Direct stochastic optical reconstruction microscopy (dSTORM) uses conventional fluorescent probes such as labeled antibodies or chemical tags for subdiffraction resolution fluorescence imaging with a lateral resolution of ∼20 nm. In contrast to photoactivated localization microscopy (PALM) with photoactivatable fluorescent proteins, dSTORM experiments start with bright fluorescent samples in which the fluorophores have to be transferred to a stable and reversible OFF state. The OFF state has a lifetime in the range of 100 milliseconds to several seconds after irradiation with light intensities low enough to ensure minimal photodestruction. Either spontaneously or photoinduced on irradiation with a second laser wavelength, a sparse subset of fluorophores is reactivated and their positions are precisely determined. Repetitive activation, localization and deactivation allow a temporal separation of spatially unresolved structures in a reconstructed image. Here we present a step-by-step protocol for dSTORM imaging in fixed and living cells on a wide-field fluorescence microscope, with standard fluorescent probes focusing especially on the photoinduced fine adjustment of the ratio of fluorophores residing in the ON and OFF states. Furthermore, we discuss labeling strategies, acquisition parameters, and temporal and spatial resolution. The ultimate step of data acquisition and data processing can be performed in seconds to minutes.

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Background: Syntaxin forms nano-sized clusters at the plasma membrane whose inner organization is unknown. Results: In the clusters, the density of proteins gradually decreases toward the periphery. Conclusion: Syntaxin reactivity is influenced by its location within the clusters. Significance: dSTORM imaging combined with cluster analysis significantly contributes to understanding membranal protein distribution and cluster organization.

SummaryIn the recent past, single-molecule based localization or photoswitching microscopy methods such as stochastic optical reconstruction microscopy (STORM) or photoactivated localization microscopy (PALM) have been successfully implemented for subdiffraction-resolution fluorescence imaging. However, the computational effort needed to localize numerous fluorophores is tremendous, causing long data processing times and thereby limiting the applicability of the technique. Here we present a new computational scheme for data processing consisting of noise reduction, detection of likely fluorophore positions, high-precision fluorophore localization and subsequent visualization of found fluorophore positions in a super-resolution image. We present and benchmark different algorithms for noise reduction and demonstrate the use of non-maximum suppression to quickly find likely fluorophore positions in high depth and very noisy images. The algorithm is evaluated and compared in terms of speed, accuracy and robustness by means of simulated data. On real biological samples, we find that real-time data processing is possible and that super-resolution imaging with organic fluorophores of cellular structures with ∼20 nm optical resolution can be completed in less than 10 s.