Cao Wenze, Song Li, Cheng Jingjing, Yi Na, Cai Luyi, Huang Fu-de, Ho Margaret
Research Center for Translational Medicine, Tongji University School of Medicine; Key Laboratory of Arrhythmias of the Ministry of Education of China, Shanghai East Hospital, Tongji University School of Medicine; Department of Anatomy and Neurobiology, Tongji University School of Medicine.
Shanghai Advanced Research Institute, University of Chinese Academy of Sciences, Chinese Academy of Sciences; Sino-Danish College, University of Chinese Academy of Sciences, Chinese Academy of Sciences.
J Vis Exp. 2017 Sep 12(127):56507. doi: 10.3791/56507.
Neurodegenerative diseases are frequently associated with a progressive loss of movement ability, reduced life span, and age-dependent neurodegeneration. To understand the mechanism of these cellular events, and their causal relationships with each other, Drosophila melanogaster, with its sophisticated genetic tools and diverse behavioral features, are used as disease models for assessing neurodegenerative phenotypes. Here we describe a high-throughput method to analyze Drosophila adult negative geotaxis behavior, as an indication for possible motor defects associated with neurodegeneration. An automated machine is designed and developed to drive fly synchronization using an initial electric impulse, later allowing the recording of negative geotaxis behavior over a course of secs to mins. Images from the digitally recorded video are then processed with the self-designed RflyDetection software for statistical data manipulation. Different from the manually controlled negative geotaxis assay based on single fly, this precise, fast, and high-throughput protocol allows data acquisition from more than hundreds of flies simultaneously, providing an efficient approach to advance our understanding in the underlying mechanism of locomotor deficits associated with neurodegeneration.
神经退行性疾病常常与运动能力的逐渐丧失、寿命缩短以及年龄依赖性神经变性相关。为了理解这些细胞事件的机制及其相互之间的因果关系，具有精密遗传工具和多样行为特征的黑腹果蝇被用作评估神经退行性表型的疾病模型。在此，我们描述一种高通量方法来分析果蝇成虫的负趋地性运动行为，以此作为与神经变性相关的可能运动缺陷的指标。设计并开发了一种自动化机器，利用初始电脉冲来驱动果蝇同步化，随后在数秒到数分钟的过程中记录负趋地性行为。然后，使用自行设计的RflyDetection软件对数字记录视频中的图像进行处理，以进行统计数据操作。与基于单只果蝇的手动控制负趋地性试验不同，这种精确、快速且高通量的方案允许同时从数百只果蝇获取数据，为推进我们对与神经变性相关的运动缺陷潜在机制的理解提供了一种有效方法。
