کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4334972 | 1614646 | 2014 | 5 صفحه PDF | دانلود رایگان |

• We describe a method for quantifying long-term behavioral phenotypes in C. elegans.
• Individual worms are placed in an array of glass wells filled with agar media.
• Machine vision analysis is used to quantify worm behavior for periods up to 3 days.
• We use the system to assay development time and mating in several strains.
• Our method is simple, inexpensive, and broadly useful in C. elegans neuroscience.
BackgroundThe nematode Caenorhabditis elegans is widely used as a model for understanding the neuronal and genetic bases of behavior. Recent studies have required longitudinal assessment of individual animal's behavior over extended periods.New methodHere we present a technique for automated monitoring of multiple worms for several days. Our method uses an array of plano-concave glass wells containing standard agar media. The concave well geometry allows worms to be imaged even at the edge of the agar surface and prevents them from burrowing under the agar. We transfer one worm or embryo into each well, and perform imaging of the array of wells using a single camera. Machine vision software is used to quantify size, activity, and/or fluorescence of each worm over time.ResultsWe demonstrate the utility of our method in two applications: (1) quantifying behavioral quiescence and developmental rate in wild-type and mutant animals, and (2) characterizing differences in mating behavior between two C. elegans strains.Comparison with existing method(s)Current techniques for tracking behavior in identified worms are generally restricted to imaging either single animals or have not been shown to work with arbitrary developmental stages; many are also technically complex. Our system works with up to 24 animals of any stages and is technically simple.ConclusionsOur multi-well imaging method is a powerful tool for quantification of long-term behavioral phenotypes in C. elegans.
Journal: Journal of Neuroscience Methods - Volume 223, 15 February 2014, Pages 35–39