Abstract
A straightforward method to achieve super-resolution consists of taking an image sequence of stochastically blinking emitters using a standard wide-field fluorescence microscope. Densely packed single molecules can be distinguished sequentially in time using high-precision localization algorithms (e.g., PALM and STORM) or by analyzing the statistics of the temporal fluctuations (SOFI). In a face-to-face comparison of the two post-processing algorithms, we show that localization-based super-resolution can deliver higher resolution enhancements but imposes significant constraints on the blinking behavior of the probes, which limits its applicability for live-cell imaging. SOFI, on the other hand, works more consistently over different photo-switching kinetics and also delivers information about the specific blinking statistics. Its suitability for low SNR acquisition reveals SOFI's potential as a high-speed super-resolution imaging technique.
Highlights
Advances in far-field fluorescence microscopy have produced a number of techniques capable of imaging features with a resolution well beyond the diffraction limit [1,2,3,4]
We investigated the performance of the statistics of the temporal fluctuations (SOFI) and stochastic optical reconstruction microscopy (STORM) algorithms under the aspects of photo-switching kinetics, labeling density and signal-to-noise ratio
We compared two post-processing algorithms for super-resolution microscopy, STORM and SOFI. Both techniques can be readily applied to standard fluorescence microscopes
Summary
Advances in far-field fluorescence microscopy have produced a number of techniques capable of imaging features with a resolution well beyond the diffraction limit [1,2,3,4]. Switching the fluorescence of single emitters sequentially on and off in either a targeted [5, 6] or stochastic manner [7, 8] enables the distinction of objects within a diffraction-limited spot. Improvements of the blinking probes and chemical buffers recently enabled live-cell STORM [11]. Further developments enabling 3D imaging include advanced localization algorithms [12], PSF engineering [13, 14], super-critical angle microscopy [15] and interferometric imaging techniques [16, 17]
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