Abstract

Much of biochemical regulation ultimately controls growth rate, particularly in microbes. Although time-lapse microscopy visualises cells, determining their growth rates is challenging, particularly for those that divide asymmetrically, like Saccharomyces cerevisiae, because cells often overlap in images. Here we present the Birth Annotator for Budding Yeast (BABY), an algorithm to determine single-cell growth rates from label-free images. Using a convolutional neural network, BABY resolves overlaps through separating cells by size and assigns buds to mothers by identifying bud necks. BABY uses machine learning to track cells and determine lineages and estimates growth rates as the rates of change of volumes. Using BABY and a microfluidic device, we show that bud growth is likely first sizer- then timer-controlled, that the nuclear concentration of Sfp1, a regulator of ribosome biogenesis, varies before the growth rate does, and that growth rate can be used for real-time control. By estimating single-cell growth rates and so fitness, BABY should generate much biological insight.

Data availability

Data is available at https://doi.org/10.7488/ds/3427 and code fromhttps://git.ecdf.ed.ac.uk/swain-lab/baby.

The following data sets were generated

Article and author information

Author details

  1. Julian MJ Pietsch

    Centre for Engineering Biology, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9992-2384
  2. Alan F Munoz

    Centre for Engineering Biology, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Diane-Yayra A Adjavon

    Centre for Engineering Biology, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Iseabail Farquhar

    Centre for Engineering Biology, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  5. Ivan BN Clark

    Centre for Engineering Biology, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Peter S Swain

    Centre for Engineering Biology, University of Edinburgh, Edinburgh, United Kingdom
    For correspondence
    peter.swain@ed.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7489-8587

Funding

Leverhulme Trust (RPG-2018-04)

  • Peter S Swain

BBSRC (BB/R001359/1)

  • Alan F Munoz

European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska Curie grant agreement (764591 - SynCrop)

  • Ivan BN Clark

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

Reviewing Editor

  1. Alan M Moses, University of Toronto, Canada

Version history

  1. Received: April 27, 2022
  2. Preprint posted: May 12, 2022 (view preprint)
  3. Accepted: July 6, 2023
  4. Accepted Manuscript published: July 7, 2023 (version 1)
  5. Version of Record published: July 26, 2023 (version 2)

Copyright

© 2023, Pietsch 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. Julian MJ Pietsch
  2. Alan F Munoz
  3. Diane-Yayra A Adjavon
  4. Iseabail Farquhar
  5. Ivan BN Clark
  6. Peter S Swain
(2023)
Determining growth rates from bright-field images of budding cells through identifying overlaps
eLife 12:e79812.
https://doi.org/10.7554/eLife.79812

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https://doi.org/10.7554/eLife.79812

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