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.