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

Single-cell RNA sequencing (scRNA-seq) has highlighted the important role of intercellular heterogeneity in phenotype variability in both health and disease. 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.

Researchers at Julius-Maximilians-University have developed single-cell, thiol-(SH)-linked alkylation of RNA for metabolic labelling sequencing (scSLAM-seq), which integrates metabolic RNA labelling, biochemical nucleoside conversion and scRNA-seq to record transcriptional activity directly by differentiating between new and old RNA for thousands of genes per single cell. They 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.

scSLAM-seq resolves transcriptional activity at the single-cell level


Overview of scSLAM-seq (top) and GRAND-SLAM (bottom) approaches. Top, nascent transcripts are labelled before or after CMV infection by adding 500 µM 4sU to the cell culture medium for 2 h. After single-cell sorting and RNA isolation, 4sU is converted into a cytosine analogue by IAA and SMART-seq libraries are prepared and sequenced. Bottom, GRAND-SLAM identifies thymine-to-cytosine mismatches and estimates both the NTR and the expression of old and new RNA. TPM, transcript per millions.

Erhard F, Baptista MAP, Krammer T, Hennig T, Lange M, Arampatzi P, Jürges CS, Theis FJ, Saliba AE, Dölken L. (2019) scSLAM-seq reveals core features of transcription dynamics in single cells. Nature 571(7765):419-423. [abstract]

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