新しい課題のための新しいツールの活用：自動化と気相分画によるネイティブプラズマプロテオミクスの最適化

Advances in high-throughput mass spectrometry have shifted the bottleneck in plasma proteomics from data acquisition to sample preparation. While enrichment and depletion strategies enable detection of low-abundance proteins, their complexity and cost limit scalability and clinical translation. Targeting midto-high abundance proteins from neat plasma offers a practical, reproducible alternative aligned with clinical workflows. Here, we combine fully automated sample preparation and Evotip loading on the Bravo AssayMAP system with extensive method optimization on the timsTOF HT and gas-phase fractionation deep spectral libraries to advance neat plasma proteomics. Automation reduced hands-on time by 88% and significantly improved robustness. Mixed-mode searching with a 1788-protein library increased identifications by up to 31% at a throughput of 100 samples per day, with less than 15% variation across plates. In a coronary artery disease cohort, we quantified 936 biologically relevant proteins and found 42 dysregulated compared to healthy controls. This streamlined, high-throughput workflow enables deep, reproducible analysis of neat plasma at scale, paving the way for population-level biomarker discovery and clinical implementation.