Integration of Two-Dimensional LC-MS with Multivariate Statistics for Comparative Analysis of Proteomic Samples
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- 作者：Marco Gaspari ; Kitty C. M. Verhoeckx ; Elwin R. Verheij ; and Jan van der Greef
- 刊名：Analytical Chemistry
- 出版年：2006
- 出版时间：April 1, 2006
- 年：2006
- 卷：78
- 期：7
- 页码：2286 - 2296
- 全文大小：291K
- 年卷期：v.78,no.7(April 1, 2006)
- ISSN：1520-6882
文摘
LC-MS-based proteomics requires methods with highpeak capacity and a high degree of automation, integratedwith data-handling tools able to cope with the massive dataproduced and able to quantitatively compare them. Thispaper describes an off-line two-dimensional (2D) LC-MSmethod and its integration with software tools for datapreprocessing and multivariate statistical analysis. The 2DLC-MS method was optimized in order to minimizepeptide loss prior to sample injection and during thecollection step after the first LC dimension, thus minimizing errors from off-column sample handling. The seconddimension was run in fully automated mode, injectingonto a nanoscale LC-MS system a series of more than100 samples, representing fractions collected in the firstdimension (8 fractions/sample). As a model study, themethod was applied to finding biomarkers for the antiinflammatory properties of zilpaterol, which are coupled tothe 
2-adrenergic receptor. Secreted proteomes fromU937 macrophages exposed to lipopolysaccharide in thepresence or absence of propanolol or zilpaterol wereanalysed. Multivariate statistical analysis of 2D LC-MSdata, based on principal component analysis, and subsequent targeted LC-MS/MS identification of peptides ofinterest demonstrated the applicability of the approach.
2-adrenergic receptor. Secreted proteomes fromU937 macrophages exposed to lipopolysaccharide in thepresence or absence of propanolol or zilpaterol wereanalysed. Multivariate statistical analysis of 2D LC-MSdata, based on principal component analysis, and subsequent targeted LC-MS/MS identification of peptides ofinterest demonstrated the applicability of the approach.