Computational Methods for Tracking, Quantitative Assessment, and Visualization of C. elegans Locomotory Behavior

• Published in: PloS one Vol. 10; no. 12; p. e0145870
• Main Authors: Moy, Kyle, Li, Weiyu, Tran, Huu Phuoc, Simonis, Valerie, Story, Evan, Brandon, Christopher, Furst, Jacob, Raicu, Daniela, Kim, Hongkyun
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 29.12.2015; Public Library of Science (PLoS)
• Subjects: Algorithms; Animal behavior; Animals; Bacteria; Behavior; Behavior, Animal; Brownian motion; Brownian movements; Caenorhabditis elegans; Caenorhabditis elegans - genetics; Caenorhabditis elegans - physiology; Caenorhabditis elegans Proteins - genetics; Circuits; Computation; Computational neuroscience; Dietary restrictions; Digital broadcasting; Exploratory behavior; Flight behavior; Food; Freeware; Locomotion; Mathematical analysis; Methods; Mutation; Nematodes; Nervous system; Neural circuitry; Neural networks; Neurons; Numerical analysis; Programming Languages; Researchers; Robustness (mathematics); Roundworms; Searching; Serotonin; Software; Source code; Tracking; Tracking systems; Wildlife conservation; Worms
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0145870

The nematode Caenorhabditis elegans provides a unique opportunity to interrogate the neural basis of behavior at single neuron resolution. In C. elegans, neural circuits that control behaviors can be formulated based on its complete neural connection map, and easily assessed by applying advanced genetic tools that allow for modulation in the activity of specific neurons. Importantly, C. elegans exhibits several elaborate behaviors that can be empirically quantified and analyzed, thus providing a means to assess the contribution of specific neural circuits to behavioral output. Particularly, locomotory behavior can be recorded and analyzed with computational and mathematical tools. Here, we describe a robust single worm-tracking system, which is based on the open-source Python programming language, and an analysis system, which implements path-related algorithms. Our tracking system was designed to accommodate worms that explore a large area with frequent turns and reversals at high speeds. As a proof of principle, we used our tracker to record the movements of wild-type animals that were freshly removed from abundant bacterial food, and determined how wild-type animals change locomotory behavior over a long period of time. Consistent with previous findings, we observed that wild-type animals show a transition from area-restricted local search to global search over time. Intriguingly, we found that wild-type animals initially exhibit short, random movements interrupted by infrequent long trajectories. This movement pattern often coincides with local/global search behavior, and visually resembles Lévy flight search, a search behavior conserved across species. Our mathematical analysis showed that while most of the animals exhibited Brownian walks, approximately 20% of the animals exhibited Lévy flights, indicating that C. elegans can use Lévy flights for efficient food search. In summary, our tracker and analysis software will help analyze the neural basis of the alteration and transition of C. elegans locomotory behavior in a food-deprived condition.