High Throughput Analysis of \({Caenorhabditis elegans}\) Odor Learning-Dependent Navigation

Abstract

Upon learning an animal acquires a different response to the same stimulus. Although learning is extensively studied in the neuroscience community, the detailed cellular and circuit mechanisms of learning remain unknown in all but the simplest cases. Yet, before one investigates how the brain changes when learning, it is helpful to first understand exactly how behavior changes as a result of learning. Here we study the nematode Caenorhabditis elegans, a small, transparent nematode that has only 302 neurons. Previous research in C. elegans had established a food-odor associative learning paradigm whereby the animal learns to either ignore or navigate towards an odor depending on the animal’s learned association. The goal of this work is to understand in richer detail how the animal navigates towards or away from the odor, so as to generate new hypotheses about what changes in the brain upon learning. This work combines the classic butanone learning assay from Torayama et al. (2007) with high-throughput video recording and computer vision behavioral analysis developed in our lab (Liu et al. 2018) to capture detailed trajectories of single animals. We modified these techniques and video-recording hardware to capture each individual animal’s trajectory during the butanone learning experiment to collectively understand the animals’ navigational behavior. During this project, we confirmed the results of a previous chemotaxis assay done on C. elegans by Torayama et al. (2007) in naïve, averse (butanone is associated with starvation), and appetitive (butanone is associated with food) conditions. We then completed a video analysis on appetitive-learned animals and observed quantitative differences in their behavior in the presence of a learned-odor target versus naïve animals. Here, we analyzed the tracks based on population-level trajectories and end points. This analysis forms the foundation for future projects in the lab that will investigate mechanisms by which neural circuitry for navigation changes upon learning.