Robust model-based analysis of single-particle tracking experiments with Spot-On

Area of Science:

• Biochemistry
• Cell Biology
• Biophysics

Background:

• Single-particle tracking (SPT) is crucial for observing protein dynamics in live cells.
• Data analysis and experimental design biases challenge accurate SPT information inference.
• Existing methods struggle with biases like out-of-focus movement and motion blur.

Purpose of the Study:

• To introduce 'Spot-On', a web-interface for unbiased SPT data analysis.
• To develop stroboscopic photo-activation SPT (spaSPT) to minimize experimental biases.
• To provide a robust framework for inferring diffusion constants and molecular subpopulations.

Main Methods:

• Developed 'Spot-On' web-interface with a kinetic modeling framework.
• Implemented stroboscopic photo-activation SPT (spaSPT) to reduce motion blur and tracking errors.
• Validated methods using realistic simulations and live mammalian cell data.

Main Results:

• Spot-On accurately infers diffusion constants and subpopulations from pooled single-molecule trajectories.
• spaSPT minimizes motion-blur bias and tracking errors in live-cell experiments.
• Spot-On outperforms existing methods in simulation and real-world applications.

Conclusions:

• Spot-On provides a robust and accurate solution for analyzing SPT data.
• The combination of Spot-On and spaSPT enhances the reliability of live-cell protein dynamics studies.
• This approach consistently infers subpopulation fractions and diffusion constants across diverse nuclear dynamics.