Persistence, period and precision of autonomous cellular oscillators from the zebrafish segmentation clock

  1. Alexis B Webb
  2. Iván M Lengyel
  3. David J Jörg
  4. Guillaume Valentin
  5. Frank Jülicher
  6. Luis G Morelli
  7. Andrew C Oates  Is a corresponding author
  1. The Francis Crick Institute, United Kingdom
  2. CONICET, Argentina
  3. Max Planck Institute for the Physics of Complex Systems, Germany
  4. Genoway, France

Abstract

In vertebrate development, the sequential and rhythmic segmentation of the body axis is regulated by a 'segmentation clock.' This clock is comprised of a population of coordinated oscillating cells that together produce rhythmic gene expression patterns in the embryo. Whether individual cells autonomously maintain oscillations, or whether oscillations depend on signals from neighboring cells is unknown. Using a transgenic zebrafish reporter line for the cyclic transcription factor Her1, we recorded single tailbud cells in vitro. We demonstrate that individual cells can behave as autonomous cellular oscillators. We described the observed variability in cell behavior using a theory of generic oscillators with correlated noise. Single cells have longer periods and lower precision than the tissue, highlighting the role of collective processes in the segmentation clock. Our work reveals a population of cells from the zebrafish segmentation clock that behave as self-sustained, autonomous oscillators with distinctive noisy dynamics.

Article and author information

Author details

  1. Alexis B Webb

    The Francis Crick Institute, London, United Kingdom
    Competing interests
    No competing interests declared.
  2. Iván M Lengyel

    Departamento de Física, FCEyN UBA and IFIBA, CONICET, Buenos Aires, Argentina
    Competing interests
    No competing interests declared.
  3. David J Jörg

    Max Planck Institute for the Physics of Complex Systems, Dresden, Germany
    Competing interests
    No competing interests declared.
  4. Guillaume Valentin

    Genoway, Lyon, France
    Competing interests
    No competing interests declared.
  5. Frank Jülicher

    Max Planck Institute for the Physics of Complex Systems, Dresden, Germany
    Competing interests
    Frank Jülicher, Reviewing editor, eLife.
  6. Luis G Morelli

    Departamento de Física, FCEyN UBA and IFIBA, CONICET, Buenos Aires, Argentina
    Competing interests
    No competing interests declared.
  7. Andrew C Oates

    Mill Hill Laboratory, The Francis Crick Institute, London, United Kingdom
    For correspondence
    andrew.oates@crick.ac.uk
    Competing interests
    No competing interests declared.

Ethics

Animal experimentation: Zebrafish experimentation was carried out in strict accordance with the ethics and regulations of the Saxonian Ministry of the Environment and Agriculture in Germany under licence Az. 74-9165.40-9-2001, and the Home Office in the United Kingdom under project licence PPL No. 70/7675.

Copyright

© 2016, Webb 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

  • 3,709
    views
  • 946
    downloads
  • 114
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

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. Alexis B Webb
  2. Iván M Lengyel
  3. David J Jörg
  4. Guillaume Valentin
  5. Frank Jülicher
  6. Luis G Morelli
  7. Andrew C Oates
(2016)
Persistence, period and precision of autonomous cellular oscillators from the zebrafish segmentation clock
eLife 5:e08438.
https://doi.org/10.7554/eLife.08438

Share this article

https://doi.org/10.7554/eLife.08438

Further reading

    1. Computational and Systems Biology
    2. Microbiology and Infectious Disease
    Ruihan Dong, Rongrong Liu ... Cheng Zhu
    Research Article

    Antimicrobial peptides (AMPs) are attractive candidates to combat antibiotic resistance for their capability to target biomembranes and restrict a wide range of pathogens. It is a daunting challenge to discover novel AMPs due to their sparse distributions in a vast peptide universe, especially for peptides that demonstrate potencies for both bacterial membranes and viral envelopes. Here, we establish a de novo AMP design framework by bridging a deep generative module and a graph-encoding activity regressor. The generative module learns hidden ‘grammars’ of AMP features and produces candidates sequentially pass antimicrobial predictor and antiviral classifiers. We discovered 16 bifunctional AMPs and experimentally validated their abilities to inhibit a spectrum of pathogens in vitro and in animal models. Notably, P076 is a highly potent bactericide with the minimal inhibitory concentration of 0.21 μM against multidrug-resistant Acinetobacter baumannii, while P002 broadly inhibits five enveloped viruses. Our study provides feasible means to uncover the sequences that simultaneously encode antimicrobial and antiviral activities, thus bolstering the function spectra of AMPs to combat a wide range of drug-resistant infections.

    1. Computational and Systems Biology
    2. Neuroscience
    Gabriel Loewinger, Erjia Cui ... Francisco Pereira
    Tools and Resources

    Fiber photometry has become a popular technique to measure neural activity in vivo, but common analysis strategies can reduce the detection of effects because they condense within-trial signals into summary measures, and discard trial-level information by averaging across-trials. We propose a novel photometry statistical framework based on functional linear mixed modeling, which enables hypothesis testing of variable effects at every trial time-point, and uses trial-level signals without averaging. This makes it possible to compare the timing and magnitude of signals across conditions while accounting for between-animal differences. Our framework produces a series of plots that illustrate covariate effect estimates and statistical significance at each trial time-point. By exploiting signal autocorrelation, our methodology yields joint 95% confidence intervals that account for inspecting effects across the entire trial and improve the detection of event-related signal changes over common multiple comparisons correction strategies. We reanalyze data from a recent study proposing a theory for the role of mesolimbic dopamine in reward learning, and show the capability of our framework to reveal significant effects obscured by standard analysis approaches. For example, our method identifies two dopamine components with distinct temporal dynamics in response to reward delivery. In simulation experiments, our methodology yields improved statistical power over common analysis approaches. Finally, we provide an open-source package and analysis guide for applying our framework.