Automated analysis of long-term grooming behavior in <i>Drosophila</i> using a <i>k</i>-nearest neighbors classifier.

Abstract

Despite being pervasive, the control of programmed grooming is poorly understood. We addressed this gap by developing a high-throughput platform that allows long-term detection of grooming in <i>Drosophila melanogaster</i>. In our method, a <i>k</i>-nearest neighbors algorithm automatically classifies fly behavior and finds grooming events with over 90% accuracy in diverse genotypes. Our data show that flies spend ~13% of their waking time grooming, driven largely by two major internal programs. One of these programs regulates the timing of grooming and involves the core circadian clock components <i>cycle</i>, <i>clock</i>, and <i>period</i>. The second program regulates the duration of grooming and, while dependent on <i>cycle</i> and <i>clock</i>, appears to be independent of <i>period</i>. This emerging dual control model in which one program controls timing and another controls duration, resembles the two-process regulatory model of sleep. Together, our quantitative approach presents the opportunity for further dissection of mechanisms controlling long-term grooming in <i>Drosophila</i>.