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

Abstract

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.

Document Details

Document Type
Pub Defense Publication
Publication Date
May 18, 2021
Source ID
10.1371/journal.pcbi.1008999

Entities

People

  • Donald Hansen
  • Elizabeth Eck
  • Hernan G. Garcia
  • Jonathan Liu
  • Meghan A. Turner
  • Simón Álamos
  • Yang Joon Kim

Organizations

  • Alfred P. Sloan Foundation
  • Burroughs Wellcome Fund
  • Hellman Foundation
  • Korea Foundation for Advanced Studies
  • National Science Foundation
  • Office of the Director
  • Shurl and Kay Curci Foundation

Tags

Fields of Study

  • Biology

Readers

  • Molecular Biology and Genetics
  • Molecular Genetics
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference