Buske Christine, Gerlai Robert
Papers/Springer SBM, London, UK (previously: University of Toronto, Department of Cell & Systems Biology); University of Toronto Mississauga, Department of Psychology, Toronto, Canada.
University of Toronto Mississauga, Department of Psychology, Toronto, Canada.
J Neurosci Methods. 2014 Aug 30;234:66-72. doi: 10.1016/j.jneumeth.2014.06.019. Epub 2014 Jun 23.
Vertebrate model organisms have been utilized in high throughput screening but only with substantial cost and human capital investment. The zebrafish is a vertebrate model species that is a promising and cost effective candidate for efficient high throughput screening. Larval zebrafish have already been successfully employed in this regard (Lessman, 2011), but adult zebrafish also show great promise. High throughput screening requires the use of a large number of subjects and collection of substantial amount of data. Collection of data is only one of the demanding aspects of screening. However, in most screening approaches that involve behavioral data the main bottleneck that slows throughput is the time consuming aspect of analysis of the collected data. Some automated analytical tools do exist, but often they only work for one subject at a time, eliminating the possibility of fully utilizing zebrafish as a screening tool. This is a particularly important limitation for such complex phenotypes as social behavior. Testing multiple fish at a time can reveal complex social interactions but it may also allow the identification of outliers from a group of mutagenized or pharmacologically treated fish. Here, we describe a novel method using a custom software tool developed within our laboratory, which enables tracking multiple fish, in combination with a sophisticated analytical approach for summarizing and analyzing high resolution behavioral data. This paper focuses on the latter, the analytic tool, which we have developed using the R programming language and environment for statistical computing. We argue that combining sophisticated data collection methods with appropriate analytical tools will propel zebrafish into the future of neurobehavioral genetic research.
脊椎动物模型生物已被用于高通量筛选，但这需要大量的成本和人力资本投入。斑马鱼是一种脊椎动物模型物种，是高效高通量筛选中一个有前景且成本效益高的候选者。斑马鱼幼体已在这方面成功应用（莱斯曼，2011年），但成年斑马鱼也显示出巨大潜力。高通量筛选需要使用大量的实验对象并收集大量数据。数据收集只是筛选中要求较高的一个方面。然而，在大多数涉及行为数据的筛选方法中，减缓通量的主要瓶颈是对收集到的数据进行分析耗时。虽然确实存在一些自动化分析工具，但它们通常一次只能处理一个实验对象，从而排除了充分利用斑马鱼作为筛选工具的可能性。对于诸如社会行为等复杂表型而言，这是一个特别重要的限制。一次测试多条鱼可以揭示复杂的社会互动，但也可能从一组诱变或药物处理的鱼中识别出异常值。在此，我们描述一种新方法，该方法使用我们实验室开发的定制软件工具，能够追踪多条鱼，并结合一种复杂的分析方法来总结和分析高分辨率行为数据。本文重点关注后者，即我们使用R编程语言和统计计算环境开发的分析工具。我们认为，将复杂的数据收集方法与适当的分析工具相结合，将推动斑马鱼在神经行为遗传学研究领域走向未来。
