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...
| Publicado en: | Journal of Biomedicine & Biotechnology pp. 258494 - 258495 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2010
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105081949&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105081949 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11107243 137K jtl: Journal of Biomedicine & Biotechnology issn: 11107243 maglogo: N pubinfo: dt: 2010 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 105081949 2010748359 10.1155/2010/258494 NLM20130815 PMC2814230 105081949 ppf: 258494 ppct: 1 formats: fmt: @attributes: type: P tig: atl: Statistical analysis of variation in the human plasma proteome. aug: au: Corzett TH Fodor IK Choi MW Walsworth VL Turteltaub KW McCutchen-Maloney SL Chromy BA sug: subj: Biological Markers Blood Blood Proteins Analysis Bioinformatics Methods Proteomics Cluster Analysis Data Analysis, Statistical Electrophoresis Methods Female Human Image Processing, Computer Assisted Methods Male Multivariate Analysis Factor Analysis Sex Factors Female Male ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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