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Super-Resolution Imaging Strategies for Cell Biologists Using a Spinning Disk Microscope

Overview of attention for article published in PLOS ONE, October 2013
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Title
Super-Resolution Imaging Strategies for Cell Biologists Using a Spinning Disk Microscope
Published in
PLOS ONE, October 2013
DOI 10.1371/journal.pone.0074604
Pubmed ID
Authors

Neveen A. Hosny, Mingying Song, John T. Connelly, Simon Ameer-Beg, Martin M. Knight, Ann P. Wheeler

Abstract

In this study we use a spinning disk confocal microscope (SD) to generate super-resolution images of multiple cellular features from any plane in the cell. We obtain super-resolution images by using stochastic intensity fluctuations of biological probes, combining Photoactivation Light-Microscopy (PALM)/Stochastic Optical Reconstruction Microscopy (STORM) methodologies. We compared different image analysis algorithms for processing super-resolution data to identify the most suitable for analysis of particular cell structures. SOFI was chosen for X and Y and was able to achieve a resolution of ca. 80 nm; however higher resolution was possible >30 nm, dependant on the super-resolution image analysis algorithm used. Our method uses low laser power and fluorescent probes which are available either commercially or through the scientific community, and therefore it is gentle enough for biological imaging. Through comparative studies with structured illumination microscopy (SIM) and widefield epifluorescence imaging we identified that our methodology was advantageous for imaging cellular structures which are not immediately at the cell-substrate interface, which include the nuclear architecture and mitochondria. We have shown that it was possible to obtain two coloured images, which highlights the potential this technique has for high-content screening, imaging of multiple epitopes and live cell imaging.

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Geographical breakdown

Country Count As %
United Kingdom 7 4%
Czechia 4 2%
Germany 3 2%
Israel 1 <1%
Sweden 1 <1%
Chile 1 <1%
Canada 1 <1%
Belgium 1 <1%
Spain 1 <1%
Other 1 <1%
Unknown 154 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 51 29%
Student > Ph. D. Student 40 23%
Student > Bachelor 14 8%
Student > Master 12 7%
Professor > Associate Professor 9 5%
Other 27 15%
Unknown 22 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 57 33%
Physics and Astronomy 27 15%
Biochemistry, Genetics and Molecular Biology 23 13%
Engineering 15 9%
Neuroscience 6 3%
Other 19 11%
Unknown 28 16%