Biofilms are spatially organized communities of microorganisms embedded in a self-produced organic matrix, conferring to the population emerging properties such as an increased tolerance to the action of antimicrobials. It was shown that some bacilli were able to swim in the exogenous matrix of pathogenic biofilms and to counterbalance these properties. Swimming bacteria can deliver antimicrobial agents in situ, or potentiate the activity of antimicrobial by creating a transient vascularization network in the matrix. Hence, characterizing swimmer trajectories in the biofilm matrix is of particular interest to understand and optimize this new biocontrol strategy in particular, but also more generally to decipher ecological drivers of population spatial structure in natural biofilms ecosystems. In this study, a new methodology is developed to analyze time-lapse confocal laser scanning images to describe and compare the swimming trajectories of bacilli swimmers populations and their adaptations to the biofilm structure. The method is based on the inference of a kinetic model of swimmer populations including mechanistic interactions with the host biofilm. After validation on synthetic data, the methodology is implemented on images of three different species of motile bacillus species swimming in a Staphylococcus aureus biofilm. The fitted model allows to stratify the swimmer populations by their swimming behavior and provides insights into the mechanisms deployed by the micro-swimmers to adapt their swimming traits to the biofilm matrix.
Data and codes have been deposited at https://forgemia.inra.fr/bioswimmers/swim-infer.Labarthe, Simon, Ravel, Guillaume, Deschamps, Julien, & Briandet, Romain. (2022). Inferring characteristics of bacterial swimming in biofilm matrix from time-lapse confocal laser scanning microscopy: compagnon code and data. Zenodo. https://doi.org/10.5281/zenodo.6560673
- Guillaume Ravel
- Romain Briandet
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Karine A Gibbs, University of California, Berkeley, United States
- Received: December 19, 2021
- Accepted: June 10, 2022
- Accepted Manuscript published: June 14, 2022 (version 1)
© 2022, Ravel et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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