Method Article

C. elegans Tracking and Behavioral Measurement

DOI:

10.3791/4094

⸱

November 17th, 2012

 ,  ,  , 
1Donnelly Centre, University of Toronto, 2Department of Physics and Astronomy, Vrije Universiteit, 3Okinawa Institute of Science and Technology, 4Department of Physics, University of Toronto

In This Article

Summary

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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.

Abstract

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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.

Protocol

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1. Description of Tracking Microscope

  1. An agar plate is illuminated by a fiber light source and imaged with a camera. This system is mounted to an X,Y translation stage.
  2. The stage is moved by standard stepper motors, which are connected to a stepper motor controller.
  3. The controller and camera are connected to the computer and controlled by custom programs written in LabVIEW.
  4. The camera images the surface of an agar plate and identifies dark objects on a light background.
  5. The image quality is adjusted so that computer program can quantify objects in real time. The gain, brightness, and shutter speed of the camera can be adjusted to provide a dark object on a white background.
  6. A calibration mark for the automatic calibration process is made by poking the agar surface with a worm pick.
  7. The filtered, binary image which is used by the tracking program can be checked.
  8. The software has an auto-calibration feature that calculates the calibration matrix by moving a test object a fixed distance.
  9. Distances in pixels is calibrated to steps taken by the stepper motor by the calibration matrix.
  10. After calibration, the system is ready to go and does not need to be recalibrated unless the magnification is changed or if the camera is repositioned.

2. Preparing Tracking Plates and C. elegans for Tracking

  1. A copper ring is used to corral the worms and keep them from migrating to the edge of the plate. The copper provides a local chemical barrier and does not affect movement of the worm otherwise for the duration of the experiment (<1 hr). Heat the ring first by placing it on a heat block or equivalent.
  2. Place ring onto a fresh agar plate (1.7% Bacto Agar, 0.25% Bacto-Peptone, 0.3% NaCl, 1 mM CaCl2, 1 mM MgSO4, 25 mM potassium phosphate buffer, 5 μg/ml cholesterol) and press down slightly to embed it into the agar surface.
  3. Pick L4 stage or young adult worms onto an agar plate filled with some NGM buffer (0.3% NaCl, 1 mM CaCl2, 1 mM MgSO4, 25 mM potassium phosphate buffer) to wash them of food residue. Let the worms swim for a few minutes.
  4. Carefully place a single worm onto the tracking plate near the center of the ring. And then place the plate onto the worm tracker.

3. Worm Tracking

  1. Run the LabVIEW program and select options if needed (location for images, types of images, measurements, camera settings).
  2. Using the joystick move the microscope until an image of the worm is in the field of view (computer screen). Press "track" to engage the tracking program.
  3. The computer program actually measures the movements of a binary filtered images as shown and can make measurements on this image in real time
  4. After tracking a reconstruction of the global trajectory can be made from the stepper motor movements, while the local shape changes of the worm can be seen in detail.

4. Data Analysis

  1. Run skeletonizing script (MATLAB) to parameterize the worm's shapes. (Figure 2)
  2. Calculate eigenmodes of the skeletonized data. (Figure 3,4)
  3. Show Figure 5. Read figure legend or representative results.

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Results

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Example: When foraging, C. elegans transitions from forward to reverse motion, often performing a reorientation (omega turn) before returning to the forward motion state. Quantifying this transition is important in understanding the foraging patterns of movement and also in the worm's motor control. The power to reveal subtle details of locomotion behavior can be seen using our tracker device.

As an example we look at the forward to reverse and reverse to forward transition by captur...

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Discussion

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The study of locomotion and natural behavior requires non-invasive tracking techniques in partner with data reduction techniques. Here we have demonstrated an easy to use tracking system that records detailed images of C. elegans behavior as it crawls on the surface of an agar plate. The amount of information contained in these images is vast and high-dimensional, and so we have also developed methods to reduce the dimensionality of the data into only four fundamental measures. These measures are comprehensive a...

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Disclosures

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No conflicts of interest declared.

Materials

List of materials used in this article
NameCompanyCatalog NumberComments
CCD cameraBaslerA601f
LensEdmund OpticsMMS series
Fiber IlluminationDolan JennerDC-950H
Translation stageDeltronLS3-4
Stepper MotorUS digitalMS23C
Stepper motor driveGeckoG201
Stepper motor controlSimpleStepSSXYZ
All programming code is available. Please send a request email to the corresponding author.

References

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  3. Pierce-Shimomura, J. T., Morse, T. M., Lockery, S. R. The fundamental role of pirouettes in Caenorhabditis elegans chemotaxis. J. Neurosci. 19 (21), 9557-9569 (1999).
  4. Gray, J. M., Hill, J. J., Bargmann, C. I. A circuit for navigation in Caenorhabditis elegans. Proc. Natl. Acad. Sci. U.S.A. 102 (9), 3184-3191 (2005).
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  7. Stephens, G. J., Johnson-Kerner, B., Bialek, W., Ryu, W. S. From modes to movement in the behavior of C. elegans. PLoS One. 5 (11), e13914(2010).
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  9. Ramot, D., Johnson, B. E., Berry, T. L. Jr, Carnell, L., Goodman, M. B. The Parallel Worm Tracker: A Platform for Measuring Average Speed and Drug-Induced Paralysis in Nematodes. PLoS One. 3 (5), e2208(2008).
  10. Swierczek, N. A., Giles, A. C., Rankin, C. H., Kerr, R. A. High-throughput behavioral analysis in C. elegans. Nat. Methods. 8 (7), 592-598 (2011).
  11. Leifer, A. M., Fang-Yen, C., Gershow, M., Alkema, M. J., Samuel, A. D. Optogenetic manipulation of neural activity in freely moving Caenorhabditis elegans. Nat. Methods. 8 (2), 147-152 (2011).
  12. Stirman, J. N., Crane, M. M., Husson, S. J., Wabnig, S., Schultheis, C., Gottschalk, A., Lu, H. Real-time multimodal optical control of neurons and muscles in freely behaving Caenorhabditis elegans. Nat. Methods. 8 (2), 153-158 (2011).
  13. Ben Arous, J., Tanizawa, Y., Rabinowitch, I., Chatenay, D., Schafer, W. R. Automated imaging of neuronal activity in freely behaving Caenorhabditis elegans. J Neurosci Methods. 187 (2), 229-234 (2010).
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Tags

C elegans TrackingWorm TrackerImage ProcessingData AnalysisAgar PlateStepper MotorLabVIEW SoftwareEigen Mode AnalysisBehavioral MeasurementDimensionality Reduction

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