Dataset Information

Single-cell nascent RNA sequencing using click-chemistry unveils coordinated transcription

ABSTRACT: Transcription is the primary regulatory step in gene expression coordinating the coding and non-coding genomic regions across space and time. Divergent initiation of transcription from promoters and enhancers produces stable RNAs from genes and unstable RNAs from enhancers. Nascent RNA capture and sequencing assays simultaneously measure gene and enhancer activity from cell populations. However, the lack of single-cell resolved view of active transcription has left fundamental questions in gene regulation unanswered. In this study, we present scGRO-seq - a single-cell nascent RNA sequencing assay using copper-catalyzed azide-alkyne cycloaddition - unveiling the coordinated regulation of dynamic transcription throughout the genome. scGRO-seq demonstrates the episodic nature of transcription and provides estimates of burst size and frequency by directly quantifying transcribing RNA polymerases. It reveals the co-transcription of functionally related genes and leverages the transcription of replication-dependent non-polyadenylated histone genes to elucidate cell-cycle dynamics. The single-nucleotide spatiotemporal resolution of scGRO-seq characterizes networks of enhancers and genes and indicates the bursting of transcription at super-enhancers before the activation of burst from associated genes. Our method offers insights into the dynamic nature of transcription, functional architecture of the genome, and serves as a powerful tool to investigate the role of the non-coding genome in gene regulation.

ORGANISM(S): Mus musculus

PROVIDER: GSE242176 | GEO | 2023/09/15

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:<p>Non-coding elements in our genomes that play critical roles in complex disease are frequently marked by highly unstable RNA species. Sequencing nascent RNAs attached to an actively transcribing RNA polymerase complex can identify unstable RNAs, including those templated from gene-distal enhancers (eRNAs). However, nascent RNA sequencing techniques remain challenging to apply in some cell lines and especially to intact tissues, limiting broad applications in fields such as cancer genomics and personalized medicine. Here we report the development of chromatin run-on and sequencing (ChRO-seq), a novel run-on technology that maps the location of RNA polymerase using virtually any frozen tissue sample, including samples with degraded RNA that are intractable to conventional RNA-seq. We used ChRO-seq to develop the first maps of nascent transcription in 23 human glioblastoma (GBM) brain tumors and patient derived xenografts. Remarkably, >90,000 distal enhancers discovered using the signature of eRNA biogenesis within primary GBMs closely resemble those found in the normal human brain, and diverge substantially from GBM cell models. Despite extensive overall similarity, 12% of enhancers in each GBM distinguish normal and malignant brain tissue. These enhancers drive regulatory programs similar to the developing nervous system and are enriched for transcription factor binding sites that specify a stem-like cell fate. These results demonstrate that GBMs largely retain the enhancer landscape associated with their tissue of origin, but selectively adopt regulatory programs that are responsible for driving stem-like cell properties. We also identified enhancers and their associated transcription factors that regulate genes characteristic of each known GBM subtype, and discovered a core group of transcription factors that control the expression of genes associated with clinical outcomes. This study uncovers new insights into the molecular etiology of GBM and introduces ChRO-seq which can now be used to map regulatory programs contributing to a variety of complex diseases.</p>

Dataset Information

Single-cell nascent RNA sequencing using click-chemistry unveils coordinated transcription

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets