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

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 mu m) 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 suc-cessfully 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.

Automated summary

Impedance cytometry combined with machine learning is used to analyze the response of bacterial single cells to antibiotic treatment in real time, with a high throughput of over one thousand cells per minute. This system provides an online training method on reference particles to distinguish between different types of particles and accurately identify susceptible cells. The results demonstrate the capability of real-time characterization and recognition of individual cells, providing a convenient and efficient method for antibiotic susceptibility testing.