Porous materials such as cellular cytosol, hydrogels, and block copolymers have nanoscale features that determine macroscale properties. Characterizing the structure of nanopores is difficult with current techniques due to imaging, sample preparation, and computational challenges. We produce a super-resolution optical image that simultaneously characterizes the nanometer dimensions of and diffusion dynamics within porous structures by correlating stochastic fluctuations from diffusing fluorescent probes in the pores of the sample, dubbed here as "fluorescence correlation spectroscopy super-resolution optical fluctuation imaging" or "fcsSOFI". Simulations demonstrate that structural features and diffusion properties can be accurately obtained at sub-diffraction-limited resolution. We apply our technique to image agarose hydrogels and aqueous lyotropic liquid crystal gels. The heterogeneous pore resolution is improved by up to a factor of 2, and diffusion coefficients are accurately obtained through our method compared to diffraction-limited fluorescence imaging and single-particle tracking. Moreover, fcsSOFI allows for rapid and high-throughput characterization of porous materials. fcsSOFI could be applied to soft porous environments such hydrogels, polymers, and membranes in addition to hard materials such as zeolites and mesoporous silica.

This document provides the appendix to "Cusp-artifacts in high order Super-resolution Optical Fluctuation Imaging". It contains more details for the derivations, simulation descriptions and comparison cases.

No abstract

Super-resolution Optical Fluctuation Imaging (SOFI) offers a simple and affordable alternative to other super-resolution (SR) imaging techniques. The theoretical resolution enhancement of SOFI scales linearly with the order of cumulants, while the imaging conditions exhibits less photo-toxicity to the living samples as compared to other SR methods. High order SOFI could, therefore, be a method of choice for dynamic live cell imaging. However, due to the cusp-artifacts and dynamic range expansion of pixel intensities, this promise has not been materialized as of yet. Here we investigated and compared high order moments vs. high order cumulants SOFI reconstructions. We demonstrate that even-order moments reconstructions are intrinsically free of cusp artifacts, allowing for a subsequent deconvolution operation to be performed, hence enhancing the resolution even further. High order moments reconstructions performance was examined for various (simulated) conditions and applied to (experimental) imaging of QD labeled microtubules in fixed cells, and actin stress fiber dynamics in live cells.

Single-molecule-localization-microscopy (SMLM) and superresolution-optical-fluctuation-imaging (SOFI) enable imaging biological samples well beyond the diffraction-limit of light. SOFI imaging is typically faster, yet has lower resolution than SMLM. Since the same (or similar) data format is acquired for both methods, their algorithms could presumably be combined synergistically for reconstruction and improvement of overall imaging performance. For that, we first defined a measure of the acquired-SNR for each method. This measure was ∼x10 to x100 higher for SOFI as compared to SMLM, indicating faster recognition and acquisition of features by SOFI. This measure also allowed fluorophore-specific optimization of SOFI reconstruction over its time-window and time-lag. We show that SOFI-assisted SMLM imaging can improve image reconstruction by rejecting common sources of background (e.g. out-of-focus emission and auto-fluorescence), especially under low signal-to-noise ratio conditions, by efficient optical sectioning and by shortening image reconstruction time. The performance and utility of our approach was evaluated by realistic simulations and by SOFI-assisted SMLM imaging of the plasma membrane of activated fixed and live T-cells (in isolation or in conjugation to antigen presenting cells). Our approach enhances SMLM performance under demanding imaging conditions and could set an example for synergizing additional imaging techniques.