A simple, freely available analysis tool and hardware for analyzing fruit flies allows opportunities for researchers in low-income settings or out in the field to screen for genes and nerve circuits underlying complex social behaviors.
The fruit fly (Drosophila melanogaster). Image credit: Mohamed Nuzrath from Pixabay
The study, published today as a Reviewed Preprint in eLife, presents what the editors describe as a valuable open-source and cost-effective method for automating the quantification of male aggression and courtship in the fruit fly (Drosophila melanogaster). They say the authors provide solid evidence that their behavioral setup, which uses readily available laboratory equipment and high-performing behavior classifiers they developed using existing software packages, can accurately and reliably characterize social behaviors in Drosophila. The work will be of interest to Drosophila neurobiologists and particularly to those working on male social behaviors.
Drosophila is one of the most well-studied model organisms for identifying nerve and gene underpinnings of social behaviors such as aggression and courtship. These behaviors exhibit stereotypical patterns such as chasing, lunging and tussling in the case of aggression, and wing extension, orienting, circling, following, and attempted and successful copulation during courtship.
Analysis by expert observers is the gold-standard method for measuring these behaviors, but it is time-consuming and unsuitable for large-scale experiments. Automating the analysis using machine learning has enabled high-throughput screening from hundreds to thousands of video frames to identify responsible genes and nerve circuits, but the models are imperfect and involve expensive experimental setups.
“There are now several types of analysis software available for classifying fruit fly social behaviors, but most use rigid rules-based classification, which results in mis-scoring behaviors or identity switching of individual fruit flies,” says lead author Sai Prathap Yadav, a PhD student at the Centre for Molecular Neurosciences, Kasturba Medical College, Manipal, India. “An alternative method is ‘supervised learning’ in which an expert-analyzed dataset is used to train a computer to predict behaviors, but although such a tool exists, the trained classifiers are not publicly available.”
To address this, the authors developed the Drosophila Aggression and Courtship Evaluator, or DANCE, a set of freely available automated software tools – based on previous supervised learning methods – for analyzing six different fruit fly behaviors.
To study aggression, they developed a ‘classifier’ that could identify lunges – defined as a male fly raising its front legs and hitting down on another fly. Other computational methods tend to underscore lunges, but the DANCE classifier was as good as expert manual scoring at identifying this behavior across all types of lunges, from low level-aggression to hyper-aggression.
“To quantify courtship behaviors, classifiers were developed for five different behaviors – which together were designed to provide a more flexible way of identifying courtship compared to conventional ‘rules-based’ algorithms,” says co-lead author Paulami Dey, a master’s thesis student at the Centre for Molecular Neurosciences, Kasturba Medical College.
These classifiers identified male fly behaviors such as wing extension, attempted copulation, successful copulation, circling and following the female flies. The DANCE classifiers were comparable with the gold-standard manual scoring across all behaviors, whereas the conventional computational method tended to underscore or overscore these behaviors.
Existing setups for conducting fly behavior experiments require complex and specialized hardware, necessitating access to sophisticated machine shops or 3D printing. For example, setting up an aggression assay requires coating chambers with a substance to prevent flies walking on the walls, and recording the videos usually requires expensive machine vision cameras and backlights.
As an alternative, the team used smartphone cameras for filming with an e-tablet providing a bright light source, and created chambers from repurposed medicine blister packs. An easily sourced acrylic sheet was used to protect the flies from the heat of the smartphone/tablet screens. Altogether this cost less than $0.30 (USD), about 10,000-fold cheaper than existing setups.
To ensure the new hardware did not impact on the flies’ aggression and courtship behavior, the team benchmarked the results of experiments with the new hardware with previous studies. This showed that the hardware reproduced past experimental results, such as aggression and courtship behaviors when flies were housed singly or in groups, or in response to food availability. The new setup also faithfully reproduced the results of RNA interference knockdown experiments, showing that the DANCE setup can be used with common neurogenetic tools used to study fruit flies. The team also used optogenetic silencing to show that a group of dopaminergic neurons mediate aggression, which was not demonstrated before.
“The DANCE assay is an easy-to-use, robust analysis pipeline and inexpensive hardware to record and quantify aggression and courtship behaviors in fruit flies, with comparable performance to more specialized and expensive setups,” concludes senior author Pavan Agrawal, Assistant Professor at the Centre for Molecular Neurosciences, Kasturba Medical College. “DANCE enables rapid behavioral screening and wider adoption by the neuroscience community, including in resource-limited settings, and can serve as a template for studying complex behaviors of other insects. Its ease of adaptability and portability should also make it useful for studying insect behaviors closer to their natural habitat.”
A figure depicting aggression and courtship behaviors of Drosophila fruit flies analyzed with the DANCE method, demonstrating differences from existing methods. Image credit: Yadav, Dey et al. (CC BY 4.0). Created in BioRender. LC, P. (2025) https://BioRender.com/9iaak8f
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