Abstract
Genetic models in Drosophila have made invaluable contributions to our understanding of the molecular mechanisms underlying neurodegeneration. In human patients, some neurodegenerative diseases lead to characteristic movement dysfunctions, such as abnormal gait and tremors. However, it is currently unknown whether similar movement defects occur in the respective fly models, which could be used to model and better understand the pathophysiology of movement disorders. To address this question, we developed a machine-learning image-analysis programme - Feature Learning-based LImb segmentation and Tracking (FLLIT) - that automatically tracks leg claw positions of freely moving flies recorded on high-speed video, generating a series of body and leg movement parameters. Of note, FLLIT requires no user input for learning. We used FLLIT to characterise fly models of Parkinson's Disease (PD) and Spinocerebellar ataxia 3 (SCA3). Between these models, walking gait and tremor characteristics differed markedly, and recapitulated signatures of the respective human diseases. Selective expression of mutant SCA3 in dopaminergic neurons led to phenotypes resembling that of PD flies, suggesting that the behavioural phenotype may depend on the circuits affected, rather than the specific nature of the mutation. Different mutations produced tremors in distinct leg pairs, indicating that different motor circuits are affected. Almost 190,000 video frames were tracked in this study, allowing, for the first time, high-throughput analysis of gait and tremor features in Drosophila mutants. As an efficient assay of mutant gait and tremor features in an important model system, FLLIT will enable the analysis of the neurogenetic mechanisms that underlie movement disorders.
