Quantification of locomotory behaviors in C. elegans

Julie Korich, Ph.D.Staff Scientist/Research Liaison

C. elegans as a Model Organism

Various disciplines employ C. elegans nematode as a model organism:• Biology: Cell Differentiation, Meiosis, and RNAi studies• Neuroscience: Neuronal function, differentiation,

behavior• Toxicology: Adverse effects of chemicals on organisms• Ecology/Soil Science: Environmental impact on soil

quality

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Modeling Locomotory Behaviors

• Locomotory assays are used in many areas of research including neurodegenerative disease, aging, and toxicology

• It is a common screening tool for genetic assays

• How do you quantify locomotory behaviors?

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Challenges with Manual Marking

• Tedious and laborious – makes large scale studies difficult

• Lack of Precision• Observer

variability

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Available Tracking Software

• Single worm trackers• WormTracker 2.0 (Shafer Lab)• Nemo (Tavernarakis Lab) • Multimodal Illumination and Tracking System (Lu Lab)• CoLBeRT (Samual Lab)• Opto-mechanical system for virtual environments (Lockery

Lab)

• Multi Worm Trackers• The Parallel Worm Tracker (Goodman Lab)• OptoTracker (Gottschalk Lab)• The Multi Worm Tracker (Kerr Lab)

Husson, S. J. et al. Keeping track of worm trackers (September 10, 2012), WormBook, ed. The C. elegans Research Community, WormBook,doi/10.1895/wormbook.1.156.1, http://www.wormbook.org.

Challenges of Automatic Tracking

• Background clutter and existing worm tracks

• Juvenile worms and eggs on medium

• Interactions• Head/Tail identification • Entanglement • Tracking large # of

worms• Velocity of movement

• Swimming/thrashing

Video provided by Dr. Kate Harwood

How WormLab Tracks

• Supports high mag and low mag (whole plate) tracking

• Composed of 2 parts:• Detection (finds new worms as the enter the movie)

• Tracking (determining changes in worm position and shape from frame to frame)

• Thresholding tools to refine background and improve detection despite moderate background clutter

• Uses geometric model, worm motion model, backtracking and Multiple Hypothesis Tracking for accurate detection

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Worm Detection

• The image is inverted and segmented to identify potential worm objects

• The algorithm measures 2 points of high curvature from a closed planar B-spline curve around the boundary of the worm object

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Head and Tail Determination

• Identification based on the worm’s shape and frequency of movement

• We apply the same spatial and temporal cues used by human observers: • The worm’s tail area is lighter than the head • The worm’s tail area is thinner than the

head • The head moves more frequently than the

tail

• Head/tail identification can be swapped for entire track by user

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Detected Head

Geometric Model

• Based on the center line of the worm and boundary

• Modeled on a spline basis to allow easy scaling and resampling at different resolutions

• User can determine the # of points along the center line used in the analysis• 3 pts: head, tail, center

• 17-19 pts: bending analysis

• 59 pts: full resolution (default)

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Worm Motion Model

• ɳ = movement along centerline (peristaltic progression factor)

• Δα = Displacement orthogonal to the trajectory

• Also use elongation and contraction to model motion

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Tracking Across Frames

• A deformable model estimation algorithm fits the geometric model from the previous frame to the current frame

• Backtracking is performed to re-establish worms with their previous tracks if lost

• Backtracking used if video starts with entangled worms

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Multiple Hypothesis Tracking

• Apply a set of hypothesized worm locations across time, thus building a hypothesis tree

• Resolve conflicts by finding the path of Maximum Fitness (best fit across frames)

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Detection of Complex Behaviors

• The geometric model, worm motion model, and MHT help identify worms in ambiguous conformations:• Coiled worms, • Overlapping worms• Omega bends• Reversing worms

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Detection of Complex Behaviors

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Editing Functions

• Manually draw a worm that is not detected prior to tracking

• Swap head and tail across a track• Join tracks• Split tracks• Delete worms per frame or across all frames

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Metrics and Analyses

• Length: Distance between head and tail along central axis

• Width is calculated from N points along the worm• Direction is the direction of travel• Postion is the center of the median axis• Instaneous speed: Velocity along the central axis from

one frame to the next• Moving Average Speed: Instantaneous speed

averaged over multiple frames• Amplitude: Amplitude of the sine wave that best fits

the worm posture• Wavelength: Period of the sine wave the best fits the

worm’s posture • Bend Angle: Bending angle at the midpoint

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Detection of Omega Bends

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• Begins when the bending angle between head-midpoint and tail-midpoint drops below 1.57 radians ( 90°) and continues until the angle exceeds 1.57 radians

Detection of Reversals

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• Reversal is defined as worm moving backwards for user defined set of frames

Head Bending Analysis

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• Indicates foraging

• Worm sampled with 19pts

• Bending angle is 3pt from head

Imaging Suggestions

• Contrast: dark solid worms on light background• Lawn: replate worms to minimize tracks• Frame Rate: 5-10fps is adequate, faster for swimming

worms• Cameras:

• Industrial machine vision cameras (CCD) work• Webcams (low cost CMOS not so much)• Recommend monochrome cameras

• Image size: • Whole plate: 2500x2500 resolution (5 Megapixels)• Single worms: 800x600, 1200x1024 and faster frame rates

              

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Video provided by Dr. Kate Harwood

WormLab Overview

• PC & MAC compatible

• Accepts video files in numerous formats

• Includes data and video export (with tracking overlay)

• Workflow based – easy to train and use

• Export metrics to Matlab and Excel

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• Control camera hardware to record videos from stereoscopes, inverted microscopes, or macro photography setup• Automatic Save• Variable Frame Rate • Scaling Tool: Calculate the pixel size• Scaling and frame rate are saved within the video file, and

automatically read by WormLab for analysis• Support DCAM/IIDC compliant cameras (Point Grey, Allied

Vision and Sony)

Camera Control

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• Track swimming, thrashing worms:

• Use a modified worm motion model to map the oscillation of the center point radially

• Quantify pharyngeal pumping

• Synchronization of stimulation and tracking

• New analyses for bending and shape interpretation

• Development of different assays – chemotaxis studies, etc.

WormLab – Future Directions

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Summary

• WormLab for automatic detection and tracking of worms

• Provide metrics including size, speed, direction• Track in complex backgrounds, entanglements,

and shapes• Capture video sequences or open previously

acquired sequences

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• I would like to thank Tony Cooke for organizing the seminar and the University of Washington

• Email questions to:

• Julie Korich at julie@mbfbioscience.com

• View our website for additional information, videos, and instructions to download a tree trial (http://www.mbfbioscience.com/wormlab)

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Acknowledgments

• Email questions to Julie Korich at julie@mbfbioscience.com

• Download a free trial www.mbfbioscience.com/wormlab

• Watch a webinar that gives an overview of WormLab www.mbfbioscience.com/webinars

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Learn More

• I would like to thank Tony Cooke for organizing the seminar and the University of Washington

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Acknowledgments