An Orthogonal Extraction Workflow for High-Depth Plasma Proteomics with Metabolomic Compatibility from Low Microliter-Scale Samples

Abstract

Plasma proteomics serves as the cornerstone of clinical biomarker development, yet standalone profiling is plagued by three critical bottlenecks: poor detection of low-abundance proteins, overlapping signals across pathologically similar diseases, and the lack of upstream/downstream molecular context to elucidate underlining mechanisms. These limitations collectively result in a low translation rate of protein biomarkers into clinical practice. A major hurdle to overcoming these challenges lies in technical barriers that hinder deep proteome coverage and the seamless integration of metabolomics into large-scale plasma proteomic studies. Conventional approaches require separate sample preparation for each omics layer, doubling experimental workload, while sequential extraction of proteins and metabolites inevitably compromises either proteome depth or metabolite stability. Herein, we report an orthogonal extraction workflow that achieves enhanced proteomic profiling depth while preserving metabolomic compatibility from a mere 10 µL of plasma. We demonstrate that a ternary solvent mixture (TSM) of methanol, acetonitrile, and acetone exhibits distinct chemical orthogonality to perchloric acid (PCA) precipitation, with each strategy recovering reproducible yet partially non-overlapping subsets of plasma proteins. By combining orthogonal extraction with PCA and TSM to deplete high-abundant proteins, we expanded plasma proteome coverage to 1136 proteins—representing a 49% increase compared to PCA alone and a 2.6-fold enhancement relative to the standard neat plasma processing protocol. This marked improvement in low-abundance protein detection enabled efficient coverage of 101 FDA-approved circulating biomarkers. For metabolomics, the workflow retained comprehensive coverage (1174 metabolites) comparable to standard methanol extraction, with superior qualitative reproducibility and quantitative consistency across technical replicates. This workflow may enable protein-centric biomarker discovery augmented by orthogonal metabolic insights from microliter-scale samples, providing a transformative tool to surmount the limitations of standalone proteomics and accelerate the clinical translation of candidate biomarkers.