Quantitative super-resolution single molecule microscopy dataset of YFP-tagged growth factor receptors
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00317585" target="_blank" >RIV/68407700:21230/18:00317585 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1093/gigascience/giy002" target="_blank" >https://doi.org/10.1093/gigascience/giy002</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1093/gigascience/giy002" target="_blank" >10.1093/gigascience/giy002</a>
Alternative languages
Result language
angličtina
Original language name
Quantitative super-resolution single molecule microscopy dataset of YFP-tagged growth factor receptors
Original language description
Background: Super-resolution single molecule localization microscopy (SMLM) is a method for achieving resolution beyond the classical limit in optical microscopes (approx. 200 nm laterally). Yellow fluorescent protein (YFP) has been used for super-resolution single molecule localization microscopy, but less frequently than other fluorescent probes. Working with YFP in SMLM is a challenge because a lower number of photons are emitted per molecule compared to organic dyes which are more commonly used. Publically available experimental data can facilitate development of new data analysis algorithms. Findings: Four complete, freely available single molecule super-resolution microscopy datasets on YFP-tagged growth factor receptors expressed in a human cell line are presented including both raw and analyzed data. We report methods for sample preparation, for data acquisition, and for data analysis, as well as examples of the acquired images. We also analyzed the SMLM data sets using a different method: super-resolution optical fluctuation imaging (SOFI). The two modes of analysis offer complementary information about the sample. A fifth single molecule super-resolution microscopy dataset acquired with the dye Alexa 532 is included for comparison purposes. Conclusion: This dataset has potential for extensive reuse. Complete raw data from SMLM experiments has typically not been published. The YFP data exhibits low signal to noise ratios, making data analysis a challenge. These data sets will be useful to investigators developing their own algorithms for SMLM, SOFI, and related methods. The data will also be useful for researchers investigating growth factor receptors such as ErbB3.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
<a href="/en/project/GA17-05840S" target="_blank" >GA17-05840S: Multicriteria Optimization of Shift-Variant Imaging System Models</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
GigaScience
ISSN
2047-217X
e-ISSN
2047-217X
Volume of the periodical
7
Issue of the periodical within the volume
3
Country of publishing house
GB - UNITED KINGDOM
Number of pages
10
Pages from-to
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UT code for WoS article
000427170500001
EID of the result in the Scopus database
2-s2.0-85048201300