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N-Glycoprotein SRMAtlas

A RESOURCE OF MASS SPECTROMETRIC ASSAYS FOR N-GLYCOSITES ENABLING CONSISTENT AND MULTIPLEXED PROTEIN QUANTIFICATION FOR CLINICAL APPLICATIONS**
  • Ruth Hüttenhain
    Footnotes
    Affiliations
    Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland

    Competence Center for Systems Physiology and Metabolic Diseases, 8093 Zurich, Switzerland
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  • Silvia Surinova
    Footnotes
    Affiliations
    Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland

    Competence Center for Systems Physiology and Metabolic Diseases, 8093 Zurich, Switzerland
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  • Reto Ossola
    Affiliations
    Biognosys AG, 8952 Schlieren, Switzerland
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  • Zhi Sun
    Affiliations
    Institute for Systems Biology, Seattle, Washington 98109–5234
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  • David Campbell
    Affiliations
    Clinic of Oncology, University Hospital Zurich, 8091 Zurich, Switzerland
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  • Ferdinando Cerciello
    Affiliations
    Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland

    Clinic of Oncology, University Hospital Zurich, 8091 Zurich, Switzerland

    NCCR Neuro Center for Proteomics, ETH Zurich and University of Zurich, 8057 Zurich, Switzerland
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  • Ralph Schiess
    Affiliations
    ProteoMediX AG, 8952 Schlieren, Switzerland
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  • Damaris Bausch-Fluck
    Affiliations
    Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland

    NCCR Neuro Center for Proteomics, ETH Zurich and University of Zurich, 8057 Zurich, Switzerland
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  • George Rosenberger
    Affiliations
    Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
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  • Jingchung Chen
    Affiliations
    Information Warehouse, Ohio State University Wexner Medical Center, Columbus, Ohio 43210
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  • Oliver Rinner
    Affiliations
    Biognosys AG, 8952 Schlieren, Switzerland
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  • Ulrike Kusebauch
    Affiliations
    Institute for Systems Biology, Seattle, Washington 98109–5234
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  • Marián Hajdúch
    Affiliations
    Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacký University, 771 26 Olomouc, Czech Republic
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  • Robert L. Moritz
    Affiliations
    Institute for Systems Biology, Seattle, Washington 98109–5234
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  • Bernd Wollscheid
    Correspondence
    To whom correspondence may be addressed: B.W. Tel.: 41-44-633-36-84; Fax:41-44-633-10-51;
    Affiliations
    Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland

    NCCR Neuro Center for Proteomics, ETH Zurich and University of Zurich, 8057 Zurich, Switzerland
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  • Ruedi Aebersold
    Correspondence
    To whom correspondence may be addressed: B.W. Tel.: 41-44-633-36-84; Fax:41-44-633-10-51;
    Affiliations
    Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland

    Competence Center for Systems Physiology and Metabolic Diseases, 8093 Zurich, Switzerland

    Faculty of Science, University of Zurich, 8057 Zurich, Switzerland
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  • Author Footnotes
    * This work was supported, in whole or in part, by American Recovery and Reinvestment Act funds through National Institutes of Health Grant R01 HG005805 (to R.L.M. and to Z.S., D.C., and U.K.), the EDRN program of the NCI (to R.A.), and in part by NIGMS Grant 2P50 GM076547 from Center for Systems Biology. This work was also supported by American Recovery and Reinvestment Act (ARRA) funds through grant number R01 HG005805 (to R.L.M.) the National Human Genome Research Institute; funding from the National Center of Competence in Research Neural Plasticity and Repair (to B.W.); the Swiss National Science Foundation Grant 31003A_135805 (to B.W.); SystemsX.ch/InfectX and SystemsX.ch/BIP (to B.W.); the Swiss National Science Foundation Grant 3100A0-107679 (to R.A.); the PRIME-XS Project funded by the European Union 7th Framework Programme Grant 262067 (to R.A.), the European Research Council Grant ERC-2008-AdG 233226 (to R.A.), funding from the Luxembourg Centre for Systems Biomedicine and the University of Luxembourg, from the National Science Foundation MRI Grant 0923536. U.K. was supported by a fellowship from the German Academic Exchange Service. Conflict of Interest: R.S. is CEO at ProteoMediX AG; O.R. is CSO at Biognosys AG, and R.O. is an employee of Biognosys AG. The other authors declare that they have no competing interests.
    This article contains Supplemental material.
    c Both authors contributed equally to this work.
Open AccessPublished:February 13, 2013DOI:https://doi.org/10.1074/mcp.O112.026617
      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.
      Protein biomarkers measured in easily accessible samples are a critical element of personalized medicine, because detecting disease states at their early onset or following a response to treatment will undoubtedly enhance treatment efficacy and boost patient wellbeing. Protein biomarkers therefore have the potential to define diseases such as cancer at a molecular level, determine the best treatment by profiling a patient's disease, and assess the therapeutic efficacy over time (
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      The abbreviations used are:
      iRT
      indexed retention time
      LOQ
      limit of quantification
      RT
      retention time
      SRM
      selected reaction monitoring.
      1The abbreviations used are:iRT
      indexed retention time
      LOQ
      limit of quantification
      RT
      retention time
      SRM
      selected reaction monitoring.
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      • Zimmerman L.J.
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      • Hall S.C.
      • Allen S.
      • Blackman R.K.
      • Borchers C.H.
      • Buck C.
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      • Cusack M.P.
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      • Ransohoff D.
      • Rodriguez H.
      • Rudnick P.A.
      • Smith D.
      • Tabb D.L.
      • Tegeler T.J.
      • Variyath A.M.
      • Vega-Montoto L.J.
      • Wahlander A.
      • Waldemarson S.
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      • Anderson N.L.
      • Fisher S.J.
      • Liebler D.C.
      • Paulovich A.G.
      • Regnier F.E.
      • Tempst P.
      • Carr S.A.
      Multisite assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma.
      ) and are generated faster and in a more cost-efficient manner. Essentially, once the peptide-specific coordinates are available for an SRM assay, it can be run on any triple quadrupole-based instrument and applied to any sample.
      Here, we developed an SRM assay library for 2007 human and 1353 murine N-glycosylated proteins. Glycoproteins can be either N- or O-glycosylated. They represent a subproteome that is particularly relevant for clinical research, because these oligosaccharide-modified proteins are typically found either secreted by cells and tissues available for remote sensing in body fluids as potential biomarkers or at the cell surface as potential drug targets (
      • Roth J.
      Protein N-glycosylation along the secretory pathway: relationship to organelle topography and function, protein quality control, and cell interactions.
      ,
      • Wollscheid B.
      • Bausch-Fluck D.
      • Henderson C.
      • O'Brien R.
      • Bibel M.
      • Schiess R.
      • Aebersold R.
      • Watts J.D.
      Mass spectrometric identification and relative quantification of N-linked cell surface glycoproteins.
      ). This is supported by the fact that the majority of the current clinically used biomarkers and drug targets are glycosylated (
      • Schiess R.
      • Wollscheid B.
      • Aebersold R.
      Targeted proteomic strategy for clinical biomarker discovery.
      ). Furthermore, the enrichment of N-glycosites (i.e. deglycosylated peptides) from blood plasma in combination with SRM was shown to reach the required LOQ (
      • Stahl-Zeng J.
      • Lange V.
      • Ossola R.
      • Eckhardt K.
      • Krek W.
      • Aebersold R.
      • Domon B.
      High sensitivity detection of plasma proteins by multiple reaction monitoring of N-glycosites.
      ) and to facilitate the quantification of N-glycoproteins over a large concentration range reaching nanogram/ml levels in plasma (
      • Cima I.
      • Schiess R.
      • Wild P.
      • Kaelin M.
      • Schüffler P.
      • Lange V.
      • Picotti P.
      • Ossola R.
      • Templeton A.
      • Schubert O.
      • Fuchs T.
      • Leippold T.
      • Wyler S.
      • Zehetner J.
      • Jochum W.
      • Buhmann J.
      • Cerny T.
      • Moch H.
      • Gillessen S.
      • Aebersold R.
      • Krek W.
      Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer.
      ). The sensitive and accurate quantification of N-glycoproteins by SRM therefore represents a promising biomarker validation strategy. For the development of the SRM assays, we used a high throughput approach based on the synthesis of equimolar synthetic peptides for 6279 N-glycosites. The synthetic peptides were employed as reference compounds to generate the corresponding fragment ion spectra and to extract the SRM assay coordinates (
      • Picotti P.
      • Rinner O.
      • Stallmach R.
      • Dautel F.
      • Farrah T.
      • Domon B.
      • Wenschuh H.
      • Aebersold R.
      High-throughput generation of selected reaction-monitoring assays for proteins and proteomes.
      ). The final library consisting of SRM assays for 5568 N-glycosites is publicly available via the SRMAtlas (
      • Picotti P.
      • Lam H.
      • Campbell D.
      • Deutsch E.W.
      • Mirzaei H.
      • Ranish J.
      • Domon B.
      • Aebersold R.
      A database of mass spectrometric assays for the yeast proteome.
      ). We show the utility of the developed SRM assay library for multiplexed quantification of selected cancer-associated N-glycoproteins in two independent cohorts of clinical specimens. The potential of the SRM assays was demonstrated by consistently quantifying 48 N-glycoproteins across 5 orders of magnitude in protein abundance and by recapitulating the abundance differences for 13 proteins in various cancer types that were previously discovered by antibody-based assays. Overall, with the N-glycoprotein SRMAtlas, we have generated a platform, which now solves the bottleneck of multiplexed, consistent, and sensitive quantification of potential biomarker candidates in suitably collected sample cohorts by using a complementary antibody-independent MS-based technology.

      DISCUSSION

      Despite the generation of extensive lists of biomarker candidates by large scale discovery-driven proteomic, transcriptomic, and genomic efforts in the last decade, almost none of the proposed biomarkers have been translated into the clinic so far. This is, to a large extent, due to the missing technological platform that allows the systematic evaluation of the clinical value of potential biomarkers in large patient cohorts in a multiplexed, consistent, and sensitive fashion. Here, we describe a platform based on SRM technology and provide an extensive collection of SRM assays that enable fast and parallel validation of biomarker candidates. The collection is focused on N-glycoproteins, because most of the currently used biomarkers are glycoproteins, and contains high quality and publicly accessible SRM assays for 2007 human and 1353 murine N-glycoproteins. The assays that were generated on three widely used mass spectrometric platforms can be queried and downloaded via the SRMAtlas webpage (
      • Picotti P.
      • Lam H.
      • Campbell D.
      • Deutsch E.W.
      • Mirzaei H.
      • Ranish J.
      • Domon B.
      • Aebersold R.
      A database of mass spectrometric assays for the yeast proteome.
      ). These instrument-specific SRM assays account for potential variation in the fragmentation pattern of peptides measured on different instrument platforms, thereby providing high quality assays for all instrument platforms currently employed in targeted MS-based proteomics. This resource covers a large number of reported biomarker candidates and proteins involved in disease-related processes, which could not yet be tested for statistical significance across clinical specimens of larger cohorts, mainly due to the lack of a suitable biomarker validation strategy. The selection of N-glycosites for the SRMAtlas was mainly guided by available empirical evidence for the detectability of the peptides by MS. However, the automated high throughput approach applied for the SRM assay generation allows for fast and cost-efficient expansion of the resource to accommodate for whole human and murine N-glycoproteome.
      The SRM assay coordinates for the SRMAtlas were extracted from fragment ion spectra derived from mixtures of synthetic peptides in equimolar amounts. In contrast to the extraction of the coordinates from the MS data of complex protein samples, the synthetic peptide approach is advantageous because it leads to the acquisition of high quality spectra for each peptide without sample-specific interferences and therefore ensures a high quality SRM assay. The N-glycoprotein SRMAtlas does not provide information about the LOQs and linear quantification ranges for the N-glycosites. These properties are dependent on sample preparation and instrument platform and should therefore be determined locally, preferentially using isotope-labeled internal standards before the validation of the proteins in large cohorts of clinical specimens.
      To evaluate the performance of the established resource, we used a subset of SRM assays for the quantification of 48 N-glycoproteins across 32 blood serum samples. The glycoproteins were selected based on previous knowledge of their typical concentrations in human serum to examine the dynamic concentration range, which can be covered using the proposed SRM-based strategy. The results show that the combined approach of N-glycosite enrichment and the highly sensitive SRM measurements allow for consistent and parallel quantification of the selected proteins across all samples down to low nanogram/ml protein concentrations in serum. In a previous study, we developed a resource of SRM assays for cancer-associated proteins, which we screened in body fluids (
      • Hüttenhain R.
      • Soste M.
      • Selevsek N.
      • Röst H.
      • Sethi A.
      • Carapito C.
      • Farrah T.
      • Deutsch E.W.
      • Kusebauch U.
      • Moritz R.L.
      • Nimèus-Malmström E.
      • Rinner O.
      • Aebersold R.
      Reproducible quantification of cancer-associated proteins in body fluids using targeted proteomics.
      ). The detectability of the cancer-associated proteins in plasma using SRM was limited to proteins with a higher or medium abundance in plasma (
      • Hüttenhain R.
      • Soste M.
      • Selevsek N.
      • Röst H.
      • Sethi A.
      • Carapito C.
      • Farrah T.
      • Deutsch E.W.
      • Kusebauch U.
      • Moritz R.L.
      • Nimèus-Malmström E.
      • Rinner O.
      • Aebersold R.
      Reproducible quantification of cancer-associated proteins in body fluids using targeted proteomics.
      ). This result showed that fractionation or enrichment of proteins in body fluids prior to the SRM measurement is inevitable to detect and quantify the low abundance tissue leakage products, which are of high interest for biomarker research. The N-glycoprotein SRMAtlas provides SRM assays that in combination with the prior enrichment step overcome the limited sensitivity in detecting low abundance proteins in body fluids, allows the quantification of clinically relevant proteins, and therefore fulfills the sensitivity requirements for biomarker validation.
      To demonstrate a direct application of the assays in a clinical setting, we selected a large cohort of 120 human plasma samples to evaluate protein abundance alterations in different cancer types. Targeted quantification of 15 previously reported cancer-associated N-glycoproteins in three malignancy groups and one control group substantially reproduced, in a single consistent measurement, the trends in abundance previously described in multiple studies. The obtained results demonstrate that the simultaneous profiling of biomarker candidates in a particular disease setting can be accomplished within complex clinical samples by using the SRM technology and the N-glycosite assays derived from the SRMAtlas. The previously reported cancer-associated proteins that have been studied in plasma comprise to a large extent plasma proteins of higher abundance underlining the need of new technologies, such as the one suggested in this study, that allow the consistent quantification of low abundance proteins in plasma for validating their clinical value systematically in large and well designed sample cohorts.
      In conclusion, it is important to reiterate that the proposed strategy and resource enables the interrogation of “The roads less traveled” (
      • Edwards A.M.
      • Isserlin R.
      • Bader G.D.
      • Frye S.V.
      • Willson T.M.
      • Yu F.H.
      Too many roads not taken.
      ). To date, biomarker validation studies were limited by the availability of antibodies, which in turn implies that the existing quantitative assays were only representing a small minority of human proteins. This resulted in a bias for proteins being analyzed, for which assays were readily available and for which extensive knowledge has been accumulated (
      • Edwards A.M.
      • Isserlin R.
      • Bader G.D.
      • Frye S.V.
      • Willson T.M.
      • Yu F.H.
      Too many roads not taken.
      ). Therefore, we believe that the resource presented here is a turning point in plasma biomarker research (Fig. 4), because it represents a large collection of assays that can be used for an unbiased analysis of proteins that were previously not addressed. The N-glycoprotein SRMAtlas holds the promise to accelerate the systematic evaluation of biomarker candidates in a cost and time efficient screening mode across large cohorts of patient specimens. We expect that the suggested approach will close the gap between proposed biomarkers and clinical usage due to the possibility of a multiplexed hypothesis testing in larger clinical cohorts without requiring antibody development (Fig. 4). Currently, the SRM technology allows for parallel quantification of around 100 peptides, including their isotope-labeled counterparts in one measurement at high sensitivity. However, newly developed MS strategies based on data-independent acquisition and targeted extraction of peptide signals (
      • Gillet L.C.
      • Navarro P.
      • Tate S.
      • Rost H.
      • Selevsek N.
      • Reiter L.
      • Bonner R.
      • Aebersold R.
      Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis.
      ,
      • Liu Y.
      • Hüttenhain R.
      • Surinova S.
      • Gillet L.C.
      • Mouritsen J.
      • Brunner R.
      • Navarro P.
      • Aebersold R.
      Quantitative measurements of N-linked glycoproteins in human plasma by SWATH-MS.
      ) hold the promise of measuring all N-glycosites present in a sample in one MS run with only a 2–3-fold reduced LOQ compared with SRM (
      • Liu Y.
      • Hüttenhain R.
      • Surinova S.
      • Gillet L.C.
      • Mouritsen J.
      • Brunner R.
      • Navarro P.
      • Aebersold R.
      Quantitative measurements of N-linked glycoproteins in human plasma by SWATH-MS.
      ). The N-glycoprotein SRMAtlas is also compatible with this newly developed MS acquisition mode. Therefore, we expect that biomarkers with clinical value will emerge from the widespread application of this unique collection of quantitative assays, and we envisage that the new bottleneck for the next phase in the biomarker pipeline (i.e. clinical validation) is going to be the availability of well annotated clinical cohorts of suitable quality and scale.
      Figure thumbnail gr4
      Fig. 4Impact of the N-glycoprotein SRMAtlas on the biomarker pipeline. The general biomarker pipeline consists of the generation of a candidate list and a lengthy and expensive development time of antibody-based assays for the validation of the candidates in clinical specimen like blood plasma. The N-glycoprotein SRMAtlas now accelerates this validation phase, because the assays for candidate quantification are publicly available. Additionally, in comparison with the gold-standard ELISA, the SRM technology facilitates multiplexed measurements of any set of candidates.

      Acknowledgments

      We acknowledge all contributors of the experimental N-glycosites. We thank Thomas Bock for help with MS measurements, Olga Schubert for helpful discussions, and Lucia Espona Pernas and Meena Choi for computational support. Author Contributions: R.H., S.S., R.O., R.L.M., B.W., and R.A. designed experiments; R.H., S.S., R.O., F.C., D.F.B., and U.K. performed the MS measurements for the SRMAtlas generation; R.S. performed the measurements in healthy blood donors; M.H. provided the patient plasma samples; S.S. performed the measurements in the patient plasma cohort; R.H., S.S., Z.S., and D.C. analyzed data; G.R. developed the script for the retention time extraction; D.C. and R.L.M. developed the platform for the accessibility of the data; J.C. selected the N-glycosites from UniProt; O.R. generated the methods for the MS measurements; R.H., S.S., B.W., and R.A. wrote the paper.

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