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



,**
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.
To whom correspondence should be addressed. E-mail: hzhang{at}systemsbiology.org
![]()
CiteULike
Complore
Connotea
Del.icio.us
Digg
Reddit
Technorati What's this?
This article has been cited by other articles:
![]() |
H. Keshishian, T. Addona, M. Burgess, E. Kuhn, and S. A. Carr Quantitative, Multiplexed Assays for Low Abundance Proteins in Plasma by Targeted Mass Spectrometry and Stable Isotope Dilution Mol. Cell. Proteomics, December 1, 2007; 6(12): 2212 - 2229. [Abstract] [Full Text] [PDF] |
||||
![]() |
H. Zhang and D. W. Chan Cancer Biomarker Discovery in Plasma Using a Tissue-targeted Proteomic Approach Cancer Epidemiol. Biomarkers Prev., October 1, 2007; 16(10): 1915 - 1917. [Full Text] [PDF] |
||||
![]() |
M. R. Larsen, S. S. Jensen, L. A. Jakobsen, and N. H. H. Heegaard Exploring the Sialiome Using Titanium Dioxide Chromatography and Mass Spectrometry Mol. Cell. Proteomics, October 1, 2007; 6(10): 1778 - 1787. [Abstract] [Full Text] [PDF] |
||||
![]() |
T. A. Drake and P. Ping Thematic review series: Systems Biology Approaches to Metabolic and Cardiovascular Disorders. Proteomics approaches to the systems biology of cardiovascular diseases J. Lipid Res., January 1, 2007; 48(1): 1 - 8. [Abstract] [Full Text] [PDF] |
||||
![]() |
H. Zhang, A. Y. Liu, P. Loriaux, B. Wollscheid, Y. Zhou, J. D. Watts, and R. Aebersold Mass Spectrometric Detection of Tissue Proteins in Plasma Mol. Cell. Proteomics, January 1, 2007; 6(1): 64 - 71. [Abstract] [Full Text] [PDF] |
||||
![]() |
T. Liu, W.-J. Qian, M. A. Gritsenko, W. Xiao, L. L. Moldawer, A. Kaushal, M. E. Monroe, S. M. Varnum, R. J. Moore, S. O. Purvine, et al. High Dynamic Range Characterization of the Trauma Patient Plasma Proteome Mol. Cell. Proteomics, October 1, 2006; 5(10): 1899 - 1913. [Abstract] [Full Text] [PDF] |
||||
![]() |
M. Ono, M. Shitashige, K. Honda, T. Isobe, H. Kuwabara, H. Matsuzuki, S. Hirohashi, and T. Yamada Label-free Quantitative Proteomics Using Large Peptide Data Sets Generated by Nanoflow Liquid Chromatography and Mass Spectrometry Mol. Cell. Proteomics, July 1, 2006; 5(7): 1338 - 1347. [Abstract] [Full Text] [PDF] |
||||
![]() |
B. L. Nunn, S. A. Shaffer, A. Scherl, B. Gallis, M. Wu, S. I. Miller, and D. R. Goodlett Comparison of a Salmonella typhimurium proteome defined by shotgun proteomics directly on an LTQ-FT and by proteome pre-fractionation on an LCQ-DUO. Brief Funct Genomic Proteomic, June 1, 2006; 5(2): 154 - 168. [Abstract] [Full Text] [PDF] |
||||
![]() |
Q. C. Ru, L. A. Zhu, J. Silberman, and C. D. Shriver Label-free Semiquantitative Peptide Feature Profiling of Human Breast Cancer and Breast Disease Sera via Two-dimensional Liquid Chromatography-Mass Spectrometry Mol. Cell. Proteomics, June 1, 2006; 5(6): 1095 - 1104. [Abstract] [Full Text] [PDF] |
||||
![]() |
X.-j. Li, E. C. Yi, C. J. Kemp, H. Zhang, and R. Aebersold A Software Suite for the Generation and Comparison of Peptide Arrays from Sets of Data Collected by Liquid Chromatography-Mass Spectrometry Mol. Cell. Proteomics, September 1, 2005; 4(9): 1328 - 1340. [Abstract] [Full Text] [PDF] |
||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| All ASBMB Journals | Journal of Biological Chemistry |
| Journal of Lipid Research | ASBMB Today |