Dataset Information

N-glycoprotein SRMAtlas: a resource of mass spectrometric assays for N-glycosites enabling consistent and multiplexed protein quantification for clinical applications.

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.

SUBMITTER: Huttenhain R

PROVIDER: S-EPMC3617325 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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Publications

N-glycoprotein SRMAtlas: a resource of mass spectrometric assays for N-glycosites enabling consistent and multiplexed protein quantification for clinical applications.

Hüttenhain Ruth R Surinova Silvia S Ossola Reto R Sun Zhi Z Campbell David D Cerciello Ferdinando F Schiess Ralph R Bausch-Fluck Damaris D Rosenberger George G Chen Jingchung J Rinner Oliver O Kusebauch Ulrike U Hajdúch Marián M Moritz Robert L RL Wollscheid Bernd B Aebersold Ruedi R

Molecular & cellular proteomics : MCP 20130213 4

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 u ...[more]

PMID: 23408683

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Project description:Because biological processes can result in different loci having different evolutionary histories, species tree estimation requires multiple loci from across multiple genomes. While many processes can result in discord between gene trees and species trees, incomplete lineage sorting (ILS), modeled by the multi-species coalescent, is considered to be a dominant cause for gene tree heterogeneity. Coalescent-based methods have been developed to estimate species trees, many of which operate by combining estimated gene trees, and so are called "summary methods". Because summary methods are generally fast (and much faster than more complicated coalescent-based methods that co-estimate gene trees and species trees), they have become very popular techniques for estimating species trees from multiple loci. However, recent studies have established that summary methods can have reduced accuracy in the presence of gene tree estimation error, and also that many biological datasets have substantial gene tree estimation error, so that summary methods may not be highly accurate in biologically realistic conditions. Mirarab et al. (Science 2014) presented the "statistical binning" technique to improve gene tree estimation in multi-locus analyses, and showed that it improved the accuracy of MP-EST, one of the most popular coalescent-based summary methods. Statistical binning, which uses a simple heuristic to evaluate "combinability" and then uses the larger sets of genes to re-calculate gene trees, has good empirical performance, but using statistical binning within a phylogenomic pipeline does not have the desirable property of being statistically consistent. We show that weighting the re-calculated gene trees by the bin sizes makes statistical binning statistically consistent under the multispecies coalescent, and maintains the good empirical performance. Thus, "weighted statistical binning" enables highly accurate genome-scale species tree estimation, and is also statistically consistent under the multi-species coalescent model. New data used in this study are available at DOI: http://dx.doi.org/10.6084/m9.figshare.1411146, and the software is available at https://github.com/smirarab/binning.

Dataset Information

N-glycoprotein SRMAtlas: a resource of mass spectrometric assays for N-glycosites enabling consistent and multiplexed protein quantification for clinical applications.

Publications

N-glycoprotein SRMAtlas: a resource of mass spectrometric assays for N-glycosites enabling consistent and multiplexed protein quantification for clinical applications.

Similar Datasets

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