The recent emphasis on comprehensive profiling of biofluids, particularly easily accessible plasma samples, is driven by promises of understanding diseases, identifying biomarkers and drug targets, facilitating companion diagnostics development, predicting toxicology effects, and enabling patient stratification for precision medicine. Despite the remarkable progress in technologies such as ligand-binding proximity extension, aptamer-based panels, and ultra-fast and sensitive LC-MS instruments the translation of large cohort data to clinical applications remains challenging. This limitation can be attributed to the complexity of multiplexed data, which reveals population-level trends but lacks applicability to individual patients. Addressing this challenge is crucial for harnessing the full potential of clinical proteomics.

Here we focused on conducting in-depth plasma LC-MS profiling proteome profiling. Our aim is to increase the number of identifications by exploring non-specific cleavage sites and fragments, as well as post-translational modifications of proteins (e.g. glycation, phosphorylation, citrullination, acetylation, glycosylation, etc.), and incorporate variety of protein isoforms. To enhance the dynamic range and mitigate biases in enrichment steps, we have improved the separation of peptides by coupling columns with different diameters: a high-capacity 1 mm ID column and a high-resolution 300 µm ID column. Additionally, we utilized a UHPLC system with a pressure during the run above 1000 bar to maintain micro-flow rates, enabling the use of a robust H-ESI source. The coupling of columns has led to an increased system void volume and reduced MS utilization time with common trap and elute or direct injection setups. Consequently, we have implemented a novel tandem UHPLC workflow that has increased MS utilization to above 95%, allowed for deep column wash in parallel to peptide separation, and eliminated delays in peptide elution. In combination with multistep and multi-search engine data processing, we have been able to drastically increase the identification rate of peptides in plasma, which is commonly observed in biofluids analysis.

The optimized proteomics workflow was employed to enhance our understanding of cardiovascular, kidney, and skeletal muscle diseases by comparing plasma proteome profiles, thereby unveiling pathways enriched in patient samples compared to controls. We also conducted comprehensive phenotyping of multiple animal species to facilitate the translation of biomarkers during the drug development process. Additionally, we completed a comprehensive study of sample preparation conditions, particularly centrifugation speed on the abundance of molecular markers in plasma and linked proteomics data with metabolomics, mRNA and miRNA profiling. Overall plasma proteomics has achieved a level of robustness that enables the generation of essential data on a day-to-day basis to support the drug development process.