Localization-based super-resolution imaging meets high-content screening

Anne Beghin, Adel Kechkar, Corey Butler, Florian Levet, Marine Cabillic, Olivier Rossier, Gregory Giannone, Rémi Galland, Daniel Choquet, Jean-Baptiste Sibarita
Nat Meth. 2017-10-30; 14(12): 1184-1190
DOI: 10.1038/nmeth.4486

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1. Nat Methods. 2017 Dec;14(12):1184-1190. doi: 10.1038/nmeth.4486. Epub 2017 Oct

Localization-based super-resolution imaging meets high-content screening.

Beghin A(1)(2), Kechkar A(3), Butler C(1)(2)(4), Levet F(1)(2)(5), Cabillic
M(1)(2), Rossier O(1)(2), Giannone G(1)(2), Galland R(1)(2), Choquet D(1)(2)(5),
Sibarita JB(1)(2).

Author information:
(1)Université de Bordeaux, Institut interdisciplinaire de Neurosciences,
Bordeaux, France.
(2)CNRS UMR 5297, Institut interdisciplinaire de Neurosciences, Bordeaux, France.
(3)Ecole Nationale Supérieure de Biotechnologie, Constantine, Algeria.
(4)Imagine Optic, Orsay, France.
(5)Bordeaux Imaging Center, CNRS, Université de Bordeaux, UMS 3420, INSERM US4,
Bordeaux, France.

Single-molecule localization microscopy techniques have proven to be essential
tools for quantitatively monitoring biological processes at unprecedented spatial
resolution. However, these techniques are very low throughput and are not yet
compatible with fully automated, multiparametric cellular assays. This
shortcoming is primarily due to the huge amount of data generated during imaging
and the lack of software for automation and dedicated data mining. We describe an
automated quantitative single-molecule-based super-resolution methodology that
operates in standard multiwell plates and uses analysis based on high-content
screening and data-mining software. The workflow is compatible with fixed- and
live-cell imaging and allows extraction of quantitative data like fluorophore
photophysics, protein clustering or dynamic behavior of biomolecules. We
demonstrate that the method is compatible with high-content screening using 3D
dSTORM and DNA-PAINT based super-resolution microscopy as well as single-particle

DOI: 10.1038/nmeth.4486
PMID: 29083400 [Indexed for MEDLINE]

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