2026-10-12T05:12:53Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33924335 2026-09-18T08:10:33Z category_4 category_146 category_7 category_12 category_21 category_873 category_734 category_931 category_811 category_64 portal_63 item_type_3 month_year_09_2026

A Robust DIA-Based Platform for Large-Scale Plasma Glycoproteomics and Biomarker Discovery Chi-Hung Lin (30220) Sayee Sawale (25071074) Mark Marispini (25071077) Adam Poltorak (25071080) Joon-Yong Lee (4259566) Natalie Smith (37259) Wan-Fang Chou (17791082) Hao Qian (154985) Philip Ma (3019515) Bruce Wilcox (478667) Biochemistry Space Science Medicine Cell Biology Biotechnology Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified Cancer workflow integrates automated diagnostic biomarker discovery customized bioinformatics pipeline concept pilot study across 560 replicates iv lung cancer associated glycosylation differences 80 per batch scale plasma glycoproteomics protein glycosylation including cancer batch cv batch coefficients stage iii specific changes solution digestion seven batches robust dia robust data results establish quantitative precision platform ’ optimized dia numerous diseases noncancer individuals ms acquisition independent acquisition glycoprotein standards detect disease cohort studies based platform 9 %. 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. 2026-09-18T00:00:00Z Dataset Dataset 10.1021/acs.jproteome.6c00522.s004 https://figshare.com/articles/dataset/A_Robust_DIA-Based_Platform_for_Large-Scale_Plasma_Glycoproteomics_and_Biomarker_Discovery/33924335 CC BY-NC 4.0