FlyVISTA, an integrated machine learning platform for deep phenotyping of sleep in <i>Drosophila</i>.

Abstract

There is great interest in using genetically tractable organisms such as <i>Drosophila</i> to gain insights into the regulation and function of sleep. However, sleep phenotyping in <i>Drosophila</i> 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 <i>Drosophila</i> and set the stage for computational analyses of microbehaviors in quiescent animals.