Computationally-efficient spatiotemporal correlation analysis super-resolves anomalous diffusion

Abstract

Anomalous diffusion dynamics in confined nanoenvironments govern the macroscale properties and interactions of many biophysical and material systems. Currently, it is difficult to quantitatively link the nanoscale structure of porous media to anomalous diffusion within them. Fluorescence correlation spectroscopy super-resolution optical fluctuation imaging (fcsSOFI) has been shown to extract nanoscale structure and Brownian diffusion dynamics within gels, liquid crystals, and polymers, but has limitations which hinder its wider application to more diverse, biophysically-relevant datasets. Here, we parallelize the least-squares curve fitting step on a GPU improving computation times by up to a factor of 40, implement anomalous diffusion and two-component Brownian diffusion models, and make fcsSOFI more accessible by packaging it in a user-friendly GUI. We apply fcsSOFI to simulations of the protein fibrinogen diffusing in polyacrylamide of varying matrix densities and super-resolve locations where slower, anomalous diffusion occurs within smaller, confined pores. The improvements to fcsSOFI in speed, scope, and usability will allow for the wider adoption of super-resolution correlation analysis to diverse research topics.

Spatially Resolving Size Effects on Diffusivity in Nanoporous Extracellular Matrix-like Materials with Fluorescence Correlation Spectroscopy Super-Resolution Optical Fluctuation Imaging

“…Owing to their larger hydrodynamic size with a radius of ∼40 nm, the larger 2000 kDa dextran molecules collide with the edge of the pores, effectively lowering the diffusion coefficients (Figure C). This decrease in diffusion coefficient is consistent with our recent work with simulated fibrinogen protein diffusion in polyacrylamide gel where we observed a slower diffusion rate when the matrix density was increased to a point where the pore size becomes comparable to the size of the fibrinogen molecules . This decrease in diffusion coefficient is also consistent with the fact that the effect of size of diffusing molecules on the diffusion coefficient in agarose hydrogel becomes more prominent when the size of the molecule reaches a critical value of ∼30 nm due to increased interaction of the diffusing molecules with the hydrogel walls .…”

“…As discussed above, fcsSOFI has been applied previously to analyze diffusion dynamics of nanoparticles within an agarose hydrogel, along with resolving structural heterogeneity of a pH-responsive, tunable allylamine functionalized poly- N -isopropylacrylamide hydrogel . Recently, our group reported an updated version of the fcsSOFI code for faster and more accurate determination of diffusion dynamics and applied it for simulations of fibrinogen diffusing in polyacrylamide hydrogels of various matrix densities …”

“…7 Recently, our group reported an updated version of the fcsSOFI code for faster and more accurate determination of diffusion dynamics and applied it for simulations of fibrinogen diffusing in polyacrylamide hydrogels of various matrix densities. 58 Here, we further improve the fcsSOFI analysis by optimizing aspects of the experimental conditions and the data analysis. Several studies on both single-point FCS 62 and camera-based FCS 18,63 discussed the effect of various experimental parameters for accurate determination of diffusion constants.…”

“…When combined, fcsSOFI allows for spatial and temporal information to be gathered and analyzed simultaneously (Figure ). Previously, we have shown that fcsSOFI can be applied to measure simulated biomolecular diffusion in simulated hydrogels of varying matrix densities . These simulations have also demonstrated the ability of fcsSOFI to accurately recover the porous structure, allowing for previously unattainable information to be gathered .…”

“…Previously, we have shown that fcsSOFI can be applied to measure simulated biomolecular diffusion in simulated hydrogels of varying matrix densities. 58 These simulations have also demonstrated the ability of fcsSOFI to accurately recover the porous structure, allowing for previously unattainable information to be gathered. 26 Other experimental studies have demonstrated the ability of fcsSOFI to extract porous structure and diffusion values in hydrogels.…”