de.mpg.escidoc.pubman.appbase.FacesBean
Deutsch
 
Hilfe Wegweiser Impressum Kontakt Einloggen
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

Systematic evaluation of reference protein normalization in proteomic experiments

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons97489

Zauber,  H.
Signalling Proteomics, Department Stitt, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97398

Schuler,  V.
Signalling Proteomics, Department Stitt, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)

Zauber-2013-Systematic evaluatio.pdf
(beliebiger Volltext), 2MB

Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Zauber, H., Schuler, V., & Schulze, W. (2013). Systematic evaluation of reference protein normalization in proteomic experiments. Frontiers in plant science, 4, 25. doi:10.3389/fpls.2013.00025.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0014-1D6D-1
Zusammenfassung
Quantitative comparative analyses of protein abundances using peptide ion intensities and their modifications have become a widely used technique in studying various biological questions. In the past years, several methods for quantitative proteomics were established using stable-isotope labeling and label-free approaches. We systematically evaluated the application of reference protein normalization (RPN) for proteomic experiments using a high mass accuracy LC-MS/MS platform. In RPN all sample peptide intensities were normalized to an average protein intensity of a spiked reference protein. The main advantage of this method is that it avoids fraction of total based relative analysis of proteomic data, which is often very much dependent on sample complexity. We could show that reference protein ion intensity sums are sufficiently reproducible to ensure a reliable normalization. We validated the RPN strategy by analyzing changes in protein abundances induced by nutrient starvation in . Beyond that, we provide a principle guideline for determining optimal combination of sample protein and reference protein load on individual LC-MS/MS systems.