Localization-based super-resolution imaging meets high-content screening
| Autor: | Adel Kechkar, Florian Levet, Daniel Choquet, Marine Cabillic, Jean-Baptiste Sibarita, Anne Beghin, Olivier Rossier, Rémi Galland, Grégory Giannone, Corey Butler |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Databases Factual ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image processing Biochemistry GeneralLiterature_MISCELLANEOUS Workflow 03 medical and health sciences Data acquisition Chlorocebus aethiops Microscopy Image Processing Computer-Assisted Animals Data Mining Humans Computer vision Molecular Biology Throughput (business) Fluorescent Dyes business.industry Membrane Proteins Cell Biology Superresolution Single Molecule Imaging Receptors Neurotransmitter Protein Transport 030104 developmental biology High-content screening COS Cells Artificial intelligence business Software HeLa Cells Biotechnology |
| Zdroj: | Nature Methods. 14:1184-1190 |
| ISSN: | 1548-7105 1548-7091 |
| DOI: | 10.1038/nmeth.4486 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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