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Nat Methods. 2017 Dec;14(12):1184-1190. doi: 10.1038/nmeth.4486. Epub 2017 Oct 30.

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

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.

Abstract

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 tracking.

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

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