Quantifying how post-transcriptional noise and gene copy number variation bias transcriptional parameter inference from mRNA distributions

  1. Xiaoming Fu
  2. Heta P Patel
  3. Stefano Coppola
  4. Libin Xu
  5. Zhixing Cao  Is a corresponding author
  6. Tineke L Lenstra  Is a corresponding author
  7. Ramon Grima  Is a corresponding author
  1. East China University of Science and Technology, China
  2. Oncode Institute, Netherlands
  3. 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.

The following data sets were generated

Article and author information

Author details

  1. Xiaoming Fu

    Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Nanjing, China
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4073-9822
  2. Heta P Patel

    Division of Gene Regulation, Oncode Institute, Amsterdam, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1618-951X
  3. Stefano Coppola

    Division of Gene Regulation, Oncode Institute, Amsterdam, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
  4. Libin Xu

    Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Nanjing, China
    Competing interests
    The authors declare that no competing interests exist.
  5. Zhixing Cao

    Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Nanjing, China
    For correspondence
    zcao@ecust.edu.cn
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2600-5806
  6. Tineke L Lenstra

    Division of Gene Regulation, Oncode Institute, Amsterdam, Netherlands
    For correspondence
    t.lenstra@nki.nl
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4440-9962
  7. Ramon Grima

    School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
    For correspondence
    ramon.grima@ed.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1266-8169

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

  1. Anna Akhmanova, Utrecht University, Netherlands

Publication history

  1. Preprint posted: November 11, 2021 (view preprint)
  2. Received: August 7, 2022
  3. Accepted: October 14, 2022
  4. Accepted Manuscript published: October 17, 2022 (version 1)
  5. 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.

Metrics

  • 720
    Page views
  • 138
    Downloads
  • 4
    Citations

Article citation count generated by polling the highest count across the following sources: PubMed Central, Crossref, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Xiaoming Fu
  2. Heta P Patel
  3. Stefano Coppola
  4. Libin Xu
  5. Zhixing Cao
  6. Tineke L Lenstra
  7. Ramon Grima
(2022)
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

Further reading

    1. Computational and Systems Biology
    Swann Floc'hlay, Ramya Balaji ... Stein Aerts
    Research Article Updated

    Wound response programs are often activated during neoplastic growth in tumors. In both wound repair and tumor growth, cells respond to acute stress and balance the activation of multiple programs, including apoptosis, proliferation, and cell migration. Central to those responses are the activation of the JNK/MAPK and JAK/STAT signaling pathways. Yet, to what extent these signaling cascades interact at the cis-regulatory level and how they orchestrate different regulatory and phenotypic responses is still unclear. Here, we aim to characterize the regulatory states that emerge and cooperate in the wound response, using the Drosophila melanogaster wing disc as a model system, and compare these with cancer cell states induced by rasV12scrib-/- in the eye disc. We used single-cell multiome profiling to derive enhancer gene regulatory networks (eGRNs) by integrating chromatin accessibility and gene expression signals. We identify a ‘proliferative’ eGRN, active in the majority of wounded cells and controlled by AP-1 and STAT. In a smaller, but distinct population of wound cells, a ‘senescent’ eGRN is activated and driven by C/EBP-like transcription factors (Irbp18, Xrp1, Slow border, and Vrille) and Scalloped. These two eGRN signatures are found to be active in tumor cells at both gene expression and chromatin accessibility levels. Our single-cell multiome and eGRNs resource offers an in-depth characterization of the senescence markers, together with a new perspective on the shared gene regulatory programs acting during wound response and oncogenesis.

    1. Cancer Biology
    2. Computational and Systems Biology
    Xiangkun Wu, Hong Yan ... Li Liang
    Research Article

    Colorectal cancer (CRC) remains a challenging and deadly disease with high tumor microenvironment (TME) heterogeneity. Using an integrative multi-omics analysis and artificial intelligence-enabled spatial analysis of whole-slide images, we performed a comprehensive characterization of TME in colorectal cancer (CCCRC). CRC samples were classified into four CCCRC subtypes with distinct TME features, namely, C1 as the proliferative subtype with low immunogenicity; C2 as the immunosuppressed subtype with the terminally exhausted immune characteristics; C3 as the immune-excluded subtype with the distinct upregulation of stromal components and a lack of T cell infiltration in the tumor core; and C4 as the immunomodulatory subtype with the remarkable upregulation of anti-tumor immune components. The four CCCRC subtypes had distinct histopathologic and molecular characteristics, therapeutic efficacy, and prognosis. We found that the C1 subtype may be suitable for chemotherapy and cetuximab, the C2 subtype may benefit from a combination of chemotherapy and bevacizumab, the C3 subtype has increased sensitivity to the WNT pathway inhibitor WIKI4, and the C4 subtype is a potential candidate for immune checkpoint blockade treatment. Importantly, we established a simple gene classifier for accurate identification of each CCCRC subtype. Collectively our integrative analysis ultimately established a holistic framework to thoroughly dissect the TME of CRC, and the CCCRC classification system with high biological interpretability may contribute to biomarker discovery and future clinical trial design.