Limits...
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.

Show MeSH
Tracking particles via spatially and temporally global assignments(a) Tracks were constructed from an image sequence by detecting particles in each frame (Step 0), linking particles between consecutive frames (Step 1), and then closing gaps and capturing merging and splitting events between the initial track segments (Step 2). (b) Cost matrix controlling particle assignments between frames. λij: cost for linking particle i in frame t to particle j in frame t + 1, x: impossible link whose cost exceeded the cutoff, d: cost for allowing particles in frame t to link to nothing in frame t + 1, b: cost for allowing particles in frame t + 1 to get linked by nothing in frame t. The lower right block is an auxiliary block required to satisfy the topological constraints of the LAP (Supplementary Note 4 online). (c) Cost matrix controlling gap closing, merging and splitting. gIJ: cost for closing a gap between the end of track segment I and the start of track segment J, mIJ: cost for the end of track segment I merging with a middle point of track segment J, sIJ: cost for the start of track segment J splitting from a middle point of track segment I. Central cross: links between track segment middle points introduced for merging and splitting were not allowed. The upper and middle right blocks, lower left and middle blocks, and lower right block were as described in (b). In (b) and (c), ‘…’ means index continuation.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2747604&req=5

Figure 1: Tracking particles via spatially and temporally global assignments(a) Tracks were constructed from an image sequence by detecting particles in each frame (Step 0), linking particles between consecutive frames (Step 1), and then closing gaps and capturing merging and splitting events between the initial track segments (Step 2). (b) Cost matrix controlling particle assignments between frames. λij: cost for linking particle i in frame t to particle j in frame t + 1, x: impossible link whose cost exceeded the cutoff, d: cost for allowing particles in frame t to link to nothing in frame t + 1, b: cost for allowing particles in frame t + 1 to get linked by nothing in frame t. The lower right block is an auxiliary block required to satisfy the topological constraints of the LAP (Supplementary Note 4 online). (c) Cost matrix controlling gap closing, merging and splitting. gIJ: cost for closing a gap between the end of track segment I and the start of track segment J, mIJ: cost for the end of track segment I merging with a middle point of track segment J, sIJ: cost for the start of track segment J splitting from a middle point of track segment I. Central cross: links between track segment middle points introduced for merging and splitting were not allowed. The upper and middle right blocks, lower left and middle blocks, and lower right block were as described in (b). In (b) and (c), ‘…’ means index continuation.

Mentions: Given the set of detected particles in a live cell time-lapse sequence (Supplementary Notes 1, 2 online present the detection algorithms used for the two applications shown in this work and their performance), we generated particle tracks in two steps (Fig. 1a): First, we constructed track segments by linking the detected particles between consecutive frames, under the condition that a particle in one frame could link to at most one particle in the previous or following frame. These track segments started and ended not only due to the true appearance and disappearance of particles, but also due to apparent disappearances associated with limitations in imaging and SNR, for example when a particle temporarily moved out of the plane in focus, or when two particles approached each other within a distance smaller than the resolution limit. The track segments obtained in this step tended to be incomplete, resulting in a systematic underestimation of particle lifetimes. In addition, because of the one-to-one assignment of particles, this step could not capture particle merges and splits, which by definition required one particle in one frame to be assigned to two particles in the previous or subsequent frame, respectively. Therefore, in a second step, we linked the initial track segments in three ways: End-to-start, in order to close gaps resulting from temporary disappearance, end-to-middle, in order to capture merging events, and start-to-middle, in order to capture splitting events.


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)

Tracking particles via spatially and temporally global assignments(a) Tracks were constructed from an image sequence by detecting particles in each frame (Step 0), linking particles between consecutive frames (Step 1), and then closing gaps and capturing merging and splitting events between the initial track segments (Step 2). (b) Cost matrix controlling particle assignments between frames. λij: cost for linking particle i in frame t to particle j in frame t + 1, x: impossible link whose cost exceeded the cutoff, d: cost for allowing particles in frame t to link to nothing in frame t + 1, b: cost for allowing particles in frame t + 1 to get linked by nothing in frame t. The lower right block is an auxiliary block required to satisfy the topological constraints of the LAP (Supplementary Note 4 online). (c) Cost matrix controlling gap closing, merging and splitting. gIJ: cost for closing a gap between the end of track segment I and the start of track segment J, mIJ: cost for the end of track segment I merging with a middle point of track segment J, sIJ: cost for the start of track segment J splitting from a middle point of track segment I. Central cross: links between track segment middle points introduced for merging and splitting were not allowed. The upper and middle right blocks, lower left and middle blocks, and lower right block were as described in (b). In (b) and (c), ‘…’ means index continuation.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2747604&req=5

Figure 1: Tracking particles via spatially and temporally global assignments(a) Tracks were constructed from an image sequence by detecting particles in each frame (Step 0), linking particles between consecutive frames (Step 1), and then closing gaps and capturing merging and splitting events between the initial track segments (Step 2). (b) Cost matrix controlling particle assignments between frames. λij: cost for linking particle i in frame t to particle j in frame t + 1, x: impossible link whose cost exceeded the cutoff, d: cost for allowing particles in frame t to link to nothing in frame t + 1, b: cost for allowing particles in frame t + 1 to get linked by nothing in frame t. The lower right block is an auxiliary block required to satisfy the topological constraints of the LAP (Supplementary Note 4 online). (c) Cost matrix controlling gap closing, merging and splitting. gIJ: cost for closing a gap between the end of track segment I and the start of track segment J, mIJ: cost for the end of track segment I merging with a middle point of track segment J, sIJ: cost for the start of track segment J splitting from a middle point of track segment I. Central cross: links between track segment middle points introduced for merging and splitting were not allowed. The upper and middle right blocks, lower left and middle blocks, and lower right block were as described in (b). In (b) and (c), ‘…’ means index continuation.
Mentions: Given the set of detected particles in a live cell time-lapse sequence (Supplementary Notes 1, 2 online present the detection algorithms used for the two applications shown in this work and their performance), we generated particle tracks in two steps (Fig. 1a): First, we constructed track segments by linking the detected particles between consecutive frames, under the condition that a particle in one frame could link to at most one particle in the previous or following frame. These track segments started and ended not only due to the true appearance and disappearance of particles, but also due to apparent disappearances associated with limitations in imaging and SNR, for example when a particle temporarily moved out of the plane in focus, or when two particles approached each other within a distance smaller than the resolution limit. The track segments obtained in this step tended to be incomplete, resulting in a systematic underestimation of particle lifetimes. In addition, because of the one-to-one assignment of particles, this step could not capture particle merges and splits, which by definition required one particle in one frame to be assigned to two particles in the previous or subsequent frame, respectively. Therefore, in a second step, we linked the initial track segments in three ways: End-to-start, in order to close gaps resulting from temporary disappearance, end-to-middle, in order to capture merging events, and start-to-middle, in order to capture splitting events.

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