Fully automated leg tracking of Drosophila neurodegeneration models reveals distinct conserved movement signatures

• Published in: PLoS biology Vol. 17; no. 6; p. e3000346
• Main Authors: Wu, Shuang, Tan, Kah Junn, Govindarajan, Lakshmi Narasimhan, Stewart, James Charles, Gu, Lin, Ho, Joses Wei Hao, Katarya, Malvika, Wong, Boon Hui, Tan, Eng-King, Li, Daiqin, Claridge-Chang, Adam, Libedinsky, Camilo, Cheng, Li, Aw, Sherry Shiying
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 27.06.2019; Public Library of Science (PLoS)
• Subjects: Animals; Artificial intelligence; Ataxia; Automation; Bioinformatics; Biology; Biology and Life Sciences; Computer and Information Sciences; Disease; Disease Models, Animal; Dopamine receptors; Drosophila; Drosophila melanogaster - anatomy & histology; Drosophila melanogaster - physiology; Drosophila Proteins - metabolism; Engineering and Technology; Etiology; Extremities; Funding; Gait; Gait - physiology; Gait Analysis - methods; Image processing; Image Processing, Computer-Assisted - instrumentation; Image Processing, Computer-Assisted - methods; Image segmentation; Insects; Learning algorithms; Leg; Machado-Joseph Disease; Machine Learning; Medical imaging; Medicine and Health Sciences; Methods; Methods and Resources; Molecular modelling; Movement - physiology; Movement disorders; Mutants; Mutation; Neurodegeneration; Neurodegenerative diseases; Neurodegenerative Diseases - genetics; Neurodegenerative Diseases - physiopathology; Neurological diseases; Neurons; Neurosciences; Parkinson Disease; Parkinson's disease; Phenotypes; Physiology; Research and Analysis Methods; Signatures; Software; Tremor; Tremors; Walking; Singapore
• ISSN: 1545-7885; 1544-9173; 1545-7885
• DOI: 10.1371/journal.pbio.3000346

Some neurodegenerative diseases, like Parkinsons Disease (PD) and Spinocerebellar ataxia 3 (SCA3), are associated with distinct, altered gait and tremor movements that are reflective of the underlying disease etiology. Drosophila melanogaster models of neurodegeneration have illuminated our understanding of the molecular mechanisms of disease. However, it is unknown whether specific gait and tremor dysfunctions also occur in fly disease mutants. To answer this question, we developed a machine-learning image-analysis program, Feature Learning-based LImb segmentation and Tracking (FLLIT), that automatically tracks leg claw positions of freely moving flies recorded on high-speed video, producing a series of gait measurements. Notably, unlike other machine-learning methods, FLLIT generates its own training sets and does not require user-annotated images for learning. Using FLLIT, we carried out high-throughput and high-resolution analysis of gait and tremor features in Drosophila neurodegeneration mutants for the first time. We found that fly models of PD and SCA3 exhibited markedly different walking gait and tremor signatures, which recapitulated characteristics of the respective human diseases. Selective expression of mutant SCA3 in dopaminergic neurons led to a gait signature that more closely resembled those of PD flies. This suggests that the behavioral phenotype depends on the neurons affected rather than the specific nature of the mutation. Different mutations produced tremors in distinct leg pairs, indicating that different motor circuits were affected. Using this approach, fly models can be used to dissect the neurogenetic mechanisms that underlie movement disorders.