ColTapp, an automated image analysis application for efficient microbial colony growth dynamics quantification

Abstract

Phenotypic heterogeneity occurs in a population of genetically identical bacteria due to stochastic molecular fluctuations and environmental variations. In extreme cases of phenotypic heterogeneity, a fraction of the bacterial population enters dormancy, and these metabolically inactive or non-dividing bacteria persist through most antibiotic challenges. These subpopulations of persister cells are difficult to study in patient samples. However, the proportion of persisters in a sample can be accessed by physically separating bacteria on a plate measuring the time until colonies become visible as dormant bacteria resume growth later than their active counterparts and form smaller colonies. Here, we present ColTapp (Colony Time-lapse app), an application dedicated to bacterial colony growth quantification, freely available for download together with its MATLAB source code or as a MacOS/Windows executable. ColTapps intuitive graphical user interface allows users without prior coding knowledge to analyze endpoint or time-lapse images of colonies on agar plates. Colonies are detected automatically, and their radius can be tracked over time. Downstream analyses to derive colony lag time and growth rate are implemented. We demonstrate here the applicability of ColTapp on a dataset of Staphyloccocus aureus colony time-lapse images. Colonies on dense plates reached saturation early, biasing lag time estimation from endpoint images. This bias can be reduced by considering the area available to each colony on a plate. By facilitating the analysis of colony growth dynamics in clinical settings, this application will enable a new type of diagnostics, oriented towards personalized antibiotic therapies.