TY - JOUR
T1 - Integration of two-dimensional LC-MS with multivariate statistics for comparative analysis of proteomic samples
AU - Gaspari, Marco
AU - Verhoeckx, Kitty C.M.
AU - Verheij, Elwin R.
AU - Van Der Greef, Jan
PY - 2006/4/1
Y1 - 2006/4/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33645677853&partnerID=8YFLogxK
U2 - 10.1021/ac052000t
DO - 10.1021/ac052000t
M3 - Article
C2 - 16579610
AN - SCOPUS:33645677853
SN - 0003-2700
VL - 78
SP - 2286
EP - 2296
JO - Analytical Chemistry
JF - Analytical Chemistry
IS - 7
ER -