ArticlePLoS computational biology2015
Deciphering Transcriptional Dynamics In Vivo by Counting Nascent RNA Molecules.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 74 citations in OpenAlex.
- Regulation of Transcriptional Bursting and Spatial Patterning in EarlybioRxiv : the preprint server for biology · 2025Article
- Two-State Stochastic Model of In Vivo Observations of Transcriptional Bursts.Brazilian journal of physics · 2025Article
- Mitigating transcription noise via protein sharing in syncytial cells.Biophysical journal · 2024Article
- The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian.Science advances · 2023Article
- Biophysical modeling with variational autoencoders for bimodal, single-cell RNA sequencing data.bioRxiv : the preprint server for biology · 2023Article
- The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian.bioRxiv : the preprint server for biology · 2023Article
- Stochastic dynamics and ribosome-RNAP interactions in transcription-translation coupling.Biophysical journal · 2023Article
- Using average transcription level to understand the regulation of stochastic gene activation.Royal Society open science · 2022Article
- Neural network aided approximation and parameter inference of non-Markovian models of gene expression.Nature communications · 2021Article
- Real-time single-cell characterization of the eukaryotic transcription cycle reveals correlations between RNA initiation, elongation, and cleavage.PLoS computational biology · 2021Article
- Statistics of Nascent and Mature RNA Fluctuations in a Stochastic Model of Transcriptional Initiation, Elongation, Pausing, and Termination.Bulletin of mathematical biology · 2020Article
- Article
- Gene Transcription as a Limiting Factor in Protein Production and Cell Growth.G3 (Bethesda, Md.) · 2020Article
- A Stochastic Model of Gene Expression with Polymerase Recruitment and Pause Release.Biophysical journal · 2020Article
- Probing Mechanisms of Transcription Elongation Through Cell-to-Cell Variability of RNA Polymerase.Biophysical journal · 2020Article
- Mammalian gene expression variability is explained by underlying cell state.Molecular systems biology · 2020Article
- Multimodal transcriptional control of pattern formation in embryonic development.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Measuring transcription at a single gene copy reveals hidden drivers of bacterial individuality.Nature microbiology · 2019Article
- Enhancer Priming Enables Fast and Sustained Transcriptional Responses to Notch Signaling.Developmental cell · 2019Article
- Gene transcription in bursting: a unified mode for realizing accuracy and stochasticity.Biological reviews of the Cambridge Philosophical Society · 2019Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deciphering how the regulatory DNA sequence of a gene dictates its expression in response to intra and extracellular cues is one of the leading challenges in modern genomics. The development of novel single-cell sequencing and imaging techniques, as well as a better exploitation of currently available single-molecule imaging techniques, provides an avenue to interrogate the process of transcription and its dynamics in cells by quantifying the number of RNA polymerases engaged in the transcription of a gene (or equivalently the number of nascent RNAs) at a given moment in time. In this paper, we propose that measurements of the cell-to-cell variability in the number of nascent RNAs provide a mostly unexplored method for deciphering mechanisms of transcription initiation in cells. We propose a simple kinetic model of transcription initiation and elongation from which we calculate nascent RNA copy-number fluctuations. To demonstrate the usefulness of this approach, we test our theory against published nascent RNA data for twelve constitutively expressed yeast genes. Rather than transcription being initiated through a single rate limiting step, as it had been previously proposed, our single-cell analysis reveals the presence of at least two rate limiting steps. Surprisingly, half of the genes analyzed have nearly identical rates of transcription initiation, suggesting a common mechanism. Our analytical framework can be used to extract quantitative information about dynamics of transcription from single-cell sequencing data, as well as from single-molecule imaging and electron micrographs of fixed cells, and provides the mathematical means to exploit the quantitative power of these technologies.
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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.