Single-cell RNA sequencing offers snapshots of whole transcriptomes but obscures the temporal dynamics of RNA biogenesis and decay. Here we present single-cell metabolically labeled new RNA tagging sequencing (scNT-Seq), a method for massively parallel analysis of newly-transcribed and pre-existing RNAs from the same cell. This droplet microfluidics-based method enables high-throughput chemical conversion on barcoded beads, efficiently marking newly-transcribed RNAs with T-to-C substitutions. We used scNT-Seq to concurrently analyze new/old transcriptomes in >50,000 single cells. These data revealed time-resolved transcription factor activities and cell state trajectories at single-cell level in response to neuronal activation. We further determined RNA kinetics parameters and revealed major regulatory strategies during stepwise conversion between pluripotent and rare totipotent two-cell-embryo-like (2C-like) stem cell states. Finally, integrating scNT-Seq with genetic perturbation identifies DNA methylcytosine dioxygenases as an epigenetic gatekeeper into 2C-like state. Time-resolved single-cell transcriptomic analysis thus opens new lines of inquiry regarding cell-type-specific RNA regulatory mechanisms. SOURCE: Peng Hu (penghu@upenn.edu) - Hao Wu University of Pennsylvanian

Pluto Bioinformatics

GSE141851: Massively parallel, time-resolved single-cell RNA sequencing with scNT-seq