A Robust DIA-Based Platform for Large-Scale Plasma Glycoproteomics and Biomarker Discovery

Abstract

Structural changes in protein glycosylation are recognized as phenotypes in numerous diseases, including cancer. Despite the promise of glycoproteomics for diagnostic biomarker discovery, large-scale studies remain limited by challenges in reproducibility, throughput, and quantitative precision. Here, we present a robust data-independent acquisition (DIA)-based plasma glycoproteomics platform that enables high-throughput and reproducible glycopeptide quantification suitable for large-cohort studies. The workflow integrates automated in-solution digestion and enrichment, optimized DIA-LC-MS acquisition, and a customized bioinformatics pipeline to achieve reproducible glycopeptide identification and quantitation. Across 560 replicates of a pooled human plasma sample processed in seven batches, the platform achieved high reproducibility, with intra-batch coefficients of variation (CVs) below 15% (n = 80 per batch) and an inter-batch CV of 22.9%. The method accurately captured expected fold changes from spiked-in glycoprotein standards. In the setting of a proof-of-concept pilot study to demonstrate the platform’s ability to detect disease-associated glycosylation differences, the platform successfully identified glycoform-specific changes and distinguished between noncancer individuals (n = 39) and those with stage III/IV lung cancer (n = 39). Together, these results establish a high-throughput DIA-based glycoproteomics workflow suitable for large-cohort studies to discover glycopeptide biomarker candidates.