Genomic encoding of transcriptional burst kinetics
Open accessAbstract
Mammalian gene expression is inherently stochastic 1,2, and results in discrete bursts of RNA molecules that are synthesized from each allele 3-7. Although transcription is known to be regulated by promoters and enhancers, it is unclear how cis-regulatory sequences encode transcriptional burst kinetics. Characterization of transcriptional bursting, including the burst size and frequency, has mainly relied on live-cell 4,6,8 or single-molecule RNA fluorescence in situ hybridization 3,5,8,9 recordings of selected loci. Here we determine transcriptome-wide burst frequencies and sizes for endogenous mouse and human genes using allele-sensitive single-cell RNA sequencing. We show that core promoter elements affect burst size and uncover synergistic effects between TATA and initiator elements, which were masked at mean expression levels. Notably, we provide transcriptome-wide evidence that enhancers control burst frequencies, and demonstrate that cell-type-specific gene expression is primarily shaped by changes in burst frequencies. Together, our data show that burst frequency is primarily encoded in enhancers and burst size in core promoters, and that allelic single-cell RNA sequencing is a powerful model for investigating transcriptional kinetics.
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Enhancers increase the frequency of transcriptional bursts without significantly affecting their amplitude and duration.
“Evidence from live imaging and single-cell transcriptomics indicates that enhancers increase the frequency of transcriptional bursts without significantly affecting their amplitude and duration [310, 505, 516].”
From Transcriptional enhancers and their communication with gene promoters (2021) · cites this paper as [516]
In the passage on Sox2 enhancer deletion (indexed as Abstract; page unavailable), deletion significantly reduced burst frequency without a significant change in burst size. The paper infers burst size from parameters it says cannot be individually identified across much of the parameter space, so its result does not establish that burst amplitude and duration were each unaffected. (LLM, conf=0.88)
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- Authors
- Larsson, A. J. M., Johnsson, P., Hagemann-Jensen, M., Hartmanis, L., Faridani, O. R., Reinius, B., Segerstolpe, Å., Rivera, C. M., Ren, B., & Sandberg, R.
- Affiliations
- Karolinska Institutet (SE)
- Broad Institute (US)
- University of California San Diego (US)
- Ludwig Cancer Research (BE)
- Corresponding author
- Sandberg, Rickard
- ORCID
- Venue
- Nature
- Published
- 2019-01
- Volume
- 565
- Issue
- 7738
- Pages
- 251-254
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC7610481
- OpenAlex
- W2906180136
- Cited by
- 534
- References
- 2
- Citations by year
- 2026: 58 · 2025: 64 · 2024: 78 · 2023: 71 · 2022: 68 · 2021: 86 · 2020: 76 · 2019: 31 · 2018: 2
- FWCI
- 17.70
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- transcriptional bursting, burst size, burst frequency, cis-regulatory elements, core promoter, TATA box, initiator element, enhancers, allele-specific expression, stochastic gene expression, cell type-specific gene expression, transcriptome-wide analysis
- MeSH terms
- Mouse Embryonic Stem Cells, Alleles, Animals, Enhancer Elements, Genetic, Fibroblasts, Genes, Humans, Kinetics, Male, Organ Specificity, Polymorphism, Genetic, Promoter Regions, Genetic, Stochastic Processes, Transcription, Genetic, TATA Box, Sequence Deletion, Sequence Analysis, RNA, Genomics, Mice, Single-Cell Analysis, Transcriptome
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