Determining growth rates from bright-field images of budding cells through identifying overlaps
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.
Metrics
-
- 944
- views
-
- 135
- downloads
-
- 5
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Cancer Biology
- Cell Biology
Cell crowding causes high-grade breast cancer cells to become more invasive by activating a molecular switch that causes the cells to shrink and spread.
-
- Cell Biology
Understanding how the brain controls nutrient storage is pivotal. Transient receptor potential (TRP) channels are conserved from insects to humans. They serve in detecting environmental shifts and in acting as internal sensors. Previously, we demonstrated the role of TRPγ in nutrient-sensing behavior (Dhakal et al., 2022). Here, we found that a TRPγ mutant exhibited in Drosophila melanogaster is required for maintaining normal lipid and protein levels. In animals, lipogenesis and lipolysis control lipid levels in response to food availability. Lipids are mostly stored as triacylglycerol in the fat bodies (FBs) of D. melanogaster. Interestingly, trpγ deficient mutants exhibited elevated TAG levels and our genetic data indicated that Dh44 neurons are indispensable for normal lipid storage but not protein storage. The trpγ mutants also exhibited reduced starvation resistance, which was attributed to insufficient lipolysis in the FBs. This could be mitigated by administering lipase or metformin orally, indicating a potential treatment pathway. Gene expression analysis indicated that trpγ knockout downregulated brummer, a key lipolytic gene, resulting in chronic lipolytic deficits in the gut and other fat tissues. The study also highlighted the role of specific proteins, including neuropeptide DH44 and its receptor DH44R2 in lipid regulation. Our findings provide insight into the broader question of how the brain and gut regulate nutrient storage.