Real-time single-cell characterization of the eukaryotic transcription cycle reveals correlations between RNA initiation, elongation, and cleavage

The eukaryotic transcription cycle consists of three main steps: initiation, elongation, and cleavage of the nascent RNA transcript. Although each of these steps can be regulated as well as coupled with each other, their in vivo dissection has remained challenging because available experimental read...

Full description

Saved in:
Bibliographic Details
Main Authors: Liu, Jonathan (Author) , Hansen, Donald (Author) , Eck, Elizabeth (Author) , Kim, Yang Joon (Author) , Turner, Meghan (Author) , Alamos, Simon (Author) , Garcia, Hernan G. (Author)
Format: Article (Journal)
Language:English
Published: May 18, 2021
In: PLoS Computational Biology
Year: 2021, Volume: 17, Issue: 5, Pages: 1-26
ISSN:1553-7358
DOI:10.1371/journal.pcbi.1008999
Online Access:lizenzpflichtig
lizenzpflichtig
Get full text
Author Notes:Jonathan Liu, Donald Hansen, Elizabeth Eck, Yang Joon Kim, Meghan Turner, Simon Alamos, Hernan G. Garcia
Description
Summary:The eukaryotic transcription cycle consists of three main steps: initiation, elongation, and cleavage of the nascent RNA transcript. Although each of these steps can be regulated as well as coupled with each other, their in vivo dissection has remained challenging because available experimental readouts lack sufficient spatiotemporal resolution to separate the contributions from each of these steps. Here, we describe a novel application of Bayesian inference techniques to simultaneously infer the effective parameters of the transcription cycle in real time and at the single-cell level using a two-color MS2/PP7 reporter gene and the developing fruit fly embryo as a case study. Our method enables detailed investigations into cell-to-cell variability in transcription-cycle parameters as well as single-cell correlations between these parameters. These measurements, combined with theoretical modeling, suggest a substantial variability in the elongation rate of individual RNA polymerase molecules. We further illustrate the power of this technique by uncovering a novel mechanistic connection between RNA polymerase density and nascent RNA cleavage efficiency. Thus, our approach makes it possible to shed light on the regulatory mechanisms in play during each step of the transcription cycle in individual, living cells at high spatiotemporal resolution.
Item Description:Gesehen am 04.08.2021
Physical Description:Online Resource
ISSN:1553-7358
DOI:10.1371/journal.pcbi.1008999