Keleş Mehmet F, Sapci Ali Osman Berk, Brody Casey, Palmer Isabelle, Mehta Anuradha, Ahmadi Shahin, Le Christin, Taştan Öznur, Keleş Sündüz, Wu Mark N
Department of Neurology, Johns Hopkins University, Baltimore, MD 21205, USA.
Department of Computer Science, Sabanci University, Tuzla, Istanbul 34956, Turkey.
Sci Adv. 2025 Mar 14;11(11):eadq8131. doi: 10.1126/sciadv.adq8131. Epub 2025 Mar 12.
There is great interest in using genetically tractable organisms such as to gain insights into the regulation and function of sleep. However, sleep phenotyping in has largely relied on simple measures of locomotor inactivity. Here, we present FlyVISTA, a machine learning platform to perform deep phenotyping of sleep in flies. This platform comprises a high-resolution closed-loop video imaging system, coupled with a deep learning network to annotate 35 body parts, and a computational pipeline to extract behaviors from high-dimensional data. FlyVISTA reveals the distinct spatiotemporal dynamics of sleep and wake-associated microbehaviors at baseline, following administration of the sleep-inducing drug gaboxadol, and with dorsal fan-shaped body drivers. We identify a microbehavior ("haltere switch") exclusively seen during quiescence that indicates a deeper sleep stage. These results enable the rigorous analysis of sleep in and set the stage for computational analyses of microbehaviors in quiescent animals.
人们对利用诸如果蝇这样易于进行基因操作的生物体来深入了解睡眠的调节和功能有着浓厚的兴趣。然而，果蝇的睡眠表型分析在很大程度上依赖于对运动不活跃的简单测量。在此，我们展示了FlyVISTA，这是一个用于对果蝇睡眠进行深度表型分析的机器学习平台。该平台包括一个高分辨率闭环视频成像系统，与一个用于注释35个身体部位的深度学习网络相结合，以及一个从高维数据中提取行为的计算管道。FlyVISTA揭示了在基线状态下、给予促眠药物加波沙朵后以及利用背侧扇形体驱动子时，睡眠和与觉醒相关的微行为的独特时空动态。我们识别出一种仅在静止期出现的微行为（“平衡棒切换”），它表明处于更深的睡眠阶段。这些结果使得能够对果蝇的睡眠进行严谨分析，并为静止动物微行为的计算分析奠定了基础。
