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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.*

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

Nature 571, 419-423 (2019)
Postprint DOI PMC
Open Access Green
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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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Nf-kappa-b; Expression; Rna
ISSN (print) / ISBN 0028-0836
e-ISSN 1476-4687
Journal Nature
Quellenangaben Volume: 571, Issue: 7765, Pages: 419-423 Article Number: , Supplement: ,
Publisher Nature Publishing Group
Publishing Place London
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)