Tracking C. elegans swimming for high-throughput phenotyping

Abstract

The model organism C. elegans is widely used for phenotype studies, where the behaviors of large numbers of individuals have to be measured accurately. Swimming motions exhibit unique aspects of their behavior, but are significantly more difficult to track than crawling motions. Filming multiple worms is necessary for high-throughput phenotype experiments, but adds to the complexity of tracking. Here we present a novel method to segment and track multiple swimming worms. Segmentation is done by low-level-vision processing followed by articulated model fitting. Tracking is performed by iteratively adjusting the articulated models to the successive frames. We tested our method on 404 videos and 2020 worms, and report results of 94.1% of worms tracked, which are valid 92.1% of the time. The models we use enable a cheap computation of a variety of measures of behavior. We report a quantitative comparison of three mutants using existing and novel phenotype measures, showing statistically significant differences of behavior.

CeleST: Computer Vision Software for Quantitative Analysis of C. elegans Swim Behavior Reveals Novel Features of Locomotion

“…Previous work based on pixel counts [25]–[27] or curvature measures [14], [15] have definitively advanced capacity to evaluate swim locomotion. However, the most sophisticated approaches available to date stop short of automated analysis programs that are applicable to all animal postures and lack measurements that have a precise meaning in terms of behavior.…”

“…This capacity for modular applications increases the number of options for use of our software components, making CeleST a particularly valuable toolbox for biologists and software developers. Additional details on the tracking method CeleST uses can be found in refs [26] , [27] .…”

“…While crawling motions have been successfully defined and measured [4] – [11] , broadly applied approaches for more complex swimming motions are not well described in the literature. Previous work based on pixel counts [25] – [27] or curvature measures [14] , [15] have definitively advanced capacity to evaluate swim locomotion. However, the most sophisticated approaches available to date stop short of automated analysis programs that are applicable to all animal postures and lack measurements that have a precise meaning in terms of behavior.…”

Desarrollo de técnicas avanzadas de seguimiento de posturas para reconocimiento de comportamientos de C. elegans

“…Currently, there are many semi-automatic or automatic applications for predicting worm pose, and tracking single or multiple worms. These applications that use traditional computer vision techniques have shown good results in pose prediction [90,41], behavior analysis [6,83,73], locomotion [8], location of multiple worms [17], calculation of the average speed of multiple worms [70], and even using low-cost tools [45]. Pose prediction is a very important technique in tracking applications because it allows us to automate all these types of tests mentioned, lifespan and healthspan, among others.…”