MiSiC, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities

  1. Swapnesh Panigrahi
  2. Dorothée Murat
  3. Antoine Le Gall
  4. Eugénie Martineau
  5. Kelly Goldlust
  6. Jean-Bernard Fiche
  7. Sara Rombouts
  8. Marcelo Nöllmann​
  9. Leon Espinosa
  10. Tâm Mignot  Is a corresponding author
  1. CNRS-Aix Marseille University, France
  2. CNRS UMR 5048, INSERM U1054, Université de Montpellier, France
  3. 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

  1. Swapnesh Panigrahi

    Laboratoire de Chimie Bactérienne, CNRS-Aix Marseille University, Marseille, France
    Competing interests
    No competing interests declared.
  2. Dorothée Murat

    Laboratoire de Chimie Bactérienne, CNRS-Aix Marseille University, Marseille, France
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5809-9267
  3. Antoine Le Gall

    Centre de Biochimie Structurale, CNRS UMR 5048, INSERM U1054, Université de Montpellier, Montpellier, France
    Competing interests
    No competing interests declared.
  4. Eugénie Martineau

    Laboratoire de Chimie Bactérienne, CNRS-Aix Marseille University, Marseille, France
    Competing interests
    No competing interests declared.
  5. Kelly Goldlust

    Laboratoire de Chimie Bactérienne, CNRS-Aix Marseille University, Marseille, France
    Competing interests
    No competing interests declared.
  6. Jean-Bernard Fiche

    Centre de Biochimie Structurale, CNRS UMR 5048, INSERM U1054, Université de Montpellier, Montpellier, France
    Competing interests
    No competing interests declared.
  7. Sara Rombouts

    Centre de Biochimie Structurale, CNRS UMR 5048, INSERM U1054, Université de Montpellier, Montpellier, France
    Competing interests
    No competing interests declared.
  8. Marcelo Nöllmann​

    Centre de Biochimie Structurale, CNRS UMR 5048, INSERM U1054, Université de Montpellier, Montpellier, France
    Competing interests
    No competing interests declared.
  9. Leon Espinosa

    Laboratoire de Chimie Bactérienne UMR7283, Centre national de la recherche scientifique, Aix Marseille Université, Marseille, France
    Competing interests
    No competing interests declared.
  10. Tâm Mignot

    Laboratoire de Chimie Bactérienne, CNRS-Aix Marseille University, Marseille, France
    For correspondence
    tmignot@imm.cnrs.fr
    Competing interests
    Tâm Mignot, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4338-9063

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

  1. Jie Xiao, Johns Hopkins University, United States

Version history

  1. Preprint posted: October 7, 2020 (view preprint)
  2. Received: November 24, 2020
  3. Accepted: September 7, 2021
  4. Accepted Manuscript published: September 9, 2021 (version 1)
  5. 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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  1. Swapnesh Panigrahi
  2. Dorothée Murat
  3. Antoine Le Gall
  4. Eugénie Martineau
  5. Kelly Goldlust
  6. Jean-Bernard Fiche
  7. Sara Rombouts
  8. Marcelo Nöllmann​
  9. Leon Espinosa
  10. Tâm Mignot
(2021)
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

Share this article

https://doi.org/10.7554/eLife.65151

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