Name: automated analysis of c. elegans swim behavior using celest software
Uploaded: 2016-12-07T12:30:00.000+00:00
Duration: 8 min 47 s
Description: Watch this Scientific Journal Video about Automated Analysis of C. elegans Swim Behavior Using CeleST Software at JoVE.com

CeleST development was supported by NIH grants R21AG027513 and U01AG045864. Data and some short video representations are adapted from Restif et al. (2014)8, which were published under the Creative Commons Attribution (CC BY) license http://creativecommons.org/licenses/by/4.0/.&#160; We thank Ricardo Laranjeiro for manuscript help.

1. Nematode Growth and Handling
<ol>
	<li>Grow C. elegans on standard Petri plates containing NGM&#160;spotted with OP50-1 Escherichia coli as food source1-3.</li>
	<li>Keep cultures in an incubator that maintains the desired temperature. C. elegans grows well from 15 to 25 &#176;C, with 20 &#176;C being the standard growth temperature. 
	NOTE: Swimming is a robust phenotype, however swim vigor can be affected by environmental factors including contamination on plates and overheating of cultures and slides. Strong efforts to maintain stable conditions should be made.</li>
	<li>Use a homemade nematode pick made of a platinum wire flame-sealed into a glass rod to handle transfer of single animals4.</li>
</ol>
2. Set up for Swim Analysis
<ol>
	<li>Use an integrated system consisting of a stereomicroscope, digital CCD camera, and digital video recording software. The CeleST software can be used on a separate computer.
	<ol>
		<li>Cover the microscope with a dark cloth (e.g., felt) to prevent glare in the swim drop area on the microscope slide and improve image quality.</li>
		<li>Adjust the microscope&#39;s working distance and magnification to full view of the swim area, and the base mirror to get sharp contrast of the animals against the background. Set the illumination to dark-field to visualize the bodies of the adults as white against the black background, which works very well for computational visualization and tracking with the swim analysis software.</li>
		<li>Take care to keep the work area, microscope slide, and swim drop very clean. Debris can interfere with&#160;the analysis by occluding the free swimming of adults, and thin threads of fiber can be detected as nonmoving animals during tracking.</li>
	</ol>
	</li>
</ol>
3. Animal Preparation for Analysis of Physical Fitness in Liquid
<ol>
	<li>Pipette 60 &#181;L&#160;of 1x M9 buffer into a 10 mm&#160;ring preprinted on a microscope slide. Make sure the drop entirely covers the inside area of the ring. The software extracts more swimming information using this drop size contained on a slide than it would if utilizing multiwell plates.</li>
	<li>Pick single adult nematodes and transfer them into the drop of 1x M9 buffer, minimizing the transport of bacteria that can cloud the liquid. Minimize bacteria by first placing the animals for transfer to a side of the plate that does not contain much bacterial lawn, letting them crawl, and then gently lifting them up.
	<ol>
		<li>When transferring the nematodes into the drop, use a dissecting scope close to the analysis station to reduce the length of swim time, which can affect the behavior of the animals25.</li>
		<li>Place only 4 adults in the drop if they move vigorously and cross paths often. As guidance to minimize overlapping swimmers (which confounds computational analysis), consider placing 4 animals per drop when they are as active as young, healthy WT adults and 5 animals per drop when they are older and show less vigor. 
		NOTE: Healthy WT nematodes instinctively start swimming soon after being placed in liquid. With the aid of a nematode pick, gently separate the adults in the drop if they clump.</li>
	</ol>
	</li>
</ol>
4. Analysis of Physical Fitness in Liquid &#8212; Video Recording of Swimming
<ol>
	<li>Position the slide loaded with adults on the base of the microscope to record their swimming. 30&#160;s&#160;videos capture details of an individual animal&#39;s behavior in liquid (Video 1), and sampling of large numbers of such records is easily accomplished when aided by CeleST software8,26; however, longer swim periods may be ideal for other specific behavioral studies25. Importantly, make sure to maintain the same magnification throughout all recordings since magnification differences affect swimming analysis and will bias comparison. 
	NOTE: These steps are provided as guidance using the digital video recording software listed in the Table of Materials; VirtualDub open access software might substitute, although we have not tested this.
	<ol>
		<li>Use the following settings as guidance for successful swim analysis: image size of 696 x 520 pixels, image resolution of 0.02 mm/pixel, and a rate of 18 frames/s. Higher resolution versions of these settings may facilitate tracking but will not affect the measures calculated by the program. 
		NOTE: The swim zone provided by the 60 &#181;L&#160;drop of 1x M9 buffer on the microscope slide does not have much depth, which minimizes upward and downward swimming motion.</li>
	</ol>
	</li>
	<li>Under Settings, go to &#39;Recording&#39;, set &#39;Limit Sequence Size&#39; to &#39;545 frames on disk&#39;. Under the same heading, check the &#39;Use Recording Manager&#39; box. Click the &#39;Edit current recording script...&#39; button, and under &#39;Commands&#39;, select &#39;Grab Frame&#39;, and under &#39;Stop Conditions&#39;, enter &#39;Stop after 0:00:30.00&#39;.</li>
	<li>To record, select &#39;New Sequence in RAM&#39; under the home heading, then press the red &#39;Record&#39; button. The software will grab 545 frames over 30 s&#160;with the digital camera and hold them in RAM memory.</li>
</ol>
5. Analysis of Physical Fitness in Liquid &#8212; Video Treatment
<ol>
	<li>Fragment the video into a sequence of image frames by clicking the home heading and selecting &#39;Export full sequence to&#8230;&#39; in the video recording software. 
	NOTE: The digital video recording software can save the frames as .bmp, .jpg, .tif, or .png, all of which can be read by the swim analysis program presented here. Note that .tif files use more storage space, but some formats such as .jpg may degrade image quality.</li>
	<li>Launch the swim analysis program from MATLAB by clicking the run button.</li>
	<li>On the home screen of the swim analysis program, use the &#39;Add one video&#39; or &#39;Add multiple videos&#39; buttons to upload sequences of image frames one at a time or in groups while attaching relevant labels such as genotype, date, trial and duration. The software creates a database of the labeled sequences.</li>
	<li>Enable simultaneous tracking of multiple animals in the uploaded sequences by clicking &#39;1. Process videos&#39;. In the new screen, select the desired videos in the left box and click, &#39;Add to the list&#39;. For each video, click its name in the top right list to display the first image of the video in the bottom right. Demarcate the swim zone by selecting 3 points inside the ring, and initiate processing by clicking &#39;Process all the videos listed above&#39;. View progress of the processing at the center bottom.</li>
	<li>Once completed, &#39;Close&#39; the video processing screen.</li>
</ol>
6. Analysis of Physical Fitness in Liquid &#8212; Measurement
<ol>
	<li>Click on &#39;2. Compute measures&#39; to open a new screen where the processed videos can be uploaded, one at a time, to validate or reject the tracking of single animals. Videos that have been processed will appear in the list on the left where the video name can be clicked to upload that video.</li>
	<li>Use this manual step to focus on areas of missed tracking (red blocks) in contrast to successful tracking (green blocks). 
	NOTE: Alternatively, one can rely on the automated output of valid tracking since the software has a robust tracking system (94.1% validity as observed in 2,020 animals from 404 videos8).
	<ol>
		<li>Find the &#39;Validity of the segmented body&#39; in the first color bar across the screen in the bottom half. The percentage of &#39;Valid frames&#39; is in green, and the percentage of &#39;Rejected frames&#39; is in red. &#8805;80% of valid frames imply that the tracking information used to compute measures comes from at least 432 frames out of 540 in a 30&#160;s swim trial recorded at 18 frames/s.</li>
		<li>Easily navigate and modify blocks of valid/rejected frames utilizing the &#39;Next block&#39;, &#39;Switch validity&#39;, &#39;Split block&#39;, and &#39;Isolate frame&#39; options. In the rare event that 2 animals crossed paths in an uncrowded swim zone, switch their identification numbers over a specific frame range with the &#39;Switch&#39; button.</li>
		<li>View the bar graphs in the bottom half of the screen for additional information on tracking and overlapping performances. The length of the segmented body (animal&#39;s body length per frame) correlates with overlapping incidences.</li>
	</ol>
	</li>
	<li>If any bright spots on the video interfere with the analysis, adjust bright areas in the swim zone by selecting the &#39;Glare zones&#39; function.</li>
	<li>Click on &#39;Save and Compute measures&#39; to save the validity input before uploading another processed video. At this stage the software computes, in the background (not shown to the user), curvature maps of the swim performance of individual animals and the parameters indicated below (Video 2). 
	NOTE: The parameters measured that are focused on here are described in Table 1. The Wave initiation rate, body wave number, asymmetry, stretch, and curling are calculated based on the curvature maps that the software computes for the animals; the Travel speed, Brush stroke, and Activity index parameters are not based on curvature maps.</li>
	<li>Click &#39;Close&#39; to exit the &#39;Compute measures&#39; screen.</li>
</ol>
7. Analysis of Physical Fitness in Liquid &#8212; Output of Results
Note: The software can report on multiple measures of the swim motion, which cover from overt to very subtle behavioral traits not readily scored by eye (Videos 3 and 4). Here the focus is on 8 parameters that typically show a good dynamic range: Wave initiation rate, Body wave number, Asymmetry, Stretch, Curling, Travel speed, Brush stroke, and Activity index.
<ol>
	<li>On the home screen of the program, click on &#39;3. Display results&#39; to obtain the analysis output.</li>
	<li>Create sample groups for statistical treatment by selecting the desired analyzed sequences of frames in the left box and moving them into a new or existing sample group by clicking the &#39;Add to the selected sample&#39; or &#39;Add to new Sample&#39; buttons.</li>
	<li>Click &#39;Show graphs for these samples&#39; to go to a second screen that displays the graphs and key statistics of the samples for each of the 8 parameters by selection on the top of the screen.
	<ol>
		<li>If desired, adjust the representation of the bar graphs at the top left of the screen using the &#39;Colors&#39;, &#39;# of histograms&#39;, and &#39;# of worms&#39; selectors.</li>
	</ol>
	</li>
	<li>Use &#39;2D histograms&#39; to plot combinations of any two parameters via the &#39;Measure on the X axis&#39; and &#39;Measure on the Y axis&#39; drop down menus on the left of the screen. Click &#39;Close&#39; to exit and return to the &#39;Display results&#39; screen.</li>
	<li>Use the &#39;Export&#8230;&#39; button on the top left of the screen to save detailed numerical data as .csv file to open and further manipulate in a spreadsheet program.</li>
	<li>&#39;Close&#39; the &#39;Display results&#39; screen to go back to the home screen and click &#39;Quit&#39; to ensure saving of the database containing the analyzed video sequences.</li>
</ol>

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A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+connectome+of+a+decision-making+neural+network.">The connectome of a decision-making neural network.</a> Science. 337 (6093), 437-444 (2012).</li><li>Greenblum, A., Sznitman, R., Fua, P., Arratia, P. E., Sznitman, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Caenorhabditis+elegans+segmentation+using+texture-based+models+for+motility+phenotyping.">Caenorhabditis elegans segmentation using texture-based models for motility phenotyping.</a> IEEE Trans Biomed Eng. 61 (8), 2278-2289 (2014).</li></ol>

The authors have nothing to disclose.

The use of C. elegans as a model system continues to increase due to its genetic malleability, experimental tractability and anatomy annotated to meticulous detail. For instance, the neuronal structure and connectivity of the C. elegans hermaphrodite is clearly mapped out34-36, greatly facilitating investigations of specific neuronal circuits that control particular behaviors. 302 neurons constitute the adult hermaphrodite&#39;s nervous system, which process a broad range of sensory inputs into basic behavioral outputs like locomotion. The structure of the more complex male nervous system has also been described37, enabling sex-specific circuitry to be addressed.37
C. elegans behavior has been extensively studied on standard culture plates containing solid media. Since WT&#160;C. elegans moves in predictable sinusoidal waves on agar-filled plates, deviations from the overall pattern can be detected by eye and scored manually. This approach, however, is subject to the experimenter&#39;s criterion and is labor intensive. Hardware and software tools designed to track and measure C. elegans locomotion on solid media remove the subjectivity bias and allow for large-scale studies, permitting more sophisticated biological questions to be addressed. The recent behavioral database created by the Schafer lab16 is an excellent example of the analytical extension and depth that has been achieved with a computational system for locomotion on plates.
When WT&#160;C. elegans is placed in liquid, the animal quickly adapts its motion to the new environment, initiating a swim. C. elegans swimming utilizes a greater range of motion than crawling and can be more irregular8. Software like CeleST is intended to fill the gap for detailed analysis of C. elegans behavior in liquid, permitting quantitation of motion-associated parameters that are not readily measured by the un-aided eye, or that can be accomplished more rapidly than manual scoring. In 8 hard h an individual could process up to 200 videos, ~1,000 records per day.
The software defines swimming assessing parameters that serve as a comprehensive fingerprint of physical fitness and behavior. In addition to enriching understanding of the complex facets of C. elegans behavior in liquid and their underlying molecular pathways, this software can be used to explore multiple aspects of biology including pharmacological responses, aging, and distinct behavior. Presented here, the overview of the quantified changes that occur in the physical performance of C. elegans adults as they age is one example of such application of the software (for a more detailed account, see Restif et al.8). In the context of aging, some measured parameters declined while others increased or did not consistently change in wild type. Trends were confirmed to a large extent by the computational profile of longevity mutants, and the relative profiles of graceful and bad ager cohorts of same-age populations kept in uniform environmental conditions. The high resolution of the software can also reveal subtle phenotypes previously unknown in extensively characterized mutants (e.g., age-1(hx546) in Figure 1).
There are a few particularly critical steps of the protocol described. Maintaining a constant temperature environment between the swim environment and the strain plate culture is important for swim reproducibility, so experimenters are highly encouraged to go to great pains to avoid random temperature changes. Swim media should be at the same temperature as the plates. Likewise, careful attention to the drop size for the swim will help ensure reproducibility. Finally, it is prudent to think in advance about the offloading of the large video files that accumulate. Processing images on a site apart from the video capture computer is recommended.
The use of the software presented here for swim analysis has certain limitations. First, although the programs can simultaneously track multiple animals, if more than five animals are jointly analyzed, there is an increased chance that the animals will swim across each other in the video images. When the program cannot unambiguously determine which animal was which, it censors those data frames. Although this program feature ensures that data for individual animals are of high quality, it limits the throughput. Second, the images should be fairly clean, that is free of dust, smudges and glare from lights, as associated signals can also confound the image analysis. As noted in protocol section 2.1.1, a very low-tech investment that can greatly aid image capture by eliminating complications with fluctuations in environmental lighting is to cover the stage area with a dark cloth that does not allow ambient light to reach the stage. Third, the program is optimized for animals at adult stage. Young larvae swim very fast and have small bodies, which increases program error. Fourth, some of the software utilizes MATLAB, and when there are version upgrades and/or operating system upgrades, some program links may be disrupted. Currently, the software is optimized for use on MATLAB 2015b and Mac OS version 10.10, but we expect soon to post a software version that is more robust against such changes. Finally, the video data files can become large fast, and require storage space to be allotted.
In summary,&#160;presented here is a simple methodology that can be easily implemented by any laboratory without much investment to create videos of C. elegans swimming for CeleST analysis. Features of the software package include extensive automation from tracking through analysis, simultaneous multi-animal tracking, and use of mathematical bases (i.e., curvature measures) for quantitation of most locomotion parameters. The software is open source, with code and demos publicly available as detailed in Restif et al.8. Although the program features advanced computer vision analysis for tracking, other published tracking systems (e.g., Greenblum et al., 201438) are compatible with the parameter analysis of the software presented here. Future improvements will be directed toward converting the software into a more robust package that does not restrict use to the specific versions of the operating systems mentioned above (also indicated in the Table of Materials).

Defining how genetics, epigenetics, experience, and environment influence behavior is a major challenge in modern biology. Simple, genetically amenable models that can be computationally tracked and measured can make critical contributions toward this goal. The nematode Caenorhabditis elegans is one such model. The purpose of this article is to demonstrate how C. elegans swimming locomotion can be tracked and quantitated to extract information on eight novel features with biological relevance.
C. elegans has a relatively short lifespan of about 2 - 3 w and a reproductive period of approximately 4 d at 20 &#176;C1,2. In standard laboratory cultures, this microscopic nematode is grown on Petri plates containing Nematode Growth Media (NGM) that are spread with a bacterial food source3,4. 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. Impairment6 or age7-12 can also dramatically alter movement on plates.
Genes that modulate behavior, healthspan or aging, can be functionally characterized by analyzing C. elegans movement patterns or locomotion. 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 C7,8,13. 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.
A growing number of trackers have elevated the objectivity, sophistication, and precision of the analysis of C. elegans movement on solid media14-18. C. elegans locomotion on plates is mostly restricted to the plane in which the animal makes contact with the solid surface of the media. (C. elegans can also induce exploration by lifting the head away from the rest of the body that is still in contact with a solid surface, positioning the body in multiple planes. However, this behavior is unusual.) When placed in liquid, this nematode initiates an undulatory movement, or swimming, that features more extensive dimensional movement, a greater range of speed and depth of motion, and an increased incidence with age of anterior moving differently from posterior as compared to animals on solid surfaces. As a quick analysis of physical fitness and response to new environment, an experimenter can place an individual animal into a liquid drop and score its frequency of body bends under the dissecting scope. Video recording can facilitate present and future scorings of the swimming vigor of the animal. However, the manual approach limits the number of features that can be scored, and is fully constrained to scoring one animal at a time.
Locomotion in liquid has been less explored than locomotion on solid media. Indeed, there are few software options that are robust and easy to implement in the laboratory to measure locomotion in liquid19-24. The software CeleST (C. elegans Swim Test) offers simplicity of use and mathematical-based measures that deliver data (curvature scores) directly relevant to the nature of the motion8; (detailed description of features and advantages in Restif et al.8). Moreover, computational analysis enables the elucidation of phenotypic features that are impossible for the human eye to score. Here, data that exemplify the resolution of this analysis approach are presented and an easy to implement protocol to record swim trials for subsequent CeleST analysis is described.

<table><tbody><tr><td>&lt;strong&gt;REAGENT&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>N2</td><td>Caenorhabditis Genetics Center (CGC)</td><td></td><td>&lt;em&gt;C. elegans&lt;/em&gt; WT (ancestral).</td></tr><tr><td>OP50 &lt;em&gt;Escherichia coli&lt;/em&gt;</td><td>Caenorhabditis Genetics Center (CGC)</td><td></td><td>Biosafety Level: BSL-1.</td></tr><tr><td>OP50-1 &lt;em&gt;Escherichia Coli&lt;/em&gt;&amp;nbsp;</td><td>Caenorhabditis Genetics Center (CGC)</td><td></td><td>Streptomycin resistant strain of OP50.&amp;nbsp; Biosafety Level: BSL-1.</td></tr><tr><td>Streptomycin sulfate salt</td><td>Sigma-Aldrich</td><td>S6501</td><td></td></tr><tr><td>Printed Microscope Slides&amp;nbsp;</td><td>Thermo Fisher Scientific</td><td>Gold Seal Fluorescent Antibody Microslides: 3032-002</td><td>have two etched 10 mm diameter circles delineated by white ceramic ink</td></tr><tr><td>Nematode Growth Medium (NGM)</td><td>For 1 L: 17 g Agar, 3 g NaCl, 2.5 g Peptone, 1 mL 1 M CaCl&lt;sub&gt;2&lt;/sub&gt;, 1 mL 5 mg/mL Cholesterol in ethanol, 25 mL 1 M KPO&lt;sub&gt;4&lt;/sub&gt; buffer, 1 mL 1 M MgSO&lt;sub&gt;4&lt;/sub&gt;, H&lt;sub&gt;2&lt;/sub&gt;O to 1 L. Sterilize by autoclaving.</td><td></td><td>Stiernagle, T. Maintenance of &lt;em&gt;C. elegans&lt;/em&gt;. WormBook, 1-11, doi:10.1895/wormbook.1.101.1 (2006)</td></tr><tr><td>M9 buffer</td><td>For 1 L: 3 g KH&lt;sub&gt;2&lt;/sub&gt;PO&lt;sub&gt;4&lt;/sub&gt;, 6 g Na&lt;sub&gt;2&lt;/sub&gt;HPO&lt;sub&gt;4&lt;/sub&gt;, 5 g NaCl, 1 mL 1 M MgSO&lt;sub&gt;4&lt;/sub&gt;, H&lt;sub&gt;2&lt;/sub&gt;O to 1 L. Sterilize by autoclaving.</td><td></td><td>Stiernagle, T. Maintenance of &lt;em&gt;C. elegans&lt;/em&gt;. WormBook, 1-11, doi:10.1895/wormbook.1.101.1 (2006)</td></tr><tr><td>&lt;strong&gt;EQUIPMENT&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>CeleST</td><td>Driscoll Lab, Rutgers University</td><td>&lt;em&gt;C. elegans&lt;/em&gt; Swim Test</td><td>Open Source, see http://celestmod.github.io/CeleST/ and http://celest.mbb.rutgers.edu/</td></tr><tr><td>MATLAB</td><td>www.mathworks.com/downloads</td><td>MatLab version 2015b (best)</td><td>The CeleST version demonstrated here has best functionality with Mac OS 10.10 and MatLab 2015b. MATLAB 2015B introduced changes to how MATLAB handled graphics, including a new coding convention and syntax. These changes resolved an issue that couldn&#039;t be resolved elegantly (primarily because the internals of MATLAB really needed the major graphics overhaul implemented in MATLAB 2015B). For this reason, CeleST should always be run on MATLAB 2015B or later versions. However for users without access to MATLAB version 2015B or later (or MATLAB at all), we have created a CeleST program that doesn&#039;t need MATLAB on the computer at al. An installer is downloaded by the prospective user and then it installs itself onto the computer through a couple prompts like most programs.&amp;nbsp;&amp;nbsp;</td></tr><tr><td>Mac OS</td><td>www.apple.com</td><td>Version 10.10</td><td>Currently, CeleST has been ported to the major operating systems (Windows, Mac, and Linux). The current code can be run on any of the operating systems and there are versions for each operating system that don&#039;t even require users to have MATLAB to use CeleST (this version requires a large download). The Windows version has been tested the least and is most prone to bugs as such. Linux has been moderately tested. And Mac has been and continues to be tested extensively (primarily because it&#039;s the operating system in our lab).</td></tr><tr><td>Stereomicroscope&amp;nbsp;</td><td>Zeiss</td><td>Stemi 2000-C&amp;nbsp;</td><td></td></tr><tr><td>Transmitted Light Base</td><td>Diagnostic Instruments</td><td>TLB 3.1</td><td></td></tr><tr><td>Digital CCD Camera</td><td>QImaging</td><td>Rolera-XR Mono Fast 1394 (ROL-XR-F-M-12)</td><td></td></tr><tr><td>Digital Video Recording Software</td><td>Norpix</td><td>Streampix Version 3.17.2</td><td></td></tr></tbody></table>

automated analysis of c. elegans swim behavior using celest software

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,&#160;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 well-characterized 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.

By analyzing locomotion in liquid (swimming), phenotypes that are not readily apparent on solid media (crawling) can be elucidated. To quantitate swimming locomotion we developed specific software that measures ten novel parameters of swimming behavior8. The eight most useful of these parameters are described in detail in Table 1. These parameters are named Wave initiation rate, Body wave number, Asymmetry, Stretch, Curling, Travel speed, Brush stroke, and Activity index. Studies exemplifying the power of the software have defined the functional decline of hundreds of aging adults with WT, behavioral or longevity mutant backgrounds8, and have analyzed the well-studied longevity mutants age-1(hx546) and da&#402;-16(mgD&#402;50), which harbor mutations that disrupt the normal insulin signaling pathway. The gene age-1 encodes for a phosphatidylinositide 3-kinase (PIK3) catalytic subunit, and, when it harbors the mutation hx546, causes lifespan extension and stress resistance27-29. The gene da&#402;-16 encodes for a forkhead box O (FOXO) transcription factor that shortens lifespan and impairs the stress response when deleted30-33.
Certain parameters of swimming such as Wave initiation rate, Travel speed, Brush stroke and Activity index gradually declined with age even in favorable genetic backgrounds (Figure 1). In line with current knowledge, long-lived age-1(hx546) mutants showed a more vigorous physical performance than WT at advanced and extremely old ages. Also as anticipated, short-lived da&#402;-16(mgD&#402;50) mutants displayed compromised performance, especially at extremely old ages. Remarkably, it was only under the scrutiny of the CeleST computer vision and mathematical algorithm package that the superior swim performance of age-1(hx546) mutants was detectable at the onset of adulthood. The fact that age-1(hx546) results in enhanced physical performance at young adult life suggests that this mutation affects normal development and/or young adult phenotype in a way not previously appreciated (Figure 1).
Body wave number, Asymmetry, Stretch, and Curling parameters trended up with age in WT and aging mutant adults (Figure 2). Interestingly, the resolution level of the software revealed finer behavioral traits like the sustained symmetry of age-1(hx546) mutants throughout their lifespan and the inability of extreme old da&#402;-16(mgD&#402;50) mutants to stretch and curl up to the extent that same-age WT and age-1(hx546) adults do.
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 homogeneous7. (By controlling genetics and environment, the possible confounding effects of these factors are minimized, unveiling the significant contribution of stochasticity to age-related degeneration.) A synchronized C. elegans population of similar genetic background kept in a controlled environment still contains a mix of different classes of individuals according to their aging peculiarities. Although all start as healthy adults, some rapidly lose their physical fitness (bad agers, class C) while others maintain vigor for longer period of time (graceful agers, class A). Bad agers thus appear to have a considerably shorter healthspan than graceful agers.
As further detailed in our study8, graceful agers maintained youthful physical fitness as observed by comparison with the swim profile of much younger adults (Figures 3, 4 and 5). This sustained fitness is comparable to the physical performance of long-lived age-1(hx546) mutants at post-reproductive age (D 11) (Figures 1 and 2). On the contrary, bad agers dramatically lost much of their physical capacity soon after reproduction, performing at levels similar to those of extreme old and progeric da&#402;-16(mgD&#402;50) adults (Figures 1 - 4). These similarities can be drawn by gross comparison, however particular signatures are readily noticeable at a closer look. For instance, although there is some correlation between the extent of both stretch and curling in extreme old wild types and aging mutants (Figure 2), this relation is not observed in bad agers (Figures 4 and 5), which show higher propensity to curl up but not to stretch in the samples tested. The software we present thus adds dimension to the analysis of physical fitness or locomotory capacity by providing the tools for more sophisticated studies that were not tractable before. In summary, CeleST provides comprehensive readouts in the form of eight novel measures highlighted here, which define the behavioral fingerprint of specific genetic, epigenetic, and environmental backgrounds, enabling the identification of unique and common parameter patterns that can be the signatures of specific conditions (environmental, pharmacological, nutritional), biological processes, or organism states such as healthspan.
<img alt="Figure 1" src="/files/ftp_upload/54359/54359fig1.jpg" /> 
Figure 1: CeleST Software Reports on Wave Initiation Rate (A), Activity Index (B), Brush Stroke (C) and Travel Speed (D) for WT, age-1(hx546), and da&#402;-16(mgD&#402;50)&#160;Adults on D 4 (young adulthood), 11 (post-reproduction) and 20 (extreme old). &#39;#&#39; in y axis means &#39;number&#39;. WTs are colored in grey, age-1 in green and da&#402;-16 in red. Error bars are the standard error of the mean (SEM). Same-age WT and aging mutants were compared for statistical significance using one-way ANOVA followed by Dunnett&#39;s multiple comparison test. **, p = 0.001 - &#60;0.01; ***, p = 0.0001 - &#60;0.001. n = 62 in each data point from four independent trials. Note that here, and for Figure 2, each individual 30 s&#160;video is made with 4 animals, and for each trial we score a total of 16 animals from 4 swim videos, this is done for 4 biological replicates for each data point shown. <a href="http://cloudflare.jove.com/files/ftp_upload/54359/54359fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/54359/54359fig2.jpg" /> 
Figure 2: Software Reports on Body Wave Number (A), Asymmetry (B), Stretch (C), and Curling (D) for WT, age-1(hx546) and da&#402;-16(mgD&#402;50)&#160;Adults on D 4 (young adulthood), 11 (post-reproduction) and 20 (extreme old). &#39;#&#39; in y axis means &#39;number&#39;. WT are colored in grey, age-1 in green, and daf-16 in red. Error bars are the standard error of the mean (SEM). Same-age WT and aging mutants were compared for statistical significance using one-way ANOVA followed by Dunnett&#39;s multiple comparison test. *, p = 0.01 - &#60;0.05; **, p = 0.001 - &#60;0.01; ***, p = 0.0001 - &#60;0.001. n = 62 in each data point from four independent, 30 s&#160;swim trials. <a href="http://cloudflare.jove.com/files/ftp_upload/54359/54359fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/54359/54359fig3.jpg" /> 
Figure 3: Software Reports on Wave Initiation Rate (A), Activity Index (B), Brush Stroke (C), and Travel Speed (D) for young WT adults (D 4), and Same-age Graceful and Bad Agers (D10 and 11). &#39;#&#39; in y axis means &#39;number&#39;. Young WTs are colored in grey, class A graceful agers in green, and class C bad agers in red. Error bars are the standard error of the mean (SEM). Class A graceful agers and class C bad agers were compared to D 4 young adults using one-way ANOVA followed by Dunnett&#39;s multiple comparison test. ****, p &#60;0.0001. n = 27 in each data point from two independent, 30 s swim trials. Graph is slightly modified from Restif et al. (2014)8, which was published under the Creative Commons Attribution (CC BY) license http://creativecommons.org/licenses/by/4.0/.&#160; <a href="http://cloudflare.jove.com/files/ftp_upload/54359/54359fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/54359/54359fig4.jpg" /> 
Figure 4: Software Reports on Body Wave Number (A), Asymmetry (B), and Curling (C) for Young WT Adults (D 4), and Same-age graceful and Bad Agers (D 10 and 11). &#39;#&#39; in y axis means &#39;number&#39;. Young WTs are colored in grey, class A graceful agers in green and class C bad agers in red. Error bars are the standard error of the mean (SEM). Class A graceful agers and class C bad agers were compared to D 4 young adults using one-way ANOVA followed by Dunnett&#39;s multiple comparison test. **, p = 0.001 - &#60;0.01; ****, p &#60;0.0001; n/a, non applicable since only one animal out of the total sample size curled. n = 27 in each data point from two independent, 30 s&#160;swim trials. Graph is slightly modified from Restif et al. (2014)8, which was published under the Creative Commons Attribution (CC BY) license http:/creativecommons.org/licenses/by/4.0/.&#160; <a href="http://cloudflare.jove.com/files/ftp_upload/54359/54359fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/54359/54359fig5.jpg" /> 
Figure 5: Software Report on Stretch for Young WT adults (D 4), and Same-age Graceful and Bad Agers (D 10 and 11). Young WTs are colored in grey, class A graceful agers in green and class C bad agers in red. Error bars are the standard error of the mean (SEM). Class A graceful agers and class C bad agers were compared to D 4 young adults using one-way ANOVA followed by Dunnett&#39;s multiple comparison test. n = 27 in each data point from two independent trials. Graph is slightly modified from Restif et al. (2014)8, which was published under the Creative Commons Attribution (CC BY) license http:/creativecommons.org/licenses/by/4.0/. <a href="http://cloudflare.jove.com/files/ftp_upload/54359/54359fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Video 1" src="/files/ftp_upload/54359/54359video1.jpg" /> 
Video 1: Swimming of a Representative group of C. elegans&#160;adults. <a href="https://www.jove.com/files/ftp_upload/54359/Video1.mov" target="_blank">Please click here to view this video. (Right-click to download.)</a>
<img alt="Video 2" src="/files/ftp_upload/54359/54359video2.jpg" /> 
Video 2: CeleST Software Computation of individual Curvature Maps of the Swim Performances of the tested Animals. Curvature maps are computed in the background; they do not appear in the software interface with the user. <a href="https://www.jove.com/files/ftp_upload/54359/Video2.mov" target="_blank">Please click here to view this video. (Right-click to download.)</a>
<img alt="Video 3" src="/files/ftp_upload/54359/54359video3.jpg" /> 
Video 3: Software calculation of swim Measures based on Individual Curvature Maps. <a href="https://www.jove.com/files/ftp_upload/54359/Video3.mov" target="_blank">Please click here to view this video. (Right-click to download.)</a>
<img alt="Video 4" src="/files/ftp_upload/54359/54359video4.jpg" /> 
Video 4: Software Calculation of Swim Measures that do not Rely on Curvature Maps. <a href="https://www.jove.com/files/ftp_upload/54359/Video4.mov" target="_blank">Please click here to view this video. (Right-click to download.)</a>

Watch this Scientific Journal Video about Automated Analysis of C. elegans Swim Behavior Using CeleST Software at JoVE.com

Automated Analysis of C. elegans Swim Behavior Using CeleST Software

An efficient and simple methodology for computer-based analysis of nematode swimming behavior in liquid is described. The method requires little to no investment for C. elegans laboratories. The hardware used is standard, and the computer software for the behavioral analysis (CeleST) is an open source one.

Automated Analysis of C. elegans Swim Behavior Using CeleST Software

Dissecting the neuronal and neuromuscular circuits that regulate behavior remains a major challenge in biology. The nematode Caenorhabditis elegans has ...

automated-analysis-of-c-elegans-swim-behavior-using-celest-software

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12.4K Views.  Rutgers University. An efficient and simple methodology for computer-based analysis of nematode swimming behavior in liquid is described. The method requires little to no investment for C. elegans laboratories. The hardware used is standard, and the computer software for the behavioral analysis (CeleST) is an open source one.

Video: Automated Analysis of C. elegans Swim Behavior Using CeleST Software

Single Wavelength Shadow Imaging of Caenorhabditis elegans Locomotion Including Force Estimates

This study demonstrates an inexpensive and straightforward technique that allows the measurement of physical properties such as position, velocity, acceleration and forces involved in the locomotory behavior of nematodes suspended in a column of water in response to single wavelengths of light. We demonstrate how to evaluate the locomotion of a microscopic organism using Single Wavelength Shadow Imaging (SWSI) using two different examples.
The first example is a systematic and statistically viable study of the average descent of C. elegans in a column of water. For this study, we used living and dead wildtype C. elegans. When we compared the velocity and direction of nematode active movement with the passive descent of dead worms within the gravitational field, this study showed no difference in descent-times. The average descent was 1.5 mm/sec &#177; 0.1 mm/sec for both the live and dead worms using 633 nm coherent light.
The second example is a case study of select individual C. elegans changing direction during the descent in a vertical water column. Acceleration and force are analyzed in this example. This case study demonstrates the scope of other physical properties that can be evaluated using SWSI while evaluating the behavior using single wavelengths in an environment that is not accessible with traditional microscopes. Using this analysis we estimated an individual nematode is capable of thrusting with a force in excess of 28 nN.
Our findings indicate that living nematodes exert 28 nN when turning, or moving against the gravitational field. The findings further suggest that nematodes passively descend in a column of water, but can actively resist the force of gravity primarily by turning direction.

Single Wavelength Shadow Imaging of Caenorhabditis elegans Locomotion Including Force Estimates

The technique presented here measures the path of freely swimming microscopic species using single wavelength exposure. C. elegans are used to demonstrate shadow imaging as an inexpensive alternative to costly microscopes. This technique can be adapted to accommodate various orientations, environments and species to measure direction, speed, acceleration and forces.

This study demonstrates an inexpensive and straightforward technique that allows the measurement of physical properties such as position, velocity, ...

Engineering

Quantitative Locomotion Study of Freely Swimming Micro-organisms Using Laser Diffraction

Soil and aquatic microscopic organisms live and behave in a complex three-dimensional environment. Most studies of microscopic organism behavior, in contrast, have been conducted using microscope-based approaches, which limit the movement and behavior to a narrow, nearly two-dimensional focal field.1 We present a novel analytical approach that provides real-time analysis of freely swimming C. elegans in a cuvette without dependence on microscope-based equipment. This approach consists of tracking the temporal periodicity of diffraction patterns generated by directing laser light through the cuvette. We measure oscillation frequencies for freely swimming nematodes.
Analysis of the far-field diffraction patterns reveals clues about the waveforms of the nematodes. Diffraction is the process of light bending around an object. In this case light is diffracted by the organisms. The light waves interfere and can form a diffraction pattern. A far-field, or Fraunhofer, diffraction pattern is formed if the screen-to-object distance is much larger than the diffracting object. In this case, the diffraction pattern can be calculated (modeled) using a Fourier transform.2
C. elegans are free-living soil-dwelling nematodes that navigate in three dimensions. They move both on a solid matrix like soil or agar in a sinusoidal locomotory pattern called crawling and in liquid in a different pattern called swimming.3 The roles played by sensory information provided by mechanosensory, chemosensory, and thermosensory cells that govern plastic changes in locomotory patterns and switches in patterns are only beginning to be elucidated.4 We describe an optical approach to measuring nematode locomotion in three dimensions that does not require a microscope and will enable us to begin to explore the complexities of nematode locomotion under different conditions.

Microscopic organisms like the free-swimming nematode C. elegans, live and behave in a complex three-dimensional environment. We report on a novel approach that provides analysis of C. elegans using diffraction patterns. This approach consists of tracking the temporal periodicity of diffraction patterns generated by directing laser light through a cuvette.

Soil and aquatic microscopic organisms live and behave in a complex three-dimensional environment. Most studies of microscopic organism behavior, in ...

Bioengineering

Microfluidic-based Electrotaxis for On-demand Quantitative Analysis of Caenorhabditis elegans' Locomotion

The nematode Caenorhabditis elegans is a versatile model organism for biomedical research because of its conservation of disease-related genes and pathways as well as its ease of cultivation. Several C. elegans disease models have been reported, including neurodegenerative disorders such as Parkinson's disease (PD), which involves the degeneration of dopaminergic (DA) neurons 1. Both transgenes and neurotoxic chemicals have been used to induce DA neurodegeneration and consequent movement defects in worms, allowing for investigations into the basis of neurodegeneration and screens for neuroprotective genes and compounds 2,3.
Screens in lower eukaryotes like C. elegans provide an efficient and economical means to identify compounds and genes affecting neuronal signaling. Conventional screens are typically performed manually and scored by visual inspection; consequently, they are time-consuming and prone to human errors. Additionally, most focus on cellular level analysis while ignoring locomotion, which is an especially important parameter for movement disorders.
We have developed a novel microfluidic screening system (Figure 1) that controls and quantifies C. elegans' locomotion using electric field stimuli inside microchannels. We have shown that a Direct Current (DC) field can robustly induce on-demand locomotion towards the cathode ("electrotaxis") 4. Reversing the field's polarity causes the worm to quickly reverse its direction as well. We have also shown that defects in dopaminergic and other sensory neurons alter the swimming response 5. Therefore, abnormalities in neuronal signaling can be determined using locomotion as a read-out. The movement response can be accurately quantified using a range of parameters such as swimming speed, body bending frequency and reversal time.
Our work has revealed that the electrotactic response varies with age. Specifically, young adults respond to a lower range of electric fields and move faster compared to larvae 4. These findings led us to design a new microfluidic device to passively sort worms by age and phenotype 6.
We have also tested the response of worms to pulsed DC and Alternating Current (AC) electric fields. Pulsed DC fields of various duty cycles effectively generated electrotaxis in both C. elegans and its cousin C. briggsae 7. In another experiment, symmetrical AC fields with frequencies ranging from 1 Hz to 3 KHz immobilized worms inside the channel 8.
Implementation of the electric field in a microfluidic environment enables rapid and automated execution of the electrotaxis assay. This approach promises to facilitate high-throughput genetic and chemical screens for factors affecting neuronal function and viability.

Microfluidic-based Electrotaxis for On-demand Quantitative Analysis of Caenorhabditis elegans' Locomotion

A semi-automated micro-electro-fluidic method to induce on-demand locomotion in Caenorhabditis elegans is described. This method is based on the neurophysiologic phenomenon of worms responding to mild electric fields (&#x201C;electrotaxis&#x201D;) inside microfluidic channels. Microfluidic electrotaxis serves as a rapid, sensitive, low-cost, and scalable technique to screen for factors affecting neuronal health.

The nematode Caenorhabditis elegans is a versatile model organism for biomedical research because of its conservation of disease-related genes and ...

C. elegans Tracking and Behavioral Measurement

We have developed instrumentation, image processing, and data analysis techniques to quantify the locomotory behavior of C. elegans as it crawls on the surface of an agar plate. For the study of the genetic, biochemical, and neuronal basis of behavior, C. elegans is an ideal organism because it is genetically tractable, amenable to microscopy, and shows a number of complex behaviors, including taxis, learning, and social interaction1,2. Behavioral analysis based on tracking the movements of worms as they crawl on agar plates have been particularly useful in the study of sensory behavior3, locomotion4, and general mutational phenotyping5. Our system works by moving the camera and illumination system as the worms crawls on a stationary agar plate, which ensures no mechanical stimulus is transmitted to the worm. Our tracking system is easy to use and includes a semi-automatic calibration feature. A challenge of all video tracking systems is that it generates an enormous amount of data that is intrinsically high dimensional. Our image processing and data analysis programs deal with this challenge by reducing the worms shape into a set of independent components, which comprehensively reconstruct the worms behavior as a function of only 3-4 dimensions6,7. As an example of the process we show that the worm enters and exits its reversal state in a phase specific manner.

C. elegans Tracking and Behavioral Measurement

We have developed a video-rate tracking microscope system that can record and quantify C. elegans behavior at high resolution and high speeds. We have also developed computational methods to reduce the dimensionality of the worm images to a fundamental set of measurements that completely describe the shape of the worm.

We have developed instrumentation, image processing, and data analysis techniques to quantify the locomotory behavior of C. elegans as it crawls on the ...

Swimming Induced Paralysis to Assess Dopamine Signaling in Caenorhabditis elegans

The swimming assay described in this protocol is a valid tool to identify proteins regulating the dopaminergic synapses. Similar to mammals, dopamine (DA) controls several functions in C. elegans including learning and motor activity. Conditions that stimulate DA release (e.g., amphetamine (AMPH) treatments) or that prevent DA clearance (e.g., animals lacking the DA transporter (dat-1) which are incapable of reaccumulating DA into the neurons) generate an excess of extracellular DA ultimately resulting in inhibited locomotion. This behavior is particularly evident when animals swim in water. In fact, while wild-type animals continue to swim for an extended period, dat-1 null mutants and wild-type treated with AMPH or inhibitors of the DA transporter sink to the bottom of the well and do not move. This behavior is termed "Swimming Induced Paralysis" (SWIP). Although the SWIP assay is well established, a detailed description of the method is lacking. Here, we describe a step-by-step guide to perform SWIP. To perform the assay, late larval stage-4 animals are placed in a glass spot plate containing control sucrose solution with or without AMPH. Animals are scored for their swimming behavior either manually by visualization under a stereoscope or automatically by recording with a camera mounted on the stereoscope. Videos are then analyzed using a tracking software, which yields a visual representation of thrashing frequency and paralysis in the form of heat maps. Both the manual and automated systems guarantee an easily quantifiable readout of the animals' swimming ability and thus facilitate screening for animals bearing mutations within the dopaminergic system or for auxiliary genes. In addition, SWIP can be used to elucidate the mechanism of action of drugs of abuse such as AMPH.

Swimming Induced Paralysis to Assess Dopamine Signaling in Caenorhabditis elegans

Swimming induced paralysis (SWIP) is a well-established behavioral assay used to study the underlying mechanisms of dopamine signaling in Caenorhabditis elegans (C. elegans). However, a detailed method to perform the assay is lacking. Here, we describe a step-by-step protocol for SWIP.

The swimming assay described in this protocol is a valid tool to identify proteins regulating the dopaminergic synapses. Similar to mammals, dopamine (DA) ...

Behavior

Comparative Analysis of Experimental Methods to Quantify Animal Activity in Caenorhabditis elegans Models of Mitochondrial Disease

Caenorhabditis elegans is widely recognized for its central utility as a translational animal model to efficiently interrogate mechanisms and therapies of diverse human diseases. Worms are particularly well-suited for high-throughput genetic and drug screens to gain deeper insight into therapeutic targets and therapies by exploiting their fast development cycle, large brood size, short lifespan, microscopic transparency, low maintenance costs, robust suite of genomic tools, mutant repositories, and experimental methodologies to interrogate both in vivo and ex vivo physiology. Worm locomotor activity represents a particularly relevant phenotype that is frequently impaired in mitochondrial disease, which is highly heterogeneous in causes and manifestations but collectively shares an impaired capacity to produce cellular energy. While a suite of different methodologies may be used to interrogate worm behavior, these vary greatly in experimental costs, complexity, and utility for genomic or drug high-throughput screens. Here, the relative throughput, advantages, and limitations of 16 different activity analysis methodologies were compared that quantify nematode locomotion, thrashing, pharyngeal pumping, and/or chemotaxis in single worms or worm populations of C. elegans at different stages, ages, and experimental durations. Detailed protocols were demonstrated for two semi-automated methods to quantify nematode locomotor activity that represent novel applications of available software tools, namely, ZebraLab (a medium-throughput approach) and WormScan (a high-throughput approach). Data from applying these methods demonstrated similar degrees of reduced animal activity occurred at the L4 larval stage, and progressed in day 1 adults, in mitochondrial complex I disease (gas-1(fc21)) mutant worms relative to wild-type (N2 Bristol) C. elegans. This data validates the utility for these novel applications of using the ZebraLab or WormScan software tools to quantify worm locomotor activity efficiently and objectively, with variable capacity to support high-throughput drug screening on worm behavior in preclinical animal models of mitochondrial disease.

Comparative Analysis of Experimental Methods to Quantify Animal Activity in Caenorhabditis elegans Models of Mitochondrial Disease

This study presents protocols for two semi-automated locomotor activity analysis approaches in C. elegans complex I disease gas-1(fc21) worms, namely, ZebraLab (a medium-throughput assay) and WormScan (a high-throughput assay) and provide comparative analysis among a wide array of research methods to quantify nematode behavior and integrated neuromuscular function.

Caenorhabditis elegans is widely recognized for its central utility as a translational animal model to efficiently interrogate mechanisms and therapies of ...

An Assay for Measuring the Effects of Ethanol on the Locomotion Speed of Caenorhabditis elegans

Alcohol use disorders are a significant public health concern, for which there are few effective treatment strategies. One difficulty that has delayed the development of more effective treatments is the relative lack of understanding of the molecular underpinnings of the effects of ethanol on behavior. The nematode, Caenorhabditis elegans (C.&#160;elegans), provides a useful model in which to generate and test hypotheses about the molecular effects of ethanol. Here, we describe an assay that has been developed and used to examine the roles of particular genes and environmental factors in behavioral responses to ethanol, in which locomotion is the behavioral output. Ethanol dose-dependently causes an acute depression of crawling on an agar surface. The effects are dynamic; animals exposed to a high concentration demonstrate an initial strong depression of crawling, referred to here as initial sensitivity, and then partially recover locomotion speed despite the continued presence of the drug. This ethanol-induced behavioral plasticity is referred to here as the development of acute functional tolerance. This assay has been used to demonstrate that these two phenotypes are distinct and genetically separable. The straightforward locomotion assay described here is suitable for examining the effects of both genetic and environmental manipulations on these acute behavioral responses to ethanol in C.&#160;elegans.

An Assay for Measuring the Effects of Ethanol on the Locomotion Speed of Caenorhabditis elegans

C. elegans is a useful model for studying the effects of ethanol on behavior. We present a behavioral assay that quantifies the effects of ethanol on the locomotion speed of crawling worms; both initial sensitivity and the development of acute functional tolerance to ethanol can be measured with this assay.

Alcohol use disorders are a significant public health concern, for which there are few effective treatment strategies. One difficulty that has delayed the ...

Automated Behavioral Analysis of Large C. elegans Populations Using a Wide Field-of-view Tracking Platform

Caenorhabditis elegans is a well-established animal model in biomedical research, widely employed in functional genomics and ageing studies. To assess the health and fitness of the animals under study, one typically relies on motility readouts, such as the measurement of the number of body bends or the speed of movement. These measurements usually involve manual counting, making it challenging to obtain good statistical significance, as time and labor constraints often limit the number of animals in each experiment to 25 or less. Since high statistical power is necessary to obtain reproducible results and limit false positive and negative results when weak phenotypic effects are investigated, efforts have recently been made to develop automated protocols focused on increasing the sensitivity of motility detection and multi-parametric behavioral profiling. In order to extend the limit of detection to the level needed to capture the small phenotypic changes that are often crucial in genetic studies and drug discovery, we describe here a technological development that enables the study of up to 5,000 individual animals simultaneously, increasing the statistical power of the measurements by about 1,000-fold compared to manual assays and about 100-fold compared to other available automated methods.

Automated Behavioral Analysis of Large C. elegans Populations Using a Wide Field-of-view Tracking Platform

We describe protocols for using the wide field-of-view nematode tracking platform (WF-NTP), which enables high-throughput phenotypic characterization of large populations of Caenorhabditis elegans. These protocols can be used to characterize subtle behavioral changes in mutant strains or in response to pharmacological treatment in a highly scalable fashion.

Caenorhabditis elegans is a well-established animal model in biomedical research, widely employed in functional genomics and ageing studies. To assess the ...

Immunology and Infection

Evaluation of Motor Impairment in C. elegans Models of Amyotrophic Lateral Sclerosis

The neurodegenerative disease amyotrophic lateral sclerosis (ALS) features progressive loss of motor neurons accompanied by muscle weakness and motor impairment that worsens with time. While considerable advances have been made in determining genetic drivers of ALS for a subset of patients, the majority of cases have an unknown etiology. Further, the mechanisms underlying motor neuron dysfunction and degeneration are not well understood; therefore, there is an ongoing need to develop and characterize representative models to study these processes. Caenorhabditis elegans can adapt their movement to the physical constraints of their surroundings, with two primary movement paradigms studied in a laboratory environment- crawling on a solid surface and swimming in liquid. These represent a complex interplay between sensation, motor neurons, and muscles. C. elegans models of ALS can exhibit impairment in one or both of these movement paradigms. This protocol describes two sensitive assays for evaluating motility in C. elegans: an optimized radial locomotion assay measuring crawling on a solid surface and an automated method for tracking and analyzing swimming in liquid (thrashing). In addition to the characterization of baseline motor impairment of ALS models, these assays can detect suppression or enhancement of the phenotypes from genetic or small molecule interventions. Thus, these methods have utility for studying ALS&#160;models and any C. elegans strain that exhibits altered motility.

Evaluation of Motor Impairment in C. elegans Models of Amyotrophic Lateral Sclerosis

This protocol describes two sensitive assays for discriminating among mild, moderate, and severe motor impairment in C. elegans models of amyotrophic lateral sclerosis, with general utility for C. elegans strains, with altered motility.

The neurodegenerative disease amyotrophic lateral sclerosis (ALS) features progressive loss of motor neurons accompanied by muscle weakness and motor ...

Swimming-Induced Paralysis (SWIP) Assay: A Method to Quantify Dopamine-Mediated Locomotion in C. elegans

Source: Kudumala, S., et al. <a href="https://www.jove.com/video/59243/swimming-induced-paralysis-to-assess-dopamine-signaling">Swimming Induced Paralysis to Assess Dopamine Signaling in&#160;Caenorhabditis elegans</a>.&#160;J. Vis. Exp.&#160;(2019).
Here, we introduce an assay to measure SWIP, a behavioral phenotype of&#160;C. elegans&#160;that occurs during vigorous motion like swimming.&#160; The example protocol discusses both manual and automated analysis approaches.

Test paraliżu wywołanego pływaniem (SWIP): metoda ilościowego określania lokomocji za pośrednictwem dopaminy u C. elegans

Source: Kudumala, S., et al. Swimming Induced Paralysis to Assess Dopamine Signaling in&#160;Caenorhabditis elegans.&#160;J. Vis. Exp.&#160;(2019).
Here, ...

Automated Analysis of C. elegans Swim Behavior Using CeleST Software

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Single Wavelength Shadow Imaging of Caenorhabditis elegans Locomotion Including Force Estimates

Quantitative Locomotion Study of Freely Swimming Micro-organisms Using Laser Diffraction

Microfluidic-based Electrotaxis for On-demand Quantitative Analysis of Caenorhabditis elegans' Locomotion

C. elegans Tracking and Behavioral Measurement

Swimming Induced Paralysis to Assess Dopamine Signaling in Caenorhabditis elegans

Comparative Analysis of Experimental Methods to Quantify Animal Activity in Caenorhabditis elegans Models of Mitochondrial Disease

An Assay for Measuring the Effects of Ethanol on the Locomotion Speed of Caenorhabditis elegans

Automated Behavioral Analysis of Large C. elegans Populations Using a Wide Field-of-view Tracking Platform

Evaluation of Motor Impairment in C. elegans Models of Amyotrophic Lateral Sclerosis

Test paraliżu wywołanego pływaniem (SWIP): metoda ilościowego określania lokomocji za pośrednictwem dopaminy u C. elegans