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Improved Blue, Green, and Red Fluorescent Protein Tagging Vectors for S. cerevisiae

Overview of attention for article published in PLOS ONE, July 2013
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Title
Improved Blue, Green, and Red Fluorescent Protein Tagging Vectors for S. cerevisiae
Published in
PLOS ONE, July 2013
DOI 10.1371/journal.pone.0067902
Pubmed ID
Authors

Sidae Lee, Wendell A. Lim, Kurt S. Thorn

Abstract

Fluorescent protein fusions are a powerful tool to monitor the localization and trafficking of proteins. Such studies are particularly easy to carry out in the budding yeast Saccharomyces cerevisiae due to the ease with which tags can be introduced into the genome by homologous recombination. However, the available yeast tagging plasmids have not kept pace with the development of new and improved fluorescent proteins. Here, we have constructed yeast optimized versions of 19 different fluorescent proteins and tested them for use as fusion tags in yeast. These include two blue, seven green, and seven red fluorescent proteins, which we have assessed for brightness, photostability and perturbation of tagged proteins. We find that EGFP remains the best performing green fluorescent protein, that TagRFP-T and mRuby2 outperform mCherry as red fluorescent proteins, and that mTagBFP2 can be used as a blue fluorescent protein tag. Together, the new tagging vectors we have constructed provide improved blue and red fluorescent proteins for yeast tagging and three color imaging.

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Geographical breakdown

Country Count As %
United States 11 2%
Germany 3 <1%
United Kingdom 2 <1%
Switzerland 1 <1%
Brazil 1 <1%
Finland 1 <1%
Israel 1 <1%
Sweden 1 <1%
China 1 <1%
Other 3 <1%
Unknown 486 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 145 28%
Researcher 105 21%
Student > Master 56 11%
Student > Bachelor 47 9%
Student > Postgraduate 20 4%
Other 70 14%
Unknown 68 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 223 44%
Biochemistry, Genetics and Molecular Biology 152 30%
Chemistry 16 3%
Engineering 7 1%
Medicine and Dentistry 6 1%
Other 36 7%
Unknown 71 14%