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

• Published in: Nature (London) Vol. 571; no. 7765; pp. 419 - 423
• Main Authors: Erhard, Florian, Baptista, Marisa A. P., Krammer, Tobias, Hennig, Thomas, Lange, Marius, Arampatzi, Panagiota, Jürges, Christopher S., Theis, Fabian J., Saliba, Antoine-Emmanuel, Dölken, Lars
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.07.2019; Nature Publishing Group
• Subjects: 13/106; 38/39; 38/91; 631/1647/514/1949; 631/326/596; 631/337/2019; 631/553; Alkylation; Animals; Bioinformatics; Cell Cycle; Cells; Cytomegalovirus; Cytomegalovirus - physiology; Deoxyribonucleic acid; DNA; DNA Methylation; Fibroblasts; Fibroblasts - metabolism; Fibroblasts - virology; Gene expression; Gene Expression Regulation - genetics; Gene sequencing; Genetic aspects; Genetic transcription; Genomics; Heterogeneity; Humanities and Social Sciences; Infections; Kinetics; Labeling; Letter; Metabolism; Methods; Mice; multidisciplinary; Observations; Phenotypes; Phenotypic variations; Principal components analysis; Promoter Regions, Genetic - genetics; Ribonucleic acid; RNA; RNA - analysis; RNA - chemistry; RNA polymerase; RNA sequencing; Science; Science (multidisciplinary); Sequence Analysis, RNA - methods; Single-Cell Analysis; Stochasticity; Sulfhydryl Compounds - chemistry; Switches; Transcription; Transcription, Genetic - genetics; Velocity; Germany
• ISSN: 0028-0836; 1476-4687
• DOI: 10.1038/s41586-019-1369-y

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. A technique known as scSLAM-seq that combines single-cell RNA sequencing with metabolic RNA labelling and nucleoside conversion is used to study the onset of cytomegalovirus infection in single mouse fibroblasts.