Resampling single-particle tracking data eliminates localization errors and reveals proper diffusion anomalies.

Area of Science:

• Soft matter physics
• Biophysics
• Statistical mechanics

Background:

• Single-particle tracking (SPT) quantifies motion in complex systems like biological specimens.
• Analysis often involves fitting time-averaged mean-square displacement (TA-MSD) to a power law (τ^α) to understand transport.
• Subdiffusion (α<1) is common in biological systems but difficult to accurately measure due to localization errors and short trajectories.

Purpose of the Study:

• To develop a robust method for accurately determining the anomalous diffusion exponent (α) from SPT data.
• To overcome challenges posed by localization errors and short trajectory lengths inherent in SPT.

Main Methods:

• A straightforward resampling approach was developed and tested.
• The method specifically targets trajectories exhibiting subdiffusive fractional Brownian motion.
• Localization errors were systematically eliminated from the TA-MSD calculations.

Main Results:

• The resampling approach effectively eliminates localization errors in TA-MSDs for subdiffusive processes.
• The mean anomalous diffusion exponent (〈α〉_{E}) of trajectory ensembles is revealed robustly.
• This method enhances the reliability of diffusion analysis in complex soft-matter systems.

Conclusions:

• The presented resampling technique provides a reliable way to quantify subdiffusion from SPT data.
• Accurate determination of anomalous diffusion exponents is crucial for understanding biological transport mechanisms.
• This method offers a significant improvement for analyzing challenging SPT datasets.