Track-A-Worm, An Open-Source System for Quantitative Assessment of C. elegans Locomotory and Bending Behavior

Under most circumstances, GABA activates chloride-selective channels and thereby inhibits neuronal activity. Here, we identify a GABA receptor in the nematode Caenorhabditis elegans that conducts cations and is therefore excitatory. Expression in Xenopus oocytes demonstrates that LGC-35 is a homopentameric cation-selective receptor of the cys-loop family exclusively activated by GABA. Phylogenetic analysis suggests that LGC-35 evolved from GABA-A receptors, but the pore-forming domain contains novel molecular determinants that confer cation selectivity. LGC-35 is expressed in muscles and directly mediates sphincter muscle contraction in the defecation cycle in hermaphrodites, and spicule eversion during mating in the male. In the locomotory circuit, GABA release directly activates chloride channels on the muscle to cause muscle relaxation. However, GABA spillover at these synapses activates LGC-35 on acetylcholine motor neurons, which in turn cause muscles to contract, presumably to drive wave propagation along the body. These studies demonstrate that both direct and indirect excitatory GABA signaling plays important roles in regulating neuronal circuit function and behavior in C. elegans.

show abstract

“…The videos were edited to capture the expulsion step for qualitative analysis. (Wang and Wang, 2013).…”

Section: High-speed Videomentioning

confidence: 99%

Spillover Transmission Is Mediated by the Excitatory GABA Receptor LGC-35 inC. elegans

et al. 2015

View full text Add to dashboard Cite

show abstract

“…A growing number of trackers have elevated the objectivity, sophistication, and precision of the analysis of C. elegans movement on solid media [14][15][16][17][18] . C. elegans locomotion on plates is mostly restricted to the plane in which the animal makes contact with the solid surface of the media.…”

Section: Introductionmentioning

confidence: 99%

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

Ibáñez-Ventoso

Herrera

Chen

et al. 2016

JoVE

View full text Add to dashboard Cite

Dissecting the neuronal and neuromuscular circuits that regulate behavior remains a major challenge in biology. The nematode Caenorhabditis elegans has proven to be an invaluable model organism in helping to tackle this challenge, from inspiring technological approaches, building the human brain connectome, to actually shedding light on the specific molecular drivers of basic functional patterns. The bulk of the behavioral studies in C. elegans have been performed on solid substrates. In liquid, animals exhibit behavioral patterns that include movement at a range of speeds in 3D, as well as partial body movements, such as a posterior curl without anterior shape change, which introduce new challenges for quantitation. The steps of a simple procedure, and use of a software that enables high-resolution analysis of C. elegans swim behavior, are presented here. The software, named CeleST, uses a specialized computer program that tracks multiple animals simultaneously and provides novel measures of C. elegans locomotion in liquid (swimming). The measures are mostly grounded in animal posture and based on mathematics used in computer vision and pattern recognition, without computational requirements for threshold cut-offs. The software tool can be used to both assess overall swimming prowess in hundreds of animals from combined small batch trials and to reveal novel phenotypes even in wellcharacterized genetic mutants. The preparation of specimens for analysis with CeleST is simple and low-tech, enabling wide adaptation by the scientific community. Use of the computational approach described here should therefore contribute to the greater understanding of behavior and behavioral circuits in the C. elegans model.

show abstract

“…However the results for Worm Track-J [55] were a success. Considering the study [56] which was conducted to tracking the path of traveling of worms helped to track our object of interest, consuming our object as the input for the algorithm and with some few changes in its movement parameters we succeeded to track the path of the face region which obtained after Thresholding and template matching steps. These tracking systems can be divided into two classes: single-object trackers and multi-objects trackers.…”

Section: Worm Track-jmentioning

confidence: 99%