Automated deep-phenotyping of the vertebrate brain
Year: 2017
Type: article
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.
Source: eLife
DOI: 10.7554/elife.23379
Language: English
Subfield: Cell Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,500
Funders: David and Lucile Packard Foundation, Epilepsy Foundation, Massachusetts General Hospital, Harvard University, Broad Institute, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Scheme for Promotion of Academic and Research Collaboration, National Institutes of Health, National Institute of Neurological Disorders and Stroke
OpenAlex ID: W2607161442