Title
Integration of two-dimensional LC-MS with multivariate statistics for comparative analysis of proteomic samples
Author
Gaspari, M.
Verhoeckx, K.C.M.
Verheij, E.R.
van der Greef, J.
TNO Kwaliteit van Leven
Publication year
2006
Abstract
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. © 2006 American Chemical Society.
Subject
Pharmacology
Analytical research
Computer aided software engineering
Data handling
Data processing
Mass spectrometry
Polysaccharides
Proteins
Data preprocessing
Lipopolysaccharide
Multivariate statistical analysis
Liquid chromatography
antiinflammatory agent
beta 2 adrenergic receptor
biological marker
lipopolysaccharide
peptide
propranolol
proteome
unclassified drug
zilpaterol
article
automation
comparative study
controlled study
human
human cell
information processing
liquid chromatography
macrophage
mass spectrometry
model
multivariate analysis
nanoparticle
nucleotide sequence
principal component analysis
proteomics
Adrenergic beta-Antagonists
Amino Acid Sequence
Anti-Inflammatory Agents
Biological Markers
Chromatography, Liquid
Humans
Lipopolysaccharides
Macrophages
Mass Spectrometry
Molecular Sequence Data
Multivariate Analysis
Principal Component Analysis
Propranolol
Proteome
Proteomics
Receptors, Adrenergic, beta-2
Reproducibility of Results
Trimethylsilyl Compounds
U937 Cells
To reference this document use:
http://resolver.tudelft.nl/uuid:df58e055-fb81-4e02-8031-9fa34764bfa9
DOI
https://doi.org/10.1021/ac052000t
TNO identifier
239205
ISSN
0003-2700
Source
Analytical Chemistry, 78 (7), 2286-2296
Document type
article