Steele Andrew D, Jackson Walker S, King Oliver D, Lindquist Susan
Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
Proc Natl Acad Sci U S A. 2007 Feb 6;104(6):1983-8. doi: 10.1073/pnas.0610779104. Epub 2007 Jan 29.
Automated analysis of mouse behavior will be vital for elucidating the genetic determinants of behavior, for comprehensive analysis of human disease models, and for assessing the efficacy of various therapeutic strategies and their unexpected side effects. We describe a video-based behavior-recognition technology to analyze home-cage behaviors and demonstrate its power by discovering previously unrecognized features of two already extensively characterized mouse models of neurodegenerative disease. The severe motor abnormalities in Huntington's disease mice manifested in our analysis by decreased hanging, jumping, stretching, and rearing. Surprisingly, behaviors such as resting and grooming were also affected. Unexpectedly, mice with infectious prion disease showed profound increases in activity at disease onset: rearing increased 2.5-fold, walking 10-fold and jumping 30-fold. Strikingly, distinct behaviors were altered specifically during day or night hours. We devised a systems approach for multiple-parameter phenotypic characterization and applied it to defining disease onset robustly and at early time points.
对小鼠行为进行自动化分析对于阐明行为的遗传决定因素、全面分析人类疾病模型以及评估各种治疗策略的疗效及其意外副作用至关重要。我们描述了一种基于视频的行为识别技术,用于分析笼内行为,并通过发现两种已被广泛研究的神经退行性疾病小鼠模型中以前未被认识到的特征来展示其功效。我们的分析表明,亨廷顿舞蹈症小鼠的严重运动异常表现为悬挂、跳跃、伸展和直立行为减少。令人惊讶的是,休息和梳理等行为也受到了影响。出乎意料的是,感染朊病毒疾病的小鼠在疾病发作时活动显著增加:直立行为增加了2.5倍,行走增加了10倍,跳跃增加了30倍。引人注目的是,不同的行为在白天或夜晚特定时间发生了改变。我们设计了一种用于多参数表型特征描述的系统方法,并将其应用于在早期时间点稳健地定义疾病发作。