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Blood plasma and serum are very useful samples for the detection, identification and quantitation of proteins associated with both health and disease. However, analysis of plasma and serum is a challenge because traces of interesting polypeptides and proteins can be dominated by the very high concentration of albumin present. Albumin may be depleted by adsorption to immunoaffinity columns or to columns containing dyes such as Cibacron Blue, or by ultrafiltration, but these methods are far from ideal. We describe a new peptide-based affinity medium which is effective for removing albumin and is very specific. The albumin-binding capacity is at least 14 mg per mL of gel. The material may be reused hundreds of times after a simple regeneration step involving NaOH, with full retention of specificity and capacity. The material was tested with human and monkey plasma and serum and rat serum, and has been used to deplete litre volumes of human plasma. The development of other peptide-based affinity media to deplete abundant proteins is briefly discussed.

Human blood plasma is a useful source of proteins associated with both health and disease. Analysis of human blood plasma is a challenge due to the large number of peptides and proteins present and the very wide range of concentrations. In order to identify as many proteins as possible for subsequent comparative studies, we developed an industrial-scale (2.5 liter) approach involving sample pooling for the analysis of smaller proteins (M(r) generally < ca. 40 000 and some fragments of very large proteins). Plasma from healthy males was depleted of abundant proteins (albumin and IgG), then smaller proteins and polypeptides were separated into 12 960 fractions by chromatographic techniques. Analysis of proteins and polypeptides was performed by mass spectrometry prior to and after enzymatic digestion. Thousands of peptide identifications were made, permitting the identification of 502 different proteins and polypeptides from a single pool, 405 of which are listed here. The numbers refer to chromatographically separable polypeptide entities present prior to digestion. Combining results from studies with other plasma pools we have identified over 700 different proteins and polypeptides in plasma. Relatively low abundance proteins such as leptin and ghrelin and peptides such as bradykinin, all invisible to two-dimensional gel technology, were clearly identified. Proteins of interest were synthesized by chemical methods for bioassays. We believe that this is the first time that the small proteins in human blood plasma have been separated and analyzed so extensively.

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.

In a previous paper we introduced a novel model-based approach (OLAV) to the problem of identifying peptides via tandem mass spectrometry, for which early implementations showed promising performance. We recently further improved this performance to a remarkable level (1-2% false positive rate at 95% true positive rate) and characterized key properties of OLAV like robustness and training set size. We present these results in a synthetic and coherent way along with detailed performance comparisons, a new scoring component making use of peptide amino acidic composition, and new developments like automatic parameter learning. Finally, we discuss the impact of OLAV on the automation of proteomics projects.