| Title |
Localization-based super-resolution imaging meets high-content screening
|
|---|---|
| Published in |
Nature Methods, October 2017
|
| DOI | 10.1038/nmeth.4486 |
| Pubmed ID | |
| Authors |
Anne Beghin, Adel Kechkar, Corey Butler, Florian Levet, Marine Cabillic, Olivier Rossier, Gregory Giannone, Rémi Galland, Daniel Choquet, Jean-Baptiste Sibarita |
| 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. |
X Demographics
The data shown below were collected from the profiles of 75 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 15 | 20% |
| France | 7 | 9% |
| United Kingdom | 6 | 8% |
| Germany | 5 | 7% |
| Japan | 2 | 3% |
| Netherlands | 2 | 3% |
| Italy | 2 | 3% |
| Switzerland | 1 | 1% |
| Canada | 1 | 1% |
| Other | 3 | 4% |
| Unknown | 31 | 41% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 45 | 60% |
| Scientists | 26 | 35% |
| Science communicators (journalists, bloggers, editors) | 3 | 4% |
| Unknown | 1 | 1% |
Mendeley readers
The data shown below were compiled from readership statistics for 258 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 258 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 65 | 25% |
| Researcher | 54 | 21% |
| Student > Master | 17 | 7% |
| Student > Doctoral Student | 17 | 7% |
| Professor > Associate Professor | 14 | 5% |
| Other | 45 | 17% |
| Unknown | 46 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 48 | 19% |
| Agricultural and Biological Sciences | 43 | 17% |
| Physics and Astronomy | 29 | 11% |
| Engineering | 28 | 11% |
| Chemistry | 18 | 7% |
| Other | 38 | 15% |
| Unknown | 54 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 39. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 June 2022.
All research outputs
#1,064,946
of 25,775,807 outputs
Outputs from Nature Methods
#1,373
of 5,417 outputs
Outputs of similar age
#21,843
of 340,861 outputs
Outputs of similar age from Nature Methods
#29
of 76 outputs
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