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

Single cell RNA sequencing so far only depicts cellular transcriptomes at a single time point with low temporal resolution for kinetic changes. Here, we present a single-cell approach based on metabolic RNA labelling for combined sequencing of total and newly transcribed RNA as well as computational analysis of the RNA present prior to labelling. We apply it to cytomegalovirus infection of fibroblasts and reveal its potential for delineating alterations in transcriptional activity and decay in single cells under perturbed experimental conditions. We performed scSLAM-seq on 107 single cells across two biological replicates and analyzed global transcriptional changes of matched bulk cells using SLAM-seq.