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High-throughput automated phenotyping of two genetic mouse models of huntington's disease

Authors :
Liliana B. Menalled
Fuat Balcı
Stephen Oakeshott
Dani Brunner
David Howland
Jul Lea Shamy
David A. Connor
Russell G. Port
Sylvie Ramboz
Bassem F. El-Khodor
Ahmad Paintdakhi
Seung Kwak
Richard Mushlin
Igor Filippov
Balcı, Fuat (ORCID 0000-0003-3390-9352 & YÖK ID 51269)
Oakeshott, Stephen
Shamy, Jul Lea T
El-Khodor, Bassem Fouad
Filippov, Igor V.
Mushlin, Richard A.
Port, Russell G.
Connor, David
Paintdakhi, Ahmad
Menalled, Liliana B.
Ramboz, Sylvie
Howland, David S.
Kwak, Seung
Brunner, Dani
College of Social Sciences and Humanities
Department of Psychology
Source :
PLOS Currents, PLoS Currents
Publication Year :
2013
Publisher :
Public Library of Science, 2013.

Abstract

Phenotyping with traditional behavioral assays constitutes a major bottleneck in the primary screening, characterization, and validation of genetic mouse modelsof disease, leading to downstream delays in drug discovery efforts. We present a novel and comprehensive one-stop approach to phenotyping, the PhenoCube™. This system simultaneously captures the cognitive performance, motor activity, and circadian patterns of group-housed mice by use of home-cage operant conditioning modules (IntelliCage) and custom-built computer vision software. We evaluated two different mouse models of Huntington's Disease (HD), the R6/2 and the BACHD in the PhenoCube™ system. Our results demonstrated that this system can efficiently capture and track alterations in both cognitive performance and locomotor activity patterns associated with these disease models. This work extends our prior demonstration that PhenoCube™ can characterize circadian dysfunction in BACHD mice and shows that this system, with the experimental protocols used, is a sensitive and efficient tool for a first pass high-throughput screening of mouse disease models in general and mouse models of neurodegeneration in particular<br />NA

Details

Language :
English
Database :
OpenAIRE
Journal :
PLOS Currents, PLoS Currents
Accession number :
edsair.doi.dedup.....e314099189b8d0189d2e1d4f902061f7