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dc.contributor.authorErhard, Florian
dc.contributor.authorBaptista, Marisa A P
dc.contributor.authorKrammer, Tobias
dc.contributor.authorHennig, Thomas
dc.contributor.authorLange, Marius
dc.contributor.authorArampatzi, Panagiota
dc.contributor.authorJürges, Christopher S
dc.contributor.authorTheis, Fabian J
dc.contributor.authorSaliba, Antoine-Emmanuel
dc.contributor.authorDölken, Lars
dc.date.accessioned2019-08-28T14:24:24Z
dc.date.available2019-08-28T14:24:24Z
dc.date.issued2019-01-01
dc.identifier.citationNature. 2019 Jul;571(7765):419-423. doi: 10.1038/s41586-019-1369-y. Epub 2019 Jul 10.en_US
dc.identifier.issn1476-4687
dc.identifier.pmid31292545
dc.identifier.doi10.1038/s41586-019-1369-y
dc.identifier.urihttp://hdl.handle.net/10033/621925
dc.description.abstractSingle-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.en_US
dc.language.isoenen_US
dc.publisherSpringer-Natureen_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.titlescSLAM-seq reveals core features of transcription dynamics in single cells.en_US
dc.typeArticleen_US
dc.contributor.departmentHIRI, Helmholtz-Institut für RNA-basierte Infektionsforschung, Josef-Shneider Strasse 2, 97080 Würzburg, Germany.en_US
dc.identifier.journalNatureen_US
dc.source.journaltitleNature


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