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<p>N-glycosylation, as a common post-translational modification with complex structures, plays key roles in protein folding, cellular recognition and signaling pathways. State-of-the-art mass spectrometry-based N-glycoproteomics has enabled deep N-glycoproteome characterization of various human organs. However, a comprehensive N-glycoproteome landscape of human organs remains lacking. Here we present a systematic human N-glycoproteome atlas spanning 74 subjects across 18 organs/tissues with identification of 57,884 N-glycan structure-level and 23,863 monosaccharide composition-level intact N-glycopeptides on 7543 N-glycosites of 5062 N-glycoproteins. Tissue-specific N-glycosylation patterns and crosstalk between sialylation and fucosylation are observed. An organ classifier using multiple machine learning models on our N-glycopeptide dataset is trained. This atlas, complemented by a unified multi-software analysis framework, provides insights into organ-specific glycobiology and establishes a fundamental reference for understanding physiological N-glycosylation characteristics of human tissues.</p>

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A comprehensive landscape of human organ N-glycoproteome

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月16日

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  • Xiaoyu Hu,
  • Tao Wang,
  • Chao Qin,
  • Yanggang Yuan,
  • Suideng Qin,
  • Hongwei Liang,
  • Zhixin Tian

摘要

N-glycosylation, as a common post-translational modification with complex structures, plays key roles in protein folding, cellular recognition and signaling pathways. State-of-the-art mass spectrometry-based N-glycoproteomics has enabled deep N-glycoproteome characterization of various human organs. However, a comprehensive N-glycoproteome landscape of human organs remains lacking. Here we present a systematic human N-glycoproteome atlas spanning 74 subjects across 18 organs/tissues with identification of 57,884 N-glycan structure-level and 23,863 monosaccharide composition-level intact N-glycopeptides on 7543 N-glycosites of 5062 N-glycoproteins. Tissue-specific N-glycosylation patterns and crosstalk between sialylation and fucosylation are observed. An organ classifier using multiple machine learning models on our N-glycopeptide dataset is trained. This atlas, complemented by a unified multi-software analysis framework, provides insights into organ-specific glycobiology and establishes a fundamental reference for understanding physiological N-glycosylation characteristics of human tissues.