GAG-Former: An LC-MS/MS data-processing workflow with a physics-informed dual-stream transformer backbone for co-elution deconvolution and simultaneous structural identification of glycosaminoglycan disaccharides in biological samples.

Authors

Chunhua Li, Shan Li, Anran Sheng, Runbo Wang, Xiaojun Sun

Published in

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences. Volume 1284. Pages 125305. Sep 24, 2026. Epub Sep 24, 2026.

Abstract

Glycosaminoglycan (GAG) disaccharide profiling by LC-MS/MS underpins heparin quality control and glycomics biomarker research. Existing workflows are constrained by co-elution of sulfation-positional isomers and C5 epimers, disconnection between structural identification and peak-area quantification, and matrix-driven transfer loss between standards and biological samples. This study develops GAG-Former, an LC-MS/MS data-processing workflow built on a physics-informed dual-stream Transformer backbone that encodes fragment spectra and chromatographic retention times in parallel. The sulfation biosynthetic order and heparin-lyase cleavage preference are embedded as non-learnable attention biases. A set-prediction triplet decoder outputs disaccharide class, retention window and concentration in a single forward pass, and a gradient-reversal layer with LayerNorm-only test-time entropy minimisation enables cross-matrix self-calibration. The workflow was evaluated on 17 GAG disaccharide standards (510 injections), 32 Latin-square gravimetric mixtures (256 injections), three biological matrices (plasma n=120, urine n=98, mouse liver/kidney/aorta n=144), an OSCS adulteration gradient (210 injections) and four animal-source heparins (96 injections). On Std-17 the workflow reached 89.7% Top-1 identification accuracy and 9.6% MAPE in concentration on the Latin-square mixtures under 5-fold cross-validation, outperforming CandyCrunch, GlycoBERT, QuanFormer and commercial automatic integration; the co-elution-pair RMSE dropped from 0.231 (QuanFormer) to 0.142, and the F1 on rare 3-O-sulfated disaccharides rose from 0.713 (bias-disabled control) to 0.864. Zero-shot identification accuracy across the three biological matrices was 86.3% (plasma), 84.1% (urine) and 80.7% (tissue). OSCS was stably detected at 0.05% (28/30 replicates, P = 0.92) and animal-source discrimination reached 95.8% accuracy. All findings were obtained in a single laboratory on one LC-MS/MS platform using anonymised archival or commercial samples; multicenter validation, blinded external testing, cross-laboratory robustness assessment and head-to-head comparison with pharmacopoeial ¹H NMR, SAX-HPLC and MRM methods have not been performed and remain prerequisites for any regulatory or clinical use. The workflow is therefore positioned as a high-throughput pre-screening approach that complements rather than replaces ¹H NMR, SAX-HPLC and MRM confirmation.

PMID:
42828965
Bibliographic data and abstract were imported from PubMed on 04 Oct 2026.

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