Recovering membrane interaction kinetics of single molecules from 3D tracking data

Area of Science:

• Microbiology
• Biophysics
• Cell Biology

Background:

• Cytosolic biomolecule and bacterial inner membrane interactions are crucial for cellular functions.
• Measuring binding kinetics of these interactions in living cells is difficult.
• Standard 2D single-molecule tracking methods are limited, especially when membrane binding doesn't change diffusion rates.

Purpose of the Study:

• To develop and validate a novel method for quantifying biomolecule-membrane interaction kinetics in live bacteria.
• To overcome limitations of conventional tracking methods in assessing membrane association.
• To establish a general framework for analyzing 3D single-molecule trajectories for interaction kinetics.

Main Methods:

• Utilized simulated 3D single-molecule tracking data of rod-shaped bacteria.
• Developed a metric to identify membrane-associated motion based on trajectory curvature.
• Applied a hidden Markov modeling framework to analyze state transitions between cytosolic and membrane-bound states.

Main Results:

• Successfully discriminated between cytosolic and membrane-bound states without relying on diffusion rate changes.
• Captured the dynamics of state transitions accurately.
• Demonstrated the method's robustness using simulated data and highlighted the importance of realistic simulations.

Conclusions:

• The presented method enables robust extraction of membrane interaction kinetics from 3D single-molecule tracking data in live bacteria.
• This approach provides a valuable tool for studying fundamental cellular processes.
• Highlights the utility of realistic microscopy simulations for quantitative analysis and bias assessment.