An Integrated Strategy Using Predicted Spectral Library and Tandem Enrichment for Large-Scale Identification of Low-Abundance Proteins and Intact N-Glycopeptides in Human Plasma.

Plasma represents a highly valuable clinical sample for protein biomarker discovery, offering a comprehensive source of physiological and pathological information. N-glycosylation plays key roles in various biological processes and enhances the sensitivity of plasma protein biomarkers for disease diagnosis. Consequently, large-scale characterization of the plasma proteome and N-glycosylation patterns by mass spectrometry (MS) is crucial for identifying biomarkers but remains highly challenging due to three major difficulties. First, plasma protein detection is limited as high-abundance proteins dominate the majority of the MS scans. Second, while plasma proteome coverage can be improved by constructing large-scale empirical spectral libraries and coupling them with DIA-MS, the labor- and time-consuming nature of experimental library generation imposes constraints on its wide adoption in clinical studies. Third, the low concentration and poor ionization of N-glycopeptides make their signals more susceptible to suppression in MS analysis. To address these issues, we developed an integrated workflow applying magnetic graphene-oxide (mGO) nanomaterial enrichment and an in silico predicted spectral library for low-abundance plasma proteome identification, along with tandem enrichment using hydrophilic interaction liquid chromatography (HILIC) for sensitive plasma N-glycoproteome profiling. In this way, 4538 plasma proteins were obtained in a single DIA-MS analysis using a QE-HF mass spectrometer, 10 times more than those obtained from direct analysis of neat plasma. Further HILIC enrichment of the mGO products enabled the identification of 7986 intact N-glycopeptides from 626 proteins with concentrations as low as the nanogram per liter range. Notably, 58.34% of these N-glycopeptides were undetectable by direct HILIC enrichment from neat plasma, highlighting the advantage of applying tandem enrichment.