ExTrack characterizes transition kinetics and diffusion in noisy single-particle tracks

Area of Science:

• Biophysics
• Cell Biology
• Microscopy

Background:

• Single-particle tracking (SPT) microscopy is vital for studying protein dynamics in live cells.
• Analysis challenges include noisy localization, short tracks, and rapid state transitions (e.g., immobile to diffusive).

Purpose of the Study:

• To introduce ExTrack, a probabilistic method for analyzing SPT data.
• To overcome limitations in analyzing noisy and complex single-particle tracks.

Main Methods:

• ExTrack utilizes full spatio-temporal track information.
• It extracts global model parameters and calculates time-point state probabilities.
• The method refines positions of bound molecules and reveals state duration distributions.

Main Results:

• ExTrack effectively analyzes tracks with a wide range of diffusion coefficients and transition rates.
• It performs well even when experimental data deviate from model assumptions.
• Demonstrated success on bacterial envelope proteins with slow diffusion and rapid transitions.

Conclusions:

• ExTrack significantly expands the scope of computationally analyzable noisy single-particle tracks.
• The method enhances the understanding of protein dynamics and molecular interactions.
• ExTrack is available as an ImageJ and Python package for broader accessibility.