Determining growth rates from bright-field images of budding cells through identifying overlaps
- University of Edinburgh, United Kingdom
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.
Article and author information
Author details
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.
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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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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