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January 15, 2026Nature Communications6 citationsOpen Access

A comprehensive N-glycoproteome atlas reveals tissue-specific glycan remodeling but non-random structural microheterogeneities

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YWYongqi WuMYMuyao YangYXYongchao Xu

Key Points

  • To characterize the N-glycoproteome across different mouse tissues and identify patterns of glycan remodeling.
  • Analysis of 24 mouse tissues for N-glycans and glycoproteins
  • Identification of 3045 N-glycans at 8681 glycosites
  • Assessment of tissue-specific structural variations and microheterogeneities
  • Co-occurrence network analyses to explore glycan attachment patterns
  • Identified 3045 N-glycans and 5026 glycoproteins in mouse tissues
  • 88.2% of glycans met high-confidence standards
  • Tissue-specific glycosylation suggests protein function adjustments
  • Non-random microheterogeneities observed at glycosites across different tissues.

Abstract

The mouse is a key model in biomedical research, yet its tissue-specific glycoproteome remains incompletely characterized due to glycan complexity and microheterogeneity. Here, we present a comprehensive N -glycoproteomic atlas across 24 mouse tissues, comprising 3045 N -glycans with distinct structural features attached at 8681 glycosites on 74,277 glycopeptides and 5026 glycoproteins. Among these glycans, 2687 (88.2%) meet the high-confidence threshold through an integrative confidence-estimation framework. Overall glycan structural patterns show enormous tissue-specific diversities, acting as superior molecular signatures of tissue identity and system origins. Notably, even commonly expressed glycoproteins undergo tissue-dependent glycan remodeling, suggesting that glycosylation may fine-tune protein functions to meet specialized biological demands. These patterns are further shaped by subcellular localization, which constrains glycan variabilities across compartments. Co-occurrence network analyses also expose substructural biases and non-random microheterogeneities among glycans attached at the same glycosites. The dataset serves as a valuable database resource for advancing the structural and functional understanding of glycoproteins.

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Cite This Study

Wu et al. (2026) studied this question.

synapsesocial.com/papers/69683e135818e7dbd7c630e2https://doi.org/10.1038/s41467-025-68186-2
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