A tessellation-based colocalization analysis approach for single-molecule localization microscopy

Florian Levet, Guillaume Julien, Rémi Galland, Corey Butler, Anne Beghin, Anaël Chazeau, Philipp Hoess, Jonas Ries, Grégory Giannone, Jean-Baptiste Sibarita
Nat Commun. 2019-05-30; 10(1):
DOI: 10.1038/s41467-019-10007-4

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Levet F(1)(2)(3)(4)(5), Julien G(1)(2), Galland R(1)(2), Butler C(1)(2), Beghin A(1)(2), Chazeau A(1)(2), Hoess P(6), Ries J(6), Giannone G(1)(2), Sibarita JB(7)(8).

Author information:
(1)Interdisciplinary Institute for Neuroscience, University of Bordeaux, Bordeaux, 33076, France.
(2)Interdisciplinary Institute for Neuroscience, Centre National de la Recherche Scientifique (CNRS) UMR 5297, Bordeaux, 33076, France.
(3)Bordeaux Imaging Center, University of Bordeaux, Bordeaux, 33076, France.
(4)Bordeaux Imaging Center, CNRS UMS 3420, Bordeaux, 33076, France.
(5)Bordeaux Imaging Center, INSERM US04, Bordeaux, 33076, France.
(6)Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, 69117, Germany.
(7)Interdisciplinary Institute for Neuroscience, University of Bordeaux, Bordeaux, 33076, France..
(8)Interdisciplinary Institute for Neuroscience, Centre National de la Recherche Scientifique (CNRS) UMR 5297, Bordeaux, 33076, France. .

Multicolor single-molecule localization microscopy (λSMLM) is a powerful technique to reveal the relative nanoscale organization and potential colocalization between different molecular species. While several standard analysis methods exist for pixel-based images, λSMLM still lacks such a standard.
Moreover, existing methods only work on 2D data and are usually sensitive to the relative molecular organization, a very important parameter to consider in quantitative SMLM. Here, we present an efficient, parameter-free colocalization analysis method for 2D and 3D λSMLM using tessellation analysis. We demonstrate that our method allows for the efficient computation of several popular colocalization estimators directly from molecular coordinates and illustrate its capability to analyze multicolor SMLM data in a robust and efficient manner.

DOI: 10.1038/s41467-019-10007-4
PMCID: PMC6542817
PMID: 31147535


Auteurs Bordeaux Neurocampus