Evidence map›Paper›PMID 40240003›Full record

ArticleNucleic acids research2025

Cell-cycle dependence of bursty gene expression: insights from fitting mechanistic models to single-cell RNA-seq data.

Augustinas Sukys, Ramon Grima

Abstract read
In one paragraph

Article in Nucleic acids research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. bioRxiv : the preprint server for biology · 2025
    Article
  9. Article
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Augustinas SukysSchool of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JH, United Kingdom.ORCID 0009-0000-9928-328X
Ramon GrimaSchool of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JH, United Kingdom.ORCID 0000-0002-1266-8169

Funding

Alan Turing InstituteAustralian Research Council FL220100005EPSRC EP/N510129/1Leverhulme Trust RPG-2020-327University of Edinburgh
6 · The paper itself

Abstract

Bursty gene expression is characterized by two intuitive parameters, burst frequency and burst size, the cell-cycle dependence of which has not been extensively profiled at the transcriptome level. In this study, we estimate the burst parameters per allele in the G1 and G2/M cell-cycle phases for thousands of mouse genes by fitting mechanistic models of gene expression to messenger RNA count data, obtained by sequencing of single cells whose cell-cycle position has been inferred using a deep-learning method. We find that upon DNA replication, the median burst frequency approximately halves, while the burst size remains mostly unchanged. Genome-wide distributions of the burst parameter ratios between the G2/M and G1 phases are broad, indicating substantial heterogeneity in transcriptional regulation. We also observe a significant negative correlation between the burst frequency and size ratios, suggesting that regulatory processes do not independently control the burst parameters. We show that to accurately estimate the burst parameter ratios, mechanistic models must explicitly account for gene copy number variation and extrinsic noise due to the coupling of transcription to cell age across the cell cycle, but corrections for technical noise due to imperfect capture of RNA molecules in sequencing experiments are less critical.

Indexed as

Cell CycleGene Expression RegulationRNA-SeqSingle-Cell AnalysisAnimalsDNA Copy Number VariationsDNA ReplicationGene Expression ProfilingMiceModels, GeneticRNA, MessengerSequence Analysis, RNASingle-Cell Gene Expression AnalysisTranscriptomeRNA, Messenger

Identifiers

PMID40240003
PMCPMC12000877

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.