MiSiC, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities - Archive ouverte HAL Access content directly
Journal Articles eLife Year : 2021

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

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1
Swapnesh Panigrahi
Dorothée Murat
Antoine Le Gall
Eugénie Martineau
  • Function : Author
Kelly Goldlust
  • Function : Author
Jean-Bernard Fiche
  • Function : Author
Sara Rombouts
  • Function : Author
Marcelo Nollmann
Leon Espinosa
Tam Mignot

Abstract

Studies of microbial communities by live imaging require 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 segmentation method that automatically segments a wide range of spatially structured bacterial communities with very little parameter adjustment, independent of the 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.

Dates and versions

hal-03097113 , version 1 (05-01-2021)

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Swapnesh Panigrahi, Dorothée Murat, Antoine Le Gall, Eugénie Martineau, Kelly Goldlust, et al.. MiSiC, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities. eLife, 2021, ⟨10.7554/eLife.65151⟩. ⟨hal-03097113⟩
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