Patterns of interdivision time correlations reveal hidden cell cycle factors
The time taken for cells to complete a round of cell division is a stochastic process controlled, in part, by intracellular factors. These factors can be inherited across cellular generations which gives rise to, often non-intuitive, correlation patterns in cell cycle timing between cells of different family relationships on lineage trees. Here, we formulate a framework of hidden inherited factors affecting the cell cycle that unifies known cell cycle control models and reveals three distinct interdivision time correlation patterns: aperiodic, alternator and oscillator. We use Bayesian inference with single-cell datasets of cell division in bacteria, mammalian and cancer cells, to identify the inheritance motifs that underlie these datasets. From our inference, we find that interdivision time correlation patterns do not identify a single cell cycle model but generally admit a broad posterior distribution of possible mechanisms. Despite this unidentifiability, we observe that the inferred patterns reveal interpretable inheritance dynamics and hidden rhythmicity of cell cycle factors. This reveals that cell cycle factors are commonly driven by circadian rhythms, but their period may differ in cancer. Our quantitative analysis thus reveals that correlation patterns are an emergent phenomenon that impact cell proliferation and these patterns may be altered in disease.
The current manuscript is a computational study, so no data have been generated for this manuscript. Modelling code is uploaded to gitHub https://github.com/fernhughes/Lineage-tree-correlation-pattern-inference.
Research data supporting Cell size control driven by the circadian clock and environment in cyanobacteriaApollo - University of Cambridge Repository.
Supplementary Data 2 supporting Hidden heterogeneity and circadian-controlled cell fate inferred from single cell lineagesNature Communications, Supplementary Data.
Mycobacteria Modify Their Cell Size Control under Sub-Optimal Carbon SourcesMiles Priestman shared the raw data.
Article and author information
EPSRC Centre for Mathematics of Precision Healthcare (EP/N014529/1)
- Fern A Hughes
MRC London Institute of Medical Sciences (MC-A658-5TY60)
- Alexis Barr
UKRI Future Leaders Fellowship (MR/T018429/1)
- Philipp Thomas
CRUK Career Development Fellowship (C63833/A25729)
- Alexis Barr
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Arvind Murugan, University of Chicago, United States
- Received: June 9, 2022
- Preprint posted: June 28, 2022 (view preprint)
- Accepted: November 14, 2022
- Accepted Manuscript published: November 15, 2022 (version 1)
- Version of Record published: January 6, 2023 (version 2)
© 2022, Hughes 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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