| Literature DB >> 29083400 |
Anne Beghin1,2, Adel Kechkar3, Corey Butler1,2,4, Florian Levet1,2,5, Marine Cabillic1,2, Olivier Rossier1,2, Gregory Giannone1,2, Rémi Galland1,2, Daniel Choquet1,2,5, Jean-Baptiste Sibarita1,2.
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.Mesh：
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Year: 2017 PMID： 29083400 DOI： 10.1038/nmeth.4486
Source DB: PubMed Journal: Nat Methods ISSN： 1548-7091 Impact factor: 28.547