Abstract
Super-resolution optical fluctuation imaging (SOFI) provides super-resolution (SR) fluorescence imaging by analyzing fluctuations in the fluorophore emission. The technique has been used both to acquire quantitative SR images and to provide SR biosensing by monitoring changes in fluorophore blinking dynamics. Proper analysis of such data relies on a fully quantitative model of the imaging. However, previous SOFI imaging models made several assumptions that can not be realized in practice. In this work we address these limitations by developing and verifying a fully quantitative model that better approximates real-world imaging conditions. Our model shows that (i) SOFI images are free of bias, or can be made so, if the signal is stationary and fluorophores blink independently, (ii) allows a fully quantitative description of the link between SOFI imaging and probe dynamics, and (iii) paves the way for more advanced SOFI image reconstruction by offering a computationally fast way to calculate SOFI images for arbitrary probe, sample and instrumental properties.
Highlights
Super-resolution optical fluctuation imaging (SOFI) provides a sub-diffraction spatial resolution in far-field fluorescence microscopy by making use of spontaneous fluctuations in the fluorophore emission [1]
Our improved model readily allows the reproduction of the measured data, and associated determination of the fluorophore blinking parameters. Overall this allows us to obtain more reliable results in a much shorter time. In this contribution we have sought to develop a quantitative model for SOFI imaging that is compatible with the limitations inherent in actual experiments
Our model can efficiently predict the SOFI image resulting from arbitrary instrumental parameters, sample structuring, and probe dynamics
Summary
Super-resolution optical fluctuation imaging (SOFI) provides a sub-diffraction spatial resolution in far-field fluorescence microscopy by making use of spontaneous fluctuations in the fluorophore emission [1]. Using changes in blinking as a contrast mechanism has been previously applied, e.g. in TRAST microscopy [16], though SOFI is one of the only techniques that can provide this information at the SR level Crucial for this analysis is the availability of a model that quantitatively describes the link between the fluorophore distribution and properties, and the resulting SOFI image. Strict stationarity is difficult to realize, but we have found that in biological imaging the fluorescence emission is usually sufficiently stationary despite some probe movement or photo-bleaching of fluorophores [19,20] This simple model was further extended to capture changes in blinking dynamics in a way that is independent of the probe concentration [10]. Our work (i) shows that SOFI images are free of bias, or can be made so, if the signal is stationary and fluorophores blink independently, (ii) allows a fully quantitative description of the link between SOFI imaging and probe dynamics under real-world imaging conditions, and (iii) paves the way for more advanced SOFI image reconstruction by offering a computationally fast way to calculate SOFI images for arbitrary probe, sample and instrumental properties
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