytometry; Machine learning; Microfluidics; Polystyrene resins; Real time; Recognition; Single cell analysis; Training
• ISSN: 0925-4005; 1873-3077
• DOI: 10.1016/j.snb.2022.132698

Impedance cytometry has enabled label-free and fast antibiotic susceptibility testing of bacterial single cells. Here, a machine learning-based impedance system is provided to score the phenotypic response of bacterial single cells to antibiotic treatment, with a high throughput of more than one thousand cells per min. In contrast to other impedance systems, an online training method on reference particles is provided, as the parallel impedance cytometry can distinguish reference particles from target particles, and label reference and target particles as the training and test set, respectively, in real time. Experiments with polystyrene beads of two different sizes (3 and 4.5 µm) confirm the functionality and stability of the system. Additionally, antibiotic-treated Escherichia coli cells are measured every two hours during the six-hour drug treatment. All results successfully show the capability of real-time characterizing the change in dielectric properties of individual cells, recognizing single susceptible cells, as well as analyzing the proportion of susceptible cells within heterogeneous populations in real time. As the intelligent impedance system can perform all impedance-based characterization and recognition of particles in real time, it can free operators from the post-processing and data interpretation. [Display omitted] •Real-time and high-throughput assessment of single bacteria.•Parallel impedance detection for the separate measurement of target and reference samples.•Artificial intelligence-based impedance system for fast antibiotic sensitivity testing.