Abstract
Aggression is a complex social behavior that remains poorly understood. Drosophila has become a powerful model system to study the underlying biology of aggression but lack of high throughput screening and analysis continues to be a barrier for comprehensive mutant and circuit discovery. Here we developed the Divider Assay, a simplified experimental procedure to make aggression analysis in Drosophila fast and accurate. In contrast to existing methods, we can analyze aggression over long time intervals and in complete darkness. While aggression is reduced in the dark, flies are capable of intense fighting without seeing their opponent. Twenty-four-hour behavioral analysis showed a peak in fighting during the middle of the day, a drastic drop at night, followed by re-engagement with a further increase in aggression in anticipation of the next day. Our pipeline is easy to implement and will facilitate high throughput screening for mechanistic dissection of aggression.
Highlights
Aggression is a complex social behavior that remains poorly understood
Using an existing machine-learning paradigm (JAABA)[36], we developed a classifier to precisely quantify lunging behavior, even during the high intensity boxing bouts, when both flies engage in rapid mutual lunging
We processed the behavioral recordings in three different ways to measure lunge numbers (Fig. 1b): (1) we manually counted lunge numbers derived from 60 pairs with varying fighting intensities through slow-motion video analysis; (2) we developed our own quantification pipeline taking advantage of existing fly-tracking software (FlyTracker)[35] and machine-learning paradigm (JAABA)[36] with an added filtering step; and (3) we used CADABRA, which is an existing automated software system that depends in part on rule-based quantification[33] (Fig. 1b)
Summary
Drosophila has become a powerful model system to study the underlying biology of aggression but lack of high throughput screening and analysis continues to be a barrier for comprehensive mutant and circuit discovery. In contrast to existing methods, we can analyze aggression over long time intervals and in complete darkness. High-throughput screens depend on assays that are fast and accurate, but such assays are harder to design for complex behaviors such as aggression. Using an existing machine-learning paradigm (JAABA)[36], we developed a classifier to precisely quantify lunging behavior, even during the high intensity boxing bouts, when both flies engage in rapid mutual lunging. Our pipeline provides a standardized method to measure aggression and will dramatically improve screening for mutant and circuit discovery
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