- Open Access
An imaging system for standardized quantitative analysis of C. elegans behavior
- Zhaoyang Feng†1,
- Christopher J Cronin†2,
- John H WittigJr1,
- Paul W Sternberg2, 3 and
- William R Schafer1Email author
© Feng et al; licensee BioMed Central Ltd. 2004
- Received: 12 May 2004
- Accepted: 26 August 2004
- Published: 26 August 2004
The nematode Caenorhabditis elegans is widely used for the genetic analysis of neuronal cell biology, development, and behavior. Because traditional methods for evaluating behavioral phenotypes are qualitative and imprecise, there is a need for tools that allow quantitation and standardization of C. elegans behavioral assays.
Here we describe a tracking and imaging system for the automated analysis of C. elegans morphology and behavior. Using this system, it is possible to record the behavior of individual nematodes over long time periods and quantify 144 specific phenotypic parameters.
These tools for phenotypic analysis will provide reliable, comprehensive scoring of a wide range of behavioral abnormalities, and will make it possible to standardize assays such that behavioral data from different labs can readily be compared. In addition, this system will facilitate high-throughput collection of phenotypic data that can ultimately be used to generate a comprehensive database of C. elegans phenotypic information.
The hardware configuration and software for the system are available from email@example.com.
- Behavioral Phenotype
- Motorize Stage
- Skeleton Point
- Parameter Estimation Module
- Touch Avoidance
The nematode Caenorhabditis elegans is among the most widely studied genetically-tractable experimental organisms. C. elegans is a soil-dwelling animal with a relatively simple and extremely well characterized anatomy; an adult hermaphrodite, for example, contains exactly 959 somatic cells, each with an identified position, morphology and cell lineage. Because of its short generation time, amenability to germline transformation, and completely sequence genome, it is ideally suited for classical and molecular genetic analysis. In particular, since the C. elegans nervous system is simple and well-characterized (the identity and connectivity of each neuron is known), it has become a facile model for studying the molecular basis for nervous system function. Robust behavioral phenotypes have been described for many C. elegans behaviors, including locomotion, egg-laying, mating and feeding, and these phenotypes have proven extremely useful for the genetic dissection of key aspects of neuronal function such as synaptic release, sensory transduction, and neuromuscular signalling .
Historically, a major limitation of such neurobiological studies in C. elegans has been the lack of quantitative methods for the evaluation of behavioral phenotypes. For example, the phenotypes of many behavioral mutants, even those defective in key aspects of neuronal signal transduction, appear subtle to a real-time human observer, and are difficult to assay without time and labor-intensive analysis of video recordings [2–4]. Even the phenotypes of mutants with grossly abnormal behavior are difficult to characterize precisely by manual observation. For example, mutants with striking defects in locomotion (uncoordinated, or Unc mutants) are typically classified using qualitative terms such as "coiler", "kinker", "sluggish", "slow" and "loopy" [5, 6]. Since these descriptions are imprecise and subjective, it is extremely difficult if not impossible to assess the phenotypic similarity between two mutants based solely on such characterizations. Another challenge occurs in the analysis of behaviors such as locomotion and egg-laying, which can fluctuate over long time scales or involve infrequently-occurring events that are difficult to evaluate through real-time observation . Furthermore, the quantitative assays that have been used in C. elegans behavioral studies (e.g. ) generally differ from lab to lab, and this lack of standardization has made effective comparison of data collected by different researchers difficult.
To address these problems, we have developed an automated tracking and image analysis system for the quantification of C. elegans behavioral patterns. Using this system, it is possible to record the behavior of individual animals at high magnification over long time periods and to simultaneously quantify a large number of behaviorally-relevant features for subsequent analysis. This system has wide applications for the dissection of complex C. elegans behaviors, and will also make it possible to comprehensively classify the behavioral patterns of C. elegans mutants on a genome-wide scale. By making this system widely available to C. elegans neuroscientists, we intend to define a software architecture that can be continually optimized and upgraded to incorporate new parameters that are useful to worm researchers, as well as a hardware platform that can be expanded to provide additional mechanical capabilities for the research community.
To effectively capture the locomotion behavior of a freely-moving worm, it is necessary to acquire a sequence of images from which the animal's position, speed and body posture at any given point in time can be derived. C. elegans are small (1 mm) animals which, in the laboratory, are normally cultured on agar plates covered with a lawn of the common laboratory bacterium Escherichia coli. Nematodes move using an approximately sinusoidal wave motion that is propagated along the anterior/posterior axis in the dorsal/ventral plane. On an agar plate, the animal will normally lie on either its left or right lateral surface, making the waveform associated with movement visible from above. When crawling at maximum speed, an adult nematode travels at a rate of approximately 500 μm/s; thus, under the relatively high magnification (40–50 X) required to measure detailed features of body posture the worm can quickly crawl outside the field of view. It is therefore necessary to incorporate into the imaging system a motorized stage that can automatically follow the animal's movements and keep it in the microscope's visual field.
Briefly, the software for the system consists of four basic modules. The first module, called the tracker, allows the system to follow the worm as it crawls around the plate by directing the movements of a motorized stage to maintain the animal in the center of the field of view. As the video acquisition board acquires digital images from the microscope field, the tracker program identifies the animal from each acquired image based on 1. size of the objective isolated from background and 2. the direction of the animal crawling in previous frame if more than two objectives are found Based on the coordinates of the animal's centroid within the field of view, the tracker directs the movements of the stage to recenter the animal in the visual field when animals approach the edge of the image frame. The program then saves an image (460 × 380) of the worm containing visual frame (i.e, the pixels composing the worm body plus the minimum enclosing rectangle), the position of the animal within the field of view, the position of the stage, the time the image was captured, and other information crucial for behavioral analysis. These data are saved into the widely used .avi multimedia format, with a MPEG-4 filter to significantly compress the size of data. Null images are not saved into the .avi format, and the user is notified of the null frame. The highest frame rate with which the tracker can perform these operations with our current hardware setting is 30 frame/sec.
Next, the system contains a module (called the converter) that processes the raw images to simplify parameter estimation. First, the grayscale image is thresholded and converted to a binary image representing the worm outline. The image is further simplified by generating a morphological skeleton along the midline of each binary image and then distributing 30 skeleton points along this skeleton. A third module (called Lineup) then orders the backbone points from head to tail. To distinguish head and tail, minor user input is required to achieve 100% accuracy. In wild type, this user input (which involves identifying the head with a mouse click) is only required on 1% of the frames. Otherwise, all image processing is completely automated.
Features measured by the automated system. List of the behavioral and morphological features. Detail algorithms to output these features are found in supplemental data. 144 statistical results (mean, maximum and minimum where applicable) of these features are output into Microsoft Access, while the values of these feature at each time when an image is grabbed are saved in Microsoft Excel.
Total area of worm
Frequency of body bends
Best-fit ellipse, major axis
Best-fit ellipse, major axis/minor axis
Equivalent ellipse ratio
Maximum skeleton point angle difference
Frequency of sideways (foraging) head movements
Specific behavior (feeding)
Angle of foraging movements
Specific behavior (feeding)
Distance moved by head during foraging
Specific behavior (feeding)
Frequency of angle change between skeleton points
Maximum distance moved from starting point
Speed of the animal's centroid movement
Ratio of head to tail movement speed
Heywood circularity factor
Local movement speed
Distance from head to tail
Length/number of pixels in skeleton
Percentage of time worms coils their body
Specific behavior (coil)
Mean perpendicular intercept
Mode of area
Mode of speed
Frequency of occurrence of modal area
Frequency of occurrence of modal speed
Local body movement speed/centroid speed
Locomotion wave efficiency
Minimum enclosing rectangle (MER) length
Minimum enclosing rectangle length/width ratio
Percentage of time that a worm performs reversals
Specific behavior (escape)
Number of reversal sessions that a worm performs
Specific behavior (escape)
Average distance traveled backward in reversals
Specific behavior (escape)
Mean value of skeleton point vector angle
Length of worm / oriented MER area
Skeleton enlongation factor
Oriented MER height
Sum of x coordinate times x coordinates of skeleton points
Sum of x coordinate times y coordinates of skeleton points
Sum of y coordinate times y coordinates of skeleton points
Average angle between skeleton points and centroid
Max angle between skeleton points and centroid
Average distance between skeleton points and centroid/length
Max distance between skeleton points and centroid/length
Min distance between skeleton points and centroid/length
Oriented MER width
Direction of centroid movement
Thickness of worm
Total distance traveled by a worm
Amplitude of waves in worm's track
Wavelength of waves in worm's track
Transparency of worm body
Percentage of time that a worm performs a sharp turn
Specific behavior (search)
Waddel disk diameter
Largest spatial span of a sine wave
Sum of X coordinates of all sktps
Sum f Y coordinates of all sktps
Sum of X coordinate * absolute value of Y coordinate
The software is available in a PC version (compiled and benchmarked on a PC with 1 G Hz Pentium-III running Windows 2000 or XP). Software is written with C/C++, Labview 7.0 and Matlab (release 13), and complied with NI LabWindow 7.0. Installation disk and dataset samples are available upon request for non-profit academic usage with a license fee ($75, charged by National Instruments for the usage of their vision library; see Additional file 3 and 4, "codes" and "filelist" for details). Worm behavioral image data are in AVI format with a standard MPEG-4 filter (Microsoft MPEG-4 v2). Quantitative morphological and behavioral data are outputted into two widely distributed formats: Microsoft Excel and Microsoft Access. Using this hardware configuration, it is possible to process a 2 Hz 1 min real time data set in less than 5 minute (from image data to final data). Thus, it is feasible to envision using the system to screen for specific behavioral phenotypes among mutagenized C. elegans.
We describe here a prototype for a standard, open-source system for automated phenotypic analysis of C. elegans behavior. We anticipate that such a system will be extremely useful to C. elegans neurobiologists, as machine vision offers a number of clear advantages over real-time observation for the characterization of behavioral phenotypes. First, it provides a precise definition of a particular mutant phenotype, facilitating quantitative comparisons between different mutant strains. For example, the waveform parameters have provided detailed information about the effects of neuronal G-protein signalling pathway genes on locomotion behavior. Even phenotypes that are extremely difficult to distinguish by eye (e.g. those of the calcium channel mutants unc-2 and unc-36) can be identified with relatively high reliability using the system . In addition, it has been possible to use our system to reliably score behavioral events without labor-and time-intensive (and potentially biased) human scoring; for example, our system has been used to automatically detect directional reversals with high reliability in a touch avoidance assay . Other specific postures such as coils can also be detected with high (>90%) reliability (Z. Feng, unpublished data).
With appropriate controls, a standardized phenotyping system also makes it possible to compare behavioral data collected by different researchers in different labs with greater precision than is possible using qualitative observer-driven approaches. In particular, a computerized system makes it possible to comprehensively assay multiple aspects of behavior simultaneously, yielding a complex phenotypic signature that can be used for bioinformatic studies . In the future, we hope to use the tools described here to generate a comprehensive C. elegans phenotypic database that could be used to explore the clustering and relative similarities of mutant phenotypes.
This work was supported by research grants from the National Institutes of Health (to W.R.S. and P.W.S.) and the Howard Hughes Medical Institute (P.W.S.). Zhaoyang Feng is a postdoctoral fellow of the Burroughs-Wellcome Fund/La Jolla Interfaces in Science interdisciplinary training program. The authors thank Anthony Kempf for critical discussion.
ZF and CJC jointly wrote the software and developed the system described here. JHW developed an early version of the system and designed the hardware configuration. PWS and WRS jointly conceived of this project, and participated in its design and coordination. WRS drafted the manuscript, CJC drafted the supplemental guide to the algorithms, and ZF drafted the supplemental hardware list. All authors read and approved the final version.
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