Introducing a new parameter for quality control of proteome profiles: Consideration of commonly expressed proteins

• Published in: Electrophoresis 2009-04, Vol.30 (8), p.1306-1328
• Main Authors: Slany, Astrid, Haudek, Verena J, Gundacker, Nina C, Griss, Johannes, Mohr, Thomas, Wimmer, Helge, Eisenbauer, Maria, Elbling, Leonilla, Gerner, Christopher
• Format: Article
• Language: English
• Subjects: 2D‐PAGE; Common proteins; Databases, Protein; Dendritic Cells - metabolism; Electrophoresis, Gel, Two-Dimensional; Endothelial Cells - metabolism; Fibroblasts - metabolism; Gene Expression; Humans; Keratinocytes - metabolism; Peptide Mapping; Proteins - analysis; Proteome profiling; Proteomics - methods; Quality control; Shotgun proteomics
• ISSN: 0173-0835; 1522-2683
• DOI: 10.1002/elps.200800440

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.