ArticleiScience2022
Deep learning explains the biology of branched glycans from single-cell sequencing data.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Single-Cell Glycomics of the Pancreatic Tumor Microenvironment: Technologies, Glyco-Immune Checkpoints, and Tumor-Immune Communication.Advanced biology · 2026Review
- The Plasma Glycome Differences Between Women with PCOS and Healthy Controls.International journal of molecular sciences · 2026Article
- Integration of RNAseq transcriptomics andChemical science · 2025Article
- Cell- and tissue-specific glycosylation pathways informed by single-cell transcriptomics.NAR genomics and bioinformatics · 2024Article
- Leveraging explainable deep learning methodologies to elucidate the biological underpinnings of Huntington's disease using single-cell RNA sequencing data.BMC genomics · 2024Article
- Designing interpretable deep learning applications for functional genomics: a quantitative analysis.Briefings in bioinformatics · 2024Review
- Review
- Interpretable feature extraction and dimensionality reduction in ESM2 for protein localization prediction.Briefings in bioinformatics · 2024Article
- Emerging technologies for single-cell glycomics.BBA advances · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glycosylation is ubiquitous and often dysregulated in disease. However, the regulation and functional significance of various types of glycosylation at cellular levels is hard to unravel experimentally. Multi-omics, single-cell measurements such as SUGAR-seq, which quantifies transcriptomes and cell surface glycans, facilitate addressing this issue. Using SUGAR-seq data, we pioneered a deep learning model to predict the glycan phenotypes of cells (mouse T lymphocytes) from transcripts, with the example of predicting β1,6GlcNAc-branching across T cell subtypes (test set F1 score: 0.9351). Model interpretation via SHAP (SHapley Additive exPlanations) identified highly predictive genes, in part known to impact (i) branched glycan levels and (ii) the biology of branched glycans. These genes included physiologically relevant low-abundance genes that were not captured by conventional differential expression analysis. Our work shows that interpretable deep learning models are promising for uncovering novel functions and regulatory mechanisms of glycans from integrated transcriptomic and glycomic datasets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.