DetecDiv, a generalist deep-learning platform for automated cell division tracking and survival analysis

  1. Théo Aspert  Is a corresponding author
  2. Didier Hentsch
  3. Gilles Charvin  Is a corresponding author
  1. Institute of Genetics and Molecular and Cellular Biology, France

Abstract

Automating the extraction of meaningful temporal information from sequences of microscopy images represents a major challenge to characterize dynamical biological processes. So far, strong limitations in the ability to quantitatively analyze single-cell trajectories have prevented large-scale investigations to assess the dynamics of entry into replicative senescence in yeast. Here, we have developed DetecDiv, a microfluidic-based image acquisition platform combined with deep learning-based software for high-throughput single-cell division tracking. We show that DetecDiv can automatically reconstruct cellular replicative lifespans with high accuracy and performs similarly with various imaging platforms and geometries of microfluidic traps. In addition, this methodology provides comprehensive temporal cellular metrics using time-series classification and image semantic segmentation. Last, we show that this method can be further applied to automatically quantify the dynamics of cellular adaptation and real-time cell survival upon exposure to environmental stress. Hence, this methodology provides an all-in-one toolbox for high-throughput phenotyping for cell cycle, stress response, and replicative lifespan assays.

Data availability

Software and documentation is fully available via Github.Data used for training classifiers is available using several zenodo repositories.A demo detecdiv project that contains all information to train users on detecdiv is available on zenodo.All the links are provided in the manuscript file.

Article and author information

Author details

  1. Théo Aspert

    Department of Developmental Biology and Stem Cells, Institute of Genetics and Molecular and Cellular Biology, Illkirch, France
    For correspondence
    aspertt@igbmc.fr
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2957-0683
  2. Didier Hentsch

    Department of Developmental Biology and Stem Cells, Institute of Genetics and Molecular and Cellular Biology, Illkirch, France
    Competing interests
    The authors declare that no competing interests exist.
  3. Gilles Charvin

    Department of Developmental Biology and Stem Cells, Institute of Genetics and Molecular and Cellular Biology, Illkirch, France
    For correspondence
    charvin@igbmc.fr
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6852-6952

Funding

Agence Nationale de la Recherche (ANR-10-LABX-0030-INRT)

  • Gilles Charvin

Agence Nationale de la Recherche (ANR-10-IDEX-0002-02)

  • Gilles Charvin

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Ihor Smal, Erasmus University Medical Center, Netherlands

Version history

  1. Preprint posted: October 5, 2021 (view preprint)
  2. Received: April 15, 2022
  3. Accepted: August 16, 2022
  4. Accepted Manuscript published: August 17, 2022 (version 1)
  5. Version of Record published: September 5, 2022 (version 2)
  6. Version of Record updated: September 12, 2022 (version 3)

Copyright

© 2022, Aspert 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. Théo Aspert
  2. Didier Hentsch
  3. Gilles Charvin
(2022)
DetecDiv, a generalist deep-learning platform for automated cell division tracking and survival analysis
eLife 11:e79519.
https://doi.org/10.7554/eLife.79519

Share this article

https://doi.org/10.7554/eLife.79519

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