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

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.