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Deep Phenotyping of Sleep in Drosophila.


ABSTRACT: Sleep is an evolutionarily conserved behavior, whose function is unknown. Here, we present a method for deep phenotyping of sleep in Drosophila, consisting of a high-resolution video imaging system, coupled with closed-loop laser perturbation to measure arousal threshold. To quantify sleep-associated microbehaviors, we trained a deep-learning network to annotate body parts in freely moving flies and developed a semi-supervised computational pipeline to classify behaviors. Quiescent flies exhibit a rich repertoire of microbehaviors, including proboscis pumping (PP) and haltere switches, which vary dynamically across the night. Using this system, we characterized the effects of optogenetically activating two putative sleep circuits. These data reveal that activating dFB neurons produces micromovements, inconsistent with sleep, while activating R5 neurons triggers PP followed by behavioral quiescence. Our findings suggest that sleep in Drosophila is polyphasic with different stages and set the stage for a rigorous analysis of sleep and other behaviors in this species.

SUBMITTER: Keles MF 

PROVIDER: S-EPMC10635029 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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FlyVISTA, an Integrated Machine Learning Platform for Deep Phenotyping of Sleep in <i>Drosophila</i>.

Keleş Mehmet F MF   Sapci Ali Osman Berk AOB   Brody Casey C   Palmer Isabelle I   Le Christin C   Taştan Öznur Ö   Keleş Sündüz S   Wu Mark N MN  

bioRxiv : the preprint server for biology 20240625


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 comp  ...[more]

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