Automated deep-phenotyping of the vertebrate brain
- 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
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Data from: Automated deep-phenotyping of the vertebrate brainAvailable at Dryad Digital Repository under a CC0 Public Domain Dedication.
Article and author information
Author details
Mehmet Fatih Yanik
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).
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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Automated deep-phenotyping of the vertebrate brain
eLife 6:e23379.
https://doi.org/10.7554/eLife.23379
