Anomalous Diffusion in Inverted Variable-Lengthscale Fluorescence Correlation Spectroscopy.

Area of Science:

• Biophysics
• Physical Chemistry
• Computational Biology

Background:

• Distinguishing diffusion processes using fluorescence correlation spectroscopy (FCS) is challenging due to model-dependent data analysis.
• Existing methods often rely on assumptions that limit their applicability to complex biological systems.

Purpose of the Study:

• To develop and validate a model-independent approach for analyzing diffusion processes using FCS.
• To demonstrate the ability of variable length-scale FCS combined with data inversion to differentiate various diffusion models.

Main Methods:

• Computer simulations were employed to model diverse diffusion scenarios, including simple diffusion, continuous-time random walk, caged diffusion, obstructed diffusion, two-state diffusion, and diffusing diffusivity.
• Variable-length-scale fluorescence correlation spectroscopy data were generated and inverted to retrieve the mean-squared displacement.
• The retrieved mean-squared displacement was analyzed to identify unique signatures of each diffusion model.

Main Results:

• Variable-length-scale FCS data, when inverted, successfully retrieved the mean-squared displacement for all simulated diffusion processes.
• The combined approach allowed for the identification of non-Gaussian diffusion behaviors.
• Accurate mean-squared displacement was obtained over several orders of magnitude in time, irrespective of the diffusion model's Gaussianity.

Conclusions:

• Variable-length-scale FCS coupled with data inversion offers a robust, model-independent method for analyzing diffusion.
• This approach enables unbiased discrimination between various diffusion models, crucial for understanding molecular dynamics in biological systems.