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

Abstract

In the effort to define genes and specific neuronal circuits that control behavior and plasticity, the capacity for high-precision automated analysis of behavior is essential. We report on comprehensive computer vision software for analysis of swimming locomotion of C. elegans, a simple animal model initially developed to facilitate elaboration of genetic influences on behavior. C. elegans swim test software CeleST tracks swimming of multiple animals, measures 10 novel parameters of swim behavior that can fully report dynamic changes in posture and speed, and generates data in several analysis formats, complete with statistics. Our measures of swim locomotion utilize a deformable model approach and a novel mathematical analysis of curvature maps that enable even irregular patterns and dynamic changes to be scored without need for thresholding or dropping outlier swimmers from study. Operation of CeleST is mostly automated and only requires minimal investigator interventions, such as the selection of videotaped swim trials and choice of data output format. Data can be analyzed from the level of the single animal to populations of thousands. We document how the CeleST program reveals unexpected preferences for specific swim “gaits” in wild-type C. elegans, uncovers previously unknown mutant phenotypes, efficiently tracks changes in aging populations, and distinguishes “graceful” from poor aging. The sensitivity, dynamic range, and comprehensive nature of CeleST measures elevate swim locomotion analysis to a new level of ease, economy, and detail that enables behavioral plasticity resulting from genetic, cellular, or experience manipulation to be analyzed in ways not previously possible.

Automated Analysis of <em>C. elegans</em> Swim Behavior Using CeleST Software

“…In addition to the overall inevitable loss of physical performance due to age, each individual adult displays a unique progression pattern through the aging process, even when genetics and environment are virtually homogeneous 7 .…”

“…WT N2 animals move actively in elegant sinusoidal waves on agar-filled plates; they change movement rates when roaming (food seeking), dwelling (browsing), or recovering from a meal (inactive satiety quiescence) 5 . Impairment 6 or age [7][8][9][10][11][12] can also dramatically alter movement on plates.…”

“…One approach to measure healthspan is to classify aging adults into three categories (e.g., A, B, and C) according to their locomotion on plates, with vigorous physical activity being class A and paralysis being class C 7,8,13 . Although such qualitative sorting is useful to reveal differences in physical fitness, the categories are broad without clear boundaries and thus their scoring is subject to experimenter bias.…”

Vision-based Kinematic Modeling of a Worm's Posture

“…In addition, behavioral characteristics of worms are practically difficult to characterize manually [5]. To address these practical problems, particularly in the last decade, there have been attempts to develop an automated image analysis system using mature machine vision technologies [5,6]. Using this approach, it is possible to readily acquire the quantitative characteristics of worms over long time periods with little error or ignorance.…”

Deep phenotyping unveils hidden traits and genetic relations in subtle mutants

“…However, the rich information encoded in fluorescence images of multicellular models has not been fully exploited at high resolution (that is, characterization of subcellular features within a living multicellular organism, thus missing the identification and characterization of phenotypic changes of weak alleles2930). Although different approaches have been used to identify chemically or genetically induced phenotypes, these have typically either screened for severe changes or have focused on behavioural or anatomical changes53132333435363738.…”