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Inferring diffusion in single live cells at the single-molecule level.

Alex Robson¹, Kevin Burrage, Mark C Leake

¹Clarendon Laboratory, Department of Physics, Oxford University, Parks Road, Oxford OX1 3PU, UK.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences

|December 26, 2012

Summary

Understanding molecular diffusion in live cells is crucial. This study introduces a novel Bayesian computational approach to accurately analyze complex diffusion behaviors at the single-molecule level.

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Area of Science:

Cell biology
Biophysics
Molecular dynamics

Background:

Molecular movement within cells is vital for biological functions.
Single-molecule and single-cell analysis provides deep insights into molecular diffusion and cellular environments.
Fluorescence microscopy, particularly total internal reflection fluorescence (TIR-FM) with fluorescent protein (FP) reporters, is key for observing dynamic molecular localization in live cells.

Purpose of the Study:

To develop a novel computational method for analyzing complex molecular diffusion modes.
To overcome the limitations of traditional methods like mean square displacement (MSD) analysis for single FP tracks.
To enable probabilistic discrimination of various diffusion behaviors at the single-molecule level.

Main Methods:

Development of a novel Bayesian ranking approach for diffusion process analysis.
Application of computational methods to analyze single-molecule tracking data from live cells.
Utilizing total internal reflection fluorescence microscopy with fluorescent protein reporters for high-contrast imaging.

Main Results:

The proposed Bayesian method allows for robust discrimination of multiple complex diffusion modes.
This approach effectively handles the stochastic behavior inherent in molecular diffusion.
It overcomes the data limitations posed by photophysical instability in single FP tracks.

Conclusions:

A new computational tool is presented for analyzing single-molecule diffusion in live cells.
This method enhances the understanding of molecular architectures and nanoscale environments.
Biologists can now probabilistically analyze complex diffusion behaviors with greater accuracy.