Integration of Two-Dimensional LC−MS with Multivariate Statistics for Comparative Analysis of Proteomic Samples

LC−MS-based proteomics requires methods with high peak capacity and a high degree of automation, integrated with data-handling tools able to cope with the massive data produced and able to quantitatively compare them. This paper describes an off-line two-dimensional (2D) LC−MS method and its integra...

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Veröffentlicht in:Analytical chemistry (Washington) 2006-04, Vol.78 (7), p.2286-2296
Hauptverfasser: Gaspari, Marco, Verhoeckx, Kitty C. M, Verheij, Elwin R, van der Greef, Jan
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Sprache:eng
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Zusammenfassung:LC−MS-based proteomics requires methods with high peak capacity and a high degree of automation, integrated with data-handling tools able to cope with the massive data produced and able to quantitatively compare them. This paper describes an off-line two-dimensional (2D) LC−MS method and its integration with software tools for data preprocessing and multivariate statistical analysis. The 2D LC−MS method was optimized in order to minimize peptide loss prior to sample injection and during the collection step after the first LC dimension, thus minimizing errors from off-column sample handling. The second dimension was run in fully automated mode, injecting onto a nanoscale LC−MS system a series of more than 100 samples, representing fractions collected in the first dimension (8 fractions/sample). As a model study, the method was applied to finding biomarkers for the antiinflammatory properties of zilpaterol, which are coupled to the β2-adrenergic receptor. Secreted proteomes from U937 macrophages exposed to lipopolysaccharide in the presence or absence of propanolol or zilpaterol were analysed. Multivariate statistical analysis of 2D LC−MS data, based on principal component analysis, and subsequent targeted LC−MS/MS identification of peptides of interest demonstrated the applicability of the approach.
ISSN:0003-2700
1520-6882
DOI:10.1021/ac052000t