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Title: Statistical Analysis of Variation in the Human Plasma Proteome

Abstract

Quantifying the variation in the human plasma proteome is an essential prerequisite for disease-specific biomarker detection. We report here on the longitudinal and individual variation in human plasma characterized by two-dimensional difference gel electrophoresis (2-D DIGE) using plasma samples from eleven healthy subjects collected three times over a two week period. Fixed-effects modeling was used to remove dye and gel variability. Mixed-effects modeling was then used to quantitate the sources of proteomic variation. The subject-to-subject variation represented the largest variance component, while the time-within-subject variation was comparable to the experimental variation found in a previous technical variability study where one human plasma sample was processed eight times in parallel and each was then analyzed by 2-D DIGE in triplicate. Here, 21 protein spots had larger than 50% CV, suggesting that these proteins may not be appropriate as biomarkers and should be carefully scrutinized in future studies. Seventy-eight protein spots showing differential protein levels between different individuals or individual collections were identified by mass spectrometry and further characterized using hierarchical clustering. The results present a first step toward understanding the complexity of longitudinal and individual variation in the human plasma proteome, and provide a baseline for improved biomarker discovery.

Authors:
 [1];  [2];  [1];  [1];  [1];  [1];  [1]
  1. Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA 94550, USA
  2. Department of Biostatistics, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1198317
Grant/Contract Number:  
AC52-07NA27344
Resource Type:
Published Article
Journal Name:
Journal of Biomedicine and Biotechnology
Additional Journal Information:
Journal Name: Journal of Biomedicine and Biotechnology Journal Volume: 2010; Journal ID: ISSN 1110-7243
Publisher:
Hindawi Publishing Corporation
Country of Publication:
Country unknown/Code not available
Language:
English

Citation Formats

Corzett, Todd H., Fodor, Imola K., Choi, Megan W., Walsworth, Vicki L., Turteltaub, Kenneth W., McCutchen-Maloney, Sandra L., and Chromy, Brett A. Statistical Analysis of Variation in the Human Plasma Proteome. Country unknown/Code not available: N. p., 2010. Web. doi:10.1155/2010/258494.
Corzett, Todd H., Fodor, Imola K., Choi, Megan W., Walsworth, Vicki L., Turteltaub, Kenneth W., McCutchen-Maloney, Sandra L., & Chromy, Brett A. Statistical Analysis of Variation in the Human Plasma Proteome. Country unknown/Code not available. doi:10.1155/2010/258494.
Corzett, Todd H., Fodor, Imola K., Choi, Megan W., Walsworth, Vicki L., Turteltaub, Kenneth W., McCutchen-Maloney, Sandra L., and Chromy, Brett A. Fri . "Statistical Analysis of Variation in the Human Plasma Proteome". Country unknown/Code not available. doi:10.1155/2010/258494.
@article{osti_1198317,
title = {Statistical Analysis of Variation in the Human Plasma Proteome},
author = {Corzett, Todd H. and Fodor, Imola K. and Choi, Megan W. and Walsworth, Vicki L. and Turteltaub, Kenneth W. and McCutchen-Maloney, Sandra L. and Chromy, Brett A.},
abstractNote = {Quantifying the variation in the human plasma proteome is an essential prerequisite for disease-specific biomarker detection. We report here on the longitudinal and individual variation in human plasma characterized by two-dimensional difference gel electrophoresis (2-D DIGE) using plasma samples from eleven healthy subjects collected three times over a two week period. Fixed-effects modeling was used to remove dye and gel variability. Mixed-effects modeling was then used to quantitate the sources of proteomic variation. The subject-to-subject variation represented the largest variance component, while the time-within-subject variation was comparable to the experimental variation found in a previous technical variability study where one human plasma sample was processed eight times in parallel and each was then analyzed by 2-D DIGE in triplicate. Here, 21 protein spots had larger than 50% CV, suggesting that these proteins may not be appropriate as biomarkers and should be carefully scrutinized in future studies. Seventy-eight protein spots showing differential protein levels between different individuals or individual collections were identified by mass spectrometry and further characterized using hierarchical clustering. The results present a first step toward understanding the complexity of longitudinal and individual variation in the human plasma proteome, and provide a baseline for improved biomarker discovery.},
doi = {10.1155/2010/258494},
journal = {Journal of Biomedicine and Biotechnology},
number = ,
volume = 2010,
place = {Country unknown/Code not available},
year = {2010},
month = {1}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1155/2010/258494

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Cited by: 24 works
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