Automated deep-phenotyping of the vertebrate brain

  1. Amin Allalou
  2. Yuelong Wu
  3. Mostafa Ghannad-Rezaie
  4. Peter M Eimon  Is a corresponding author
  5. Mehmet Fatih Yanik  Is a corresponding author
  1. Massachusetts Institute of Technology, United States

Abstract

Here we describe an automated platform suitable for large-scale deep-phenotyping of zebrafish mutant lines, which uses optical projection tomography to rapidly image brain-specific gene expression patterns in 3D at cellular resolution. Registration algorithms and correlation analysis are then used to compare 3D expression patterns, to automatically detect all statistically significant alterations in mutants, and to map them onto a brain atlas. Automated deep-phenotyping of a mutation in the master transcriptional regulator fezf2 not only detects all known phenotypes but also uncovers important novel neural deficits that were overlooked in previous studies. In the telencephalon, we show for the first time that fezf2 mutant zebrafish have significant patterning deficits, particularly in glutamatergic populations. Our findings reveal unexpected parallels between fezf2 function in zebrafish and mice, where mutations cause deficits in glutamatergic neurons of the telencephalon-derived neocortex.

Data availability

The following data sets were generated

Article and author information

Author details

  1. Amin Allalou

    Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Yuelong Wu

    Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Mostafa Ghannad-Rezaie

    Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Peter M Eimon

    Massachusetts Institute of Technology, Cambridge, United States
    For correspondence
    peter.eimon@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0447-517X
  5. Mehmet Fatih Yanik

    Massachusetts Institute of Technology, Cambridge, United States
    For correspondence
    yanik@ethz.ch
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8963-2893

Funding

National Institutes of Health (Director's Pioneer Award DP1-NS082102)

  • Mehmet Fatih Yanik

David and Lucile Packard Foundation (Packard Award in Science and Engineering)

  • Mehmet Fatih Yanik

The Eli and Edythe L. Broad Institute of MIT and Harvard (SPARC Award)

  • Mehmet Fatih Yanik

Epilepsy Foundation (Fellowship)

  • Amin Allalou

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: All procedures on live animals were performed in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. Protocols were approved by the Massachusetts Institute of Technology Committee on Animal Care (protocol #0312-025-15).

Reviewing Editor

  1. Didier YR Stainier, Max Planck Institute for Heart and Lung Research, Germany

Publication history

  1. Received: December 28, 2016
  2. Accepted: April 7, 2017
  3. Accepted Manuscript published: April 13, 2017 (version 1)
  4. Version of Record published: May 23, 2017 (version 2)

Copyright

© 2017, Allalou 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. Amin Allalou
  2. Yuelong Wu
  3. Mostafa Ghannad-Rezaie
  4. Peter M Eimon
  5. Mehmet Fatih Yanik
(2017)
Automated deep-phenotyping of the vertebrate brain
eLife 6:e23379.
https://doi.org/10.7554/eLife.23379

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