Developing a computational tool for high-throughput analyses of single-cell microscopy data in antimicrobial pharmacology

Antimicrobial resistance poses significant medical challenge worldwide. Misuse, overuse or suboptimal dosing of antibiotics are major driving factors of antimicrobial resistance. Pharmacokinetic/pharmacodynamic (PK/PD) modelling is critical for designing optimal antimicrobial therapies to maximise the efficacy and minimise the emergence of resistance. However, conventional PK/PD modelling is generally based on viable counting on agar plates after overnight culture and employs a population approach. Therefore, a high-throughput, real-time approach is required to capture the dynamics of antimicrobial killing and resistance at single-cell level for accurate antimicrobial PK/PD modelling.
We have developed a time-lapse microscopy method using a microfluidic chamber to record morphological changes of bacterial cells following antimicrobial treatment. However, automatic cell lineage tracking is challenging and necessitates the development of a robust and efficient computational tool to solve this problem.
This multidisciplinary project will develop and validate an advanced computational toolkit to automate cell segmentation, cytometry analysis, cell lineage tracking and single-cell based PK/PD modelling. The developed imaging-based single-cell PK/PD approach will accurately capture bacterial population heterogeneity following antimicrobial treatment, and shift the paradigm of conventional antimicrobial pharmacological research.