High-Throughput Computational Analysis of Biofilm Formation from Time-Lapse Microscopy

Abstract

Candida albicans biofilm formation in the presence of drugs can be examined through time-lapse microscopy. In many cases, the images are used qualitatively, which limits their utility for hypothesis testing. We employed a machine-learning algorithm implemented in the Orbit Image Analysis program to detect the percent area covered by cells from each image. This is combined with custom R scripts to determine the growth rate, growth asymptote, and time to reach the asymptote as quantitative proxies for biofilm formation. We describe step-by-step protocols that go from sample preparation for time-lapse microscopy through image analysis parameterization and visualization of the model fit. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Sample preparation. Basic Protocol 2: Time-lapse microscopy: Evos protocol. Basic Protocol 3: Batch file renaming. Basic Protocol 4: Machine learning analysis of Evos images with Orbit. Basic Protocol 5: Parametrization of Orbit output in R. Basic Protocol 6: Visualization of logistic fits in R.