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Analysis of microtubule dynamic instability using a plus-end growth marker

Nature Methods volume 7, pages 761–768 (2010)Cite this article

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Abstract

Regulation of microtubule dynamics is essential for many cell biological processes and is likely to be variable between different subcellular regions. We describe a computational approach to analyze microtubule dynamics by detecting growing microtubule plus ends. Our algorithm tracked all EB1-EGFP comets visible in an image time-lapse sequence allowing the detection of spatial patterns of microtubule dynamics. We introduce spatiotemporal clustering of EB1-EGFP growth tracks to infer microtubule behaviors during phases of pause and shortening. We validated the algorithm by comparing the results to data for manually tracked, homogeneously labeled microtubules and by analyzing the effects of well-characterized inhibitors of microtubule polymerization dynamics. We used our method to analyze spatial variations of intracellular microtubule dynamics in migrating epithelial cells.

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Figure 1: EB1-EGFP comet detection.
Figure 2: EB1-EGFP object tracking.
Figure 3: EB1-EGFP growth-track clustering.
Figure 4: Effects of microtubule inhibitors.
Figure 5: Effects of tubulin acetylation and spatiotemporal microtubule regulation in migrating cells.

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Acknowledgements

This work was supported by US National Institutes of Health grants U01 GM067230 to G.D. and R01 GM079139 to T.W. This research was in part conducted in a facility constructed with support from the Research Facilities Improvement Program grant C06 RR16490 from the National Center for Research Resources of the National Institutes of Health. We thank A. Wheeler (Imperial College London) for the EB1-EGFP–expressing HaCaT cell line.

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Author notes

  1. Gaudenz Danuser

    Present address: Present address: Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA.,

Authors and Affiliations

  1. Department of Cell Biology, The Scripps Research Institute, La Jolla, California, USA

    Alexandre Matov, Kathryn Applegate & Gaudenz Danuser

  2. Department of Cell and Tissue Biology, University of California, San Francisco, California, USA

    Praveen Kumar & Torsten Wittmann

  3. Institute of Cell Biology, Swiss Federal Institute of Technology, Zurich, Switzerland

    Claudio Thoma & Wilhelm Krek

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  1. Alexandre Matov
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Contributions

A.M. designed and implemented algorithms for comet detection and track clustering, and performed computer vision and statistical analysis. P.K. and T.W. acquired EB1-EGFP and mCherry-tubulin datasets. C.T. and W.K. generated data for the comparison of EB3-EGFP and CLIP170-EGFP comet dynamics in pVHL knockdown cells. G.D. conceived the algorithm for growth-track clustering and designed validation experiments. T.W. directed image acquisition and contributed to data analysis. All authors contributed to the interpretation of the results and to the discussion of improvements to the software. G.D., T.W. and A.M. wrote the manuscript.

Corresponding authors

Correspondence to Gaudenz Danuser or Torsten Wittmann.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6, Supplementary Tables 1–2 and Supplementary Notes 1–9 (PDF 1679 kb)

Supplementary Software 1

ClusterTrack software for detection, tracking and growth track clustering of plus end-labeled microtubules in the analysis of microtubule dynamics. (ZIP 143932 kb)

Supplementary Video 1

Overlay of computer-generated growth tracks (yellow) onto a EB1-EGFP time-lapse sequence. Total length is 30 s (75 frames). Images were acquired every 0.4 s. Video plays at 15 frames s−1, and is thus accelerated 6×. (MOV 5518 kb)

Supplementary Video 2

Dual-wavelength time-lapse sequence of mCherry-tubulin and EB1-EGFP used for validation of the clustering algorithm. Hand-tracked microtubule ends are indicated by blue crosses. Total length is 60 s (97 frames). Images were acquired every 0.62 s. Video plays at 15 frames s−1, and is thus accelerated 9.3×. (MOV 7161 kb)

Supplementary Video 3

Same sequence as Supplementary Video 2 with automatically detected EB1-EGFP comets highlighted by bright green dots. (MOV 6352 kb)

Supplementary Video 4

Region of the dual-wavelength time-lapse sequence in Supplementary Video 2 showing examples of microtubule trajectories inferred by the clustering algorithm. Shown are the microtubule channel (top left), EB1 channel (bottom left), microtubule and EB1 color overlay (top right), and microtubule trajectory overlaid on the EB1 channel (bottom right). Green indicates growth tracks, and red are backward and blue are forward gaps. Such videos were used for the visual validation documented in Supplementary Table 2. This video displays a trajectory deemed as correct. (MOV 4968 kb)

Supplementary Video 5

Second example of a probably correct computer-inferred microtubule trajectory. Different channels and color-coded lines are as indicated for Supplementary Video 4. Note that the growth phase before the last backward gap was tracked as too short because of the low signal-to-noise ratio of the EB1 comet and its partial overlap with another comet. This inaccuracy will lead to an underestimation of the inferred shortening speed and overestimation of the inferred catastrophe rate for this specific trajectory. However, because all phases are assigned properly, this trajectory does not produce a false positive or a false negative. (MOV 6882 kb)

Supplementary Video 6

Example of a computer-inferred microtubule trajectory probably containing a false positive growth event at the end of the trajectory. The trajectory appears to switch to a different microtubule during the last forward gap. Different channels and color-coded lines are as indicated for Supplementary Video 4. (MOV 4636 kb)

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Matov, A., Applegate, K., Kumar, P. et al. Analysis of microtubule dynamic instability using a plus-end growth marker. Nat Methods 7, 761–768 (2010). https://doi.org/10.1038/nmeth.1493

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