Robust single-particle tracking in live-cell time-lapse sequences


Single-particle tracking (SPT) is often the rate-limiting step in live-cell imaging studies of subcellular dynamics. Here we present a tracking algorithm that addresses the principal challenges of SPT, namely high particle density, particle motion heterogeneity, temporary particle disappearance, and particle merging and splitting. The algorithm first links particles between consecutive frames and then links the resulting track segments into complete trajectories. Both steps are formulated as global combinatorial optimization problems whose solution identifies the overall most likely set of particle trajectories throughout a movie. Using this approach, we show that the GTPase dynamin differentially affects the kinetics of long- and short-lived endocytic structures and that the motion of CD36 receptors along cytoskeleton-mediated linear tracks increases their aggregation probability. Both applications indicate the requirement for robust and complete tracking of dense particle fields to dissect the mechanisms of receptor organization at the level of the plasma membrane.

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Figure 1: Tracking particles via spatially and temporally global assignments.
Figure 2: Validation of tracking algorithm on simulated tracks.
Figure 3: CCP lifetime is regulated by dynamin.
Figure 4: CD36 receptor aggregation activity depends on motion type.


  1. 1

    Sako, Y., Minoguchi, S. & Yanagida, T. Single-molecule imaging of EGFR signalling on the surface of living cells. Nat. Cell Biol. 2, 168–172 (2000).

  2. 2

    Fujiwara, T., Ritchie, K., Murakoshi, H., Jacobson, K. & Kusumi, A. Phospholipids undergo hop diffusion in compartmentalized cell membrane. J. Cell Biol. 157, 1071–1081 (2002).

  3. 3

    Groc, L. et al. Differential activity-dependent regulation of the lateral mobilities of AMPA and NMDA receptors. Nat. Neurosci. 7, 695–696 (2004).

  4. 4

    Meijering, E., Smal, I. & Danuser, G. Tracking in molecular bioimaging. IEEE Signal Process. Mag. 23, 46–53 (2006).

  5. 5

    Kalaidzidis, Y. Intracellular objects tracking. Eur. J. Cell Biol. 86, 569–578 (2007).

  6. 6

    Ghosh, R.N. & Webb, W.W. Automated detection and tracking of individual and clustered cell-surface low-density-lipoprotein receptor molecules. Biophys. J. 66, 1301–1318 (1994).

  7. 7

    Crocker, J.C. & Grier, D.G. Methods of digital video microscopy for colloidal studies. J. Colloid Interface Sci. 179, 298–310 (1996).

  8. 8

    Cheezum, M.K., Walker, W.F. & Guilford, W.H. Quantitative comparison of algorithms for tracking single fluorescent particles. Biophys. J. 81, 2378–2388 (2001).

  9. 9

    Goulian, M. & Simon, S.M. Tracking single proteins within cells. Biophys. J. 79, 2188–2198 (2000).

  10. 10

    Schmidt, T., Schutz, G.J., Baumgartner, W., Gruber, H.J. & Schindler, H. Imaging of single molecule diffusion. Proc. Natl. Acad. Sci. USA 93, 2926–2929 (1996).

  11. 11

    Manley, S. et al. High-density mapping of single-molecule trajectories with photoactivated localization microscopy. Nat. Methods 5, 155–157 (2008).

  12. 12

    Reid, D.B. An algorithm for tracking multiple targets. IEEE Trans. Automat. Contr. 24, 843–854 (1979).

  13. 13

    Blackman, S.S. & Popoli, R. Design and Analysis of Modern Tracking Systems. (Artech House, Norwood, Massachusetts, 1999).

  14. 14

    Veenman, C.J., Reinders, M.J.T. & Backer, E. Resolving motion correspondence for densely moving points. IEEE T Pattern Anal. 23, 54–72 (2001).

  15. 15

    Chetverikov, D. & Verestoy, J. Feature point tracking for incomplete trajectories. Computing 62, 321–338 (1999).

  16. 16

    Shafique, K. & Shah, M. A noniterative greedy algorithm for multiframe point correspondence. IEEE T Pattern Anal. 27, 51–65 (2005).

  17. 17

    Sbalzarini, I.F. & Koumoutsakos, P. Feature point tracking and trajectory analysis for video imaging in cell biology. J. Struct. Biol. 151, 182–195 (2005).

  18. 18

    Genovesio, A. et al. Multiple particle tracking in 3-D+t microscopy: method and application to the tracking of endocytosed quantum dots. IEEE Trans. Image Process. 15, 1062–1070 (2006).

  19. 19

    Tvarusko, W. et al. Time-resolved analysis and visualization of dynamic processes in living cells. Proc. Natl. Acad. Sci. USA 96, 7950–7955 (1999).

  20. 20

    Jiang, S., Zhou, X.B., Kirchhausen, T. & Wong, S.T.C. Tracking molecular particles in live cells using fuzzy rule-based system. Cytometry A 71A, 576–584 (2007).

  21. 21

    Bonneau, S., Dahan, M. & Cohen, L.D. Single quantum dot tracking based on perceptual grouping using minimal paths in a spatiotemporal volume. IEEE Trans. Image Process. 14, 1384–1395 (2005).

  22. 22

    Genovesio, A. & Olivo-Marin, J.-C. Split and merge data association filter for dense multi-target tracking. IEEE Int. Conf. Pattern Recognit. 4, 677–680 (2004).

  23. 23

    Racine, V. et al. Visualization and quantification of vesicle trafficking on a three-dimensional cytoskeleton network in living cells. J. Microsc. 225, 214–228 (2007).

  24. 24

    Burkard, K.E. & Cela, E. in Handbook of Combinatorial Optimization. Supplement volume A (eds. Du, D. Z. & Pardalos, P. M.) 75–149 (Kluwer Academic Publishers, Dordrecht, The Netherlands, 1999).

  25. 25

    Jonker, R. & Volgenant, A. A shortest augmenting path algorithm for dense and sparse linear assignment problems. Computing 38, 325–340 (1987).

  26. 26

    Ehrlich, M. et al. Endocytosis by random initiation and stabilization of clathrin-coated pits. Cell 118, 591–605 (2004).

  27. 27

    Merrifield, C.J., Feldman, M.E., Wan, L. & Almers, W. Imaging actin and dynamin recruitment during invagination of single clathrin-coated pits. Nat. Cell Biol. 4, 691–698 (2002).

  28. 28

    Rappoport, J.Z. & Simon, S.M. Real-time analysis of clathrin-mediated endocytosis during cell migration. J. Cell Sci. 116, 847–855 (2003).

  29. 29

    Merrifield, C.J., Perrais, D. & Zenisek, D. Coupling between clathrin-coated-pit invagination, cortactin recruitment, and membrane scission observed in live cells. Cell 121, 593–606 (2005).

  30. 30

    Olivo-Marin, J.-C. Extraction of spots in biological images using multiscale products. Pattern Recognit. 35, 1989–1996 (2002).

  31. 31

    Conner, S.D. & Schmid, S.L. Regulated portals of entry into the cell. Nature 422, 37–44 (2003).

  32. 32

    Macia, E. et al. Dynasore, a cell-permeable inhibitor of dynamin. Dev. Cell 10, 839–850 (2006).

  33. 33

    Febbraio, M., Hajjar, D.P. & Silverstein, R.L. CD36: a class B scavenger receptor involved in angiogenesis, atherosclerosis, inflammation, and lipid metabolism. J. Clin. Invest. 108, 785–791 (2001).

  34. 34

    Thomann, D., Rines, D.R., Sorger, P.K. & Danuser, G. Automatic fluorescent tag detection in 3D with super-resolution: application to the analysis of chromosome movement. J. Microsc. 208, 49–64 (2002).

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This research was supported by the US National Institutes of Health R01 grant GM73165 (to G.D. and S.L.S.), by grants from the Heart and Stroke Foundation of Canada and the Canadian Institutes for Health Research (to S.G.), and by a postdoctoral fellowship from The Helen Hay Whitney Foundation–The Agouron Institute (to K.J.). We thank T. Kirchhausen (Harvard Medical School) for providing BSC1 cells stably expressing rat brain clathrin light chain–EGFP.

Author information

K.J. designed and implemented the tracking algorithm, analyzed CD36 aggregation, and wrote the majority of the manuscript. D.L. analyzed CCP lifetimes and wrote the related sections in the manuscript. M.M. and H.K. acquired live-cell image sequences of clathrin light chain-EGFP and Cy3-Fab fragment–labeled CD36 receptors, respectively. S.G., S.L.S. and G.D. initiated the study and helped edit the manuscript.

Correspondence to Khuloud Jaqaman.

Supplementary information

Supplementary Text and Figures

Supplementary Figure 1, Supplementary Table 1, Supplementary Notes 1–9, Supplementary Methods (PDF 1275 kb)

Supplementary Video 1

TIR-FM imaged clathrin-coated pits in a BSC1 cell; image size: 150×150 pixels, pixel size: 67 nm. Left: original image sequence. Right: Original image sequence overlaid with detection markers and drag-tails. Symbol legend: Open circles: Detected and tracked particles — White: A particle that spans the whole movie; Purple: A particle that appears in the movie and stays till the end or a particle that is there from the beginning of the movie but then disappears; Red: A particle that appears and disappears in the movie; Green: A particle in its first frame; Yellow: A particle in its last frame. Asterisks: Closed gaps — Cyan: A gap whose length is smaller than both track segments it links; Blue: A gap whose length is larger than at least one of the track segments it links. Drag-tails: The drag-tail of a track that exists in frame t represents all of its positions from its initiation up to frame t. After a particle permanently disappears, its track is no longer shown. (MOV 5149 kb)

Supplementary Video 2

Time averaging enhances detection efficiency in single molecule CD36 movies recorded with epifluorescence microscopy. Left: Raw movie. Middle: Detected particles without time averaging. Right: Detected particles with time averaging using a window of three. (MOV 4917 kb)

Supplementary Video 3

CD36 receptors in human macrophages; image size: 256×256 pixels, pixel size: 67 nm. Left: Control cell. Middle: Blebbistatin-treated cell. Right: Nocodazole-treated cell. Symbols: Open circles and asterisks: See legend of Video 1. Diamonds: Merging and splitting events — Green: Splitting event (when a particle in frame t — 1 splits into two particles in frame t, both the particle in frame t — 1 and the two particles in frame t are shown as green diamonds); Yellow: Merging event (when two particles in frame t — 1 merge into one particle in frame t, both the two particles in frame t — 1 and the one particle in frame t are shown as yellow diamonds). (MOV 4867 kb)

Supplementary Software (ZIP 915 kb)

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Jaqaman, K., Loerke, D., Mettlen, M. et al. Robust single-particle tracking in live-cell time-lapse sequences. Nat Methods 5, 695–702 (2008).

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