Research Article: Dynamical analysis of bacteria in microscopy movies

Date Published: June 6, 2019

Publisher: Public Library of Science

Author(s): Teun Vissers, Nick Koumakis, Michiel Hermes, Aidan T. Brown, Jana Schwarz-Linek, Angela Dawson, Wilson C. K. Poon, Michael Eisenbach.


Recent advances in microscopy, computing power and image processing have enabled the analysis of ever larger datasets of movies of microorganisms to study their behaviour. However, techniques for analysing the dynamics of individual cells from such datasets are not yet widely available in the public domain. We recently demonstrated significant phenotypic heterogeneity in the adhesion of Escherichia coli bacteria to glass surfaces using a new method for the high-throughput analysis of video microscopy data. Here, we present an in-depth analysis of this method and its limitations, and make public our algorithms for following the positions and orientations of individual rod-shaped bacteria from time-series of 2D images to reconstruct their trajectories and characterise their dynamics. We demonstrate in detail how to use these algorithms to identify different types of adhesive dynamics within a clonal population of bacteria sedimenting onto a surface. The effects of measurement errors in cell positions and of limited trajectory durations on our results are discussed.

Partial Text

The ability of microbes to move on and adhere to surfaces is an essential part of their survival strategies [1]. Motility allows them to explore new niches and swim towards nutrients or oxygen [2, 3] to optimise growth and division. Adhesion allows them to colonise surfaces and grow protective biofilms [4, 5]. In this state, bacteria are typically more difficult to dislodge by fluid flows and can enjoy increased resistance to antimicrobials [6, 7]. As a result, they become sources of infection that are hard to eradicate [8].

The automated analysis and interpretation of microscopy images containing microorganisms is of academic and practical relevance. Studying bacteria on the level of single cells has the potential to reveal new important phenomena related to bacterial adhesion and population heterogeneities [9, 20, 31–35]. Therefore, algorithms that can extract this information from videos are highly relevant to study differences between individual cells within a population, which has potential applications in developing medical diagnostics methods.




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