Quantifying how post-transcriptional noise and gene copy number variation bias transcriptional parameter inference from mRNA distributions
- East China University of Science and Technology, China
- Oncode Institute, Netherlands
- University of Edinburgh, United Kingdom
Abstract
Transcriptional rates are often estimated by fitting the distribution of mature mRNA numbers measured using smFISH (single molecule fluorescence in situ hybridization) with the distribution predicted by the telegraph model of gene expression, which defines two promoter states of activity and inactivity. However, fluctuations in mature mRNA numbers are strongly affected by processes downstream of transcription. In addition, the telegraph model assumes one gene copy, but in experiments cells may have two gene copies as cells replicate their genome during the cell cycle. Whilst it is often presumed that post-transcriptional noise and gene copy number variation affect transcriptional parameter estimation, the size of the error introduced remains unclear. To address this issue, here we measure both mature and nascent mRNA distributions of GAL10 in yeast cells using smFISH and classify each cell according to its cell cycle phase. We infer transcriptional parameters from mature and nascent mRNA distributions, with and without accounting for cell cycle phase and compare the results to live-cell transcription measurements of the same gene. We find that: (i) correcting for cell cycle dynamics decreases the promoter switching rates and the initiation rate, and increases the fraction of time spent in the active state, as well as the burst size; (ii) additional correction for post-transcriptional noise leads to further increases in the burst size and to a large reduction in the errors in parameter estimation. Furthermore, we outline how to correctly adjust for measurement noise in smFISH due to uncertainty in transcription site localisation when introns cannot be labelled. Simulations with parameters estimated from nascent smFISH data, which is corrected for cell cycle phases and measurement noise, leads to autocorrelation functions that agree with those obtained from live-cell imaging.
Data availability
The 4 smFISH datasets are available from https://osf.io/d5nvj/. These datasets include the maximum intensity projected images, the spot localization results, the nuclear and cellular masks used for merged, G1 and G2 cells and the analyzed results of the mature and nascent data. The analysis code of the smFISH microscopy data is available at https://github.com/Lenstralab/smFISH. The code for the the synthetic simulations and the parameter inference is available at https://github.com/palmtree2013/RNAInferenceTool.jl.
Article and author information
Author details
Funding
National Natural Science Foundation of China (61988101)
- Xiaoming Fu
- Libin Xu
- Zhixing Cao
National Natural Science Foundation of China (6207313)
- Xiaoming Fu
- Libin Xu
- Zhixing Cao
H2020 European Research Council (755695 BURSTREG)
- Tineke L Lenstra
Leverhulme Trust (RPG-2020-327)
- Ramon Grima
Shanghai Action Plan for Technological Innovation Grant (22ZR1415300)
- Xiaoming Fu
- Libin Xu
- Zhixing Cao
Shanghai Action Plan for Technological Innovation Grant (22511104000)
- Xiaoming Fu
- Libin Xu
- Zhixing Cao
Shanghai Sailing Program (22YF1410700)
- Xiaoming Fu
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Anna Akhmanova, Utrecht University, Netherlands
Version history
- Preprint posted: November 11, 2021 (view preprint)
- Received: August 7, 2022
- Accepted: October 14, 2022
- Accepted Manuscript published: October 17, 2022 (version 1)
- Version of Record published: November 10, 2022 (version 2)
Copyright
© 2022, Fu et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Quantifying how post-transcriptional noise and gene copy number variation bias transcriptional parameter inference from mRNA distributions
eLife 11:e82493.
https://doi.org/10.7554/eLife.82493
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