1. Computational and Systems Biology
  2. Developmental Biology
Download icon

Dynamics and heterogeneity of a fate determinant during transition towards cell differentiation

  1. Nicolás Peláez
  2. Arnau Gavalda-Miralles
  3. Bao Wang
  4. Heliodoro Tejedor Navarro
  5. Herman Gudjonson
  6. Ilaria Rebay
  7. Aaron R Dinner
  8. Aggelos K Katsaggelos
  9. Luis AN Amaral
  10. Richard W Carthew  Is a corresponding author
  1. Northwestern University, United States
  2. Universitat Rovira i Virgili, United States
  3. Howard Hughes Medical Institute, Northwestern University, United States
  4. University of Chicago, United States
Research Article
  • Cited 19
  • Views 4,313
  • Annotations
Cite this article as: eLife 2015;4:e08924 doi: 10.7554/eLife.08924

Abstract

Yan is an ETS-domain transcription factor responsible for maintaining Drosophila eye cells in a multipotent state. Using a fluorescent reporter for Yan expression, we observed a biphasic distribution of Yan in multipotent cells. Transitions to various differentiated states occurred over the course of this dynamic process, suggesting that Yan expression level does not strongly determine cell potential. Consistent with this conclusion, perturbing Yan expression by varying gene dosage had no effect on cell fate transitions. However, we observed that as cells transited to differentiation, Yan expression became highly heterogeneous and this heterogeneity was transient. Signals received via the EGF Receptor were necessary for the transience in Yan noise since genetic loss caused sustained noise. Since these signals are essential for eye cells to differentiate, we suggest that dynamic heterogeneity of Yan is a necessary element of the transition process, and cell states are stabilized through noise reduction.

Article and author information

Author details

  1. Nicolás Peláez

    Department of Molecular Biosciences, Northwestern University, Evanston, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Arnau Gavalda-Miralles

    Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Bao Wang

    Department Electrical Engineering and Computer Science, Northwestern University, Evanston, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Heliodoro Tejedor Navarro

    Department of Chemical and Biological Engineering, Howard Hughes Medical Institute, Northwestern University, Evanston, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Herman Gudjonson

    James Franck Institute, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Ilaria Rebay

    Ben May Department for Cancer Research, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Aaron R Dinner

    James Franck Institute, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Aggelos K Katsaggelos

    Department Electrical Engineering and Computer Science, Northwestern University, Evanston, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Luis AN Amaral

    Department of Chemical and Biological Engineering, Howard Hughes Medical Institute, Northwestern University, Evanston, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Richard W Carthew

    Department of Molecular Biosciences, Northwestern University, Evanston, United States
    For correspondence
    r-carthew@northwestern.edu
    Competing interests
    The authors declare that no competing interests exist.

Reviewing Editor

  1. Naama Barkai, Weizmann Institute of Science, Israel

Publication history

  1. Received: May 22, 2015
  2. Accepted: November 18, 2015
  3. Accepted Manuscript published: November 19, 2015 (version 1)
  4. Version of Record published: January 13, 2016 (version 2)

Copyright

© 2015, Peláez 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

  • 4,313
    Page views
  • 752
    Downloads
  • 19
    Citations

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

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)

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

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

Further reading

    1. Computational and Systems Biology
    2. Medicine
    James A Timmons et al.
    Short Report

    Insulin resistance (IR) contributes to the pathophysiology of diabetes, dementia, viral infection, and cardiovascular disease. Drug repurposing (DR) may identify treatments for IR; however, barriers include uncertainty whether in vitro transcriptomic assays yield quantitative pharmacological data, or how to optimise assay design to best reflect in vivo human disease. We developed a clinical-based human tissue IR signature by combining lifestyle-mediated treatment responses (>500 human adipose and muscle biopsies) with biomarkers of disease status (fasting IR from >1200 biopsies). The assay identified a chemically diverse set of >130 positively acting compounds, highly enriched in true positives, that targeted 73 proteins regulating IR pathways. Our multi-gene RNA assay score reflected the quantitative pharmacological properties of a set of epidermal growth factor receptor-related tyrosine kinase inhibitors, providing insight into drug target specificity; an observation supported by deep learning-based genome-wide predicted pharmacology. Several drugs identified are suitable for evaluation in patients, particularly those with either acute or severe chronic IR.

    1. Computational and Systems Biology
    2. Neuroscience
    Ling-Qi Zhang et al.
    Research Article

    We developed an image-computable observer model of the initial visual encoding that operates on natural image input, based on the framework of Bayesian image reconstruction from the excitations of the retinal cone mosaic. Our model extends previous work on ideal observer analysis and evaluation of performance beyond psychophysical discrimination, takes into account the statistical regularities of the visual environment, and provides a unifying framework for answering a wide range of questions regarding the visual front end. Using the error in the reconstructions as a metric, we analyzed variations of the number of different photoreceptor types on human retina as an optimal design problem. In addition, the reconstructions allow both visualization and quantification of information loss due to physiological optics and cone mosaic sampling, and how these vary with eccentricity. Furthermore, in simulations of color deficiencies and interferometric experiments, we found that the reconstructed images provide a reasonable proxy for modeling subjects' percepts. Lastly, we used the reconstruction-based observer for the analysis of psychophysical threshold, and found notable interactions between spatial frequency and chromatic direction in the resulting spatial contrast sensitivity function. Our method is widely applicable to experiments and applications in which the initial visual encoding plays an important role.