Consistency of a two clinical site sample collection: a proteomics study.

PURPOSE: We investigated the ability to perform a clinical proteomic study using samples collected at different times from two independent clinical sites. EXPERIMENTAL DESIGN: Label-free 2-D-LC-MS proteomic analysis was used to differentially quantify tens of thousands of peptides from human plasma. We have asked whether samples collected from two sites, when analyzed by this type of peptide profiling, reproducibly contain detectable peptide markers that are differentially expressed in the plasma of disease (advanced renal cancer) patients relative to healthy normals. RESULTS: We have demonstrated that plasma proteins enriched in disease patients are indeed detected reproducibly in both clinical collections. Regression analysis, unsupervised hierarchical clustering and PCA detected no systematic bias in the data related to site of sample collection and processing. Using a genetic algorithm, support vector machine classification method, we were able to correctly classify disease samples at 88% sensitivity and 94% specificity using the second site as an independent validation set. CONCLUSIONS AND CLINICAL RELEVANCE: We conclude that multiple site collection, when analyzed by label-free 2-D-LC-MS, generates data that are sufficiently reproducible to guide reliable biomarker discovery.

Quantification of proteins and metabolites by mass spectrometry without isotopic labeling.

We demonstrate the quantification capability and robustness of a new integrated liquid chromatography-mass spectrometry (LC-MS) approach for large-scale profiling of proteins and metabolites. This approach to determine differential expression relies on linearity of signal vs molecular concentration using electrospray ionization LC-MS, reproducibility of sample processing, a novel normalization strategy and associated data analysis software. No isotopic tagging or spiking of internal standards is required. The method is general and applicable to the proteome and metabolome from all biological fluids and tissues. Small or large numbers of samples can be profiled in a single experiment. Differential profiling of 6000 molecular ions per sample by one-dimensional chromatography LC-MS and 30,000 molecular ions per sample by two-dimensional chromatography LC-MS is demonstrated using rheumatoid arthritis patient samples compared with control samples. A new approach to peptide identification is described that involves building libraries of previously identified peptides, circumventing the need to acquire MS/MS data during profiling. Robustness of the platform was tested by repeating sample preparation and LC-MS differential expression analysis after 10 mo, using independent serum aliquots stored at -80 degrees C. To the best of our knowledge, this is the first demonstration of long-term robustness of a platform for quantitative proteomics and metabolomics.

Comprehensive phenotyping in multiple sclerosis: discovery based proteomics and the current understanding of putative biomarkers.

Currently, there is no single test for multiple sclerosis (MS). Diagnosis is confirmed through clinical evaluation, abnormalities revealed by magnetic resonance imaging (MRI), and analysis of cerebrospinal fluid (CSF) chemistry. The early and accurate diagnosis of the disease, monitoring of progression, and gauging of therapeutic intervention are important but elusive elements of patient care. Moreover, a deeper understanding of the disease pathology is needed, including discovery of accurate biomarkers for MS. Herein we review putative biomarkers of MS relating to neurodegeneration and contributions to neuropathology, with particular focus on autoimmunity. In addition, novel assessments of biomarkers not driven by hypotheses are discussed, featuring our application of advanced proteomics and metabolomics for comprehensive phenotyping of CSF and blood. This strategy allows comparison of component expression levels in CSF and serum between MS and control groups. Examination of these preliminary data suggests that several CSF proteins in MS are differentially expressed, and thus, represent putative biomarkers deserving of further evaluation.