A stochastic single-cell-based framework for MIC determination

Antimicrobial resistance, viewed through the One Health approach, represents a global public health challenge connecting humans, animals, and the environment. This study aimed to develop a probabilistic framework linking single-cell variability with population-level minimum inhibitory concentration (MIC) determination, providing a realistic, quantitative understanding of antimicrobial susceptibility and improving interpretation of bacterial responses to antibiotic exposure.

The behavior of individual Escherichia coli cells exposed to gentamicin was examined by time-lapse phase-contrast microscopy, while population growth was quantified using a turbidimetric system simulating the broth microdilution method. Microscopic observations revealed variability in single-cell division and micro-colony formation under antibiotic stress. The maximum micro-colony size (Nmax) decreased with gentamicin concentration, indicating concentration-dependent growth limitation, although limited divisions persisted at 3 μg mL-1. Population-level analysis showed that the first detectable increase in optical density in broth microdilution assays corresponded to approximately 7 log CFU mL-1, defining the operational detection threshold of the conventional MIC assay. Monte Carlo simulations incorporating experimentally estimated single-cell growth probabilities described how detectable growth depends on antibiotic concentration and inoculum size, reframing MIC as a probabilistic value. This framework explained the inoculum effect and the range of antibiotic concentrations near inhibitory concentrations where detection becomes probabilistic due to stochastic responses.

These results demonstrate that variability among individual cells influences the apparent inhibitory effect observed at the population level. Viewing MIC in a probabilistic manner provides a realistic understanding of bacterial behavior under antibiotic stress and can support reliable interpretations of antimicrobial susceptibility, with potential implications for safety and clinical microbiology.