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bioRxiv · 10.1101/2023.10.30.564733

Deep Phenotyping of Sleep in Drosophila

Keles, M. F.·Sapcı, A. O. B.·Brody, C.·Palmer, I.·Le, C.·Tastan, O.·Keles, S.·Wu, M.
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Abstract

Animal behavior depends on internal state. While subtle movements can signify significant changes in internal state, computational methods for analyzing these "microbehaviors" are lacking. Here, we present FlyVISTA, a machine-learning platform to characterize microbehaviors in freely-moving flies, which we use to perform deep phenotyping of sleep. 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-associated microbehaviors in flies. We further show that stimulation of dorsal fan-shaped body neurons induces micromovements, not sleep, whereas activating R5 ring neurons triggers rhythmic proboscis extension followed by persistent sleep. Importantly, we identify a novel microbehavior ("haltere switch") exclusively seen during quiescence that indicates a deeper sleep stage. These findings enable the rigorous analysis of sleep in Drosophila and set the stage for computational analyses of microbehaviors.

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Keles, M. F., Sapcı, A. O. B., Brody, C., Palmer, I., Le, C., Tastan, O., Keles, S., Wu, M.. 2023-11-02. Deep Phenotyping of Sleep in Drosophila. https://doi.org/10.1101/2023.10.30.564733

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