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

Jaqaman K, Loerke D, Mettlen M, Kuwata H, Grinstein S, Schmid SL, Danuser G - Nat. Methods (2008)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Cell Biology, The Scripps Research Institute, 10550 N. Torrey Pines Rd, La Jolla, California 92037, USA. kjaqaman@scripps.edu

ABSTRACT
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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CD36 receptor aggregation activity depends on motion type(a) Epifluorescence image of CD36 immuno-labeled in a control macrophage using a primary Fab fragment followed by a Cy3-conjugated secondary Fab fragment. (b) CD36 tracks in a control macrophage. (c) CD36 tracks in a Blebbistatin (Bleb.) treated macrophage. (d) CD36 tracks in a Nocodazole (Noc.) treated macrophage. All tracks are from 10s/100 frame movies. Tracks are classified as linear (red) or random (cyan) (Supplementary Note 10 online). Scale bar = 1 µm. (e) Fraction of particles undergoing linear motion. (f) Two sample trajectories represented as x-coordinate, y-coordinate and amplitude over time, highlighting merging events (green ovals), splitting events (purple ovals) and closed gaps (orange ovals). The two colors (pink and blue) highlight the two track segments brought together by capturing merge and split events. (g) Conditional probabilities of merging and splitting while undergoing linear motion and while undergoing random motion, and ratio of conditional probability while undergoing linear motion to conditional probability while undergoing random motion. In (e) and (g), error bars indicate standard deviation as calculated from a sample of size 200 generated by the bootstrap method. ** p-value < 10−10. Statistics were calculated from 14 control cells (7527 trajectories ≥ 5 frames long), 11 Blebbistatin-treated cells (5148 trajectories ≥ 5 frames long) and 12 Nocodazole-treated cells (4926 trajectories ≥ 5 frames long). Trajectories shorter than 5 frames were excluded as non-classifiable with respect to motion type.
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Figure 4: CD36 receptor aggregation activity depends on motion type(a) Epifluorescence image of CD36 immuno-labeled in a control macrophage using a primary Fab fragment followed by a Cy3-conjugated secondary Fab fragment. (b) CD36 tracks in a control macrophage. (c) CD36 tracks in a Blebbistatin (Bleb.) treated macrophage. (d) CD36 tracks in a Nocodazole (Noc.) treated macrophage. All tracks are from 10s/100 frame movies. Tracks are classified as linear (red) or random (cyan) (Supplementary Note 10 online). Scale bar = 1 µm. (e) Fraction of particles undergoing linear motion. (f) Two sample trajectories represented as x-coordinate, y-coordinate and amplitude over time, highlighting merging events (green ovals), splitting events (purple ovals) and closed gaps (orange ovals). The two colors (pink and blue) highlight the two track segments brought together by capturing merge and split events. (g) Conditional probabilities of merging and splitting while undergoing linear motion and while undergoing random motion, and ratio of conditional probability while undergoing linear motion to conditional probability while undergoing random motion. In (e) and (g), error bars indicate standard deviation as calculated from a sample of size 200 generated by the bootstrap method. ** p-value < 10−10. Statistics were calculated from 14 control cells (7527 trajectories ≥ 5 frames long), 11 Blebbistatin-treated cells (5148 trajectories ≥ 5 frames long) and 12 Nocodazole-treated cells (4926 trajectories ≥ 5 frames long). Trajectories shorter than 5 frames were excluded as non-classifiable with respect to motion type.

Mentions: We immuno-labeled surface-bound CD36 receptors in primary macrophages by a primary Fab fragment and a Cy3-conjugated secondary Fab fragment, and recorded single-molecule movies using epi-fluorescence microscopy at a frame rate of 10 Hz (Fig. 4a, Supplementary Video 2 online). Both individual receptors and receptor aggregates generated diffraction-limited image features, i.e. particles. Thus, we estimated their positions by first detecting local maxima, and then fitting Gaussian kernels in areas around these local maxima to achieve sub-pixel localization34. To enhance detection efficiency under the low SNR conditions of single molecule movies, we performed the search for local maxima in time-averaged images, followed by Gaussian kernel fitting in individual frames (Supplementary Note 2, Supplementary Video 2 online).


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

Jaqaman K, Loerke D, Mettlen M, Kuwata H, Grinstein S, Schmid SL, Danuser G - Nat. Methods (2008)

CD36 receptor aggregation activity depends on motion type(a) Epifluorescence image of CD36 immuno-labeled in a control macrophage using a primary Fab fragment followed by a Cy3-conjugated secondary Fab fragment. (b) CD36 tracks in a control macrophage. (c) CD36 tracks in a Blebbistatin (Bleb.) treated macrophage. (d) CD36 tracks in a Nocodazole (Noc.) treated macrophage. All tracks are from 10s/100 frame movies. Tracks are classified as linear (red) or random (cyan) (Supplementary Note 10 online). Scale bar = 1 µm. (e) Fraction of particles undergoing linear motion. (f) Two sample trajectories represented as x-coordinate, y-coordinate and amplitude over time, highlighting merging events (green ovals), splitting events (purple ovals) and closed gaps (orange ovals). The two colors (pink and blue) highlight the two track segments brought together by capturing merge and split events. (g) Conditional probabilities of merging and splitting while undergoing linear motion and while undergoing random motion, and ratio of conditional probability while undergoing linear motion to conditional probability while undergoing random motion. In (e) and (g), error bars indicate standard deviation as calculated from a sample of size 200 generated by the bootstrap method. ** p-value < 10−10. Statistics were calculated from 14 control cells (7527 trajectories ≥ 5 frames long), 11 Blebbistatin-treated cells (5148 trajectories ≥ 5 frames long) and 12 Nocodazole-treated cells (4926 trajectories ≥ 5 frames long). Trajectories shorter than 5 frames were excluded as non-classifiable with respect to motion type.
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Figure 4: CD36 receptor aggregation activity depends on motion type(a) Epifluorescence image of CD36 immuno-labeled in a control macrophage using a primary Fab fragment followed by a Cy3-conjugated secondary Fab fragment. (b) CD36 tracks in a control macrophage. (c) CD36 tracks in a Blebbistatin (Bleb.) treated macrophage. (d) CD36 tracks in a Nocodazole (Noc.) treated macrophage. All tracks are from 10s/100 frame movies. Tracks are classified as linear (red) or random (cyan) (Supplementary Note 10 online). Scale bar = 1 µm. (e) Fraction of particles undergoing linear motion. (f) Two sample trajectories represented as x-coordinate, y-coordinate and amplitude over time, highlighting merging events (green ovals), splitting events (purple ovals) and closed gaps (orange ovals). The two colors (pink and blue) highlight the two track segments brought together by capturing merge and split events. (g) Conditional probabilities of merging and splitting while undergoing linear motion and while undergoing random motion, and ratio of conditional probability while undergoing linear motion to conditional probability while undergoing random motion. In (e) and (g), error bars indicate standard deviation as calculated from a sample of size 200 generated by the bootstrap method. ** p-value < 10−10. Statistics were calculated from 14 control cells (7527 trajectories ≥ 5 frames long), 11 Blebbistatin-treated cells (5148 trajectories ≥ 5 frames long) and 12 Nocodazole-treated cells (4926 trajectories ≥ 5 frames long). Trajectories shorter than 5 frames were excluded as non-classifiable with respect to motion type.
Mentions: We immuno-labeled surface-bound CD36 receptors in primary macrophages by a primary Fab fragment and a Cy3-conjugated secondary Fab fragment, and recorded single-molecule movies using epi-fluorescence microscopy at a frame rate of 10 Hz (Fig. 4a, Supplementary Video 2 online). Both individual receptors and receptor aggregates generated diffraction-limited image features, i.e. particles. Thus, we estimated their positions by first detecting local maxima, and then fitting Gaussian kernels in areas around these local maxima to achieve sub-pixel localization34. To enhance detection efficiency under the low SNR conditions of single molecule movies, we performed the search for local maxima in time-averaged images, followed by Gaussian kernel fitting in individual frames (Supplementary Note 2, Supplementary Video 2 online).

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Cell Biology, The Scripps Research Institute, 10550 N. Torrey Pines Rd, La Jolla, California 92037, USA. kjaqaman@scripps.edu

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus