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 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 beta2-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.
Analytical Sciences Department, TNO Quality of Life, P.O. Box 360, Zeist, The Netherlands. Gaspari@unicz.it
Marco
M
Gaspari
Kitty C M
KC
Verhoeckx
Elwin R
ER
Verheij
Jan
J
van der Greef
D000319:Adrenergic beta-Antagonists / Q000494:pharmacology; D000595:Amino Acid Sequence; D000893:Anti-Inflammatory Agents / Q000494:pharmacology; D015415:Biomarkers / Q000032:analysis; D002853:Chromatography, Liquid / Q000379:methods*; D006801:Humans; D008070:Lipopolysaccharides / Q000494:pharmacology; D008264:Macrophages / Q000187:drug effects / Q000378:metabolism; D013058:Mass Spectrometry / Q000379:methods*; D008969:Molecular Sequence Data; D015999:Multivariate Analysis; D025341:Principal Component Analysis; D011433:Propranolol / Q000494:pharmacology; D020543:Proteome / Q000032:analysis* / Q000737:chemistry; D040901:Proteomics / Q000379:methods*; D018343:Receptors, Adrenergic, beta-2 / Q000378:metabolism; D015203:Reproducibility of Results; D014297:Trimethylsilyl Compounds / Q000494:pharmacology; D020298:U937 Cells / Q000187:drug effects / Q000378:metabolism
D000319:Adrenergic beta-Antagonists; D000893:Anti-Inflammatory Agents; D015415:Biomarkers; D008070:Lipopolysaccharides; D020543:Proteome; D018343:Receptors, Adrenergic, beta-2; D014297:Trimethylsilyl Compounds; C475840:Zilpaterol; D011433:Propranolol