Robust single particle tracking in live cell time-lapse sequences

By K. Jaqaman, D. Loerke, Marcel Mettlen, H. Kuwata, S. Grinstein, S. Schmid, G. Danuser. Nature Methods, 2008

1,990 citations recorded by the source corpus.

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.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9a3b0cd3c589277366dc30b4063be5675b48ae3e

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Citations in the corpus

The ImageJ ecosystem: Open‐source software for image visualization, processing, and analysis (2020, methodology): “29,70 TrackMate includes several tracking algorithms, such as LAP tracking, 69 and experienced users can implement their own custom algorithms.”
Visualization of image data from cells to organisms (2010, neutral): “Often these studies require reliable tracking algorithms 12,13 that enable researchers to follow the same object (for example, single molecules or cells) over time and to extract and visualize trajectories and other measurements (for example, color-coded speed of cells in a developing embryo 14 ).”
Automated and semi‐automated cell tracking: addressing portability challenges (2011, methodology): “Poor detection quality leads to poor tracking quality (Jaqaman et al. , 2008; or our evaluation in the section ‘Evaluation of NENIA’).”
Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data (2014, methodology): “In general, computational approaches to cell segmentation and tracking can be divided into three main categories: contour evolution (for example, level sets) 21 , state-space models (for example, particle filters) 22 and data association methods (for example, graph matching) 20,23 .”
Methods for cell and particle tracking. (2012, neutral): “, 2005) and graph-based optimization approaches (Sbalzarini and Koumoutsakos, 2005; Jaqaman et al., 2008).”
Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software (2011, methodology): “While retaining the successful attributes of CellProfiler 1.0 (Supplemental Figure 1 and Supplemental Table 1), CellProfiler 2.0 compares favorably to CellProfiler 1.0 in terms of performance (Supplemental Figure 2) and features (Supplemental Table 2).”
Objective comparison of particle tracking methods (2014, methodology): “METHOD 4: PARAMETERS The user parameters of this method (and typical values) are:  Number of iterations of convolution with the Gaussian kernel (10-500).”
A High Precision Survey of the Molecular Dynamics of Mammalian Clathrin-Mediated Endocytosis (2011, methodology): “scission-undetected CCSs most likely correspond to the ‘‘abortive’’ CCSs described previously [3,27,28], although some of these structures may have represented endosomal clathrin.”
Deep learning for cellular image analysis (2019, methodology): “Complex behaviors such as disappearance, splitting, and merging can be treated with linear programming, as in the now classic investigation by Jaqaman et al. 119 .”