Automated analysis of long-term grooming behavior in Drosophila using a k -nearest neighbors classifier

ELife
Bing QiaoSheyum Syed

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 Drosophila melanogaster. In our method, a k-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 cycle, clock, and period. The second program regulates the duration of grooming and, while dependent on cycle and clock, appears to be independent of period. 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 Drosophila.

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Citations

Apr 9, 2019·Journal of Neurogenetics·Yuya OnoderaNobuhiro Yamagata
Feb 26, 2019·Science Advances·Quentin GeissmannGiorgio F Gilestro
Dec 24, 2018·Nature Methods·Talmo D PereiraJoshua W Shaevitz
Jun 27, 2019·PLoS Computational Biology·Joshua M MuellerJean M Carlson
Sep 23, 2021·Journal of Biological Rhythms·Carson V FulghamDaniel J Cavanaugh

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Methods Mentioned

BETA
feature extraction
dissection
fluorescence microscopy
genetic modifications

Software Mentioned

Matlab

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