Deciphering Transcriptional Dynamics In Vivo by Counting Nascent RNA Molecules

PLoS Comput Biol. 2015 Nov 6;11(11):e1004345. doi: 10.1371/journal.pcbi.1004345. eCollection 2015 Nov.

Abstract

Deciphering how the regulatory DNA sequence of a gene dictates its expression in response to intra and extracellular cues is one of the leading challenges in modern genomics. The development of novel single-cell sequencing and imaging techniques, as well as a better exploitation of currently available single-molecule imaging techniques, provides an avenue to interrogate the process of transcription and its dynamics in cells by quantifying the number of RNA polymerases engaged in the transcription of a gene (or equivalently the number of nascent RNAs) at a given moment in time. In this paper, we propose that measurements of the cell-to-cell variability in the number of nascent RNAs provide a mostly unexplored method for deciphering mechanisms of transcription initiation in cells. We propose a simple kinetic model of transcription initiation and elongation from which we calculate nascent RNA copy-number fluctuations. To demonstrate the usefulness of this approach, we test our theory against published nascent RNA data for twelve constitutively expressed yeast genes. Rather than transcription being initiated through a single rate limiting step, as it had been previously proposed, our single-cell analysis reveals the presence of at least two rate limiting steps. Surprisingly, half of the genes analyzed have nearly identical rates of transcription initiation, suggesting a common mechanism. Our analytical framework can be used to extract quantitative information about dynamics of transcription from single-cell sequencing data, as well as from single-molecule imaging and electron micrographs of fixed cells, and provides the mathematical means to exploit the quantitative power of these technologies.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Computational Biology / methods*
  • Cytoplasm / metabolism
  • DNA-Directed RNA Polymerases / metabolism
  • Models, Genetic*
  • RNA / analysis*
  • RNA / genetics
  • RNA / metabolism*
  • Transcription, Genetic / genetics*
  • Yeasts / genetics
  • Yeasts / metabolism

Substances

  • RNA
  • DNA-Directed RNA Polymerases

Grants and funding

This work was supported by National Science Foundation: grant DMR-1206146 to JK. AS was funded by a Junior Fellowship from the Rowland Institute at Harvard. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.