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 computational technique 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 with high precision. These measurements, combined with theoretical modeling, reveal a significant 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.

Neural network aided approximation and parameter inference of non-Markovian models of gene expression

“…Hence Region IV can be understood as delay-induced bimodality or a delay-induced zeroinflation phenomenon. Since there is evidence that the delay time is stochastic rather than fixed 34,35 we also used the NN-CME to investigate how the nascent RNA distributions change with variance in the delay time when the mean is kept constant: as shown in Fig. 5c, we find that a large increase in the variability of the delay time tends to destroy the peak at zero.…”

Collective polymerase dynamics emerge from DNA supercoiling during transcription

“…Further measurements will offer insights into the respective contributions of initiation and elongation to gene expression statistics. For instance, strategically placing single-molecule reporter systems such as MS2 and PP7 in a single gene, one near the promoter and the other near the termination site [18, 40] would allow for the de-convolution of flux due to initiation and its evolution during elongation.…”

Neural network aided approximation and parameter inference of stochastic models of gene expression

“…Hence Region IV can be understood as delay-induced bimodality or a delay-induced zero-inflation phenomenon. Since there is evidence that the delay time is stochastic rather than fixed [26, 27] we also used the NN-CME to investigate how the nascent RNA distributions change with variance in the delay time when the mean is kept constant: as shown in Fig. 4c we find that a large increase in the variability of the delay time tends to destroy the peak at zero.…”