Dimalis: A complete standalone pipeline to analyse prokaryotic cell growth from time-lapse imaging

Abstract

ABSTRACTReal-time imaging of bacterial cell division, population growth and behaviour is essential for our understanding of microbial-catalyzed processes at the microscale. However, despite the relative ease by which high resolution imaging data can be acquired, the extraction of relevant cell features from images remains cumbersome. Here we present a versatile pipeline for automated extraction of bacterial cell features from standalone or time-resolved image series, with standardized data output for easy downstream processing. The input consist of phase-contrast images with or without additional fluorescence details, which are denoised to account for potential out-of-focus regions, and segmented to outline the morphologies of individual cells. Cells are then tracked over subsequent time frame images to provide genealogy or microcolony spatial information. We test the pipeline with eight different bacterial strains, cultured in microfluidics systems with or without nutrient flow, or on agarose miniature surfaces to follow microcolony growth. Examples of downstream processing in form of extraction of growth kinetic parameters or bistable cell differentiation are provided. The pipeline is wrapped in a Docker to facilitate installation, consistent processing and avoiding constant software updates.