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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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.
Supplementary Figure 1, Supplementary Table 1, Supplementary Notes 1–9, Supplementary Methods (PDF 1275 kb)
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)
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)
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)
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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). https://doi.org/10.1038/nmeth.1237
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