scGlycoAI: machine learning analysis of single-cell glycan and RNA sequencing data for cell-type annotation and malignancy-associated glycan discovery
Abstract
Single-cell glycan and RNA sequencing (scGR-seq) enables characterization of cellular glycan heterogeneity. Here, we present scGlycoAI, an interpretable machine learning framework that integrates scGR-seq data for glycan-centered biological discovery. Using lectin-binding profiles as input, scGlycoAI employs hierarchical machine learning to accurately classify cell types and subtypes within complex tumor microenvironments. Applied to pancreatic ductal adenocarcinoma (PDAC) datasets, scGlycoAI achieved an overall eight-class classification accuracy of 90.3% under leave-one-patient-out validation using glycan features alone. These results demonstrate that glycan signatures, despite their lower dimensionality compared with transcriptomic profiles, are sufficient for robust cell-type annotation and represent cell-type-specific molecular signatures. Feature importance analysis identified PVL lectin binding, which reflects recognition of sialylated and/or GlcNAc-containing glycans, as a key feature of aggressive cancer states. A neural-network model combined with SHAP-based interpretation further revealed that elevated PVL binding was associated with transcriptional programs linked to epithelial–mesenchymal transition, KRAS signaling, extracellular matrix remodeling, and inflammatory responses. Higher expression of the PVL-associated gene signature was associated with poorer overall survival in the TCGA pancreatic adenocarcinoma cohort. Collectively, these findings suggest that glycan-based machine learning enables accurate cell classification while providing biological insights into malignancy-associated glycan states at single-cell resolution. This framework provides a foundation for glycan-centered biomarker discovery and glycan-targeted therapeutic development.
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BibTeX
@article{anh2026scglycoai,
title = {{scGlycoAI: machine learning analysis of single-cell glycan and RNA sequencing data for cell-type annotation and malignancy-associated glycan discovery}},
author = {Dinh Xuan Tuan Anh and Hiroaki Tateno},
journal = {Glycobiology},
year = {2026},
month = oct,
doi = {10.1093/glycob/cwag100},
url = {https://doi.org/10.1093/glycob/cwag100},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - scGlycoAI: machine learning analysis of single-cell glycan and RNA sequencing data for cell-type annotation and malignancy-associated glycan discovery
AU - Dinh Xuan Tuan Anh
AU - Hiroaki Tateno
PY - 2026
DA - 2026/10/24
JO - Glycobiology
DO - 10.1093/glycob/cwag100
UR - https://doi.org/10.1093/glycob/cwag100
AB - Single-cell glycan and RNA sequencing (scGR-seq) enables characterization of cellular glycan heterogeneity. Here, we present scGlycoAI, an interpretable machine learning framework that integrates scGR-seq data for glycan-centered biological discovery. Using lectin-binding profiles as input, scGlycoAI employs hierarchical machine learning to accurately classify cell types and subtypes within complex tumor microenvironments. Applied to pancreatic ductal adenocarcinoma (PDAC) datasets, scGlycoAI achieved an overall eight-class classification accuracy of 90.3% under leave-one-patient-out validation using glycan features alone. These results demonstrate that glycan signatures, despite their lower dimensionality compared with transcriptomic profiles, are sufficient for robust cell-type annotation and represent cell-type-specific molecular signatures. Feature importance analysis identified PVL lectin binding, which reflects recognition of sialylated and/or GlcNAc-containing glycans, as a key feature of aggressive cancer states. A neural-network model combined with SHAP-based interpretation further revealed that elevated PVL binding was associated with transcriptional programs linked to epithelial–mesenchymal transition, KRAS signaling, extracellular matrix remodeling, and inflammatory responses. Higher expression of the PVL-associated gene signature was associated with poorer overall survival in the TCGA pancreatic adenocarcinoma cohort. Collectively, these findings suggest that glycan-based machine learning enables accurate cell classification while providing biological insights into malignancy-associated glycan states at single-cell resolution. This framework provides a foundation for glycan-centered biomarker discovery and glycan-targeted therapeutic development.
SN - 0959-6658
SN - 1460-2423
ER -