"If the Shoe Fits?"-Benchmarking Plasma Proteomic Sample-Preparation Workflows Across Human and Rat Biofluids.

Abstract

The push for new clinical biomarkers has seen rapid innovation in biofluid analysis, particularly for plasma. For mass-spectrometry (MS)-based analysis, achieving depth and quantitative accuracy whilst ensuring throughput continues to shape plasma methods development. Numerous workflows have emerged that mitigate high-abundance suppression and expand dynamic range, especially when paired with next-generation MS instrumentation. Yet systematic evaluations that also consider biological variables (e.g., biofluid type, species) and technical parameters (e.g., MS methods) are limited. Here, we benchmarked eight sample-preparation workflows spanning neat approaches (SP3, STrap), depletion (perchloric acid, PerCA), and corona-enrichment strategies (MagNet HILIC/SAX, Enrich-iST, ProteoNano). We compared their performance across human plasma, human serum, and rat plasma, analyzing all samples on an Orbitrap Astral (Thermo) using two plasma-optimized data-independent acquisition (DIA) methods: one discovery-maximized and one throughput-maximized. We identified 2726 human and 3767 rat proteins across workflows and methods, including ∼1000 from neat plasma. Increasing throughput incurred a ∼20 to 30% reduction in depth, depending on workflow and species. EV-enrichment produced the deepest proteomes but with distinct compositions relative to neat, depleted, and secreted-protein-enriched samples, revealing a unique sub-proteome niche. Several workflows also performed markedly better in rat plasma, supporting improved sensitivity for preclinical analyses. Enrichment or depletion dramatically reshaped the balance of tissue- and cell-specific proteins detectable in plasma, suggesting that workflow choice should be guided by the organs, immune targets, or inflammatory signals most relevant to the study. In this vein, statistical analysis of differentially abundant proteins showed that >90% of detected proteins were significantly altered between workflows, with the largest numbers arising from the corona-enrichment strategies, underscoring how strongly workflow choice shapes the downstream proteome. Taken together, these findings emphasize a rapidly expanding plasma methodological landscape, where the most effective workflow is the one most precisely tailored to a cohort's biology.