A multi-animal tracker for studying complex behaviors

BMC Biology

Methodology Article

Amir Levine¹  Ehud Cohen¹  Alon Zaslaver²  Eyal Itskovits³ 

[1] Biochemistry and Molecular Biology, The Institute for Medical Research Israel – Canada (IMRIC), School of Medicine, The Hebrew University of Jerusalem, 91120, Jerusalem, Israel;Department of Genetics, The Silberman Institute of Life Science, Edmond J. Safra Campus, The Hebrew University of Jerusalem, 91904, Jerusalem, Israel;Department of Genetics, The Silberman Institute of Life Science, Edmond J. Safra Campus, The Hebrew University of Jerusalem, 91904, Jerusalem, Israel;School of Computer Science and Engineering, Hebrew University, Jerusalem, Israel;

关键词: Multi-animal tracking;    Image analyses;    Chemotaxis;    C. elegans;    Locomotion;   

DOI  :  10.1186/s12915-017-0363-9

 received in 2017-02-09, accepted in 2017-03-06,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundAnimals exhibit astonishingly complex behaviors. Studying the subtle features of these behaviors requires quantitative, high-throughput, and accurate systems that can cope with the often rich perplexing data.ResultsHere, we present a Multi-Animal Tracker (MAT) that provides a user-friendly, end-to-end solution for imaging, tracking, and analyzing complex behaviors of multiple animals simultaneously. At the core of the tracker is a machine learning algorithm that provides immense flexibility to image various animals (e.g., worms, flies, zebrafish, etc.) under different experimental setups and conditions. Focusing on C. elegans worms, we demonstrate the vast advantages of using this MAT in studying complex behaviors. Beginning with chemotaxis, we show that approximately 100 animals can be tracked simultaneously, providing rich behavioral data. Interestingly, we reveal that worms’ directional changes are biased, rather than random – a strategy that significantly enhances chemotaxis performance. Next, we show that worms can integrate environmental information and that directional changes mediate the enhanced chemotaxis towards richer environments. Finally, offering high-throughput and accurate tracking, we show that the system is highly suitable for longitudinal studies of aging- and proteotoxicity-associated locomotion deficits, enabling large-scale drug and genetic screens.ConclusionsTogether, our tracker provides a powerful and simple system to study complex behaviors in a quantitative, high-throughput, and accurate manner.

【 授权许可】

CC BY   
© Zaslaver et al. 2017