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scSLAM-seq reveals core features of transcription dynamics in single cells
Open accessPublished version of a preprint
Abstract
Single-cell RNA sequencing (scRNA-seq) has highlighted the important role of intercellular heterogeneity in phenotype variability in both health and disease 1. However, current scRNA-seq approaches provide only a snapshot of gene expression and convey little information on the true temporal dynamics and stochastic nature of transcription. A further key limitation of scRNA-seq analysis is that the RNA profile of each individual cell can be analysed only once. Here we introduce single-cell, thiol-(SH)-linked alkylation of RNA for metabolic labelling sequencing (scSLAM-seq), which integrates metabolic RNA labelling 2, biochemical nucleoside conversion 3 and scRNA-seq to record transcriptional activity directly by differentiating between new and old RNA for thousands of genes per single cell. We use scSLAM-seq to study the onset of infection with lytic cytomegalovirus in single mouse fibroblasts. The cell-cycle state and dose of infection deduced from old RNA enable dose-response analysis based on new RNA. scSLAM-seq thereby both visualizes and explains differences in transcriptional activity at the single-cell level. Furthermore, it depicts 'on-off' switches and transcriptional burst kinetics in host gene expression with extensive gene-specific differences that correlate with promoter-intrinsic features (TBP-TATA-box interactions and DNA methylation). Thus, gene-specific, and not cell-specific, features explain the heterogeneity in transcriptomes between individual cells and the transcriptional response to perturbations.
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“The study highlighted a critical insight: most DEGs are primarily detectable in new RNA, eluding detection in analyses of old or total RNA.”
From Single-cell sequencing to multi-omics: technologies and applications (2024) · cites this paper as [32]
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Metadata
- Authors
- Erhard, F., Baptista, M. A. P., Krammer, T., Hennig, T., Lange, M., Arampatzi, P., Jürges, C. S., Theis, F. J., Saliba, A.-E., & Dölken, L.
- Affiliations
- University of Würzburg (DE)
- Helmholtz Institute for RNA-based Infection Research (DE)
- Helmholtz Munich (DE)
- Helmholtz Centre for Infection Research (DE)
- Corresponding author
- Erhard, Florian
- Saliba, Antoine-Emmanuel
- Dölken, Lars
- ORCID
- Venue
- Nature
- Published
- 2019-07-10
- Volume
- 571
- Issue
- 7765
- Pages
- 419-423
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2957836762
- Cited by
- 233
- References
- 55
- Citations by year
- 2026: 31 · 2025: 23 · 2024: 43 · 2023: 35 · 2022: 31 · 2021: 33 · 2020: 33 · 2019: 4
- FWCI
- 8.44
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- HZI (Helmholtz Centre for Infection Research) (submittedVersion, cc-by-nc-sa)
- Site cant be reached (submittedVersion)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
- Funders
- Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie (0703/89374/3/2017, 0703/68674/5/2017 and 0703/89374/3/2017, 0703/68674/5/2017)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- transcriptional dynamics, single-cell transcriptomics, transcriptional bursts, cytomegalovirus infection, mouse fibroblasts, dose-response relationship, DNA methylation, TATA box, TATA-binding protein, intercellular heterogeneity
- MeSH terms
- Alkylation, Animals, Cell Cycle, Cytomegalovirus, Fibroblasts, Gene Expression Regulation, Kinetics, Promoter Regions, Genetic, RNA, Sulfhydryl Compounds, Transcription, Genetic, Sequence Analysis, RNA, DNA Methylation, Mice, Single-Cell Analysis
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