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使用开源MATLAB应用程序对多孔板中斑马鱼幼体运动功能进行量化分析。

Quantification of larval zebrafish motor function in multiwell plates using open-source MATLAB applications.

作者信息

Zhou Yangzhong, Cattley Richard T, Cario Clinton L, Bai Qing, Burton Edward A

机构信息

1] Pittsburgh Institute for Neurodegenerative Diseases, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. [2] Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. [3] Tsinghua University Medical School, Beijing, China.

1] Pittsburgh Institute for Neurodegenerative Diseases, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. [2] Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

出版信息

Nat Protoc. 2014 Jul;9(7):1533-48. doi: 10.1038/nprot.2014.094. Epub 2014 Jun 5.

Abstract

This article describes a method to quantify the movements of larval zebrafish in multiwell plates, using the open-source MATLAB applications LSRtrack and LSRanalyze. The protocol comprises four stages: generation of high-quality, flatly illuminated video recordings with exposure settings that facilitate object recognition; analysis of the resulting recordings using tools provided in LSRtrack to optimize tracking accuracy and motion detection; analysis of tracking data using LSRanalyze or custom MATLAB scripts; and implementation of validation controls. The method is reliable, automated and flexible, requires <1 h of hands-on work for completion once optimized and shows excellent signal:noise characteristics. The resulting data can be analyzed to determine the following: positional preference; displacement, velocity and acceleration; and duration and frequency of movement events and rest periods. This approach is widely applicable to the analysis of spontaneous or stimulus-evoked zebrafish larval neurobehavioral phenotypes resulting from a broad array of genetic and environmental manipulations, in a multiwell plate format suitable for high-throughput applications.

摘要

本文介绍了一种使用开源MATLAB应用程序LSRtrack和LSRanalyze来量化多孔板中斑马鱼幼体运动的方法。该方案包括四个阶段:使用便于物体识别的曝光设置生成高质量、光照均匀的视频记录;使用LSRtrack中提供的工具分析所得记录,以优化跟踪精度和运动检测;使用LSRanalyze或自定义MATLAB脚本分析跟踪数据;以及实施验证控制。该方法可靠、自动化且灵活,一旦优化,完成操作只需不到1小时的实际操作时间,并且具有出色的信噪比特性。所得数据可用于分析以确定以下内容:位置偏好;位移、速度和加速度;以及运动事件和休息期的持续时间和频率。这种方法广泛适用于分析由多种遗传和环境操作导致的自发或刺激诱发的斑马鱼幼体神经行为表型,采用适合高通量应用的多孔板形式。

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