Abstract
We review single-molecule localization microscopy techniques with a focus on computational techniques and algorithms necessary for their use. The most common approach to single-molecule localization, Gaussian fitting at positions pre-estimated from local maxima, is illustrated in depth and techniques for two- and three-dimensional data analysis are highlighted. After an introduction explaining the principle requirements of single-molecule localization microscopy, we discuss and contrast novel approaches such as maximum likelihood estimation and model-less fitting. Finally, we give an overview over the existing, scientifically available software and show how these techniques can be combined to quickly and easily obtain super-resolution images.
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Acknowledgment
We would like to thank the Biophotonics Initiative of the BMBF for financial support (Grants #13N11019 and #13N12507).
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Wolter, S., Holm, T., van de Linde, S., Sauer, M. (2014). Data Analysis for Single-Molecule Localization Microscopy. In: Fornasiero, E., Rizzoli, S. (eds) Super-Resolution Microscopy Techniques in the Neurosciences. Neuromethods, vol 86. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-983-3_6
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DOI: https://doi.org/10.1007/978-1-62703-983-3_6
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