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.

Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: The rate-limiting step number

“…6A ; see Supplementary Text F) where Θ is the set of parameters. Then, as done in previous work ( 69 ), the likelihood function of Θ for given data x = ( x t 1 , …, x t K ) is constructed as where p ( x ∣μ, σ 2 ) is the probability density function of the normal distribution with mean μ and variance σ 2 , and the and were calculated using the derived moments (see Supplementary Texts D to F for details). This likelihood function was used to estimate parameters for a single cell ( Fig.…”

Minimal synthetic enhancers reveal control of the probability of transcriptional engagement and its timing by a morphogen gradient

“…As a next step in our theoretical dissection, we tested the performance of our theoretical model in explaining the rate of transcription after loci become active. Typically, in MS2 experiments, the loading rate is measured from the initial slope of spot fluorescence traces (Garcia et al, 2013;Eck et al, 2020;Liu et al, 2021). However, due to the weak expression driven by our enhancers, it was not possible to perform this analysis with confidence (Fig.…”

“…MCP-mCherry and His-iRFP were described before by (Liu et al, 2021). The Dorsal-mVenus transgene was developed by Reeves et al (2012).…”

Application of Domain- and Genotype-Specific Models to Infer Post-Transcriptional Regulation of Segmentation Gene Expression in Drosophila

“…It is highly likely that the duration of these processes demonstrates both temporal and spatial variation in the embryo. New experimental methods emerge that, in combination with modeling, provide important data on the processes, for example, allowing for the estimation of various parameters of the transcription cycle [67]. However, we still lack the necessary information to properly extend the constant τ to biologically reliable alternatives in the form of a function of time and space.…”