bioRxivDevelopmental BiologyPreprint
Simple Methods to Acutely Measure Multiple Timing Metrics among Sexual Repertoire of Male Drosophila
Yutong SongiD, Hongyu MiaoiD, Dongyu Sun, Xiao LiuiD, Fan Jiang, Xuejiao YangiD, Woo Jae KimiD
- Citations
- 1
Abstract
Male Drosophila courtship behavior is a key model for studying temporal decision-making. While courtship index (CI) is widely used to quantify mating activity, other timing metrics like courtship latency, copulation latency (CL), and mating duration (MD) remain understudied. Traditional methods for quantifying these behaviors are often labor-intensive and prone to human error. In this study, we present a protocol combining a modular chamber system and automated software (DrosoMating) to quantify 6 key timing metrics during male courtship. Our image-based video analysis enables precise identification of courtship and copulation events, as well as quantification of their timing and duration under controlled conditions. Validation shows <0.05% error rates and 98-99% agreement with manual scoring for CL, CI, and MD. The protocol supports genetic and neural circuit manipulations, detecting subtle genetic, social, and environmental behavioral variations. By minimizing manual effort and standardizing data collection, this approach facilitates scalable, reproducible studies on adaptive trade-offs, learning, and neural mechanisms in mating behavior. This method streamlines timing analysis in male courtship, offering reproducible metrics for behavioral genetics. HighlightA high-throughput software pipeline for automated temporal profiling of Drosophila melanogaster mating behavior after brief user-guided calibration. An accompanying, open-hardware platform that can be assembled at minimal cost while maintaining experimental rigor. The system attains near-manual accuracy and outputs Temporal Measurement Parameters data that are readily adaptable to--and quantifiable within--diverse behavioral paradigms.
- organism: Drosophila
- assay: imaging
- research focus: cell type annotation
- research focus: clustering
- research focus: dimensionality reduction
- approach: experimental assay
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