1. Computational and Systems Biology
  2. Chromosomes and Gene Expression
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Single-cell analysis of transcription kinetics across the cell cycle

  1. Samuel O Skinner
  2. Heng Xu
  3. Sonal Nagarkar-Jaiswal
  4. Pablo R Freire
  5. Thomas P Zwaka
  6. Ido Golding  Is a corresponding author
  1. Baylor College of Medicine, United States
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Cite this article as: eLife 2016;5:e12175 doi: 10.7554/eLife.12175

Abstract

Transcription is a highly stochastic process. To infer transcription kinetics for a gene-of-interest, researchers commonly compare the distribution of mRNA copy-number to the prediction of a theoretical model. However, the reliability of this procedure is limited because the measured mRNA numbers represent integration over the mRNA lifetime, contribution from multiple gene copies, and mixing of cells from different cell-cycle phases. We address these limitations by simultaneously quantifying nascent and mature mRNA in individual cells, and incorporating cell-cycle effects in the analysis of mRNA statistics. We demonstrate our approach on Oct4 and Nanog in mouse embryonic stem cells. Both genes follow similar two-state kinetics. However, Nanog exhibits slower ON/OFF switching, resulting in increased cell-to-cell variability in mRNA levels. Early in the cell cycle, the two copies of each gene exhibit independent activity. After gene replication, the probability of each gene copy to be active diminishes, resulting in dosage compensation.

Article and author information

Author details

  1. Samuel O Skinner

    Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Heng Xu

    Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Sonal Nagarkar-Jaiswal

    Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Pablo R Freire

    Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Thomas P Zwaka

    Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Ido Golding

    Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, United States
    For correspondence
    golding@bcm.edu
    Competing interests
    The authors declare that no competing interests exist.

Reviewing Editor

  1. Robert H Singer, Albert Einstein College of Medicine, United States

Publication history

  1. Received: October 9, 2015
  2. Accepted: January 28, 2016
  3. Accepted Manuscript published: January 29, 2016 (version 1)
  4. Version of Record published: March 7, 2016 (version 2)

Copyright

© 2016, Skinner 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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  1. Further reading

Further reading

    1. Computational and Systems Biology
    2. Medicine
    Xuan Xu et al.
    Research Article

    Background: Potential therapy and confounding factors including typical co‐administered medications, patient's disease states, disease prevalence, patient demographics, medical histories, and reasons for prescribing a drug often are incomplete, conflicting, missing, or uncharacterized in spontaneous adverse drug event (ADE) reporting systems. These missing or incomplete features can affect and limit the application of quantitative methods in pharmacovigilance for meta-analyses of data during randomized clinical trials.

    Methods: Data from patients with hypertension were retrieved and integrated from the FDA Adverse Event Reporting System. 134 antihypertensive drugs out of 1151 drugs were filtered and then evaluated using the Empirical Bayes Geometric Mean (EBGM) of the posterior distribution to build ADE-drug profiles with an emphasis on the pulmonary ADEs (pADE). Afterward, the Graphical Least Absolute Shrinkage and Selection Operator (GLASSO) captured drug associations based on pADEs by correcting hidden factors and confounder misclassification. Selected drugs were then compared using the Friedman test in drug classes and clusters obtained from GLASSO.

    Results: Following multiple filtering stages to exclude insignificant and noise-driven reports, we found that drugs from antihypertensives agents, urologicals, and antithrombotic agents (macitentan, bosentan, epoprostenol, selexipag, sildenafil, tadalafil, and beraprost) form a similar class with a significantly higher incidence of pADEs. Macitentan and bosentan were associates with 64% and 56% of pADEs, respectively. Because these two medications are prescribed in diseases affecting pulmonary function and may be likely to emerge among the highest reported pADEs, in fact, they serve to validate the methods utilized here. Conversely, doxazosin and rilmenidine were found to have the least pADEs in selected drugs from hypertension patients. Nifedipine and candesartan were also found by signal detection methods to form a drug cluster, shown by several studies an effective combination of these drugs on lowering blood pressure and appeared an improved side effect profile in comparison with single-agent monotherapy.

    Conclusions: We consider pADE profiles in multiple long-standing groups of therapeutics including antihypertensive agents, antithrombotic agents, beta-blocking agents, calcium channel blockers, or agents acting on the renin-angiotensin system, in patients with hypertension associated with high-risk for COVID-19. We found that several individual drugs have significant differences between their drug classes and compared to other drug classes. For instance, macitentan and bosentan from endothelin receptor antagonists show major concern while doxazosin and rilmenidine exhibited the least pADEs compared to the outcomes of other drugs. Using techniques in this study, we assessed and confirmed the hypothesis that drugs from the same drug class could have very different pADE profiles affecting outcomes in acute respiratory illness.

    Funding: GJW and MJD accepted funding from BioNexus KC for funding on this project, but BioNexus KC had no direct role in this article.

    1. Chromosomes and Gene Expression
    2. Computational and Systems Biology
    Lucy Ham et al.
    Research Article Updated

    Single-cell expression profiling opens up new vistas on cellular processes. Extensive cell-to-cell variability at the transcriptomic and proteomic level has been one of the stand-out observations. Because most experimental analyses are destructive we only have access to snapshot data of cellular states. This loss of temporal information presents significant challenges for inferring dynamics, as well as causes of cell-to-cell variability. In particular, we typically cannot separate dynamic variability from within cells (‘intrinsic noise’) from variability across the population (‘extrinsic noise’). Here, we make this non-identifiability mathematically precise, allowing us to identify new experimental set-ups that can assist in resolving this non-identifiability. We show that multiple generic reporters from the same biochemical pathways (e.g. mRNA and protein) can infer magnitudes of intrinsic and extrinsic transcriptional noise, identifying sources of heterogeneity. Stochastic simulations support our theory, and demonstrate that ‘pathway-reporters’ compare favourably to the well-known, but often difficult to implement, dual-reporter method.