Localization microscopy is a class of super-resolution fluorescence microscopy techniques. Localization microscopy methods are characterized by stochastic temporal isolation of fluorophore emission, i.e., making the fluorophores blink so rapidly that no two are likely to be photoactive at the same time close to each other. Well-known localization microscopy methods include dSTORM, STORM, PALM, FPALM, or GSDIM. The biological community has taken great interest in localization microscopy, since it can enhance the resolution of common fluorescence microscopy by an order of magnitude at little experimental cost. However, localization microscopy has considerable computational cost since millions of individual stochastic emissions must be located with nanometer precision. The computational cost of this evaluation, and the organizational cost of implementing the complex algorithms, has impeded adoption of super-resolution microscopy for a long time. In this work, I describe my algorithmic framework for evaluating localization microscopy data. I demonstrate how my novel open-source software achieves real-time data evaluation, i.e., can evaluate data faster than the common experimental setups can capture them. I show how this speed is attained on standard consumer-grade CPUs, removing the need for computing on expensive clusters or deploying graphics processing units. The evaluation is performed with the widely accepted Gaussian point spread function (PSF) model and a Poissonian maximum-likelihood noise model. I extend the computational model to show how robust, optimal two-color evaluation is realized, allowing correlative microscopy between multiple proteins or structures. By employing cubic B-splines, I show how the evaluation of three-dimensional samples can be made simple and robust, taking an important step towards precise imaging of micrometer-thick samples. I uncover the behavior and limits of localization algorithms in the face of increasing emission densities. Finally, I show up algorithms to extend localization microscopy to common biological problems. I investigate cellular movement and motility by considering the in vitro movement of myosin-actin laments. I show how SNAP-tag fusion proteins enable imaging with bright and stable organic fluorophores in live cells. By analyzing the internal structure of protein clusters, I show how localization microscopy can provide new quantitative approaches beyond pure imaging.
|Advisor:||Sauer , Markus , Heilemann , Mike , Beier , Dagmar|
|School:||Bayerische Julius-Maximilians-Universitaet Wuerzburg (Germany)|
|Source:||DAI-C 81/1(E), Dissertation Abstracts International|
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