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Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

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A stochastic single-cell-based framework for MIC determination.

Styliani Dimitra Papagianeli¹, Zafeiro Aspridou², Konstantinos Koutsoumanis¹

¹Laboratory of Food Microbiology and Hygiene, Department of Food Science and Technology, School of Agriculture, Faculty of Agriculture, Forestry and Natural Environment, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece.

Journal of Applied Microbiology

|May 20, 2026

Summary

This summary is machine-generated.

Single-cell variability in bacterial populations affects antimicrobial resistance outcomes. This study introduces a probabilistic framework to better understand bacterial responses to antibiotics, improving antimicrobial susceptibility testing.

Keywords:

Escherichia coli O157:H7 antimicrobial susceptibility testing broth microdilution minimum inhibitory concentration (MIC)single-cell variability stochastic modeling

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Area of Science:

Microbiology
Computational Biology
Public Health

Background:

Antimicrobial resistance (AMR) is a global health threat requiring a One Health approach, integrating human, animal, and environmental factors.
Current methods for determining Minimum Inhibitory Concentration (MIC) may not fully capture bacterial responses due to inherent single-cell variability.

Purpose of the Study:

To develop a probabilistic framework linking single-cell variability to population-level MIC determination.
To provide a quantitative understanding of antimicrobial susceptibility and bacterial responses to antibiotics.

Main Methods:

Time-lapse phase-contrast microscopy to observe individual Escherichia coli cell behavior under gentamicin exposure.
Turbidimetric system simulating broth microdilution for population growth quantification.
Monte Carlo simulations incorporating single-cell growth probabilities to model population-level responses.

Main Results:

Single-cell variability in division and micro-colony formation was observed under antibiotic stress.
Gentamicin concentration directly impacted maximum micro-colony size, demonstrating concentration-dependent growth limitation.
A probabilistic framework was established, reframing MIC and explaining the inoculum effect and stochastic responses near inhibitory concentrations.

Conclusions:

Individual cell variability significantly influences population-level antimicrobial effects.
A probabilistic view of MIC offers a more realistic understanding of bacterial behavior under antibiotic pressure.
This approach can enhance the reliability of antimicrobial susceptibility interpretations for clinical microbiology and safety.