Statistical Analysis of Variation in the Human Plasma Proteome
| Autor: | Todd H. Corzett, Imola K Fodor, Sandra L. McCutchen-Maloney, Kenneth W. Turteltaub, Brett A. Chromy, Vicki L. Walsworth, Megan W. Choi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2010 |
| Předmět: |
Male
Proteome Article Subject Health Toxicology and Mutagenesis Difference gel electrophoresis lcsh:Biotechnology lcsh:Medicine Computational biology Biology Bioinformatics lcsh:Chemical technology lcsh:Technology Sex Factors lcsh:TP248.13-248.65 Image Processing Computer-Assisted Genetics Cluster Analysis Humans Electrophoresis Gel Two-Dimensional lcsh:TP1-1185 Biomarker discovery Molecular Biology Principal Component Analysis lcsh:T lcsh:R Computational Biology Blood Proteins General Medicine Blood proteins Hierarchical clustering Variation (linguistics) Data Interpretation Statistical Multivariate Analysis Principal component analysis Molecular Medicine Biomarker (medicine) Female Biomarkers Research Article Biotechnology |
| Zdroj: | Journal of Biomedicine and Biotechnology, Vol 2010 (2010) Journal of Biomedicine and Biotechnology |
| ISSN: | 1110-7251 1110-7243 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|