iTRAQ data interpretation.

Quantitative proteomic analysis can help elucidating unexplored biological questions; it, however, relies on highly reproducible experiments and reliable data processing. Among the existing strategies, iTRAQ is known as an easy to use method allowing relative comparison of up to eight multiplexed samples.Once the data is acquired it is important that the final protein quantification reflects the actual amounts in the samples.

Robust workflow for iTRAQ-based peptide and protein quantification.

Quantitative proteomics has become a routinely used technique to globally compare protein content and expression profiles of biological samples, for instance after differential stimulation. In this context, chemical stable isotope-based labeling techniques, such as ICAT and iTRAQ, have been successfully applied in a large variety of studies. Since iTRAQ labels are isobaric, quantitation is conducted on the MS/MS level.

Systematic and quantitative comparison of digest efficiency and specificity reveals the impact of trypsin quality on MS-based proteomics.

Trypsin is the most frequently used proteolytic enzyme in mass spectrometry-based proteomics. Beside its good availability, it also offers some major advantages such as an optimal average peptide length of ~14 amino acids, and typically the presence of at least two defined positive charges at the N-terminus as well as the C-terminal Arg/Lys, rendering tryptic peptides well suited for CID-based LC-MS/MS. Here, we conducted a systematic study of different types of commercially available trypsin in order to qualitatively and quantitatively compare cleavage specificity, efficiency as well as reproducibility and the potential impact on quantitation and proteome coverage.

Quality control of nano-LC-MS systems using stable isotope-coded peptides.

In analytical sciences, there is a general need for quality control to assess whether a product or a process meets defined requirements. Especially in proteomics, which implies analysis of ten thousands of analytes within a complex mixture, quality control to validate LC-MS performance and method setup is inevitable to achieve day-to-day-, inter-system-, as well as inter-user reproducibility. Thus, results deriving from LC-MS analyses can be benchmarked and the need for system maintenance can be revealed.