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Quantitative super-resolution single molecule microscopy dataset of YFP-tagged growth factor receptors
T. Lukeš, J. Pospíšil, K. Fliegel, T. Lasser, GM. Hagen,
Jazyk angličtina Země Spojené státy americké
Typ dokumentu časopisecké články, práce podpořená grantem
NLK
BioMedCentral Open Access
od 2012
Directory of Open Access Journals
od 2012
Free Medical Journals
od 2012
PubMed Central
od 2012
Europe PubMed Central
od 2012
Open Access Digital Library
od 2011-01-01
Open Access Digital Library
od 2012-01-01
Open Access Digital Library
od 2012-01-01
Oxford Journals Open Access Collection
od 2011
ROAD: Directory of Open Access Scholarly Resources
od 2012
- MeSH
- algoritmy MeSH
- bakteriální proteiny chemie MeSH
- fluorescenční barviva chemie MeSH
- lidé MeSH
- luminescentní proteiny chemie MeSH
- receptory růstových faktorů chemie izolace a purifikace MeSH
- zobrazení jednotlivé molekuly metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
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 with 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 datasets using a different method: super-resolution optical fluctuation imaging (SOFI). The 2 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. Conclusions: This dataset has potential for extensive reuse. Complete raw data from SMLM experiments have typically not been published. The YFP data exhibit low signal-to-noise ratios, making data analysis a challenge. These datasets 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.
Citace poskytuje Crossref.org
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- $a 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 with 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 datasets using a different method: super-resolution optical fluctuation imaging (SOFI). The 2 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. Conclusions: This dataset has potential for extensive reuse. Complete raw data from SMLM experiments have typically not been published. The YFP data exhibit low signal-to-noise ratios, making data analysis a challenge. These datasets 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.
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