Dendritic cells (DCs), the most potent and specialized antigen-presenting cells, play a key role in the regulation of the adaptive immunity. Immature DCs were generated by in vitro culturing of peripheral blood monocytes and functionally activated with the classical pathogen-associated molecular pattern lipopolysaccharide (LPS). Alternative activation resulting in Th-2 polarization was induced with lipid oxidation products derived from 1-palmitoyl-2-arachidoyl-sn-glycerol-3-phosphorylcholin (OxPAPC). Tolerogenic cells were obtained by treating DCs with human rhinovirus (HRV). The aim of this study was the identification of proteome profiles related to the functionally different dendritic cell phenotypes. Cytoplasmic proteins were analyzed by shotgun proteomics resulting in the identification of 1690 proteins. While mature and alternatively activated DCs displayed highly distinct protein expression profiles, HRV-treated DCs showed minor proteome alterations. As DCs exert many specific functions via secretion, we investigated the secretomes by a combination of 2D-PAGE and shotgun proteomics. We successfully identified a broad variety of cytokines (e.g., GM-CSF, TNF-alpha, interleukin-1beta, 6, 12 beta, 28B and 29), chemokines (e.g., CCL3, 5, 8, 17, 18, 19, 24, CXCL1, 2, 9 and 10) and growth factors (growth/differentiation factor 8, C-type lectin domain family 11 member A). The relative composition of secretome profiles, although comprising much less proteins, was found to be much more affected by functional alteration of cells than the cytoplasmic protein composition. In conclusion, we demonstrate that functional distinct subsets of DCs display distinct proteome profiles which comprise biomarker candidates. These proteins may prove useful for the interpretation of complex clinical proteomics data.

Interpretation of proteome data with a focus on biomarker discovery largely relies on comparative proteome analyses. Here, we introduce a database-assisted interpretation strategy based on proteome profiles of primary cells. Both 2-D-PAGE and shotgun proteomics are applied. We obtain high data concordance with these two different techniques. When applying mass analysis of tryptic spot digests from 2-D gels of cytoplasmic fractions, we typically identify several hundred proteins. Using the same protein fractions, we usually identify more than thousand proteins by shotgun proteomics. The data consistency obtained when comparing these independent data sets exceeds 99% of the proteins identified in the 2-D gels. Many characteristic differences in protein expression of different cells can thus be independently confirmed. Our self-designed SQL database (CPL/MUW - database of the Clinical Proteomics Laboratories at the Medical University of Vienna accessible via www.meduniwien.ac.at/proteomics/database) facilitates (i) quality management of protein identification data, which are based on MS, (ii) the detection of cell type-specific proteins and (iii) of molecular signatures of specific functional cell states. Here, we demonstrate, how the interpretation of proteome profiles obtained from human liver tissue and hepatocellular carcinoma tissue is assisted by the Clinical Proteomics Laboratories at the Medical University of Vienna-database. Therefore, we suggest that the use of reference experiments supported by a tailored database may substantially facilitate data interpretation of proteome profiling experiments.

Interpretation of proteome profiling experiments largely relies on comparative analyses. False-positive identifications may cause fatal misinterpretation of data. On the other hand, proteome analysis may also suffer from false negatives, when proteins that are actually present are not detected. This circumstance may be as fatal as false-positive identifications and was hardly considered until now. Appropriate positive controls would facilitate quality assessment of proteome profiling experiments. Based on cell biology knowledge, our aim was to generate a list of commonly expressed proteins, which may serve as positive control. Following a pragmatic experimental strategy, we compared the cytoplasmic fractions of four largely differing kinds of cells, which were human DCs, endothelial cells, fibroblasts and keratinocytes. Proteome profiling was performed by 2D-PAGE in addition to shotgun analysis. By shotgun analysis, 665 proteins were identified, which occurred in each of the four cells types; 360 proteins of those were also detectable in the corresponding 2-D gels. We consider these proteins as common proteins. All shotgun analysis data, including mass fragmentation spectra of the corresponding peptides, are accessible via the proteomics identification database (http://www.ebi.ac.uk/pride). As expected, most of the common proteins could be clearly assigned to at least one of the following functional categories: chaperones, cytoskeleton, energy metabolism, redox regulation, nucleic acid processing, protein turnover, membrane transport, protein synthesis and signaling. We suggest that the present data may prove helpful for data assessment, quality control and interpretation of a large variety of experiments based on proteome profiling.

Clinical proteome analysis will almost inevitably be confronted with blood constituents. Purified plasma, serum, cell or tissue samples may easily be contaminated with some other constituents, affecting the final proteome analysis result. To recognize proteins which are potentially indicative for the presence of major blood constituents, we purified T cells, monocytes, neutrophils, erythrocytes, platelets and plasma and performed comparative proteome profiling employing 2D-PAGE in addition to shotgun proteomics. By mass analysis, 594 different proteins were identified in the 2D gels. Six of the 594 proteins displayed a highly specific expression pattern. A total of 1774 proteins were identified by shotgun proteomics, including 50 proteins with highly specific expression patterns. Indeed, proteins specific for each of the constituents were successfully identified. All protein lists including mass spectrometry details and expression specificity are freely available via the PRIDE database and the CPL/MUW database. The present protein maps of each of the constituents may serve as references for comparative analyses and will aid the interpretation of proteome profiles of clinical samples.

In postmenopausal women, PGS does not significantly reduce hot flashes within a 12-week observation period, but further studies are needed to investigate the long-term effect.