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

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  • Christophe Restif
  • Carolina Ibáñez-Ventoso
  • Mehul M Vora
  • Suzhen Guo
  • Dimitris Metaxas
  • Monica Driscoll

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.

Suggested Citation

  • Christophe Restif & Carolina Ibáñez-Ventoso & Mehul M Vora & Suzhen Guo & Dimitris Metaxas & Monica Driscoll, 2014. "CeleST: Computer Vision Software for Quantitative Analysis of C. elegans Swim Behavior Reveals Novel Features of Locomotion," PLOS Computational Biology, Public Library of Science, vol. 10(7), pages 1-12, July.
    • Handle: RePEc:plo:pcbi00:1003702
    DOI: 10.1371/journal.pcbi.1003702
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    1. Laura A. Herndon & Peter J. Schmeissner & Justyna M. Dudaronek & Paula A. Brown & Kristin M. Listner & Yuko Sakano & Marie C. Paupard & David H. Hall & Monica Driscoll, 2002. "Stochastic and genetic factors influence tissue-specific decline in ageing C. elegans," Nature, Nature, vol. 419(6909), pages 808-814, October.
    2. Greg J Stephens & Bethany Johnson-Kerner & William Bialek & William S Ryu, 2008. "Dimensionality and Dynamics in the Behavior of C. elegans," PLOS Computational Biology, Public Library of Science, vol. 4(4), pages 1-10, April.
    3. Scott Ogg & Suzanne Paradis & Shoshanna Gottlieb & Garth I. Patterson & Linda Lee & Heidi A. Tissenbaum & Gary Ruvkun, 1997. "The Fork head transcription factor DAF-16 transduces insulin-like metabolic and longevity signals in C. elegans," Nature, Nature, vol. 389(6654), pages 994-999, October.
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    1. Li-Chun Lin & Han-Sheng Chuang, 2017. "Analyzing the locomotory gaitprint of Caenorhabditis elegans on the basis of empirical mode decomposition," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-14, July.

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