Literature DB >> 15595720

Proteomic analysis of human serum by two-dimensional differential gel electrophoresis after depletion of high-abundant proteins.

Brett A Chromy1, Arlene D Gonzales, Julie Perkins, Megan W Choi, Michele H Corzett, Brian C Chang, Christopher H Corzett, Sandra L McCutchen-Maloney.   

Abstract

Two-dimensional differential gel electrophoresis (2-D DIGE) was used to analyze human serum following the removal of albumin and five other high-abundant serum proteins. After protein removal, serum was analyzed by SDS-PAGE as a preliminary screen, and significant differences between four high-abundant protein removal methods were observed. Antibody-based albumin removal and high-abundant protein removal methods were found to be efficient and specific. To further characterize serum after protein removal, 2-D DIGE was employed, enabling multiplexed analysis of serum through the use of three fluorescent protein dyes. Comparison between crude serum and serum after removal of high-abundant proteins clearly illustrates an increase in the number of lower abundant protein spots observed. Approximately 850 protein spots were detected in crude serum whereas over 1500 protein spots were exposed following removal of six high-abundant proteins, representing a 76% increase in protein spot detection. Several proteins that showed a 2-fold increase in intensity after depletion of high-abundant proteins, as well as proteins that were depleted during abundant protein removal methods, were further characterized by mass spectrometry. This series of experiments demonstrates that high-abundant protein removal, combined with 2-D DIGE, is a practical approach for enriching and characterizing lower abundant proteins in human serum. Consequently, this methodology offers advances in proteomic characterization, and therefore, in the identification of biomarkers from human serum.

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Year:  2004        PMID: 15595720     DOI: 10.1021/pr049921p

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  34 in total

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