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TestSTORM: Simulator for optimizing sample labeling and image acquisition in localization based super-resolution microscopy

TestSTORM: Simulator for optimizing sample and image acquisition in localization based super-resolution microscopy, Sinkó J, Kákonyi R, Rees E, Metcalf D, Knight AE, Kaminski CF, Szabó G, Erdélyi, Biomed. Opt. Exp. (2014), 5 (3), pp. 778-787

DOI: 10.1364/BOE.5.000778 | pdf


Abstract

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.

 


TestSTORMsingle moleculesuperresolution

Figure: Test models to assess localisation precision in superresolution images.  The models mimic features of typical biological structures, e.g. labelled proteins, vesicles, protein fibres, etc.  The model takes the stochastic photophysics / photochemistry of blinking fluorophores into account.