| Video-based particle tracking monitors the microscopic movement of labeled biomolecules, subcellular organelles and fluorescent probes within a complex cellular environment. Measurements obtained by this technique, mainly the mean square displacement (MSD) of the tracers, enable us to extract the dynamic information of subcellular processes and the intracellular mechanical properties. However, MSD measurements are highly susceptible to static error introduced by noise in the image acquisition process that leads to an incorrect positioning of the particle. An approach that greatly increases the accuracy of MSD measurements is developed herein by combining experimental data with Monte Carlo simulations. In addition, the trajectories obtained from video-based particle tracking can provide insight into transient subcellular transportation such as endocytosis. In this work, it is shown that the Kalman filter, a robust algorithm to estimate the state of a linear dynamic system from noisy measurements, can be applied to particle tracking to dramatically reduce positioning error while retaining the intrinsic fluctuations of a cellular dynamic process. Hence, the Kalman filter can serve as a powerful tool to infer a trajectory of ultra-high fidelity from noisy images, revealing the details of dynamic cellular processes. Finally, the short time dynamics of NAC-1 nuclear bodies (NB) are measured to study their interactions with chromatin since the chromatin architecture is suspected to be highly associated with gene regulation. I demonstrate that the dynamics of NAC-1 NBs are determined by these associations with chromatin as well as the organization of the chromatin structure through the utilization of the developed high accuracy particle tracking analysis method. |
