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Single Particle Tracking (SPT) can aid in understanding complexspatio-temporal processes. However, quantifying diffusivity and forces fromindividual live cell trajectories is complicated by inter- & intra-trajectorykinetic heterogeneity, thermal fluctuations, and statistical temporaldependence inherent to the underlying molecule's time correlated confineddynamics experienced in the cell. Experimental artifacts such as localizationuncertainty and motion blur also obscure the data. We introduce a new maximumlikelihood estimation (MLE) technique that decouples the above noise sourcesand systematically treats temporal correlation via a likelihood function(permitting more reliable extraction of effective forces from position vs. timedata). Our estimator is demonstrated to be consistent over a wide range ofexposure times, diffusion coefficients, and confinement "radii". The algorithmand corresponding software can reliably extract motion parameters independentof exposure time in trajectories exhibiting confined and/or non-stationarydynamics and will aid in directly comparing trajectories obtained fromdifferent imaging modalities.

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