Quantitative Proteomics Core

We determine the identity of proteins that are dissolved in buffer or from cut gel bands. This approach is straightforward and allows the detection of known and unknown amino acid sequences and their modifications, enzymatic digestion sites, as well as the detection of post-translational modifications. Under ideal experimental conditions, we reach a coverage of at least 70 % of the protein sequence. Further improvements in coverage are achieved by the combination of trypsin with other proteolytic enzymes, such as LysN, AspN, GluC, or ArgC.

The total protein inventory in cellular extracts is typically analyzed via trypsination followed by chromatographic separation of purified peptides and collection of data on MS instruments. One of the most common experimental strategies are label-free approaches, where the peak areas of detectable peptides are used to compare their abundance between samples. Depending on the experimental requirements, we offer a versatile set of targeted and untargeted MS detection modes to survey global proteome profiles at different sensitivities and specificities via relative or absolute quantitation. The core is equipped with high-resolution nanoLC-systems that enable deep proteome studies with the highest possible protein coverage and sensitivity. For higher throughput, our research infrastructure also includes a microflow-HPLC systems allowing the collection of data from large sample cohorts with higher throughput, but lower sensitivity and protein coverage than nanoLC.

Labeling of proteins with stable heavy isotopes is a widely used method for quantitative proteomics. SILAC is most commonly based on the direct supplementation of 13C and 15N enriched arginine and lysine isotopes into the cell culture medium. After protein extraction and digestion, up to three protein samples are multiplexed and the isotopic labels are resolved via LC-MS for quantitation. The use of multiplexed samples eliminates problems that are commonly observed in label-free experiments across individually measured samples, such as zero-values and technical variability, which diminish the statistical power of the assays. SILAC has been widely applied to characterize differences in proteome compositions, analyze the dynamics of post-translational modifications or protein-protein interactions, and to monitor the turnover of proteins with high accuracy and reproducibility proteome-wide.

TMT is a class of labelling reagents that is used to detect proteins in multiplexed samples. Unlike SILAC, the labelling with TMT occurs after sample extraction, and targets peptides via a chemical derivatization that is specific for free amines. TMT labels are designed to have an amine-reactive linker group, a spacer arm, and a mass reporter and the labels contain different numbers and combinations of 13C and 15N isotopes resulting in MS2 reporter ions that are unique for each label. After labelling, up to 18 peptide samples are mixed in one sample and identical peptides with different labels show the same elution profile during chromatographic separation. The TMT labels are designed so that the MS2 reporter group is detectable in high-resolution tandem mass spectrometers and the resulting fragment ions are used to determine the peptide abundance of multiple samples in a single LC-MS run. Since the use of TMT allows for the quantitation of peptides with greater accuracy and reproducibility than many other methods, this technology is particularly useful to monitor post-translational modifications or to collect data from very complex experimental designs, such as time-series experiments or sample populations with many experimental groups.

Post-translation modifications are required for controlling almost every biological process to regulate protein functions in a relatively short time scales. The Quantitative Proteomics Core has implemented multiple experimental strategies to enrich and analyze post-translational modifications, such as phosphorylation, acetylation, and ubiquitination for single proteins and complex proteome extracts. We also offer the identification of protease target sites via PALEO (protease activity labeling employing 18O-enriched water). The sample extraction and MS-detection methods for novel or more uncommon post-translational modifications can be implemented if required. We recommend the use of TMT labelling techniques, to maximize the accuracy and reproducibility in the detection of functional groups.

Proteins typically do not act as single players but form complex cellular network of protein interactions that are tightly regulated and adapt dynamically to external and internal signals. The formation of these macromolecular complexes is central for almost every biochemical process, and provides deeper insight into biochemical reactions, regulation of cellular processes, mechanisms of disease and biomarkers, as well as the discovery of drug targets. Consequently, the study of protein interaction networks and their dynamics is a key-factor to define a protein’s function. To survey the total set of protein interactions in biological samples, the Quantitative Proteomics facility employs a variety of techniques, such as: immunoprecipitation, cross-linking mass spectrometry (XL-MS), and proximity-labelling techniques (e.g., APEX2 labelling).

We offer many different sample preparation protocols to extract proteins from a wide range of matrices, such as bacteria, plants, cell cultures, tissues, and biofluids. However, there may always be a need to tailor sample preparation procedures of the individual research project to obtain optimal results. We have 15 years of experience in the optimization of workflows for the isolation of low abundance analytes and the processing of recalcitrant samples. We can also provide assessments for the feasibility of studies and alternative experimental strategies. If you want to establish a method for a particularly difficult sample or simply need an assessment of different available methods, our team is happy to help.

Our facility possesses a versatile set of data-analysis tools to extract and process raw MS1 and MS2 spectra, determine the sequence of proteins, conduct proteome-wide data analyses, identify post-translational modifications, analyze protein interactions, and survey the turnover of proteins. For the processing of highly complex proteome raw data files, we currently employ the Proteome Discoverer 3.0 platform. We also offer the analysis of datasets with MaxQuant, Peaks Studio Xpro, Skyline, DIA-NN, and MSfragger. The Quantitative Proteomics Core uses publicly available R-scripts for the processing of raw data, but also has a very dynamic collaboration with the Whitehead Bioinformatics and Research Computing Core (BaRC) to extract the best possible results from your experiments.