Current proteomics methods and instrumentation represent today a powerful tool to get new insights into complex biological systems, in particular for:

• Characterization of complex samples (proteomes, sub-proteomes, biological fluids, protein complexes…) with maximal analytical depth (number of proteins identified)
• Precise relative quantification of each protein in different samples using repeatable and reproducible analytical methods
• Semi-absolute quantification of different proteins in a complex mixture

The ProFI teams have expertise in discovery proteomics, e.g. all methods dedicated to the systematic, non-hypothesis-driven, and large-scale characterization of complex mixtures of proteins, in a qualitative and quantitative manner. Thus, we apply bottom-up strategies based on the enzymatic digestion of proteins with a specific protease (e.g. trypsin) and high-throughput analysis of resulting peptides by liquid nanochromatography coupled to mass spectrometry (nanoLC-MS), using fast-sequencing instruments. According to the project, we design optimized analytical workflows including the following steps:

• Sample preparation (total cellular lysates, sub-cellular fractionation, enrichment techniques, handling of biological fluids etc.)
• Analytical pre-fractionation. In order to increase the analytical depth for highly complex proteomes, samples can be fractionated at protein or peptide level, prior to nanoLC-MS analysis (gel electrophoresis, IEF, SCX, HPLC)
• Quantitative methods using label-free or isotopic labelling methods (SILAC, iTRAQ, TMT)
• NanoLC-MS analysis with benchmarked and quality controlled systems to ensure a sustained level of performance and with optimum chromatographic gradients and MS modes (DDA, DIA).
• Bioinformatic processing. We use several software tools installed on clusters and/or multicore servers to process large scale data. Our in-house developed tools (Proline, MS-Angel, mz-Scope) offer an optimized pipeline solution for data handling, processing and inspection.
• Statistical analysis. We offer support to interpret large-scale proteomic data using state-of-the-art statistical methods and tools, in order to identify differentially abundant proteins and control false-discovery rate.