N-Glycoprotein SRMAtlas

Ruth Hüttenhain(ETH Zurich), Silvia Šurinová(ETH Zurich), Reto Ossola(Biognosys (Switzerland)), Zhi Sun(Institute for Systems Biology), David Campbell(University Hospital of Zurich), Ferdinando Cerciello(University of Zurich), Ralph Schiess(Proteome Sciences (United Kingdom)), Damaris Bausch‐Fluck(University of Zurich), George Rosenberger(ETH Zurich), J. Chen(The Ohio State University Wexner Medical Center), Oliver Rinner(Biognosys (Switzerland)), Ulrike Kusebauch(Institute for Systems Biology), Marián Hajdúch(Institute of Molecular and Translational Medicine), Robert L. Moritz(Institute for Systems Biology), Bernd Wollscheid(University of Zurich), Ruedi Aebersold(University of Zurich)
Molecular & Cellular Proteomics
February 13, 2013
Cited by 50Open Access
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Abstract

Protein biomarkers have the potential to transform medicine as they are clinically used to diagnose diseases, stratify patients, and follow disease states. Even though a large number of potential biomarkers have been proposed over the past few years, almost none of them have been implemented so far in the clinic. One of the reasons for this limited success is the lack of technologies to validate proposed biomarker candidates in larger patient cohorts. This limitation could be alleviated by the use of antibody-independent validation methods such as selected reaction monitoring (SRM). Similar to measurements based on affinity reagents, SRM-based targeted mass spectrometry also requires the generation of definitive assays for each targeted analyte. Here, we present a library of SRM assays for 5568 N-glycosites enabling the multiplexed evaluation of clinically relevant N-glycoproteins as biomarker candidates. We demonstrate that this resource can be utilized to select SRM assay sets for cancer-associated N-glycoproteins for their subsequent multiplexed and consistent quantification in 120 human plasma samples. We show that N-glycoproteins spanning 5 orders of magnitude in abundance can be quantified and that previously reported abundance differences in various cancer types can be recapitulated. Together, the established N-glycoprotein SRMAtlas resource facilitates parallel, efficient, consistent, and sensitive evaluation of proposed biomarker candidates in large clinical sample cohorts.


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