Impact of biological matrix on inflammatory protein biomarker quantification based on targeted mass spectrometry.

BACKGROUND: Serum and plasma are widely used matrices in biological and clinical studies. To improve reliability and consistency of markers quantification, the influence of these matrices on proteins was evaluated by targeted mass spectrometry. RESULTS: 65 proteins were quantified in matched blood samples collected in serum, ethylenediaminetetraacetic acid and heparin plasma tubes from 40 healthy and 10 pathological individuals. Only 52% of the proteins were not impacted by any of the biological matrices tested, and the effects on quantification of proteins affected was matrix and protein dependent. CONCLUSION: Matrix comparisons using mass spectrometry is therefore recommended to assess the relevance of using surrogate matrix, performing biomarker discovery study or evaluating the clinical use of biomarkers in large clinical cohorts.

In vitro and in silico processes to identify differentially expressed proteins.

We present an integrated proteomics platform designed for performing differential analyses. Since reproducible results are essential for comparative studies, we explain how we improved reproducibility at every step of our laboratory processes, e.g. by taking advantage of the powerful laboratory information management system we developed. The differential capacity of our platform is validated by detecting known markers in a real sample and by a spiking experiment. We introduce an innovative two-dimensional (2-D) plot for displaying identification results combined with chromatographic data. This 2-D plot is very convenient for detecting differential proteins. We also adapt standard multivariate statistical techniques to show that peptide identification scores can be used for reliable and sensitive differential studies. The interest of the protein separation approach we generally apply is justified by numerous statistics, complemented by a comparison with a simple shotgun analysis performed on a small volume sample. By introducing an automatic integration step after mass spectrometry data identification, we are able to search numerous databases systematically, including the human genome and expressed sequence tags. Finally, we explain how rigorous data processing can be combined with the work of human experts to set high quality standards, and hence obtain reliable (false positive < 0.35%) and nonredundant protein identifications.