ArticleeLife2016
Single-cell analysis of transcription kinetics across the cell cycle.
Article in eLife, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers.
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Who cites it
93 citing papers in PubMed.
- Integrating zero-inflation correction and transcriptional kinetics for single-cell transcriptomic analysis.PLoS computational biology · 2026Article
- Using the simple telegraph model to decipher transcriptional burst regulation across genome-wide data.iScience · 2026Article
- Nuclear compartmentalization at the G1/S transition plays a key role in DNA replication control.Nature communications · 2026Article
- Ligand-dependent enhancer activation indirectly modulates non-target promoters in a chromatin domain.eLife · 2026Article
- Efficiency, accuracy and robustness of probability generating function based parameter inference method for stochastic biochemical reactions.PLoS computational biology · 2026Article
- From noise to models to numbers: Evaluating negative binomial models and parameter estimations in single-cell RNA-seq.PLoS computational biology · 2026Article
- Subtle alteration in transcriptional memory governs the lineage-level cell cycle duration heterogeneities of mammalian cells.iScience · 2025Article
- Single-Cell Multiomic Analysis of Circadian Rhythmicity in Mouse Liver.bioRxiv : the preprint server for biology · 2025Article
- Cell-cycle dependence of bursty gene expression: insights from fitting mechanistic models to single-cell RNA-seq data.Nucleic acids research · 2025Article
- Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression.Nature communications · 2025Article
- Gene trajectory inference for single-cell data by optimal transport metrics.Nature biotechnology · 2025Article
- Transcriptome analysis of two isolates of the tomato pathogen Cladosporium fulvum, uncovers genome-wide patterns of alternative splicing during a host infection cycle.PLoS pathogens · 2024Article
- AI-powered simulation-based inference of a genuinely spatial-stochastic gene regulation model of early mouse embryogenesis.PLoS computational biology · 2024Article
- Control of cell state transitions by post-transcriptional regulation.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Review
- Functional consequences of somatic polyploidy in development.Development (Cambridge, England) · 2024Review
- Quantifying and correcting bias in transcriptional parameter inference from single-cell data.Biophysical journal · 2024Article
- Article
- Review
- Stochastic modeling of the mRNA life process: A generalized master equation.Biophysical journal · 2023Article
- SLAM-Drop-seq reveals mRNA kinetic rates throughout the cell cycle.Molecular systems biology · 2023Article
33 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
Abstract
Transcription is a highly stochastic process. To infer transcription kinetics for a gene-of-interest, researchers commonly compare the distribution of mRNA copy-number to the prediction of a theoretical model. However, the reliability of this procedure is limited because the measured mRNA numbers represent integration over the mRNA lifetime, contribution from multiple gene copies, and mixing of cells from different cell-cycle phases. We address these limitations by simultaneously quantifying nascent and mature mRNA in individual cells, and incorporating cell-cycle effects in the analysis of mRNA statistics. We demonstrate our approach on Oct4 and Nanog in mouse embryonic stem cells. Both genes follow similar two-state kinetics. However, Nanog exhibits slower ON/OFF switching, resulting in increased cell-to-cell variability in mRNA levels. Early in the cell cycle, the two copies of each gene exhibit independent activity. After gene replication, the probability of each gene copy to be active diminishes, resulting in dosage compensation.
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Registered trials
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.