Motion Blur Filtering: A Statistical Approach for Extracting Confinement Forces and Diffusivity from a Single Blurred Trajectory

Abstract

Single Particle Tracking (SPT) can aid in understanding complex spatio-temporal processes. However, quantifying diffusivity and forces from individual live cell trajectories is complicated by inter- & intra-trajectory kinetic heterogeneity, thermal fluctuations, and statistical temporal dependence inherent to the underlying molecule's time correlated confined dynamics experienced in the cell. Experimental artifacts such as localization uncertainty and motion blur also obscure the data. We introduce a new maximum likelihood estimation (MLE) technique that decouples the above noise sources and systematically treats temporal correlation via a likelihood function (permitting more reliable extraction of effective forces from position vs. time data). Our estimator is demonstrated to be consistent over a wide range of exposure times, diffusion coefficients, and confinement "radii". The algorithm and corresponding software can reliably extract motion parameters independent of exposure time in trajectories exhibiting confined and/or non-stationary dynamics and will aid in directly comparing trajectories obtained from different imaging modalities.

Companion Python and Jupyter / Ipython notebooks maintained at: http://www.github.com/calderoc/MotionBlurFilter

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• Tug-of-war as a cooperative mechanism for bidirectional cargo transport by molecular motors
• Residence times of receptors in dendritic spines analyzed by simulations in empirical domains
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• Local Analysis of Heterogeneous Intracellular Transport: Slow and Fast moving Endosomes
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• Fitting a function to time-dependent ensemble averaged data
• Fisher information matrix for single molecules with stochastic trajectories
• Effect of Pixelation on the Parameter Estimation of Single Molecule Trajectories
• Errors in energy landscapes measured with particle tracking
• Limits of accuracy for parameter estimation and localisation in Single-Molecule Microscopy via sequential Monte Carlo methods