Abstract
Super-resolution optical fluctuation imaging (SOFI) allows one to perform sub-diffraction fluorescence microscopy of living cells. By analyzing the acquired image sequence with an advanced correlation method, i.e. a high-order cross-cumulant analysis, super-resolution in all three spatial dimensions can be achieved. Here we introduce a software tool for a simple qualitative comparison of SOFI images under simulated conditions considering parameters of the microscope setup and essential properties of the biological sample. This tool incorporates SOFI and STORM algorithms, displays and describes the SOFI image processing steps in a tutorial-like fashion. Fast testing of various parameters simplifies the parameter optimization prior to experimental work. The performance of the simulation tool is demonstrated by comparing simulated results with experimentally acquired data.
Highlights
The emergence of sub-diffraction fluorescence microscopy [1,2,3,4,5] has opened the door for novel insights in the life sciences by imaging features well beyond the diffraction limit [6]
Super-resolved single molecule localization methods such as photoactivation localization microscopy (PALM) [7] and stochastic optical reconstruction microscopy (STORM) [8] rely on stochastic emissions of photon bursts produced by independently blinking emitters
We developed an simulation tool equipped with a graphical user interface (GUI)
Summary
The emergence of sub-diffraction fluorescence microscopy [1,2,3,4,5] has opened the door for novel insights in the life sciences by imaging features well beyond the diffraction limit [6]. Super-resolved single molecule localization methods such as photoactivation localization microscopy (PALM) [7] and stochastic optical reconstruction microscopy (STORM) [8] rely on stochastic emissions of photon bursts produced by independently blinking emitters. PALM and STORM analyze a sequence of image frames showing sparse sub-sets of emitting labels such that the emitters can be localized individually. The emitter localizations are combined into a spatially super-resolved image of the sample. In contrast to this frame-by-frame localization, super-resolution optical fluctuation imaging (SOFI) [9, 10] exploits the image sequence as a whole by using higher order statistics, i.e. higher.
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