MiSiC, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities
- CNRS-Aix Marseille University, France
- CNRS UMR 5048, INSERM U1054, Université de Montpellier, France
- Aix Marseille Université, France
Abstract
Studies of bacterial communities, biofilms and microbiomes, are multiplying due to their impact on health and ecology. Live imaging of microbial communities requires new tools for the robust identification of bacterial cells in dense and often inter-species populations, sometimes over very large scales. Here, we developed MiSiC, a general deep-learning-based 2D segmentation method that automatically segments single bacteria in complex images of interacting bacterial communities with very little parameter adjustment, independent of the microscopy settings and imaging modality. Using a bacterial predator-prey interaction model, we demonstrate that MiSiC enables the analysis of interspecies interactions, resolving processes at subcellular scales and discriminating between species in millimeter size datasets. The simple implementation of MiSiC and the relatively low need in computing power make its use broadly accessible to fields interested in bacterial interactions and cell biology.
Data availability
The tensorflow model describe in this article is available in GitHub :https://github.com/pswapnesh/MiSiChttps://github.com/leec13/MiSiCguiSource data files have been provided for Figures 2, 3, 4 and 5
Article and author information
Author details
Funding
ERC advanced grant (JAWS 885145)
- Tâm Mignot
AMIDEX
- Eugénie Martineau
ANR (IBM (ANR-14-CE09-0025-01))
- Marcelo Nöllmann
ANR (HiResBacs (ANR-15-CE11-0023))
- Marcelo Nöllmann
CNRS 80-prime
- Swapnesh Panigrahi
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Jie Xiao, Johns Hopkins University, United States
Version history
- Preprint posted: October 7, 2020 (view preprint)
- Received: November 24, 2020
- Accepted: September 7, 2021
- Accepted Manuscript published: September 9, 2021 (version 1)
- Version of Record published: September 28, 2021 (version 2)
Copyright
© 2021, Panigrahi 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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MiSiC, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities
eLife 10:e65151.
https://doi.org/10.7554/eLife.65151
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