Benchmarking of plasma proteomics workflows reveals complementarity of deep mass spectrometry and affinity-based...

Plasma proteomics holds promise for biomarker discovery, yet its characterization remains limited by the complex dynamic range of protein concentrations. We benchmarked seven proteomics workflows including five MS-based strategies (NEAT, PCA-N, TOP14, ENRICH-iST and SEER) on the Orbitrap Astral and Exploris 480, and Olink Target Inflammation and Reveal on 80 lithium heparin plasma samples from two clinical cohorts. Workflows were evaluated for proteome depth, reproducibility, HPPP catalogue coverage and recovery of aging-associated biological signatures. The Astral outperformed the Exploris 480 three-fold in protein identifications at four-fold higher throughput. Proteome coverage ranged from 1159 (NEAT) to 6003 protein groups (SEER), the latter providing over 70% HPPP coverage. TOP14 and ENRICH-iST achieved intermediate depth with high reproducibility. ENRICH-iST further demonstrated applicability to lithium heparin plasma via a simple protocol adaptation. Both Olink assays quantified their complete target panels, capturing low-abundance markers partially inaccessible to MS. Despite limited numerical overlap between MS and Olink datasets, fold-change directionality was broadly concordant. SEER and Olink Reveal uniquely recovered aging-related pathway signatures, including telomere maintenance and immune modulation. No single workflow provided exhaustive plasma proteome coverage. Integration of MS and affinity-based assays should be considered in plasma biomarker research for comprehensive coverage of the plasma proteome. SIGNIFICANCE: The present work provides a systematic and integrated benchmarking of multiple contemporary plasma proteomics workflows evaluated on two clinically relevant cohorts. By comparing advanced MS strategies and high-plex affinity-based assays under harmonized analytical conditions, this study goes beyond isolated performance metrics to examine how depth of coverage, reproducibility, throughput, and platform-specific biases influence biological interpretation. By clarifying the strengths and limitations of each approach, this work contributes to improving experimental design, facilitating cross-study comparability, and supporting the gradual standardization required for clinical adoption. In the longer term, these results lay the groundwork for integrative analytical pipelines and harmonized data generation strategies that may enhance the reliability and interpretability of plasma proteomics in biomarker validation, longitudinal monitoring, and future clinical research settings.