A novel, MS-based approach for the relative quantification of proteins, relying on the derivatization of primary amino groups in intact proteins using isobaric tag for relative and absolute quantitation (iTRAQ) is presented. Due to the isobaric mass design of the iTRAQ reagents, differentially labeled proteins do not differ in mass; accordingly, their corresponding proteolytic peptides appear as single peaks in MS scans. Because quantitative information is provided by isotope-encoded reporter ions that can only be observed in MS/MS spectra, we analyzed the fragmentation behavior of ESI and MALDI ions of peptides generated from iTRAQ-labeled proteins using a TOF/TOF and/or a QTOF instrument. We observed efficient liberation of reporter ions for singly protonated peptides at low-energy collision conditions. In contrast, increased collision energies were required to liberate the iTRAQ label from lysine side chains of doubly charged peptides and, thus, to observe reporter ions suitable for relative quantification of proteins with high accuracy. We then developed a quantitative strategy that comprises labeling of intact proteins by iTRAQ followed by gel electrophoresis and peptide MS/MS analyses. As proof of principle, mixtures of five different proteins in various concentration ratios were quantified, demonstrating the general applicability of the approach presented here to quantitative MS-based proteomics.

One of the major challenges for large scale proteomics research is the quality evaluation of results. Protein identification from complex biological samples or experimental setups is often a manual and subjective task which lacks profound statistical evaluation. This is not feasible for high-throughput proteomic experiments which result in large datasets of thousands of peptides and proteins and their corresponding mass spectra. To improve the quality, reliability and comparability of scientific results, an estimation of the rate of erroneously identified proteins is advisable. Moreover, scientific journals increasingly stipulate that articles containing considerable MS data should be subject to stringent statistical evaluation. We present a newly developed easy-to-use software tool enabling quality evaluation by generating composite target-decoy databases usable with all relevant protein search engines. This tool, when used in conjunction with relevant statistical quality criteria, enables to reliably determine peptides and proteins of high quality, even for nonexperienced users (e.g. laboratory staff, researchers without programming knowledge). Different strategies for building decoy databases are implemented and the resulting databases are characterized and compared. The quality of protein identification in high-throughput proteomics is usually measured by the false positive rate (FPR), but it is shown that the false discovery rate (FDR) delivers a more meaningful, robust and comparable value.

Leaf senescence represents the final stage of leaf development and is associated with fundamental changes on the level of the proteome. For the quantitative analysis of changes in protein abundance related to early leaf senescence, we designed an elaborate double and reverse labeling strategy simultaneously employing fluorescent two-dimensional DIGE as well as metabolic N labeling combined with MS showed that results obtained by both quantification methods correlated well for proteins showing low to moderate regulation factors. Nano HPLC/ESI-MS/MS analysis of 21 protein spots that consistently exhibited abundance differences in nine biological replicates based on both DIGE and MS resulted in the identification of 13 distinct proteins and protein subunits that showed significant regulation in Arabidopsis mutant plants displaying advanced leaf senescence. Ribulose 1,5-bisphosphate carboxylase/oxygenase large and three of its four small subunits were found to be down-regulated, which reflects the degradation of the photosynthetic machinery during leaf senescence. Among the proteins showing higher abundance in mutant plants were several members of the glutathione S-transferase family class phi and quinone reductase. Up-regulation of these proteins fits well into the context of leaf senescence since they are generally involved in the protection of plant cells against reactive oxygen species which are increasingly generated by lipid degradation during leaf senescence. With the exception of one glutathione S-transferase isoform, none of these proteins has been linked to leaf senescence before. Molecular & Cellular Proteomics 7:, 108 -120.A major focus of proteome research is the simultaneous identification and quantification of proteins in cells, tissues, or organisms in dependence on the developmental stage, different physiological conditions, environmental influences, or genotypes. This quantitative, mass spectrometry (MS)1 -based description of proteomes was facilitated by the development of various stable isotope labeling techniques that have since been applied to proteomics studies in a multitude of organisms (1, 2). In plant proteomics, however, the most frequently used method for comparative, quantitative studies so far has been two-dimensional PAGE (3). In traditional two-dimensional PAGE approaches, quantitative differences in protein abundance between biological samples are revealed by comparing spot patterns in individual gels based on densitometric analysis following silver or Coomassie Blue staining. Limitations of this method regarding reproducibility, sensitivity, and dynamic range of protein quantification were improved significantly by introducing the DIGE technology (4). The DIGE technique employs spectrally resolvable fluorescent cyanine dyes (CyDyes) to label proteins prior to separation by twodimensional PAGE (5). Using the minimal labeling approach, two distinct protein samples are separately labeled with the fluorescent dyes Cy3 and Cy5, respectively, while an internal standard consisting of equa...

No abstract

Commonly, prior to mass spectrometry based analysis of proteins or protein mixtures, the proteins are subjected to specific enzymatic proteolysis. For this purpose trypsin is most frequently used. However, the process of proteolysis is not unflawed. For example, some side activities of trypsin are known and have already been described in the literature (e.g., chymotryptic activity). Here, we describe the occurrence of transpeptidated peptides during standard proteome analysis using two-dimensional polyacrylamide gel electrophoresis followed by mass spectrometric protein identification. Different types of transpeptidated peptides have been detected. The most frequently observed transpeptidation reaction is N-terminal addition of arginine or lysine to peptides. Furthermore, addition of two amino acids to the N-terminus of a peptide has also been detected. Another transpeptidation that we observed, is combination of two peptides, which were originally located in different regions of the analyzed protein. Currently, the full amount of peptides generated by transpeptidation is not clear. However, it should be recognized that protein information is presently lost as these effects are not detectable with available database search software.

The newly available techniques for sensitive proteome analysis and the resulting amount of data require a new bioinformatics focus on automatic methods for spectrum reprocessing and peptide/protein validation. Manual validation of results in such studies is not feasible and objective enough for quality relevant interpretation. The necessity for tools enabling an automatic quality control is, therefore, important to produce reliable and comparable data in such big consortia as the Human Proteome Organization Brain Proteome Project. Standards and well-defined processing pipelines are important for these consortia. We show a way for choosing the right database model, through collecting data, processing these with a decoy database and end up with a quality controlled protein list merged from several search engines, including a known false-positive rate.

The HUPO Brain Proteome Project is an initiative coordinating proteomics studies to characterise human and mouse brain proteomes. Proteins identified in human brain samples during the project's pilot phase were put into biological context through integration with various annotation sources followed by a bioinformatics analysis. The data set was related to the genome sequence via the genes encoding identified proteins including an assessment of splice variant identification as well as an analysis of tissue specificity of the respective transcripts. Proteins were furthermore categorised according to subcellular localisation, molecular function and biological process, grouped into protein families and mapped to biological pathways they are known to act in. Involvement in pathological conditions was examined based on association with entries in the online version of Mendelian Inheritance in Man and an interaction network was derived from curated protein-proteininteraction data. Overall a non-redundant set of 1804 proteins was identified in human brain samples. In the majority of cases splice variants could be unambiguously identified by unique peptides, including matches to several hypothetical transcripts of known as well as predicted genes.

The pilot phase of the Brain Proteome Project (BPP), the Human Proteome Organization (HUPO) initiative that focuses on studies of the brain of both humans and mice, has now been completed. Participating laboratories studied the proteomes of two human samples derived from biopsy and autopsy as well as three mouse samples from various developmental stages. With the combined and centrally reprocessed data now available, a comparison in terms of protein identifications and project organization is made between the HUPO BPP pilot and three other proteomics studies: the HUPO Plasma Proteome Project (PPP) pilot, a proteome of human blood platelets and a recently published comprehensive mouse proteome. Finally, as any comparison between large-scale proteomics datasets is decidedly non-trivial, we also evaluate and discuss several ways to go about comparing such different result sets.