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scSLAM-seq reveals core features of transcription dynamics in single cells

Author

Listed:
  • Florian Erhard

    (Julius-Maximilians-University Würzburg)

  • Marisa A. P. Baptista

    (Julius-Maximilians-University Würzburg)

  • Tobias Krammer

    (Helmholtz-Center for Infection Research (HZI))

  • Thomas Hennig

    (Julius-Maximilians-University Würzburg)

  • Marius Lange

    (Helmholtz Zentrum München–German Research Center for Environmental Health
    Technische Universität München)

  • Panagiota Arampatzi

    (University of Würzburg)

  • Christopher S. Jürges

    (Julius-Maximilians-University Würzburg)

  • Fabian J. Theis

    (Helmholtz Zentrum München–German Research Center for Environmental Health
    Technische Universität München)

  • Antoine-Emmanuel Saliba

    (Helmholtz-Center for Infection Research (HZI))

  • Lars Dölken

    (Julius-Maximilians-University Würzburg
    Helmholtz-Center for Infection Research (HZI))

Abstract

Single-cell RNA sequencing (scRNA-seq) has highlighted the important role of intercellular heterogeneity in phenotype variability in both health and disease1. 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 labelling2, biochemical nucleoside conversion3 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.

Suggested Citation

  • Florian Erhard & Marisa A. P. Baptista & Tobias Krammer & Thomas Hennig & Marius Lange & Panagiota Arampatzi & Christopher S. Jürges & Fabian J. Theis & Antoine-Emmanuel Saliba & Lars Dölken, 2019. "scSLAM-seq reveals core features of transcription dynamics in single cells," Nature, Nature, vol. 571(7765), pages 419-423, July.
  • Handle: RePEc:nat:nature:v:571:y:2019:i:7765:d:10.1038_s41586-019-1369-y
    DOI: 10.1038/s41586-019-1369-y
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    Citations

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    Cited by:

    1. Kun Yin & Yiling Xu & Ye Guo & Zhong Zheng & Xinrui Lin & Meijuan Zhao & He Dong & Dianyi Liang & Zhi Zhu & Junhua Zheng & Shichao Lin & Jia Song & Chaoyong Yang, 2024. "Dyna-vivo-seq unveils cellular RNA dynamics during acute kidney injury via in vivo metabolic RNA labeling-based scRNA-seq," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    2. Carmen Aguilar & Mindaugas Pauzuolis & Malvika Pompaiah & Ehsan Vafadarnejad & Panagiota Arampatzi & Mara Fischer & Dominik Narres & Mastura Neyazi & Özge Kayisoglu & Thomas Sell & Nils Blüthgen & Mar, 2022. "Helicobacter pylori shows tropism to gastric differentiated pit cells dependent on urea chemotaxis," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    3. Jasper Panten & Tobias Heinen & Christina Ernst & Nils Eling & Rebecca E. Wagner & Maja Satorius & John C. Marioni & Oliver Stegle & Duncan T. Odom, 2024. "The dynamic genetic determinants of increased transcriptional divergence in spermatids," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    4. Andrew J. Heindel & Jeffrey W. Brulet & Xiantao Wang & Michael W. Founds & Adam H. Libby & Dina L. Bai & Michael C. Lemke & David M. Leace & Thurl E. Harris & Markus Hafner & Ku-Lung Hsu, 2023. "Chemoproteomic capture of RNA binding activity in living cells," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    5. Shichao Lin & Kun Yin & Yingkun Zhang & Fanghe Lin & Xiaoyong Chen & Xi Zeng & Xiaoxu Guo & Huimin Zhang & Jia Song & Chaoyong Yang, 2023. "Well-TEMP-seq as a microwell-based strategy for massively parallel profiling of single-cell temporal RNA dynamics," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    6. Bibiana Costa & Jennifer Becker & Tobias Krammer & Felix Mulenge & Verónica Durán & Andreas Pavlou & Olivia Luise Gern & Xiaojing Chu & Yang Li & Luka Čičin-Šain & Britta Eiz-Vesper & Martin Messerle , 2024. "Human cytomegalovirus exploits STING signaling and counteracts IFN/ISG induction to facilitate infection of dendritic cells," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    7. Teresa Rummel & Lygeri Sakellaridi & Florian Erhard, 2023. "grandR: a comprehensive package for nucleotide conversion RNA-seq data analysis," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    8. Lior Fishman & Avani Modak & Gal Nechooshtan & Talya Razin & Florian Erhard & Aviv Regev & Jeffrey A. Farrell & Michal Rabani, 2024. "Cell-type-specific mRNA transcription and degradation kinetics in zebrafish embryogenesis from metabolically labeled single-cell RNA-seq," Nature Communications, Nature, vol. 15(1), pages 1-20, December.

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