Biomechanical Profiling of Caenorhabditis elegans Motility

Krajacic, Predrag; Shen, Xing; Purohit, Prashant K.; Arratia, Paulo E.; Lamitina, Todd

doi:10.1534/genetics.112.141176

Cited by 43 publications

(57 citation statements)

References 26 publications

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“…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 C. elegans Swim Behavior Using CeleST Software

Ibáñez-Ventoso

Herrera

Chen

et al. 2016

JoVE

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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.

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Section: Introductionmentioning

confidence: 99%

Automated Analysis of C. elegans Swim Behavior Using CeleST Software

Ibáñez-Ventoso

Herrera

Chen

et al. 2016

JoVE

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“…Several recent reports describe systems that begin to address this gap by automatically recording and quantifying spontaneous behavior in animals ranging from worms (7)(8)(9)(10)(11)(12)(13)(14)(15) to flies (16)(17)(18)(19), fish (20,21), and mice (22,23). The advantage of these approaches is that they provide a means to quantify movement parameters such as velocity precisely and in some cases to automatically detect predefined behaviors based on a manually annotated training data set.…”

mentioning

confidence: 99%

A dictionary of behavioral motifs reveals clusters of genes affecting Caenorhabditis elegans locomotion

Brown

Yemini

Grundy

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

209

252

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Visible phenotypes based on locomotion and posture have played a critical role in understanding the molecular basis of behavior and development in Caenorhabditis elegans and other model organisms. However, it is not known whether these human-defined features capture the most important aspects of behavior for phenotypic comparison or whether they are sufficient to discover new behaviors. Here we show that four basic shapes, or eigenworms, previously described for wild-type worms, also capture mutant shapes, and that this representation can be used to build a dictionary of repetitive behavioral motifs in an unbiased way. By measuring the distance between each individual's behavior and the elements in the motif dictionary, we create a fingerprint that can be used to compare mutants to wild type and to each other. This analysis has revealed phenotypes not previously detected by real-time observation and has allowed clustering of mutants into related groups. Behavioral motifs provide a compact and intuitive representation of behavioral phenotypes.phenotyping | imaging | ethology | nematode T he study of unconstrained spontaneous behavior is the core of ethology, and it has also made significant contributions to behavioral genetics in model organisms. A powerful approach has been the careful expert observation of mutants to identify those with visible locomotor phenotypes, as demonstrated for many model organisms (1-6). However, as with most manually scored experiments, subjectivity can reduce reproducibility, whereas subtle quantitative changes or those that happen on very short or long time-scales are likely to be missed. Furthermore, manual observations are not scalable, and this has led to a widening gap between our ability to sequence and manipulate genomes and our ability to assess the effects of genetic variation and mutation on behavior.Several recent reports describe systems that begin to address this gap by automatically recording and quantifying spontaneous behavior in animals ranging from worms (7-15) to flies (16-19), fish (20, 21), and mice (22,23). The advantage of these approaches is that they provide a means to quantify movement parameters such as velocity precisely and in some cases to automatically detect predefined behaviors based on a manually annotated training data set. This automated analysis eliminates some of the problems of a purely manual approach, but it still relies on preselected behavioral parameters that may not be optimal for phenotypic comparisons and precludes the discovery of new behaviors that have not already been observed by eye. An alternative approach is to use unsupervised learning, which attempts to use the inherent structure of a data set to identify informative patterns; to do this, we first needed to extract worm postures from movie data and have as compact and complete a representation of worm behavior as possible.

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“…Despite decreased swimming speeds, both unit power (63.6 6 37.9 pW/mm) and unit propulsion (4.3 6 3.0 nN/mm) significantly increased as expected. 2,31 The large standard deviations can be attributed to the insufficient number of interrogation window (N ¼ 19 625) as compared with the suggested N ¼ 57 600 in the current study. An improvement can be achieved with smaller particles and a camera with finer pixel size.…”

Section: Discussionmentioning

confidence: 60%

“…4(d) shows comparisons of the measured motility and the data reported in the prior literature. [31][32][33] The time-averaged power and propulsion over three swimming cycles were measured to be 5.2 6 3.2 pW and 0.8 6 0.5 nN, respectively (see the data in the supplementary material 30 ). The reported time-averaged power and propulsion range from 1.3 pW to 3 pW and from 1.7 to 7.6 nN, respectively.…”

Section: B Measurements Of Power and Propulsion Of The Wild-type Wormmentioning

confidence: 99%

“…A medium with viscosity of 10 mPa s was made by mixing dextran (FL-95771/MW ¼ 2 000 000, Sigma) with the NGM buffer to achieve a 6% dextran solution. Numerous studies 2,8,9,31 have reported that C. elegans is able to sense the ambient viscosity change and make a behavioral response according to the coordination between sensory and motor neurons. A representative trajectory of movement and a relationship of power and propulsion similar to the previous N2 and kin-2 are depicted in Figs.…”

Section: Effects Of Strain and Liquid Viscosity To Motility Changesmentioning

confidence: 99%

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Characterizations of kinetic power and propulsion of the nematode Caenorhabditis elegans based on a micro-particle image velocimetry system

2014

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Quantifying the motility of micro-organisms is beneficial in understanding their biomechanical properties. This paper presents a simple image-based algorithm to derive the kinetic power and propulsive force of the nematode Caenorhabditis elegans. To avoid unnecessary disturbance, each worm was confined in an aqueous droplet of 0.5 ll. The droplet was sandwiched between two glass slides and sealed with mineral oil to prevent evaporation. For motion visualization, 3-lm fluorescent particles were dispersed in the droplet. Since the droplet formed an isolated environment, the fluid drag and energy loss due to wall frictions were associated with the worm's kinetic power and propulsion. A microparticle image velocimetry system was used to acquire consecutive particle images for fluid analysis. The shorttime interval (Dt < 20 ms) between images enabled quasi real-time measurements. A numerical simulation of the flow in a straight channel showed that the relative error of this algorithm was significantly mitigated as the image was divided into small interrogation windows. The time-averaged power and propulsive force of a N2 adult worm over three swimming cycles were estimated to be 5.2 6 3.1 pW and 1.0 6 0.8 nN, respectively. In addition, a mutant, KG532 [kin-2(ce179) X], and a wild-type (N2) worm in a viscous medium were investigated. Both cases showed an increase in the kinetic power as compared with the N2 worm in the nematode growth medium due to the hyperactive nature of the kin-2 mutant and the high viscosity medium used. Overall, the technique deals with less sophisticated calculations and is automation possible. V C 2014 AIP Publishing LLC.

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Biomechanical Profiling of Caenorhabditis elegans Motility

Cited by 43 publications

References 26 publications

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

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

A dictionary of behavioral motifs reveals clusters of genes affecting Caenorhabditis elegans locomotion

Characterizations of kinetic power and propulsion of the nematode Caenorhabditis elegans based on a micro-particle image velocimetry system

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