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

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 sca...

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Autores principales:	Swapnesh Panigrahi, Dorothée Murat, Antoine Le Gall, Eugénie Martineau, Kelly Goldlust, Jean-Bernard Fiche, Sara Rombouts, Marcelo Nöllmann, Leon Espinosa, Tâm Mignot
Formato:	article
Lenguaje:	EN
Publicado:	eLife Sciences Publications Ltd 2021
Materias:	Deep learning image analysis microscopy myxococcus xanthus biofilms Medicine R Science Q Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/b911c8ea9855474b86e9b6372224a944
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id	oai:doaj.org-article:b911c8ea9855474b86e9b6372224a944
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spelling	oai:doaj.org-article:b911c8ea9855474b86e9b6372224a9442021-11-15T06:46:24ZMisic, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities10.7554/eLife.651512050-084Xe65151https://doaj.org/article/b911c8ea9855474b86e9b6372224a9442021-09-01T00:00:00Zhttps://elifesciences.org/articles/65151https://doaj.org/toc/2050-084XStudies 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.Swapnesh PanigrahiDorothée MuratAntoine Le GallEugénie MartineauKelly GoldlustJean-Bernard FicheSara RomboutsMarcelo NöllmannLeon EspinosaTâm MignoteLife Sciences Publications LtdarticleDeep learningimage analysismicroscopymyxococcus xanthusbiofilmsMedicineRScienceQBiology (General)QH301-705.5ENeLife, Vol 10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Deep learning image analysis microscopy myxococcus xanthus biofilms Medicine R Science Q Biology (General) QH301-705.5
spellingShingle	Deep learning image analysis microscopy myxococcus xanthus biofilms Medicine R Science Q Biology (General) QH301-705.5 Swapnesh Panigrahi Dorothée Murat Antoine Le Gall Eugénie Martineau Kelly Goldlust Jean-Bernard Fiche Sara Rombouts Marcelo Nöllmann Leon Espinosa Tâm Mignot Misic, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities
description	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.
format	article
author	Swapnesh Panigrahi Dorothée Murat Antoine Le Gall Eugénie Martineau Kelly Goldlust Jean-Bernard Fiche Sara Rombouts Marcelo Nöllmann Leon Espinosa Tâm Mignot
author_facet	Swapnesh Panigrahi Dorothée Murat Antoine Le Gall Eugénie Martineau Kelly Goldlust Jean-Bernard Fiche Sara Rombouts Marcelo Nöllmann Leon Espinosa Tâm Mignot
author_sort	Swapnesh Panigrahi
title	Misic, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities
title_short	Misic, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities
title_full	Misic, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities
title_fullStr	Misic, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities
title_full_unstemmed	Misic, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities
title_sort	misic, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities
publisher	eLife Sciences Publications Ltd
publishDate	2021
url	https://doaj.org/article/b911c8ea9855474b86e9b6372224a944
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Misic, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities

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