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Institute
In recent years three-dimensional (3D) super-resolution fluorescence imaging by single-molecule localization (localization microscopy) has gained considerable interest because of its simple implementation and high optical resolution. Astigmatic and biplane imaging are experimentally simple methods to engineer a 3D-specific point spread function (PSF), but existing evaluation methods have proven problematic in practical application. Here we introduce the use of cubic B-splines to model the relationship of axial position and PSF width in the above mentioned approaches and compare the performance with existing methods. We show that cubic B-splines are the first method that can combine precision, accuracy and simplicity.
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 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 filaments. 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.
Super-resolution fluorescence imaging based on inglemolecule localization relies critically on the availability of efficient processing algorithms to distinguish, identify, and localize emissions of single fluorophores. In multiple current applications, such as threedimensional, time-resolved or cluster imaging, high densities of fluorophore emissions are common. Here, we provide an analytic tool to test the performance and quality of localization microscopy algorithms and demonstrate that common algorithms encounter difficulties for samples with high fluorophore density. We demonstrate that, for typical single-molecule localization microscopy methods such as dSTORM and the commonly used rapidSTORM scheme, computational precision limits the acceptable density of concurrently active fluorophores to 0.6 per square micrometer and that the number of successfully localized fluorophores per frame is limited to 0.2 per square micrometer.