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Visualization of Localization Microscopy Data

Published online by Cambridge University Press:  18 January 2010

David Baddeley ,
Mark B. Cannell  and
Christian Soeller
David Baddeley
Affiliation:
Department of Physiology, School of Medical Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
Mark B. Cannell
Affiliation:
Department of Physiology, School of Medical Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
Christian Soeller*
Affiliation:
Department of Physiology, School of Medical Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
*
Corresponding author. E-mail: [email protected]
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Abstract

Localization microscopy techniques based on localizing single fluorophore molecules now routinely achieve accuracies better than 30 nm. Unlike conventional optical microscopies, localization microscopy experiments do not generate an image but a list of discrete coordinates of estimated fluorophore positions. Data display and analysis therefore generally require visualization methods that translate the position data into conventional images. Here we investigate the properties of several widely used visualization techniques and show that a commonly used algorithm based on rendering Gaussians may lead to a 1.44-fold loss of resolution. Existing methods typically do not explicitly take sampling considerations into account and thus may produce spurious structures. We present two additional visualization algorithms, an adaptive histogram method based on quad-trees and a Delaunay triangulation based visualization of point data that address some of these deficiencies. The new visualization methods are designed to suppress erroneous detail in poorly sampled image areas but avoid loss of resolution in well-sampled regions. A number of criteria for scoring visualization methods are developed as a guide for choosing among visualization methods and are used to qualitatively compare various algorithms.

Keywords

single moleculeslocalization microscopynanoscopycaveolinsuper-resolutionfluorescence
Type
Biological Imaging: Techniques Development and Applications
Information
Microscopy and Microanalysis , Volume 16 , Issue 1 , February 2010 , pp. 64 - 72
Copyright
Copyright © Microscopy Society of America 2010

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References

REFERENCES

Baddeley, D., Jayasinghe, I.D., Cremer, C., Cannell, M.B. & Soeller, C. (2009). Light-induced dark states of organic fluochromes enable 30 nm resolution imaging in standard media. Biophys J 96, L22–L24.CrossRefGoogle ScholarPubMed
Betzig, E., Patterson, G.H., Sougrat, R., Lindwasser, O.W., Olenych, S., Bonifacino, J.S., Davidson, M.W., Lippincott-Schwartz, J. & Hess, H.F. (2006). Imaging intracellular fluorescent proteins at nanometer resolution. Science 313, 1642–1645.CrossRefGoogle ScholarPubMed
De Berg, M., Cheong, O. & van Kreveld, M. (2008). Computational Geometry: Algorithms and Applications. Berlin, Heidelberg: Springer-Verlag.CrossRefGoogle Scholar
Egner, A., Geisler, C., von Middendorff, C., Bock, H., Wenzel, D., Medda, R., Andresen, M., Stiel, A.C., Jakobs, S., Eggeling, C., Schönle, A. & Hell, S.W. (2007). Fluorescence nanoscopy in whole cells by asynchronous localization of photoswitching emitters. Biophys J 93, 3285–3290.CrossRefGoogle ScholarPubMed
Finkel, R. & Bentley, J. (1974). Quad trees a data structure for retrieval on composite keys. Acta Informatica 4, 1–9.Google Scholar
Fölling, J., Bossi, M., Bock, H., Medda, R., Wurm, C., Hein, B., Jakobs, S., Eggeling, C. & Hell, S. (2008). Fluorescence nanoscopy by ground-state depletion and single-molecule return. Nat Methods 5, 943–945.CrossRefGoogle ScholarPubMed
Heilemann, M., van de Linde, S., Schüttpelz, M., Kasper, R., Seefeldt, B., Mukher-jee, A., Tinnefeld, P. & Sauer, M. (2008). Subdiffraction-resolution fluorescence imaging with conventional fluorescent probes. Angew Chem Int Ed 47, 6172–6176.CrossRefGoogle ScholarPubMed
Heintzmann, R. & Sheppard, C. (2007). The sampling limit in fluorescence microscopy. Micron 38, 145–149.CrossRefGoogle ScholarPubMed
Hess, S.T., Girirajan, T.P.K. & Mason, M.D. (2006). Ultra-high resolution imaging by fluorescence photoactivation localization microscopy. Biophys J 91, 4258–4272.Google Scholar
Rust, M.J., Bates, M. & Zhuang, X. (2006). Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (storm). Nat Methods 3, 793–795.Google Scholar
Shannon, C. (1949). Communication in the presence of noise. Proc IRE 37, 10–21.CrossRefGoogle Scholar
Shroff, H., Galbraith, C.G., Galbraith, J.A. & Betzig, E. (2008). Live-cell photoactivated localization microscopy of nanoscale adhesion dynamics. Nat Methods 5, 417–423.CrossRefGoogle ScholarPubMed
Thompson, R.E., Larson, D.R. & Webb, W.W. (2002). Precise nanometer localization analysis for individual fluorescent probes. Biophys J 82, 2775–83.CrossRefGoogle ScholarPubMed
van Oijen, A., Köhler, J., Schmidt, J., Müller, M. & Brakenhoff, G. (1998). 3-Dimensional super-resolution by spectrally selective imaging. Chem Phys Lett 292, 183–187.CrossRefGoogle Scholar
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