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Tracking the Motion of the Outer Ends of Microtubules

Tracking the Motion of the Outer Ends of Microtubules. Stathis Hadjidemetriou, Derek Toomre, James S. Duncan Yale University, School of Medicine, New Haven, CT 06520 . Microtubule (MT) Assembly. Self-assembling biopolymers of cytoplasm, width≈25 nm. Provide structure and support to the cell.

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Tracking the Motion of the Outer Ends of Microtubules

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  1. Tracking the Motion of the Outer Ends of Microtubules Stathis Hadjidemetriou, Derek Toomre, James S. Duncan Yale University, School of Medicine, New Haven, CT 06520

  2. Microtubule (MT) Assembly • Self-assembling biopolymers of cytoplasm,width≈25 nm • Provide structure and support to the cell MTs Fluorescence labeling in confocal microscopy, ≈200 nm/pixel

  3. Why are Microtubule Dynamics Interesting? • Dynamic ‘highways’ for trafficking of vesicles • Regulate cell migration and cell division MT: tracks for granules (dark dots) in fish cell MT: spindle in yeast cell

  4. Importance in Cell Pathology • Anticancer drugs (e.g. Taxol®, Taxotere®) inhibit polymerization of MTs to retard: • Cancer growth through cell division • Cancer spread through cell migration • Deficient neurotransmitter transport implicated in neurodegenerative diseases, e.g. Alzheimer’s

  5. Objective Fibroblast & epithelial cells • Segment of MTs [Hadjidemetriou et al, 05] • Tracking of MTs motion[Hadjidemetriou et al, 04]

  6. Previous Work in Segmentation of Biomedical Curvilinear Structures • Microscopy: • Actin, chromosomes [Noordmans et al, 98] • Organ imaging: • Colon [Cohen et al, 01] • Vasculature [Chung et al, 01], bronchial tree [Fridman et al, 03] • White matter tractography [Parker et al, 02][Jackowski et al, 04]

  7. Previous Work in Motion Tracking of Biomedical Structures • Microscopy: • Manual: Annotation of video [Waterman et al, 00] • Automated: Single molecules, cytoplasmic particles [Cheezum et al 01] • Organ imaging: • Lungs, heart [Papademetris et al, 02] • Cineangiography of coronary arteries [Shechter et al, 03] • Registration of longitudinal data [Weiner et al, 2004]

  8. Confocal Microscopy • Pinhole microscopy, 2D or 3D • Resolution: x-y≈200 nm/pixel, z≈500 nm/pixel, weff≈2-3 pixels • Laser causes photobleaching I:D→[0, Imax],fluorescence

  9. Preprocessing • Filtering: Largest eigenvalue of matrix of 2nd derivatives

  10. MT Segmentation in Initial Frame • Segment MTs starting from the ends • Get pinner pinner Microtubule Microtubule end

  11. Cumulative Cost Over Valid Neighborhood • 1. Start frompinner tocompute cost map Uo: • P1: Isotropic, favors MT fluorescence: . 1 P1(x) . Imax Ix

  12. Centerline Component • P2: Anisotropic, favors MT centerline: • 2. Solve with one pass algorithm . x

  13. Extraction of Curve Segment • 3. Curve segment is streamline of U0 • Backtrack along Uofrom starting point:

  14. Extraction of First MT Segment Null region of Ulo Valid region for Ulo, r=5weff Microtubule end Microtubule

  15. MT tangent Extraction of Subsequent MT Segments Valid region for Ulo, r=5weff 2weff Null region of Ulo Microtubule Most recently segmented point, pstart

  16. Evaluation of Curve Segment Contrast measure across its axis: Microtubule segment weff weff weff Outer zone Outer zone Inner zone

  17. Microtubule 2D Segmentation Compute Uloin segment neighborhood Backtrack along Ulo to get segment Compute Uloin segment neighborhood Backtrack along Ulo to get segment Yes Evaluate curve

  18. Examples of Segmentations of 2D MT

  19. MT Extrapolation in Average Frame • Compute average image over time:

  20. MT Extrapolation in Average Frame • Extrapolate MTs starting from the outer ends • Get pouter

  21. Thresholding of Data • Estimatemean and st.dev. of intensitieson MT centerlines • Threshold=mean-st.dev.

  22. . Lt pouter Streamline Passing from Microtubule • For all t=tstart+1→tend: • Compute local U0t starting from pinner • Extract streamline Ltbetween pinner and pouter • Ltincludes microtubule MT . pinner

  23. Examples of Streamlines Through Microtubules

  24. u Compute MT End Feature For all t=tstart+1→tend: • Compute end feature , RalongstreamlineLt, R=directional derivative of Uoalong tangentu ofLt: . . Lt pouter pinner .

  25. . tend etend . Lt et . t Lt-1 et-1 MT End Point Trajectory • Condition 1: Limit motion shift: • |et-et-1| < shift • et -MT end at time t • Condition 2: Point of maximum R • along Lt: . . . . L1 . etstart tstart

  26. tend t pouter . . pinner Outline of Motion Tracking Algorithm Preprocessing Segment microtubules to get pinner . Microtubule tstart Extrapolate microtubules to getpouter Next subsequence MT end For all t: Extract streamline, Lt btw pinnerand pouter For allt:Compute MT end feature, R • Implementation in C++ • Enhancement for visualization Form MT end point trajectory

  27. Phantom Sequence for Noise • Green: Ground truth • Red: Algorithm Size=150x150x100, Time=2 min 45 sec

  28. Phantom Sequence for Proximity • Size=150x150x100, • Time=2 min 38 sec • Green: Ground truth • Red: Algorithm

  29. Examples 1 of Real Sequence

  30. Example 1: Ground Truth • Size=635x471x100, • Time=30 min 6 sec • Error=3.3 pixels • Green: Ground truth • Red: Algorithm

  31. Example 2 of Real Sequence

  32. Example 2: Ground Truth • Error in MTs tracked=17/19 • Error in tracking=2.6 pixels • Green: Ground truth • Red: Algorithm

  33. Example 3 of Real Sequence • Size=136x112x100, • Time=5 min 7 sec

  34. Example 4 of Real Sequence • Size=350x262x36, • Time=3 min 25 sec, • Error in MTs tracked=17/19 • Error in tracking=2.6 pixels • Green: Ground truth • Red: Algorithm

  35. Summary • Preprocessing • MT segmentation and extrapolation: • First and average image • Consecutive MT segments • MT end feature ≡derivative of Uo along streamline • Form MT end trajectory

  36. Discussion • Evaluate:Phantom, real • Robust: • Noise, • Deformation, • Proximity • MT end polymerization • Sensitive: • Intersections, • Lateral motion

  37. End

  38. Example 4 of Real Sequence

  39. No aperture Aperture Laser illumination Wide field of view Narrow field of view Confocal vs Conventional Microscopy Confocal Microscopy Widefield Microscopy CCD CCD Beam splitter Beam splitter Light source Objective lens Objective lens Specimen Specimen

  40. Quantification of Microtubule Motion • Specification of tips at first frame • Motion tracking of ends • Measure average (de)-polymerization: • Duration, • Rates

  41. C R P D Q R C Quantification of Microtubule End Motion • Microtubule states: • Polymerization, • Depolymerization, • Quiescent • Statistics: • States:(de)polymerization rates, avg time duration • State transitions: Rescue, catastrophe

  42. Examples of Motion Statistics

  43. Example 2: Statistics • Size=636x472x100, • Time= min sec

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