Motion blur filtering: A statistical approach for extracting confinement forces and diffusivity from a single blurred...

Area of Science:

• Biophysics
• Cell Biology
• Statistical Mechanics

Background:

• Single particle tracking (SPT) is crucial for studying cellular processes.
• Quantifying molecular movement is challenging due to kinetic heterogeneity and experimental artifacts like motion blur and localization uncertainty.
• Existing methods struggle to accurately analyze complex, time-correlated dynamics in live cells.

Purpose of the Study:

• To develop a robust method for extracting kinetic parameters from single live-cell trajectories.
• To address and decouple noise sources including motion blur and localization uncertainty.
• To enable reliable quantification of diffusivity and confinement forces in cellular environments.

Main Methods:

• Developed a maximum likelihood estimation (MLE) technique based on time series analysis.
• Modified the Kalman filter to incorporate a motion blur model and handle confined dynamics.
• Utilized an exact likelihood function within a time domain MLE framework.

Main Results:

• The new MLE technique accurately quantifies diffusivity and confinement forces, even with motion blur and localization uncertainty.
• Demonstrated consistency across a wide range of experimental parameters (exposure times, diffusion coefficients, confinement widths).
• Showed that neglecting motion blur or confinement significantly biases kinetic parameter estimation.

Conclusions:

• The developed MLE method provides a reliable way to analyze single particle tracking data.
• This technique overcomes limitations of traditional methods like mean squared displacement analysis.
• Enables accurate comparison of trajectories across different experiments and imaging modalities.