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

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....

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Bibliographic Details
Published in:Nature communications 2019-05, Vol.10 (1), p.2379-2379, Article 2379
Main Authors: Levet, Florian, Julien, Guillaume, Galland, Rémi, Butler, Corey, Beghin, Anne, Chazeau, Anaël, Hoess, Philipp, Ries, Jonas, Giannone, Grégory, Sibarita, Jean-Baptiste
Format: Article
Language:English
Subjects:
14/63
631/114/1314
631/114/794
Bioinformatics
Biotechnology
Cellular Biology
Computer Science
Humanities and Social Sciences
Life Sciences
Localization
Microscopy
multidisciplinary
Parameter sensitivity
Science
Science (multidisciplinary)
Tessellation
Two dimensional analysis
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Summary: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. Multicolour single-molecule localization microscopy lacks a standard analysis method. Here Levet et al. introduce Coloc-Tesseler, a parameter-free colocalisation analysis method based on tessellation analysis for the efficient analysis of multicolour SMLM data.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-019-10007-4