Localization-based super-resolution microscopy image quality depends on several factors such as dye choice and labeling strategy, microscope quality and user-defined parameters such as frame rate and number as well as the image processing algorithm. Experimental optimization of these parameters can be time-consuming and expensive so we present TestSTORM, a simulator that can be used to optimize these steps. TestSTORM users can select from among four different structures with specific patterns, dye and acquisition parameters. Example results are shown and the results of the vesicle pattern are compared with experimental data. Moreover, image stacks can be generated for further evaluation using localization algorithms, offering a tool for further software developments.

Biomedical Optics Express, Vol. 5, Issue 3, pp. 778-787 (2014)

Localization microscopy software generally contains three elements: a localization algorithm to determine fluorophore positions on a specimen, a quality control method to exclude imprecise localizations, and a visualization technique to reconstruct an image of the specimen. Such algorithms may be designed for either sparse or partially overlapping (dense) fluorescence image data, and making a suitable choice of software depends on whether an experiment calls for simplicity and resolution (favouring sparse methods), or for rapid data acquisition and time resolution (requiring dense methods). We discuss the factors involved in this choice. We provide a full set of MATLAB routines as a guide to localization image processing, and demonstrate the usefulness of image simulations as a guide to the potential artefacts that can arise when processing.

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Localization based super-resolution microscopy techniques require precise drift correction methods because the achieved spatial resolution is close to both the mechanical and optical performance limits of modern light microscopes. Multi-color imaging methods require corrections in addition to those dealing with drift due to the static, but spatially dependent, chromatic offset between images. We present computer simulations to quantify this effect, which is primarily caused by the high- NA objectives used in super-resolution microscopy. Although the chromatic offset in well corrected systems is only a fraction of an optical wavelength in magnitude (<50 nm) and thus negligible in traditional diffraction limited imaging, we show that object colocalization by multi-color super-resolution methods is impossible without appropriate image correction. The simulated data are in excellent agreement with experiments using fluorescent beads excited and localized at multiple wavelengths. Finally we present a rigorous and practical calibration protocol to correct for chromatic optical offset, and demonstrate its efficacy for the imaging of transferrin receptor protein colocalization in HeLa cells using two-color direct stochastic optical reconstruction microscopy (dSTORM). more…