Fourier Transform Fluorescence Recovery After Photobleaching with Patterned Illumination

Fourier transform fluorescence recovery after photobleaching (FT-FRAP) with patterned illumination is demonstrated for quantitatively evaluating normal and anomalous diffusion. Diffusion characterization is routinely performed to assess mobility in cell biology, pharmacology, and food science. Conventional FRAP is noninvasive, has low sample volume requirements, and can rapidly measure diffusion over distances of a few micrometers. However, conventional point-bleach measurements are complicated by signal-to-noise limitations, the need for precise knowledge of the photobleach beam profile, potential for bias due to sample heterogeneity, and poor compatibility with multi-photon excitation due to local heating. In FT-FRAP with patterned illumination, the time-dependent fluorescence recovery signal is concentrated to puncta in the spatial Fourier domain through patterned photobleaching, with substantial improvements in signal-to-noise, mathematical simplicity, representative sampling, and multiphoton compatibility. A custom nonlinear-optical beam-scanning microscope enabled patterned illumination for photobleaching through two-photon excitation. Measurements in the spatial Fourier domain removed dependence on the photobleach profile, suppressing bias from imprecise knowledge of the point spread function and enabled flow analysis through the spatial phase shift. Simultaneous measurement of diffusion at multiple length scales was enabled through analysis of multiple spatial harmonics of the photobleaching pattern. Anomalous diffusion was characterized by FT-FRAP through a nonlinear fit to multiple spatial harmonics of the fluorescence recovery. Furthermore, FT-FRAP with patterned illumination enabled simultaneous diffusion measurements at every position throughout the field of view for normal and anomalous diffusion. Inverse Fourier transformation of peaks shifted to the origin in the spatial frequency domain produced fluorescence recovery maps in real space based on the spatial-frequency peak shape. Diffusion contrast across the field of view was determined through image segmentation and fitting the integrated fluorescence recoveries to a diffusion model