u-track 3D: measuring and interrogating dense particle dynamics in three dimensions

Particle tracking is a ubiquitous task in the study of dynamic molecular and cellular processes by live microscopy. Light-sheet microscopy has recently opened a path to acquiring complete cell volumes for investigation in 3-dimensions (3D). However, hypothesis formulation and quantitative analysis have remained difficult due to fundamental challenges in the visualization and the verification of large sets of 3D particle trajectories. Here we describe u-track 3D, a software package that addresses these two challenges with three algorithmic innovations. Building on the established framework of globally optimal particle association in space and time implemented in the u-track package and recent advances in gaining association robustness in the case of erratic motion, we first report a complete and versatile pipeline for particle tracking. We then present the concept of dynamic region of interest (dynROI), which allows an experimenter to interact with dynamic 3D processes in 2D views amenable to visual inspection. Third, we present an estimator of trackability, which provides for every trajectory a confidence score, thereby overcoming the challenges of visual validation of trajectories in dense particle fields. With these combined strategies, u-track 3D provides a framework for the unbiased study of molecular processes in complex volumetric sequences.

[1]  Wesley R. Legant,et al.  Lattice light-sheet microscopy: Imaging molecules to embryos at high spatiotemporal resolution , 2014, Science.

[2]  K. Jaqaman,et al.  Robust single particle tracking in live cell time-lapse sequences , 2008, Nature Methods.

[3]  Léo Guignard,et al.  Multi-view light-sheet imaging and tracking with the MaMuT software reveals the cell lineage of a direct developing arthropod limb , 2018, eLife.

[4]  Wesley R. Legant,et al.  Single-Molecule Dynamics of Enhanceosome Assembly in Embryonic Stem Cells , 2014, Cell.

[5]  Karl Rohr,et al.  Tracking virus particles in fluorescence microscopy images via a particle Kalman filter , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[6]  Ty C. Voss,et al.  Dynamic Exchange at Regulatory Elements during Chromatin Remodeling Underlies Assisted Loading Mechanism , 2011, Cell.

[7]  Isabelle Bloch,et al.  Multiple Hypothesis Tracking for Cluttered Biological Image Sequences , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Wesley R. Legant,et al.  Augmin accumulation on long-lived microtubules drives amplification and kinetochore-directed growth , 2019, The Journal of cell biology.

[9]  A. Volgenant,et al.  A shortest augmenting path algorithm for dense and sparse linear assignment problems , 1987, Computing.

[10]  Gaudenz Danuser,et al.  Traction stress in focal adhesions correlates biphasically with actin retrograde flow speed , 2008, The Journal of cell biology.

[11]  G. Hager,et al.  Single-molecule analysis of steroid receptor and cofactor action in living cells , 2017, Nature Communications.

[12]  Gaudenz Danuser,et al.  plusTipTracker: Quantitative image analysis software for the measurement of microtubule dynamics. , 2011, Journal of structural biology.

[13]  P. Koumoutsakos,et al.  Feature point tracking and trajectory analysis for video imaging in cell biology. , 2005, Journal of structural biology.

[14]  A. Bhardwaj,et al.  In situ click chemistry generation of cyclooxygenase-2 inhibitors , 2017, Nature Communications.

[15]  Gaudenz Danuser,et al.  Piecewise-Stationary Motion Modeling and Iterative Smoothing to Track Heterogeneous Particle Motions in Dense Environments , 2017, IEEE Transactions on Image Processing.

[16]  Gaudenz Danuser,et al.  Analysis of Microtubule Dynamic Instability Using a Plus End Growth Marker , 2010, Nature Methods.

[17]  Sandra L Schmid,et al.  Advances in analysis of low signal-to-noise images link dynamin and AP2 to the functions of an endocytic checkpoint. , 2013, Developmental cell.

[18]  Carlos R Reis,et al.  Diagonally Scanned Light-Sheet Microscopy for Fast Volumetric Imaging of Adherent Cells , 2016, Biophysical journal.

[19]  Gaudenz Danuser,et al.  Quantifying Modes of 3D Cell Migration. , 2015, Trends in cell biology.

[20]  Brian P. Mehl,et al.  Bright photoactivatable fluorophores for single-molecule imaging , 2016, Nature Methods.

[21]  Gaudenz Danuser,et al.  Deconvolution-free Subcellular Imaging with Axially Swept Light Sheet Microscopy , 2015, Biophysical journal.

[22]  Erik H. W. Meijering,et al.  Quantitative comparison of multiframe data association techniques for particle tracking in time-lapse fluorescence microscopy , 2015, Medical Image Anal..

[23]  William J. Godinez,et al.  Objective comparison of particle tracking methods , 2014, Nature Methods.

[24]  Charles Kervrann,et al.  A Guided Tour of Selected Image Processing and Analysis Methods for Fluorescence and Electron Microscopy , 2016, IEEE Journal of Selected Topics in Signal Processing.

[25]  R. Heald,et al.  Thirty years of search and capture: The complex simplicity of mitotic spindle assembly , 2015, The Journal of cell biology.