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Data Analysis for Single-Molecule Localization Microscopy

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Super-Resolution Microscopy Techniques in the Neurosciences

Part of the book series: Neuromethods ((NM,volume 86))

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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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Authors and Affiliations

  1. Lehrstuhl für Biotechnologie und Biophysik, University of Würzburg, Würzburg, Germany

    Steve Wolter, Thorge Holm, Sebastian van de Linde & Markus Sauer

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  1. Steve Wolter
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  2. Thorge Holm
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  3. Sebastian van de Linde
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  4. Markus Sauer
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Editors and Affiliations

  1. Department of Neuro- and Sensory Physiology,University of Göttingen Medical Center, Göttingen, Germany

    Eugenio F. Fornasiero

  2. Department of Neuro- and Sensory Physiology,University of Göttingen Medical Center, Göttingen, Germany

    Silvio O. Rizzoli

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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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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-982-6

  • Online ISBN: 978-1-62703-983-3

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