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Statistical Analysis of Variation in the Human Plasma Proteome

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 fr...

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Autores principales: Corzett, Todd H., Fodor, Imola K., Choi, Megan W., Walsworth, Vicki L., Turteltaub, Kenneth W., McCutchen-Maloney, Sandra L., Chromy, Brett A.
Formato: Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2814230/
https://www.ncbi.nlm.nih.gov/pubmed/20130815
http://dx.doi.org/10.1155/2010/258494
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author Corzett, Todd H.
Fodor, Imola K.
Choi, Megan W.
Walsworth, Vicki L.
Turteltaub, Kenneth W.
McCutchen-Maloney, Sandra L.
Chromy, Brett A.
author_facet Corzett, Todd H.
Fodor, Imola K.
Choi, Megan W.
Walsworth, Vicki L.
Turteltaub, Kenneth W.
McCutchen-Maloney, Sandra L.
Chromy, Brett A.
author_sort Corzett, Todd H.
collection PubMed
description 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.
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spelling pubmed-28142302010-02-03 Statistical Analysis of Variation in the Human Plasma Proteome Corzett, Todd H. Fodor, Imola K. Choi, Megan W. Walsworth, Vicki L. Turteltaub, Kenneth W. McCutchen-Maloney, Sandra L. Chromy, Brett A. J Biomed Biotechnol Research Article 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. Hindawi Publishing Corporation 2010 2010-01-14 /pmc/articles/PMC2814230/ /pubmed/20130815 http://dx.doi.org/10.1155/2010/258494 Text en Copyright © 2010 Todd H. Corzett et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
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
title Statistical Analysis of Variation in the Human Plasma Proteome
title_full Statistical Analysis of Variation in the Human Plasma Proteome
title_fullStr Statistical Analysis of Variation in the Human Plasma Proteome
title_full_unstemmed Statistical Analysis of Variation in the Human Plasma Proteome
title_short Statistical Analysis of Variation in the Human Plasma Proteome
title_sort statistical analysis of variation in the human plasma proteome
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2814230/
https://www.ncbi.nlm.nih.gov/pubmed/20130815
http://dx.doi.org/10.1155/2010/258494
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