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Molecular & Cellular Proteomics 4:144-155, 2005.
© 2005 by The American Society for Biochemistry and Molecular Biology, Inc.

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Research

High Throughput Quantitative Analysis of Serum Proteins Using Glycopeptide Capture and Liquid Chromatography Mass Spectrometry ^*,S

Hui Zhang^,, Eugene C. Yi, Xiao-jun Li, Parag Mallick, Karen S. Kelly-Spratt^¶, Christophe D. Masselon^||, David G. Camp, II^||, Richard D. Smith^||, Christopher J. Kemp^¶ and Ruedi Aebersold^,**

From the Institute for Systems Biology, Seattle, Washington 98103, ^¶ Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, ^|| Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, and ^** Swiss Federal Institute of Technology (ETH) Zurich, and Faculty of Natural Sciences, University of Zurich, CH-8093 Zurich, Switzerland

It is expected that the composition of the serum proteome can provide valuable information about the state of the human body in health and disease and that this information can be extracted via quantitative proteomic measurements. Suitable proteomic techniques need to be sensitive, reproducible, and robust to detect potential biomarkers below the level of highly expressed proteins, generate data sets that are comparable between experiments and laboratories, and have high throughput to support statistical studies. Here we report a method for high throughput quantitative analysis of serum proteins. It consists of the selective isolation of peptides that are N-linked glycosylated in the intact protein, the analysis of these now deglycosylated peptides by liquid chromatography electrospray ionization mass spectrometry, and the comparative analysis of the resulting patterns. By focusing selectively on a few formerly N-linked glycopeptides per serum protein, the complexity of the analyte sample is significantly reduced and the sensitivity and throughput of serum proteome analysis are increased compared with the analysis of total tryptic peptides from unfractionated samples. We provide data that document the performance of the method and show that sera from untreated normal mice and genetically identical mice with carcinogen-induced skin cancer can be unambiguously discriminated using unsupervised clustering of the resulting peptide patterns. We further identify, by tandem mass spectrometry, some of the peptides that were consistently elevated in cancer mice compared with their control littermates.

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