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  Single‐cell segmentation in bacterial biofilms with an optimized deep learning method enables tracking of cell lineages and measurements of growth rates

Jelli, E., Ohmura, T., Netter, N., Abt, M., Jiménez‐Siebert, E., Neuhaus, K., et al. (2023). Single‐cell segmentation in bacterial biofilms with an optimized deep learning method enables tracking of cell lineages and measurements of growth rates. Molecular Microbiology, online ahead of print. doi:10.1111/mmi.15064.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000D-03D0-4 Version Permalink: https://hdl.handle.net/21.11116/0000-000D-45EB-D
Genre: Journal Article

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Molecular Microbiology - 2023 - Jelli - Single‐cell segmentation in bacterial biofilms with an optimized deep learning.pdf (Publisher version), 6MB
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Molecular Microbiology - 2023 - Jelli - Single‐cell segmentation in bacterial biofilms with an optimized deep learning.pdf
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Copyright Date:
2023
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http://creativecommons.org/licenses/by-nc/4.0/

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https://onlinelibrary.wiley.com/doi/10.1111/mmi.15064 (Publisher version)
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OA-Status:
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 Creators:
Jelli, Eric1, 2, Author                 
Ohmura, Takuya1, Author
Netter, Niklas1, Author
Abt, Martin1, Author
Jiménez‐Siebert, Eva1, Author
Neuhaus, Konstantin1, Author
Rode, Daniel K. H.1, Author
Nadell, Carey D.1, Author
Drescher, Knut1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Department of Computational Neuroethology, Max Planck Institute for Neurobiology of Behavior – caesar, Max Planck Society, ou_3361762              

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Free keywords: 3D segmentation, biofilm, deep learning, image analysis, image cytometry, Vibrio cholerae
 Abstract: Bacteria often grow into matrix-encased three-dimensional (3D) biofilm communities, which can be imaged at cellular resolution using confocal microscopy. From these 3D images, measurements of single-cell properties with high spatiotemporal resolution are required to investigate cellular heterogeneity and dynamical processes inside biofilms. However, the required measurements rely on the automated segmentation of bacterial cells in 3D images, which is a technical challenge. To improve the accuracy of single-cell segmentation in 3D biofilms, we first evaluated recent classical and deep learning segmentation algorithms. We then extended StarDist, a state-of-the-art deep learning algorithm, by optimizing the post-processing for bacteria, which resulted in the most accurate segmentation results for biofilms among all investigated algorithms. To generate the large 3D training dataset required for deep learning, we developed an iterative process of automated segmentation followed by semi-manual correction, resulting in >18,000 annotated Vibrio cholerae cells in 3D images. We demonstrate that this large training dataset and the neural network with optimized post-processing yield accurate segmentation results for biofilms of different species and on biofilm images from different microscopes. Finally, we used the accurate single-cell segmentation results to track cell lineages in biofilms and to perform spatiotemporal measurements of single-cell growth rates during biofilm development.

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Language(s): eng - English
 Dates: 2023-04-17
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1111/mmi.15064
 Degree: -

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Project name : -
Grant ID : TARGET-Biofilms
Funding program : TARGET-Biofilms
Funding organization : Bundesministerium für Bildung und Forschung
Project name : -
Grant ID : DR 982/5-1
Funding program : -
Funding organization : Deutsche Forschungsgemeinschaft
Project name : -
Grant ID : DR 982/6-1
Funding program : -
Funding organization : Deutsche Forschungsgemeinschaft
Project name : -
Grant ID : 955910
Funding program : -
Funding organization : European Commission
Project name : -
Grant ID : 716734
Funding program : -
Funding organization : European Commission
Project name : Human Frontier Science Program
Grant ID : LT000013/2019-C
Funding program : -
Funding organization : -
Project name : -
Grant ID : -
Funding program : -
Funding organization : Minna-James-Heineman- Stiftung
Project name : -
Grant ID : 51NF40_180541
Funding program : -
Funding organization : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Project name : -
Grant ID : TMCG-3_ 213801
Funding program : -
Funding organization : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Source 1

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Title: Molecular Microbiology
  Abbreviation : Mol Microbiol
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Oxford : Blackwell Science
Pages: - Volume / Issue: - Sequence Number: , online ahead of print Start / End Page: - Identifier: ISSN: 0950-382X
CoNE: https://pure.mpg.de/cone/journals/resource/954925574950