Transcriptomics

Dataset Information

0

Massively parallel and time-resolved RNA sequencing in single cells with scNT-Seq


ABSTRACT: Single-cell RNA sequencing offers snapshots of whole transcriptomes but obscures the temporal RNA dynamics. 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 mRNAs from the same cell. This droplet microfluidics-based method enables high-throughput chemical conversion on barcoded beads, efficiently marking newly-transcribed mRNAs with T-to-C substitutions. With scNT-Seq, we jointly profiled new and old transcriptomes in ~55,000 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 rates of RNA biogenesis and decay to uncover RNA 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 barrier into 2C-like cell state. Time-resolved single-cell transcriptomic analysis thus opens new lines of inquiry regarding cell-type-specific RNA regulatory mechanisms.

ORGANISM(S): Mus musculus Homo sapiens

PROVIDER: GSE141851 | GEO | 2020/07/21

REPOSITORIES: GEO

Dataset's files

Source:
Action DRS
Other
Items per page:
1 - 1 of 1

Similar Datasets

2020-10-15 | GSE143523 | GEO
2022-04-04 | GSE199933 | GEO
| PRJNA392921 | ENA
| PRJNA594978 | ENA
| PRJNA392923 | ENA
2021-06-07 | GSE172073 | GEO
2021-06-07 | GSE165161 | GEO
2021-06-07 | GSE165160 | GEO
2022-02-01 | GSE194357 | GEO
| PRJNA392924 | ENA