A translational, high-throughput proteomics platform for the deep analysis of plasma samples

Translational Proteomics, crucial for protein analysis in clinical samples, aims to achieve three key objectives: i) analyze extensive cohorts rapidly; ii) quantify a high number of proteins per sample using hypothesis-free and non-targeted analysis; and iii) integrate robust statistical models for quantitative data analysis. In an effort to attain these goals, we report here a novel, rapid and hypothesis-free workflow using multiplexed labelling that allows the analysis of thousands of plasma samples without the need for high-stable mass spectometers. This approach enables accurate and reproducible quantification of over a thousand of proteins. The described workflow successfully analyzed more than 1300 non-depleted plasma samples (across 130 TMT experiments), yielding highly reproducible results. The analysis of 600 proteins per experiment was achieved without peptide fractionation in a swift 6-hour timeframe per experiment, completing the entire process in just two weeks. Moreover, peptide fractionation, while extending the analysis to over 1000 proteins, required 30 hours per experiment, spanning a two-month duration. Although protein yield was higher in depleted samples, depletion introduced a quantification bias affecting approximately half of the plasma proteome, as revealed by hierarchical clustering analysis. Notably, a high correlation between biochemical quantitation and mass spectrometry measurements was observed for several proteins (p<1e-17). Additionally, the selection of a longitudinal prospective cohort (baseline and three-years follow up) enabled us to analyze the long-term stability of the plasma proteome. We observed that proteins from 84% of the individuals showed a significant correlation (FDR<5%) along time. Finally, the study identified gender-discriminating proteins. These results highlight the robustness of the new method for high-throughput quantitative analysis of the deep plasma proteome that could facilitate and improve clinical proteomics discovery in human blood plasma.