A Novel Computational Approach for Simultaneous Tracking and Feature Extraction of C. elegans Populations in Fluid Environments

The nematode Caenorhabditis elegans (C. elegans) is a genetic model widely used to dissect conserved basic biological mechanisms of development and nervous system function. C. elegans locomotion is under complex neuronal regulation and is impacted by genetic and environmental factors; thus, its analysis is expected to shed light on how genetic, environmental, and pathophysiological processes control behavior. To date, computer-based approaches have been used for analysis of C. elegans locomotion; however, none of these is both high resolution and high throughput. We used computer vision methods to develop a novel automated approach for analyzing the C. elegans locomotion. Our method provides information on the position, trajectory, and body shape during locomotion and is designed to efficiently track multiple animals (C. elegans) in cluttered images and under lighting variations. We used this method to describe in detail C. elegans movement in liquid for the first time and to analyze six unc-8, one mec-4, and one odr-1 mutants. We report features of nematode swimming not previously noted and show that our method detects differences in the swimming profile of mutants that appear at first glance similar.