Newly synthesized RNA Sequencing Characterizes Transcription Dynamics in Three Pluripotent States
Shao, R.; Kumar, B.; Liedschreiber, K.; Lidschreiber, M. M.; Cramer, P.; Elsässer, S. J.
Show abstract
Unique transcriptomes define naive, primed and paused pluripotent states in mouse embryonic stem cells. Here we perform transient transcriptome sequencing (TT-seq) to de novo define and quantify coding and non-coding transcription units (TUs) in different pluripotent states. We observe a global reduction of RNA synthesis, total RNA amount and turnover rates in ground state naive cells (2i) and paused pluripotency (mTORi). We demonstrate that elongation velocity can be reliably estimated from TT-seq nascent RNA and RNA polymerase II occupancy and observe a transcriptome-wide attenuation of elongation velocity in the two inhibitor-induced states. We also discover a relationship between elongation velocity and termination read-through distance. Our analysis suggests that steady-state transcriptomes in mouse ES cells are controlled predominantly on the level of RNA synthesis, and that signaling pathways governing different pluripotent states immediately control key parameters of transcription.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Orchestration of pluripotent stem cell genome reactivation during mitotic exit 97%
- SLAMseq resolves the kinetics of maternal and zygotic gene expression in early zebrafish embryogenesis 96%
- Transcriptional architecture and Pol II regulation at promoters, enhancers, and enhancer clusters in Canis lupus familiaris 95%
Similar papers in this journal
- Functionally distinct promoter classes initiate transcription via different mechanisms reflected in focused versus dispersed initiation patterns 95%
- Cell cycle-driven transcriptome maturation confers multilineage competence to cardiopharyngeal progenitors 94%
- Local rewiring of genome - nuclear lamina interactions by transcription. 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.