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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...
Autores principales: | , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2010
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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. |
format | Text |
id | pubmed-2814230 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
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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