Abstract
Image-based high-throughput screening strategies for quantifying morphological phenotypes have proven widely successful. Here we describe a combined experimental and multivariate image analysis approach for systematic large-scale phenotyping of morphological dynamics in bacteria. Using off-the-shelf components and software, we established a workflow for high-throughput time-resolved microscopy. We then screened the single‐gene deletion collection of Escherichia coli for antibiotic-induced morphological changes. Using single-cell quantitative descriptors and supervised classification methods, we measured how different cell morphologies developed over time for all strains in response to the β-lactam antibiotic cefsulodin. 191 strains exhibit significant variations under antibiotic treatment. Phenotypic clustering provided insights into processes that alter the antibiotic response. Mutants with stable bulges show delayed lysis, contributing to antibiotic tolerance. Lipopolysaccharides play a crucial role in bulge stability. This study demonstrates how multiparametric phenotyping by high-throughput time-resolved imaging and computer-aided cell classification can be used for comprehensively studying dynamic morphological transitions in bacteria.
Highlights
Image-based high-throughput screening strategies for quantifying morphological phenotypes have proven widely successful
High-throughput microscopy screening assays have been successful in finding genes involved in various biological processes[3,4,5], building disease models[6] and discovering proteomic changes induced by perturbations[7]
We establish a multivariate data analysis workflow for the classification of single cells into different morphological classes and for the characterization of the morphological dynamics of each strain based on the time evolution of morphological classes at the population level
Summary
Image-based high-throughput screening strategies for quantifying morphological phenotypes have proven widely successful. We establish a multivariate data analysis workflow for the classification of single cells into different morphological classes and for the characterization of the morphological dynamics of each strain based on the time evolution of morphological classes at the population level We applied this methodology to assess the morphological changes induced by the β-lactam antibiotic cefsulodin in 4218 strains from the Keio collection[18]. The aforementioned workflow identified 191 genetic perturbations that produced significant phenotypic variation from wild-type E. coli Functional analysis of these genes together with similarity-based clustering of their phenotypes revealed different types of atypical morphological dynamics and highlighted the cellular processes that can affect the outcome of cefsulodin treatment in E. coli. The simple experimental workflow and the comprehensive image classification approach described here is highly adaptable, providing an ideal platform for future high-throughput image-based studies of dynamic processes in bacteria as well as in diminutive cell types of higher organisms
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