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.