Robust single particle tracking in live cell time-lapse sequences

Department of Cell Biology, The Scripps Research Institute, 10550 N. Torrey Pines Rd, La Jolla, California 92037, USA.
Nature Methods (Impact Factor: 25.95). 08/2008; 5(8):695-702. DOI: 10.1038/nmeth.1237
Source: PubMed

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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Available from: Sandra L Schmid, Aug 22, 2014
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    • "For single-molecule localization, a number of algorithms have been developed to fit discrete single-molecule images or even high-density overlapping single-molecules (reviewed and compared in Small and Stahlheber, 2014). The U-track platform and the MTT program can be used to extract trajectories of single-molecule movement from localization data (Jaqaman et al., 2008; Sergé et al., 2008). For diffusion analysis , a range of diffusion models have been established based on the mean square displacement (MSD) curves of long SPT trajectories (Saxton and Jacobson, 1997). "
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    Molecular cell 05/2015; 58(4):644-659. DOI:10.1016/j.molcel.2015.02.033 · 14.46 Impact Factor
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    • "Imaged molecules of CX 3 CL1 or ADAM10 were detected and tracked as described by Jaqaman et al. (2008). In brief, subdiffraction particle positions and intensities were estimated by 1) detecting significant local intensity maxima and 2) fitting Gaussian kernels approximating the two-dimensional point spread function of the microscope . "
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    Molecular Biology of the Cell 12/2014; 25(24):3884-99. DOI:10.1091/mbc.E13-11-0633 · 4.55 Impact Factor
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    • "In order to perform segment joining , a similarity is first defined between every pair of segments based on compatibility factors such as their start / end frame , location , and speed . Then the Hungarian algorithm ( Munkres , 1957 ) is used to find a globally optimal mapping between segments based on the similarity matrix ( Bise et al . , 2011 ; Jaqaman et al . , 2008 ; Perera et al . , 2006 ) . Out of these mapped assignments , segments are only joined if their similarity falls above some threshold . The two - tiered approach to tracking aims to be computationally efficient by implementing an unsophisticated , greedy nearest neighbor algorithm when the tracking scenario is simple , and a more comple"
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