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

Domain: chemistry
Published: 2026-01-13
Editorial outcome: major_revision

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.

Introduction
The mouse is an indispensable animal model in biomedical research1. Decades of efforts have mapped the molecular landscapes of mice, including genome2, transcriptome3, proteome4, and phosphoproteome5, deepening our understanding of gene regulation and signaling dynamics across tissues. Glycans, another important class of macromolecules, have also shown tissue-specific patterns through previous glycomics studies6,7, which provided an additional dimension to decode the biochemical underpinnings of tissue functions and specialization. However, glycomic studies lack contextual information on protein carriers and specific glycosites, limiting their functional interpretability. Although existing glycoproteomic investigations have also been performed on several individual organs or biofluids, such as liver8, brain9,10, and serum11, the whole landscape of site-specific glycosylation at the proteome-wide level across various tissues remains underexplored.

The microheterogeneity and nonlinear structural complexity of N-glycans pose considerable analytical challenges. Fortunately, numerous computational tools have been developed for intact glycopeptide analysis over the past decade, including Byonic12, GPQuest13, pGlyco314, MSFragger-Glyco15, O-pair Search16, Glyco-Decipher17, and GlycanFinder18, which are capable of revealing the microheterogeneities of site-specific glycosylation19. Nevertheless, most of these tools only identify the predefined glycan compositions, limiting their ability to distinguish functional glycan structures and resolve isomeric glycoforms12,13,20. Furthermore, the majority of analytical software suffer from the lack of well-established glycan databases for most species as they adopt a glycan database-dependent strategy. To address these limitations, we previously developed a modular strategy that determines detailed structural features of N-glycans at specific glycosites by leveraging characteristic B- and Y-ions in tandem mass spectra (MS/MS) acquired with low higher-energy collisionnal dissociation (HCD) collision energy21. The StrucGP software, based on this strategy, enables the high-throughput de novo structural interpretation of site-specific N-glycans21, achieving high-resolution glycoproteomic analyses across diverse biomedical samples.

In this study, we generated a structural and site-specific N-glycoproteome atlas across 24 mouse tissues using the StrucGP software. This comprehensive dataset provides an unprecedented insight into the tissue-specific landscape of protein N-glycosylation, revealing how distinct glycan structures and site-specific glycosylation contribute to tissue identity and specialization. We identified molecular signatures defined by unique glycopeptides, which robustly distinguish tissue types and offer an additional dimension for understanding cell and organ differentiation beyond gene and protein expressions. Importantly, we demonstrate that even commonly expressed proteins across various tissues undergo tissue-dependent N-glycan remodeling, suggesting that glycosylation fine-tunes protein function in response to specific physiological contexts. Compartment-specific glycosylation patterns further highlight the alignment between glycan features and biosynthetic pathways or functional demands. Through glycan co-occurrence network analyses, we also reveal highly organized, non-random patterns of glycan usage that illuminate the organizational logic of glycoproteome diversity. Collectively, this study not only expands the scope of glycoproteomics but also establishes a foundational resource for functional and mechanistic investigations of glycosylation in tissue biology and mammalian physiology.

Results
A comprehensive N-glycoproteome atlas across 24 mouse tissues
In this study, we created a comprehensive atlas of the mouse N-glycoproteome across 24 distinct tissues, encompassing 16 major organs, 7 brain regions, and the serum, collectively representing all eight major anatomical systems. Seven brain regions included the medulla, cerebellum, white matter, hypothalamus, cortex, hippocampus, and olfactory bulb (Fig. 1a). For each tissue, equal amounts of proteins were pooled from five male and five female C57BL/6N mice (8–9 weeks) to generate 24 tissue-specific samples. Glycopeptides were enriched using hydrophilic interaction chromatography (HILIC) followed by mixed anion-exchange columns (MAX)22 and subjected to at least triplicate LC-MS/MS analyses per sample. For 22 tissues (excluding gallbladder and ovary due to limited sample size), the glycopeptides enriched by HILIC were further separated into six fractions and analyzed by at least duplicate LC-MS/MS runs per fraction. These resulted in a total of 456 raw data (Fig. 1b). During mass spectrometry analyses, each glycopeptide peak was first fragmented by a relatively high HCD energy (HCD=33%), followed by a relatively low HCD energy (HCD=20%) for peptide sequence and glycan structure analyses, respectively21.

Using StrucGP, we identified 17,806 glycosite-containing peptides from 1,950,741 oxonium ion containing spectra, representing 13,934 N-glycosites on 7365 N-glycoproteins from 24 mouse tissues within 1% false discovery rate (FDR) at the peptide level (regardless of their attached glycans) (Fig. 1c). After further filtering by FDR < 1% at the glycan level, a total of 74,277 unique intact N-glycopeptides (IGPs) were identified from 1,258,537 high-quality MS/MS spectra, comprising 3045 N-glycans with distinct structural features and 8681 N-glycosites from 5026 N-glycoproteins (Fig. 1c; Supplementary Data 1 and 2), constituting one of the most extensive tissue-specific glycoproteomic resources to date. Among 3045 identified glycans, 76.3% and 34.2% have been reported in GlyTouCan at the compositional and structural levels, respectively (Supplementary Fig. 1a and Supplementary Data 2).

To further evaluate the reliability of the identified N-glycan structures, we created an integrative confidence-estimation framework based on a logistic regression model trained by using a manually annotated gold-standard subset for glycan confidence scoring (Supplementary Data 3). The logistic model demonstrated robust and discriminative performance in distinguishing structurally reasonable from ambiguous glycans (AUC=0.79; precision=0.77; recall=0.69; F1=0.70), indicating reliable predictive power. The calibrated probability outputs were used to assign confidence levels according to statistically optimized thresholds (t_high=0.82, t_low=0.46) (Supplementary Fig. 1b). Applying this framework, 88.2% (2687/3045) of glycans were classified as high-confidence, 7.2% (218/3045) as intermediate-confidence, and 4.6% (140/3045) as low-confidence (Supplementary Fig. 1b and Supplementary Data 2). To ensure the robustness of subsequent biological interpretations, all glycan-level analyses were performed using only high-confidence glycans, whereas substructure-based analyses included all glycans. These high-confidence glycoproteins collectively covered 2582 of the 4065 N-glycoproteins reviewed in UniProtKB (63.5%) and delineated 4783 previously unannotated glycoproteins, including 2795 novel glycoproteins filtered by FDR < 1% at both peptide and glycan levels (Supplementary Fig. 1c). Comparative analysis with MSFragger-Glyco, Byonic, and pGlyco3 revealed that each algorithm identified distinct subsets of glycopeptides, highlighting that integrating results from multiple tools can therefore enhance overall glycopeptide coverage (Supplementary Fig. 1d). Motif analysis further confirmed a higher prevalence of the N-X-T motif over N-X-S as reported previously23–25 (Supplementary Fig. 1e).

Across the 24 mouse tissues, glycoprotein and glycopeptide identifications varied substantially across tissues. The gallbladder yielded 1275 unique N-linked IGPs from 284 glycoproteins, while 11,723 IGPs from 1400 glycoproteins derived from the kidney (Fig. 1d). This disparity is likely arising from the absence of fractionation in the gallbladder samples, as fractionation markedly increased the identification of glycopeptides, glycoproteins, glycosites, and glycans in matched tissues (Supplementary Fig. 1f). Notably, even with an equal number of LC-MS/MS runs, the number of identified glycoproteins and glycopeptides varied significantly across tissues20,26. As shown in Fig. 1e, the liver identified the most glycoproteins and glycopeptides, whereas the seminal vesicles identified the fewest, which is consistent with previous protein identifications from multi-tissue proteomics27. Replicate analyses of the same fraction revealed high correlation coefficients at both glycan (R=0.59–0.88) and glycosite (R=0.61–0.83) levels (Supplementary Fig. 2a), and the inter-tissue variability greatly exceeded technical variation, further confirming the robustness of the dataset (Supplementary Fig. 2b).

We further profiled tissue-specific N-glycosylation landscapes and uncovered pronounced differences in glycan diversity and complexity across organs. By quantifying the number of identified N-glycans per tissue and analyzing glycosite-specific occupancy, we found that the kidney harbored the most glycosites (2133 glycosites modified with 898 unique glycans) at both peptide and glycopeptide levels (FDR < 1%) (Fig. 1f and Supplementary Fig. 3). Besides the influence of mass spectrometry depth, this likely reflects the intrinsic glycoprotein richness of the kidney, as previously reported28. In contrast, nervous system tissues (seven brain regions) showed relatively fewer glycosites (1322–1714) but featured the greatest structural diversity, with 818–992 unique glycans identified across brain regions. These patterns were further illustrated by the glycan-to-glycosite ratios: the kidney showed a relatively low ratio of 0.42, while the seven brain regions ranged from 0.54–0.75; notably, the liver exhibited the lowest ratio at 0.25 (Fig. 1f). In neural tissues, individual glycosites carried up to 257–367 distinct glycan structures, highlighting exceptional microheterogeneities compared with other tissues (Fig. 1g). In addition, the diversity of N-glycans in neural tissues was further evidenced by the larger number of structural isomers identified for individual glycan compositions. Isomeric analysis revealed that each glycan composition could be further expanded into 6–12 isomers across tissue, with the nervous system containing the most (Fig. 1h). These structural isomers were distinguished by characteristic B and Y ions in low HCD energy of MS/MS, as exemplified by the 12 isomers of the composition N5H5F1S1 (Fig. 1i and Supplementary Fig. 4a), and isomers derived from the same peptide sequence were often further resolved by C18 chromatography based on their distinct retention times (Supplementary Fig. 4b). Despite the remarkable structural diversity, N-glycans in the nervous system tended to exhibit relatively low complexity, as the glycans in nervous system were predominantly mono-antennary, consistent with previous finding in neuronal tissues29. In contrast, bi- and tri-antennary glycans dominated most tissues, while tri- and tetra-antennary glycans were especially abundant in exocrine organs like the pancreas and seminal vesicles. Notably, highly branched glycans were observed in the stomach and intestine (Fig. 1j and Supplementary Fig. 5).

We also investigated sex differences in the renal glycosylation landscape, given the large number of N-glycopeptides identified in the kidney. Overall, glycopeptide, glycosite, and glycoprotein profiles were comparable between sexes, with strong overlap and correlation (Supplementary Fig. 6a–c). However, subtle differences emerged: female kidneys showed higher levels of LacdiNAc and Neu5Ac-rich glycans, while male kidneys had more Neu5Gc-modified structures (Supplementary Fig. 6d, e). Functionally, female-enriched glycoproteins were linked to immune and reproductive pathways, and male-enriched ones to metabolism and localization (Supplementary Fig. 6f). These findings suggest that despite overall similarity, sex-specific glycosylation patterns may underlie functional differences.

Site-specific glycans show tissue-specific structural features
To investigate tissue-specific glycan preferences, we first extracted the top ten most abundant N-glycans in each tissue based on the number of their modified glycosites, excluding commonly high-abundant oligo-mannose glycans. The nervous system and kidney were enriched in core-fucosylated bisecting glycans (N3H3F1). In contrast, although both the kidney and seminal vesicle exhibited high levels of Lewisx/a and Lewisy/b structures, bisecting glycans were far less abundant in the seminal vesicle. Liver and serum were characterized by complex, sialylated glycans with relatively low levels of core fucosylation. Testis-specific glycans were notably enriched in terminal GlcNAc. In addition, the pancreas, seminal vesicle, and several digestive tissues—such as the intestine, colon, and stomach—shared an enrichment of terminal galactose. Despite this common feature, the most abundant glycans in the pancreas were hybrid type, while digestive tissues predominantly carried bisected core structures. The seminal vesicle, by contrast, was distinguished by strong fucosylation on terminal galactosylated glycans, lacking the bisecting core features observed in the digestive tract (Fig. 2a).

Based on the modular structures of identified N-glycans across mouse tissues, three glycan subtypes with four core structures and 17 branch structures were observed (Fig. 2b). Among glycan subtypes, serum glycoproteins were predominantly modified with complex glycans (74.9% IGPs), with minimal oligo-mannose glycans (3.1% IGPs). Complex glycans were also attached at more than 50% IGPs in the bladder, kidney, gallbladder, liver, white matter, cortex, hippocampus, and hypothalamus. Hybrid glycans were highly expressed in the pancreas (30.7%), lung, heart, spleen, and uterus (25.3%); whereas oligo-mannose glycans were primarily detected in the testis (38.1%), ovary, olfactory bulb, intestine, liver (34.2%), etc (Fig. 2c). Among four core structures, typical core structure (HexNAc2Hex3, Core I) was the most prevalent, showing notably higher abundance in serum (73% vs. 48.5% across all tissues) and liver (74.3%). The fucosylated core structure (core II) was enriched in the seminal vesicle (51.9% vs. 33.6% across all tissues), heart (46.1%), and lung (45.2%). The bisected core structure (core III), besides being enriched in the nervous system (averaged 7.2% in brain regions vs. 4.8% across all tissues), was also prominent in digestive and exocrine organs such as the kidney (12.8%)30,31, intestine (12.1%)6, and colon (8.7%). In contrast, the fucosylated bisecting core (core IV) was specifically elevated in neural tissues6,32, averaging 29.4% across brain regions compared to 13.1% in all tissues (Fig. 2d). Interestingly, more pronounced tissue-specific distributions emerged when examining distinct glycan branch structures. The nervous system predominantly featured terminal GlcNAc33, unbranched structures (labeled as Ø), Lewisx/a, and various sialylated branches (except sialyl-LacNAc) mainly modified by N-acetylneuraminic acid (Neu5Ac) but lacking the N-glycolylneuraminic acid (Neu5Gc), in line with previous reports34. Conversely, serum and liver glycans displayed a predominance of Neu5Gc-containing branches in the absence of Neu5Ac35. Heart and muscle showed a relatively high abundance of sialyl-LacNAc via Neu5Ac, or with an additionally branched Neu5Gc.

Additionally, rare branch structures such as LacdiNAc-containing glycans (excluding sialyl-LacdiNAc which tended to cluster with other sialylated branch structures in neural tissues) were selectively enriched in digestive organs including the stomach, colon, and intestine, while branches with antenna-fucosylation (without sialylation) were highly expressed in kidney, and the Lewisy/b epitope was also highly expressed in the seminal vesicle (Fig. 2e).

Given that glycosyltransferases (GTs) are key enzymes governing N-glycans biosynthesis36, we examined their expression patterns using a previously reported RNA-sequencing dataset4 covering 10 peripheral tissues and 4 brain regions overlapping with our study. While RNA and protein expression levels are not always tightly correlated, the RNA-seq data offer a complementary view of tissue-specific GT expression. As shown in Fig. 2f, these 209 GTs displayed distinct tissue-resolved expression patterns (Supplementary Data 4). Notably, the four brain regions exhibited a unique GT expression pattern, consistent with the specific distribution patterns of N-glycans observed in our study. As expected, the elevated levels of core III and core IV in the brain regions strongly correlated with high expression of the Mgat3 gene (Fig. 2g). Similarly, the high expression of Fut9 in the kidney and brain regions also aligned with our observation of elevated fucosylated N-glycans in these tissues (Fig. 2h). Collectively, these findings delineate a detailed and tissue-resolved map of the tissue-specific N-glycan patterns, highlighting the complex and diverse glycosylation in mouse tissues.

Subcellular localization constrains the conservation and diversification of N-glycosylation
To investigate the underlying determinants of glycosylation variability, we examined whether subcellular localization contributes to the diversity of site-specific glycans. Subcellular localization analyses based on UniProtKB database revealed that glycoproteins identified at the glycopeptide level were predominantly located in the endoplasmic reticulum (ER) (444), Golgi apparatus (311), plasma membrane (169), and etc (Supplementary Data 5 and Fig. 3a). Notably, distinct glycosylation patterns were observed across these compartments (Fig. 3b), highlighting compartment-specific regulation of N-glycan biosynthesis37. For example, glycoproteins in the ER were mainly decorated with oligo-mannose glycans, characterized by simple branch structures composed solely of hexose (Hex) and unbranched residues, consistent with early-stage glycan biosynthesis37. In terms of core structures, core I was most abundant in the ER. In contrast, glycoproteins associated with the extracellular matrix (ECM) and plasma membrane predominantly carried complex glycans featuring extensive branch structures and terminal capping such as sialylation and fucosylation, indicative of advanced processing in the Golgi apparatus (Fig. 3b). Consistently, core II was the dominant structure in the ECM and plasma membrane compartments. GO enrichment analysis further revealed that ER-localized glycoproteins performed conserved functions, including mediating protein maturation, quality control, and vesicular trafficking, while plasma membrane glycoproteins support more diverse roles regarding signal modulation, ion transport, and cell-type-specific processes-functions that may demand higher structural variability and contextual responsiveness (Fig. 3c).

To examine whether glycosylation conservation or diversity is linked to subcellular compartmentalization across tissues, we specifically compared the glycan profiles of ER- and plasma membrane-localized glycoproteins in multiple tissues and serum. Although tissue-specific differences were still evident, the overall compartment-based glycan feature remained highly conserved. Specifically, N-glycan maturation followed a consistent intracellular-to-extracellular trajectory, progressing from oligo-mannose to structurally complex glycans (Fig. 3d). To quantify the extent of conservation within compartments, we selected shared glycoproteins identified in ≥12 tissues, categorized them by subcellular location, and assessed inter-tissue glycan similarity. ER-localized glycoproteins exhibited the highest glycan similarity across tissues, reflecting conserved biosynthetic constraints. Conversely, ECM and plasma membrane glycoproteins showed significantly greater glycan heterogeneities, consistent with their specialized extracellular functions (Fig. 3e). Overall, the subcellular localization of glycoproteins shapes N-glycosylation patterns, with increasing structural complexity from the ER to the extracellular compartments.

Diverse glycosylation remodels common glycoproteins across tissues
Building on the observation that N-glycosylation patterns vary markedly across tissues, we next examined whether conserved glycoproteins undergo tissue-specific glycan remodeling to support functional specialization. Among the 62 glycoproteins ubiquitously detected, 24 were expressed in all tissues, while the other 38 were only absent from serum. Notably, 51 glycoproteins were exclusively detected in neural tissues, suggesting a conserved and specialized glycoproteomic signature within the nervous system (Supplementary Data 6 and Fig. 4a). To further explore how a common proteome is customized by glycosylation across tissues, we compared the N-glycan profiles of conserved glycoproteins across at least 12 arbitrary tissues. Despite the invariant protein backbones, the attached N-glycan structures varied markedly among tissues, revealing extensive tissue-specific glycan remodeling (Fig. 4b). To validate this observation, we repeated the analysis using a more stringently defined set of 24 universally expressed glycoproteins, reaffirming the reproducibility and biological relevance of these tissue-specific glycosylation profiles (Supplementary Fig. 7). It should be noted that the differences in glycan clustering between the two figures reflect variation in the underlying glycoprotein sets, rather than inconsistency in the observed trends. Remarkably, analyzing glycan variants on 24 shared glycoproteins yielded more robust and specific tissue signatures than global glycan profiling. For instance, a trisialylated N-glycan structure was highly abundant in the hippocampus but minimally present in the medulla, olfactory bulb, and hypothalamus, a regional distinction less evident in global glycan profiling, suggesting that differential glycosylation of commonly expressed proteins offers a more sensitive and precise readout of tissue-specific glycan features than total glycan analysis. To further investigate the basis of this remodeling, we categorized the commonly expressed glycoproteins by their subcellular localization and assessed glycan similarity within each category across tissues. Consistent with our earlier findings, glycoproteins localized to the ER exhibited the highest glycan similarity, while those at the cell surface and in the ECM displayed the lowest similarity. These observations suggest that glycosylation patterns may be influenced by compartment-specific biosynthetic environments and potentially reflect distinct functional roles associated with different subcellular localizations (Fig. 4c).

To further explore how N-glycosylation modulates protein function in a tissue-dependent manner, we performed site-specific glycosylation analysis on these commonly expressed glycoproteins. The mannose-6-phosphate receptor (M6pr), which exhibited the highest number of N-glycans on the common glycosite among the non-serum shared glycoproteins, served as a representative example. Glycosylation at Asn-84 of M6pr displayed remarkable heterogeneities across tissues, with distinct N-glycan structures found even at this single glycosite depending on tissue types (Fig. 4d). Similarly, Laminin subunit gamma-1 (Lamc1) exhibited tissue-dependent glycan heterogeneity at Asn-1221, reinforcing the notion that the same glycosite can undergo diverse remodeling across organs. In contrast, cell surface hyaluronidase (Cemip2) showed highly conserved glycosylation at Asn-914 across tissues, highlighting that some sites maintain stable glycan composition irrespective of tissue context (Supplementary Data 7 and Supplementary Fig. 8).

To probe the functional relevance of this site-specific glycan remodeling, we aligned glycan heterogeneities with known tissue-specific roles of the corresponding proteins. Although the amino acid sequences of these glycoproteins remain invariant, variations in their N-glycan structures likely modulate their biochemical properties and functional outputs in a tissue-dependent manner. As an example, M6pr is essential for lysosomal enzyme trafficking and is broadly expressed across tissues38. However, the glycosylation at the glycosite Asn-84 varied substantially among tissues. In the liver, M6pr predominantly carried complex glycans with extensive sialylation (Fig. 4e), a modification associated with prolonged protein half-life and enhanced endosomal recycling39. In contrast, in the seminal vesicle and pancreas, Asn-84 was mainly decorated with hybrid glycans, but sialylation was largely absent in the seminal vesicle (Fig. 4e). Collectively, tissue-specific remodeling of glycan structures on shared proteins exemplifies how post-translational modifications adapt core biological machinery to diverse physiological demands.

Site-specific N-glycosylation displays pronounced performance in tissue identity
Correlation and unsupervised clustering analyses demonstrated that glycopeptides outperform both glycoproteins and total glycans in distinguishing tissues, underscoring the superior discriminatory power of site-specific glycosylation profiling. Clustering heatmaps and principal component analysis (PCA) plots based on glycopeptide data revealed distinct tissue-specific patterns. Notably, all seven brain regions clustered tightly together, exhibiting glycosylation profiles markedly different from other tissues. Serum formed a distinct cluster, while anatomically adjacent organs grouped more closely, reflecting shared physiological functions. Conversely, organs with distinct roles, such as those within the female reproductive system, displayed greater divergence (Fig. 5a, b). Functional specialization was further reflected by the abundance of key glycoproteins in specific tissues (Supplementary Fig. 9). In the nervous system, the predominant glycoproteins included Slc1a2, Thy1, and Syp, all of which are critical for synaptic transmission, neuronal signaling, and synaptic integrity40,41. In contrast, Muc2 was highly expressed in the intestine and colon, consistent with its role in mucus layer formation, gut homeostasis, and pathogen defense42 (Supplementary Data 8 and Fig. 5c).

Beyond individual glycoproteins, distinct tissue-specific glycan compositions and structures were observed across 20 and 22 tissues, respectively. These abundant glycan features served as molecular signatures that robustly distinguish tissues and serum. Brain regions, for instance, exhibited unique fucosylated glycoforms mostly containing multiple Neu5Ac residues, including N5H6F1S6 in the cerebellum, N4H6F2S3 in the cortex, N4H5F1S6 in the hippocampus, N4H7F3S2 in the hypothalamus, N8H6F3S3 in the medulla, N4H7F3S1 in the olfactory bulb, and N6H7F5S2G1 in the white matter. In contrast, serum displayed highly specific glycan compositions such as N5H5G4, N5H7G2, and N5H8G1, whereas the gallbladder and ovary lacked unique glycan features, likely due to their lower identification coverage (Fig. 5d). Importantly, glycan structural information provided greater resolution for tissue discrimination than compositional data alone. For example, although the lung and seminal vesicle could not be clearly distinguished from other tissues based on glycan compositions, they exhibited distinct structural isomers, highlighting the importance of glycan isomerism in tissue-specific differentiation (Supplementary Data 9 and Fig. 5e). These isomeric forms were further confirmed by their characteristic B/Y ions in MS2 spectra (Supplementary Fig. 10). Together, these findings highlight the role of site-specific N-glycosylation patterns and glycan structural isomers as molecular signatures driving tissue identity.

Tissue-enriched glycans may perform specialized functions
To investigate the tissue-specific distribution and functional roles of sialylation and fucosylation, which usually play essential roles in mammalian development39, we systematically quantified the number of fucose and sialic acid residues on N-glycans across 24 mouse tissues. Fucosylated N-glycans were ubiquitous across all tissues, with particularly high enrichment in the brain regions (average 75%), kidney (76.7%), and seminal vesicle (85.4%) (Fig. 6a). In contrast, sialylated glycans exhibited a more heterogeneous distribution. Although both the kidney and seminal vesicle displayed elevated fucosylation, the seminal vesicle showed relatively low sialylation (5.4%). Notably, the composition of sialic acid varied by tissues, with Neu5Gc demonstrating greater tissue specificity than Neu5Ac. Neu5Gc was virtually absent from neural tissue but abundant in serum and liver (Fig. 6b, c). The stomach exhibited the most extensive fucosylation per glycan, with up to nine fucose residues per structure, while the cerebellum, hippocampus, uterus, and liver showed the highest levels of sialylation per glycan, each with distinct Neu5Ac/Neu5Gc profiles (Fig. 6d). Across all tissues, the average Neu5Gc-to-Neu5Ac ratio was 1.7, but extreme values were observed in serum (17.6), liver (4.7), ovary (3.4), and neural tissues (0.03–0.13) (Fig. 6e). These observations reflect tissue-specific glycan structures and reaffirm that the murine brain N-glycome is dominated by Neu5Ac with only trace Neu5Gc, while serum is rich in Neu5Gc6. In addition, we found that 0.7%–22% of N-glycans per tissue carried both Neu5Ac and Neu5Gc, with the highest co-occurrence in heart (22%), muscle (10.7%), testis (10.3%), lung (9.9%), and liver (9.7%) (Supplementary Fig. 11a, b).

Ploy-sialic acid (ploySia) glycans, particularly those modified by Neu5Ac, were highly enriched in all seven brain regions, with considerable structural variations in branches (Supplementary Fig. 11c). The hippocampus showed robust expression of polySia, including disialylated and trisialylated forms. Gene Ontology (GO) analysis indicated that glycoproteins modified by trisialylated glycans were predominantly localized to synapses, presynaptic membrane, and axon, implicating them in cell adhesion, transmembrane transport, and trans-synaptic signaling. Disialylated glycoproteins, also abundant in the hippocampus, were similarly involved in transmembrane transporter and cell adhesion, and were additionally enriched for functions related to cognition, learning, and memory (Fig. 6f). Interestingly, sialylated LacdiNAc glycans clustered more closely with ploySia structures than with other LacdiNAc glycans categorized by branch features (Fig. 2e). These sialylated LacdiNAc structures showed highest abundance in the olfactory bulb and were associated with neuron differentiation and synapse organization, with a subcellular localization pattern similar to that of polySia glycans (Supplementary Fig. 11d).

Tissue-specific enrichment was also observed for Lewis epitopes. Lewisx/a was abundant in the kidney, while Lewisy/b was preferentially expressed in the seminal vesicle (Fig. 2e and Supplementary Fig. 11e). GO enrichment analysis revealed that Lewisx/a modified glycoproteins in the kidney localized to vesicles, extracellular region, and lysosome, and were functionally linked to peptidase activity, protein maturation, reproduction, and lysosomal transport. In parallel, Lewisy/b modified glycoproteins in the seminal vesicle shared similar localization patterns and were associated with protein binding and signaling receptor binding, with significant implications for reproduction, response to stimulus, and female pregnancy (Fig. 6g). Notably, sialylated Lewis epitopes with Neu5Ac or Neu5Gc preferentially clustered with glycans carrying the same type of sialic acid (Fig. 2e).

LacdiNAc glycans were most enriched in the kidney, colon, stomach, and intestine. Among these, the intestine had the highest abundance levels of LacdiNAc structure, whereas the kidney exhibited the most abundant fucosylated LacdiNAc (Supplementary Fig. 11f). In the intestine, LacdiNAc-modified glycoproteins were primarily localized to the ECM, cell surface, and plasma membrane, with functions related to cell adhesion, cell development, and differentiation. In the kidney, fucosylated LacdiNAc glycoproteins were enriched at the cell surface and in the laminin complex, where they were linked to peptidase activities, cell adhesion, and organ development (Fig. 6h). These results indicate that specialized glycan structures are preferentially attached to functionally relevant glycoproteins in specific tissues, suggesting a coordinated mechanism by which glycan modifications fine-tune protein function to meet localized physiological demands.

Site-specific N-glycans show non random microheterogeneities
To investigate prevalent patterns of intact N-glycans at specific glycosites, we performed a systematic analysis of co-occurrence possibility across 24 mouse tissues. Specifically, to ensure data robustness, we used stringent filtering criteria, including glycosites with at least 10 distinct glycan structures, with each glycan detected a minimum of 10 times per tissue. Glycan pairs were further filtered by requiring a co-occurrence probability ≥ 0.5 at the same glycosite. Based on these criteria, we systematically examined 24 mouse tissues, focusing on six predominant co-occurrence patterns as well as other combinations, including (i) isomeric glycans; (ii) glycans differing by one or more sialic acid residues; (iii) glycans differing by one or more fucose residues; (iv) glycans differing by one or more mannose/galactose residues; (v) glycans differing by both fucose and sialic acid residues; and (vi) glycans differing by a mannose or galactose residue with additional random variations in fucose and sialic acid content. Among these patterns, pattern (ii)-glycans differing by sialic acid content-was particularly common in several tissues. In muscle, for example, 63.3% of co-occurring glycan pairs differed in sialic acid residues, as did the gallbladder (53.3%). Pattern (iii), involving differences in one or more fucose residues, was notable in cerebellum (29.1%) and seminal vesicle (26.7%) (Fig. 7a). These findings suggest that the site-specific glycan microheterogeneities may often arise from dynamic additions or removals of terminal sialic acids and fucose, rather than from divergent biosynthetic pathways.

To further illustrate the pattern of dynamic additions or removals of terminal sialic acids and fucose, we examined tissues enriched in sialylated, fucosylated, or mannose glycans. For each tissue, we identified the most abundant glycan structure and calculated its conditional co-occurrence probabilities with others. In serum, the most frequently observed glycan was a biantennary structure carrying two terminal sialic acid residues (HexNAc4Hex5Fuc1NeuAc2, N4H5F1S1). Notably, when this structure was detected at a glycosite, up to 148 other biantennary structures containing one sialic acid were concurrently identified at the same site, with a co-occurrence probability of 98.7%. Additionally, 88 asialylated biantennary structures were also frequently present, with a co-occurrence probability of 97.8%. Furthermore, a total of 89 pairs of monosialylated and asialylated biantennary glycans co-occurred at individual sites with a high probability of 98.9% (Fig. 7b). To assess the generality and tissue specificity of this co-occurrence pattern, we extended our analysis to the remaining 23 mouse tissues. These triplets (di-, mono-, and asialylated glycans) were reproducibly identified across tissues, such as liver, heart, ovary, and muscle (Supplementary Fig. 12a). Interestingly, while both serum and liver exhibited these patterns, their dominant glycan structures differed. In serum, disialylated and monosialylated biantennary glycans predominated, whereas asialylated counterparts were more abundant in liver. This divergence may reflect tissue-specific glycoprotein turnover requirements: sialylation is known to prolong the circulatory half-life of glycoproteins43, while the absence of terminal sialic acids facilitates rapid clearance and renewal of proteins in the liver, thereby supporting its metabolic functions.

In addition to the sialylated biantennary glycans, similar co-occurrence patterns were also observed for mannosylated and fucosylated glycans in tissue-specific contexts. In both the small intestine and testis, numerous glycosites exhibited the simultaneous presence of oligo-mannose differing by only one or a few mannose residues. For example, Man7, Man8, and Man9 frequently co-occurred at the same sites in the small intestine, often appearing in pairs (Supplementary Fig. 12b). Likewise, in the seminal vesicle and kidney, fucosylated glycans displayed a similar trend: glycans differing by one or more fucose residues frequently coexisted at individual sites (Supplementary Fig. 12c). Notably, a distinct pattern was observed in the mouse brain, where all seven brain regions predominantly expressed bisected complex glycans modified with core fucose (core IV). However, unlike other tissues where co-occurring glycans typically varied by single monosaccharide units, glycosites in the mouse brain exhibited more heterogeneous and structurally diverse co-occurrence profiles, which further supports our earlier observation that the nervous system featured the greatest structural diversity. Specifically, in the white matter, the three most frequently coexisting glycans with the core-fucosylated bisecting glycans represented distinct glycan types—complex, hybrid, and oligo-mannose—each characterized by short and simple configurations (Fig. 7b and Supplementary Fig. 12d). These results underscore the unique glycosylation landscape of the brain, where co-occurrence patterns reflect a broader range of biosynthetic pathways and structural motifs than those observed in other tissues. Overall, our findings provide a detailed perspective on the structural micro-heterogeneities of N-glycans, offering further insights into the dynamic nature of glycosylation across tissues, which has not been systematically addressed in previous studies.

Co-occurrence preferences across modular structural features of N-glycans
We further explored the interplay between the core and branch structure of N-glycan through calculating the probability of specific branch structures co-occurring with particular core structures, as well as the likelihood of co-occurrence between different branch structures. Core I showed a strong preference for mannose (Hex), consistent with oligo-mannose characteristics, while core II exhibited a notable preference for LacNAc (HexNAc + Hex). Core III and core IV, both of which represent bisected core structures, tend to associate with branch structures lacking sialic acid or fucose capping33 (Fig. 7c (i) and Supplementary Fig. 13a). Branch–branch associations revealed more intricate patterns. Branches with more complex structures (on the right side of the network) had a higher probability of co-occurring with either a single GlcNAc or LacNAc. Additionally, ploySia-containing branches frequently co-occurred with Lewisx/a structures, while fucosylated branches tended to cluster together. Sialylation on one branch increased the likelihood of sialylation on the opposite branch, with Neu5Ac and Neu5Gc modifications preferentially pairing with their respective types (Fig. 7c (ii) and Supplementary Fig. 13b). Further analysis of branch-to-core structure associations uncovered additional insights. Branch types, such as the unbranched structure (labeled as Ø), Hex, and Neu5Gc-modified structures, showed a stronger preference to core I, whereas LacNAc, Neu5Ac-modified branches, and fucosylated branch structures preferentially attach to core II. The low-abundance bisected N-glycan structures, core III and core IV, exhibited minimal branch association, except with simple structures like GlcNAc. This might be due to the inhibitory effect of bisected glycans on key branching GTs (GnTII, GnTIV, and GnTV) and fucosyltransferase (FUT8), limiting N-glycan branching and elongation33 (Fig. 7c (iii) and Supplementary Fig. 13c). Together, these findings reveal that substructural co-occurrence patterns are shaped not only by terminal modifications of N-glycans but also by intrinsic constraints between core and branch elements, contributing to the observed glycan microheterogeneity across tissues.

Methods
Tissue preparation
Ten adult C57BL/6N mice (8–9 weeks old; five females and five males) were obtained from Huachuang Xinnuo Pharmaceutical Technology Co. (Jiangsu, China). The mice were anesthetized using avertin and sacrificed following perfusion with 0.9% NaCl. A total of 24 tissues, including the white matter, hippocampus, olfactory bulb, medulla oblongata, hypothalamus, cerebellum, cortex, seminal vesicle, kidney, colon, intestine, testis, bladder, lung, ovary, uterus, pancreas, heart, liver, spleen, stomach, muscle, gallbladder, and serum, were collected for glycoproteomics analysis. All tissues were flash-frozen in liquid nitrogen and stored at −80°C until protein extraction.

Protein extraction and trypsin digestion
Each tissue was cut into around 1 mm2 fragments and washed three times with precooled phosphate-buffered saline (PBS, pH 7.4). The fragments were then homogenized in 0.5 ml of 8M urea (cat. no. U5128, Sigma, Germany)/1M NH4HCO3 (cat. no. 11213, Sigma, Germany) at 4°C using a high-throughput tissue grinding machine (Shanghai Jingxin, China). The crude extract was briefly sonicated until the solution was clear. Lysates were clarified by centrifugation at 15,000 × g for 15 min at room temperature (RT). Protein concentrations were determined by BCA protein assay reagent (cat. no. P0012, Beyotime, China). All proteins from mouse tissue were reduced by 5mM dithiothreitol (DTT) (cat. no. 43819, Sigma, Germany) at 37°C for 1 h, and alkylated by 15mM iodoacetamide (IAM) (cat. no. I6125, Sigma, Germany) at RT in the dark for 30 min. Another 2.5mM DTT was then added and incubated at RT for 10 min. The solutions were diluted two times with deionized water prior to the first cycle of protein digestion by incubating the samples with the sequencing grade trypsin (cat. no. V5113, Promega, USA, WI; proteins: enzyme, 100:1, w/w) at 37°C for 2 h with shaking. The solutions were further diluted four-fold with deionized water, and the same relative amount of trypsin (proteins: enzyme, 100:1, w/w) was then added and incubated at 37°C overnight with shaking. The pH of the solution was adjusted with 10% trifluoroacetic acid (TFA) (cat. no. 3020031, Sigma, Germany) till pH < 2. The samples were centrifuged at 15,000 × g for 10 min to remove any particulate matter, and the peptides in the supernatants were desalted by the C18 column (cat. no. WAT054955, Waters, USA). Peptides were eluted from the C18 column in 50% acetonitrile (ACN) (cat. no. A9554, Thermo Fisher Scientific, USA)/0.1% TFA solution and the peptide concentrations were measured by the UV absorbance at 215 nm using a DS-11 spectrophotometer (DeNovix, USA).

Intact glycopeptide enrichment
The intact glycopeptides were enriched from 24 tissues using two enrichment methods: Oasis mixed anion-exchange (MAX) SPE column59 (cat. no. 186000366, Waters, USA), and in-house HILIC micro-column (HILIC beads spec: 5μm, 100Å, cat. no. P19-00385, Agela Technologies, China). Briefly, the tryptic peptides from mouse tissue were purified by C18 and were enriched in 50% ACN/0.1% TFA. The peptide solution (~6 mg peptides) was diluted by 100% ACN and 50% TFA to a final solvent composition of 80% ACN/0.2% TFA. The HILIC micro-column was washed three times each with 0.1% TFA and 80% ACN/0.2% TFA, the peptide sample and beads were mixed and incubated at RT for 2 h, followed by three washes with 80% ACN/0.2% TFA. The glycopeptides bound to the beads were eluted in 0.4 mL of 0.1% TFA solution. About 20 μg glycopeptides were used for LC-MS/MS analysis, and the remaining glycopeptides were used for high-pH HPLC fractionation. The flow-through of the HILIC micro-column was loaded onto an anion exchange reversed phase (MAX) column to maximize the glycopeptide enrichment. Briefly, the flow through from the HILIC micro-column was diluted by 100% ACN and 50% TFA to the final concentration of 95% ACN/1% TFA. The MAX column was washed three times each by 100% ACN, 100mM triethylammonium acetate buffer, deionized water, and 95% ACN/1% TFA, followed by sample loading twice and washes four times with 95% ACN/1% TFA. The glycopeptides bound to the column were eluted in 400 μL of 50% ACN/0.1% formic acid (FA). The sample was dried by RVC 2–18 CDplus concentrator (Christ, Osterode am Harz, Germany) and resuspended in 50 μL of 0.1% FA.

High-pH HPLC fractionation
HILIC-enriched intact glycopeptides from 22 mouse tissues were separately injected via a 1260 series HPLC (Agilent Technologies, USA) into a Zorbax Extend-C18 analytical column containing 1.8 μm particles at a flow rate of 0.2 mL/min. The mobile-phase A consisted of 10mM ammonium formate (pH 10), and B composed of 10mM ammonium formate and 90% ACN (pH 10). Sample separation was accomplished using the following linear gradient: 0–2% B for 10 min, 2–8% B for 5 min, 8–35% B for 85 min, 35–95% B for 5 min, 95–95% B for 15 min. Peptides were detected at 215 nm, and 96 fractions were collected along with the LC separation in a time-based mode from 16 to 112 min. The 96 fractions of one tissue were concatenated into six fractions. The samples were then dried in a speed-vacuum and stored at −80°C until LC-MS/MS analysis. This high-pH reversed-phase HPLC fractionation effectively reduces sample complexity and enhances glycopeptide identification depth in downstream MS analysis.

LC-MS/MS analysis
The enriched glycopeptides were separated on an Orbitrap Fusion Lumos Mass Spectrometer equipped with an Easy-nLCTM 1200 system (Thermo Scientific, San Jose, CA, USA). Peptide separation was performed on a 75 μm × 50 cm Acclaim PepMap 100 C18 column (cat. no. 164570, Thermo Fisher Scientific, USA) with a 75 μm × 2 cm pre-column (cat. no. 164946, Thermo Fisher Scientific, USA). The flow rate was kept at 200 nL/min using mobile phase A (0.1% FA in water) and mobile phase B (0.1% FA in 80% ACN). The gradient profile (90 min) was set as follows: 3–25% B for 65 min, 25–40% B for 4 min, 40–99% B for 3 min, 99% B for 18 min. The spray voltage was set at 2.4 kV. Orbitrap MS1 spectra (AGC 4.0 × 105) were collected from 375 to 2000 m/z at a resolution of 60K followed by oxonium ions triggered data-dependent higher-energy collision dissociation (HCD) MS/MS at a resolution of 15K using an isolation width of 0.7 m/z for 20% collision energy and 2 m/z for 30% collision energy. Charge state screening was enabled to reject unassigned and singly charged ions. A dynamic exclusion time of 20 s was used to discriminate against previously selected ions.

Intact glycopeptides identification by StrucGP
Intact glycopeptides were identified using the StrucGP software21. Briefly, the intact glycopeptide analyses were performed using the built-in glycan branch structure database (containing 17 branch structures) from StrucGP and the Mus musculus UniProt protein database (UP000000589, downloaded on 18 Oct. 2023) for the analysis of 24 tissues. The protein enzymatic digestion was set as trypsin with max 2 missing cleavage sites, and the potential glycosite-containing peptides were screened with the N-X-S/T motif (X is any amino acid except Proline). Mass tolerances were set to 10 ppm for precursor ions and 20 ppm for fragment ions. For the Y ions determination, an optional mass shift of ±1 Da or ±2 Da was allowed in addition to the 20 ppm mass tolerance in MS2. Finally, 1% FDR was set for both peptides and glycans was applied to the quality control of the intact glycopeptide identifications.

Inter-tissue glycan similarity
In the analysis of glycan similarity within GOCC terms, we focused on evaluating the overall overlapping glycan distribution across tissues for all proteins associated with each term. Given that an individual protein may harbor distinct types and numbers of glycans in different tissues, we employed a modified Jaccard similarity metric to comprehensively assess inter-tissue glycan composition. Specifically, for each GOCC term, we first enumerated all relevant glycans identified on its constituent proteins across tissues, then calculated the total number of occurrences of the intersecting glycans in all tissues. This value was compared against the total number of all glycans observed, thereby capturing both the overall consistency and richness of glycan distribution among tissues. By fully considering the summed occurrences of shared glycans across tissues, this approach more accurately reflects the commonality of glycan profiles and aligns closely with research objectives.

For the analysis of glycan structural similarity at individual glycosylation sites on single glycoproteins, our primary interest was the degree of overlap in glycan structural types observed at a given site across tissues. In this context, the frequency of occurrence of each glycan structure was not central to our analysis, and we therefore employed the conventional Jaccard similarity metric. Specifically, for each glycosylation site, we determined the set of glycan structures present across all tissues and compared the overlapping glycan types to the total number observed. This method effectively measures the consistency in glycan structural composition at specific sites across tissues, providing an intuitive assessment of conservation and diversity at the molecular level.

Data quality control and confidence framework for glycan classification
To ensure the reliability and consistency of the glycoproteomic data across all 24 mouse tissues, we performed an internal quality assessment by evaluating the correlation of glycan structures and glycosylation site profiles between technical replicates. Specifically, for each tissue, we selected two representative data frames, either from the same fraction or from MAX, and calculated Spearman correlation coefficients for (i) the presence of unique glycan structures and (ii) the distribution of glycosylation sites. This correlation-based filtering enabled us to confirm the reproducibility of glycan and site-level profiling within each tissue sample.

To further evaluate the reliability of glycan structural assignments, we established a data-driven confidence framework that integrates both biological plausibility and statistical evidence. The framework was implemented as a supervised learning-based system calibrated with manually verified spectra. A total of 472 N-glycan structures were manually annotated in 774 spectra (1–3 spectra per glycan) to construct a gold-standard dataset. These structures were selected to provide a representative coverage of the full spectrum of glycan confidence levels encountered in our scoring framework, ensuring the inclusion of both clearly interpretable and potentially ambiguous cases. Among them, 332 glycans were classified as structurally reasonable and 140 as structurally ambiguous, providing a balanced set of positive and negative examples for model training. Five quantitative indicators were used as model features: Highest_GlycanScore, GlyTouCan match, GlycanType probability, all branches probability, and pathway status. Biosynthetic constraints were applied to filter implausible N-glycan compositions, excluding compositions with N < 2 or N > 8, H < 3 or H > 9, S > 4, F > 3 (particularly F > 4, which rarely conforms to canonical pathways), structures with disproportionately high sialylation relative to N/H (e.g., N2H3S5), and G > 2. For oligo-mannose type glycans, only N2H5 to N2H9 without fucosylation, sialylation, or Neu5Gc modification were retained as biosynthetically plausible. GlycanScore and the probability of GlycanType and all branches, representing the degree of MS/MS spectral matching, was calculated as previously described21. Although GlycanScore was only one of the features incorporated into the integrative model, higher GlycanScores generally corresponded to spectra with more complete and higher-quality Y-ion series and were therefore qualitatively associated with higher structural confidence. A logistic regression classifier was trained to predict structural validity, using five-fold cross-validation (5-fold CV) combined with grid search (GridSearchCV) for hyperparameter optimization. The final configuration was: LogisticRegression(max_iter=100, C=1, class_weight=“balanced”, penalty=“l1”, solver=“saga”). Model coefficients, which indicated the relative contribution of each feature to confidence estimation, were listed as the following order: pathway (1.25) > all branches probability (0.49) > GlyTouCan match (0.45) > Highest_GlycanScore (0.41) > GlycanType probability (0.03). The output probabilities were calibrated using isotonic regression to ensure that model-derived probabilities could be directly interpreted as empirical confidence levels. Confidence categories were defined by scanning calibrated probability outputs and evaluating positive and negative predictive values at different thresholds.

Glycan substructural co-occurrence probability and structural network construction
To investigate structural preferences and co-occurrence patterns among core structures and branch structures, we constructed a substructural-level interaction analysis. First, all unique glycan structures identified across the 24 tissues were decomposed into their constituent motifs. For each glycan, we enumerated motif pairs of three specific types: core–branch, branch–branch, and branch–core. We then quantified the frequency of each motif pair across all glycans and tissues, yielding a global co-occurrence probability matrix. The resulting matrix was visualized as a structural interaction network using NetworkX60,61, enabling intuitive interpretation of motif pairing tendencies and connectivity.

Glycan-glycan co-occurrence and transition pattern analysis at the glycosite level
At the glycosite level, we analyzed the co-occurrence of glycan structures to assess potential structural transitions and substitution patterns. For this, we restricted the analysis to unique glycosylation sites with a minimum of 10 glycan observations to ensure statistical robustness. For each qualifying glycosite, we counted the number of times each pair of glycan structures co-occurred, resulting in a glycan co-occurrence matrix. A co-occurrence probability matrix was then derived by normalizing the pairwise frequencies against the total occurrence count of the reference glycan within the same site, thereby accounting for asymmetric occurrence likelihoods.

To further dissect potential structural transitions, glycan pairs that satisfied both a minimum co-occurrence count of ≥ 11 and a co-occurrence probability ≥ 0.5 were selected for comparative classification. Each glycan pair was categorized into one of seven transition types based on their compositional differences in Hexose (H), Fucose (F), Sialic acid (S), and N-acetylglucosamine (G): (i) isoform: isomeric glycans; (ii) G/S: identical H and F counts; differ only in G or S; (iii) F: identical H, G, and S counts; differ only in F; (iv) only H: differ only in H; identical G, S, and F counts; (v) F and GS: identical H; differ in both F and either G or S; (vi) H and GSF: differences in H and at least one of G, S, or F; (vii) others: all other unclassified variations. This detailed classification system provided insight into the potential biosynthetic flexibility and microheterogeneity at the glycosite level.

Tissue-specific expression profiling of glycosyltransferases. To investigate tissue-specific expression patterns of GTs, 728 mouse GT entries were first retrieved from the UniProt database. After removing entries with redundant gene names, a nonredundant list of 384 GT genes was obtained. These were then matched to the transcriptomic dataset from ArrayExpress (E-MTAB-10276)4, resulting in 209 GTs with available expression data. Gene expression values for these 209 GTs were visualized using a heatmap to illustrate tissue-specific expression patterns.

Other bioinformatic analysis
GO enrichment analysis was conducted with the database for annotation, visualization, and integrated discovery (DAVID) (david.ncifcrf.gov) to identify enriched biological process, cellular component, and molecular function62. Motif was plotted by bioinformatics.com.cn (last accessed on June 20th, 2024), an online platform for data analysis and visualization63. Heatmap generation was performed using the seaborn library64 from Python, where hierarchical clustering was applied to both rows and columns to reveal global similarity patterns. The linkage method used for dendrogram construction was Ward’s minimum variance method65, which minimizes the total within-cluster variance. Distance metrics were based on Euclidean distance. For dimensionality reduction, we applied PCA implemented in the sklearn.decomposition66 module. All scatter plots, including PCA projections and correlation plots, were generated using matplotlib67. Upset plots were performed using the UpSetRR package (v1.4).

Ethical statement
All animal experiments were conducted in accordance with relevant ethical regulations and guidelines. The experimental protocols were reviewed and approved by the Ethics Committee of Northwest University, China (approval number: NWU-AWC-20241206M).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript by Wu et al. provides an extensive high-resolution N glycoproteomic comparative atlas for 24 mouse tissues. It reports the detection of distinct 3,045 N-glycan structures that are attached to 8,681 glycopeptide sites, resulting in the cumulative compilation of 74,277 glycopeptides and 5,026 glycoproteins. The overall scope and breadth of this data set is unprecedented for any mammalian species.\nIn Figures 1-3, and 6, the authors provide the broad comparisons of the glycan and glycopeptide species present in the 24 tissues. In Figures 4 and 5, comparative glycopeptide and glycosite analysis is provided. The demonstration of the different glycans that are detected specifically at Asn-84 in the mannose-6-phosphate receptor glycoprotein in all tissues most effectively illustrates the types of unprecedented analyses that can be done with this type of data set. Figure 7 describes co- occurrence network analysis of glycans present in each tissues. Supplemental data figures, comparative tables and glycopeptide data sets for each tissue are provided at an appropriate level.\nThe technical and experimental approaches used are established, and have been used extensively by the authors in prior studies. Because of the scope of the project, some experimental and study design compromises were necessary. The authors provide their rationale for these decisions, which are reasonable.\nThe novelty and unprecedented amounts and types of glycoproteomic data generated are impressive, and overall well summarized. There are some concerns, clarification questions and suggestions for the authors to consider, as follows.\n- Including the data summary of what different search algorithms generated is important, but more details for how the different glycopeptide search algorithm comparisons were done would be helpful. Is it known why each algorithm identified unique glycopeptides relative to each other?\n- There does not appear to be any sulfated or phosphorylated N-glycans glycopeptides reported by any algorithm. Is the reason for this known?\n- In the Discussion, limitations, mentioning the differences in mouse relative to human (like NeuGc, alpha-Gal) could be helpful. Most mouse models do not replicate human disease in many aspects, and it has been known for a long time in the glycobiology community that the glycan differences are major factors.\n- Demonstrating that the same glycoprotein has different glycans dependent on organ location is powerful, and highlights the type of comparisons that could only be done after generating the data. The authors should consider highlighting more examples of this, like glycoproteins detected in at least half of the tissues, in a supplementary table perhaps? Are there examples that are essentially invariant for site N-glycan composition across the tissues?\n- In several comparisons, it would be helpful to include some additional summary tables (supplemental) of the specific identities of the shared glycopeptides. A list of the top 20 glycoproteins for each organ, for example. In particular a list of the protein identities of the 62 ubiquitous glycoproteins detected, the 24 expressed in all tissues and the 38 in all samples except serum. And the 51 proteins detected only in neuronal tissues.\n- Data for the brain and kidney were highlighted throughout as they have quite distinct N-glycomes relative to other organs, something that many other groups have noted in previous studies. While it is not possible or relevant to cite every previous study, there are a few suggestions below for the authors to consider addressing, in the context of data comparison for glycopeptides and spatial glycomics (just for mouse brain and kidney). Co-occurrence of N-glycans has been reported in multiple mouse tissues using spatial glycomic mass spectrometry imaging methods.\n- Spatial Glycomic references:\nGustafsson OJ, et al. MALDI imaging mass spectrometry of N‐linked glycans on formalin‐fixed paraffin‐embedded murine kidney. Anal Bioanal Chem. 2015;407(8):2127‐2139.\nClarke HA, et al. Spatial mapping of the brain metabolome lipidome and glycome. Nat Commun. 2025 May 12;16(1):4373. doi: 10.1038/s41467-025-59487-7.\n- Murine brain and kidney glycopeptide studies:\nKawade H, et al. Bisecting GlcNAc modification of angiotensin-related glycoproteins in mouse kidney. J Biochem. 2025 Jun 13:mvaf033.\nBradberry MM, et al. N-glycoproteomics of brain synapses and synaptic vesicles. Cell Rep. 2023 Apr 25;42(4):112368.\nMinor comments:\n- The NeuGc and NeuAc comparisonsin Figure 5 are very useful. Were any glycan species with both NeuAc and NeuGc on the same glycopeptide detected.\n- Were male versus female comparisons possible for some of the most abundant tissue types?\n- Can a mouse specific glycan list be provided (as compared to humans for example)?\n- Ref 7 and 43 are the same paper\n- Table S4, cite the source of the RNA seq data (it was cited in the main text)\n- Check again for misspelled words and a few occurrences of grammatical errors", "reviewer_2": "Overall evaluation\nThe manuscript reports the identification of 8,681 glycosylation sites across 5,026 glycoproteins and 3,045 glycans, representing an impressive scale of coverage. It also attempts to provide detailed structural information beyond glycan composition—such as core structures, bisecting GlcNAc, and terminal branched motifs—using the previously developed StrucGP tool. These structural features are indeed challenging to resolve using conventional methods.\nHowever, several major concerns remain regarding the accuracy and confidence of glycan assignments. First, multiple glycan structures reported in the dataset appear inconsistent with known biosynthetic pathways, raising serious doubts about their validity. In some cases, the MS/MS spectra are unlikely to support the assigned glycan compositions, suggesting potential misassignments during interpretation.\nSecond, although the manuscript emphasizes resolution of glycan isomers, it lacks methodological detail on how such isomeric differentiation was achieved and validated. Given the inherent difficulty of resolving structural isomers in glycoproteomics, this is a critical point that must be clearly addressed.\nTo ensure the structural reliability of glycan annotations, we strongly encourage the authors to complement the de novo interpretation tool with rigorous validation strategies. These may include manual MS/MS spectrum interpretation, cross-database comparison, assessment of reproducibility across samples or datasets, evaluation using alternative computational tools, and orthogonal structural characterization following purification and analysis by chemical or enzymatic methods where applicable. Particular attention should be paid to biosynthetic plausibility, and the manuscript should clearly explain the logic and confidence criteria used for isomer discrimination. Without such supporting evidence, novel glycan assignments risk misinterpretation or overstatement.\nAt present, the manuscript falls short of the standards required for publication. Substantial revisions and validation are needed before it can be considered further.\nMajor comments\nQ1. Glycan classification is inconsistent with biosynthetic pathways.\n*N2H4F1 is categorized as high-mannose, but typically, fucose does not occur in high-mannose glycans. This composition more likely represents a hybrid structure with core fucosylation.\n*Similarly, structures like N2H9F1 and N2H8F1, which include fucose within high-mannose-type glycans, have also been identified; however, from a biosynthetic perspective, such structures are unlikely to be produced. Potential misassignments in MS/MS spectral interpretations were found, please see Q3 for details.\n*Structures like N3H8 also frequently appear in the identification table. However, biosynthetically, glycans with three HexNAc residues should no longer be considered high-mannose-type; the pathway toward hybrid or complex types requires mannose trimming down to N2H5 before HexNAc addition, making N3H8 unlikely to form. Potential misassignments in MS/MS spectral interpretations were found, please see Q3 for details.\nQ2. Potential misassignments in MS/MS spectral interpretations\n*Spectra #25304 and #25306 (sample: mouse_pancreas_IGP_hilic_50cm_4h_20200122_1, peptide: FANNTSVEPQK+N2H9F1)\nAlthough the core peptide sequence FANNTSVEPQK appears to be correctly assigned at first glance, the Y-ion signals in these spectra only extend up to the fragment corresponding to the peptide with N3H6F1 attached (Pep+N3H6F1). There are no fragment ions supporting the presence of the larger assigned glycan (N2H9F1). Moreover, this peptide contains two potential N-glycosylation sites. Additionally, the spectra show a signal at m/z 284.044, which corresponds to a HexNAc residue modified with a sulfate group. This observation suggests that, instead of the originally assigned glycan, an alternative composition—such as N3H2F1(Sul)1 + N2H3—may provide a better explanation for the data.\n*Spectra #11204 and #11206 (sample: mouse_female_kidney_mix_IGP_hilic_4h_50cm_1, peptide: FM[Oxidation]KELSDDSFENSNK+N3H8)\nIn spectra #11204 and #11206, the peptide was assigned as FM[Oxidation]KELSDDSFENSNK with an N3H8 glycan. However, there is only a single b-ion supporting this peptide, which is insufficient evidence to confidently confirm the identification. Meanwhile, the spectra contain numerous b-ions matching a different peptide, SHTNTSHVMQYGNK. This suggests that the original peptide assignment is likely incorrect. It seems more reasonable that the spectra actually come from SHTNTSHVMQYGNK with an N4H8, which has two possible sites for glycan attachment.\nQ3. In the authors’ 2021 publication describing the development of the StrucGP software, they reported identifications from both standard glycoproteins and approximately 600 distinct glycans detected in mouse brain samples. In contrast, the current manuscript reports around 3,000 glycans identified across multiple mouse tissues. Given this substantial increase in the number of identified glycans and their structural complexity, I would like the authors to clarify in detail how they ensured the accuracy of glycan assignments.\nSpecifically, how do the authors address glycan isomers that occur on the same glycosylation site and peptide? In Figure S4B, the N5H5F1S1 isomers are shown to be present on different proteins, and the observed retention time differences are likely attributable primarily to differences in the peptide sequences. Therefore, this data alone does not sufficiently support the claim that retention time is effective for separating glycan structural isomers.\nQ4. For N-glycans with more than four antennas, how do the authors determine whether the structure is a bisecting or non- bisecting isomer? In addition, how the authors determine the number of branches in the glycan backbone? For example, in Supplementary Figure S5, the authors propose a hexa-antennary structure instead of a bisecting penta-antennary.\nPlease provide paired MS/MS spectra of isomeric structures—one with bisecting GlcNAc and one without—to illustrate the diagnostic fragment ions and explain the logic of how you distinguished these two possibilities.\nMinor comments\nQ5. line 102, page 5\n\"This disparity is likely arising from the absence of fractionation in the gallbladder samples, highlighting the critical role of sample fractionation in enhancing the depth of analysis for complex tissues.\"\nIf the authors wish to demonstrate the effect of fractionation, comparative data from the same tissue with and without fractionation should be included.\nQ6. Line 106, page 5\nWhile the general statement that \"the number of identifications can vary between tissues even under the same MS conditions\" is relevant, the referenced study by Giansanti (Ref. 27) does not directly provide data on glycoproteins or glycopeptides. Therefore, the authors should consider citing a more appropriate reference.\nQ7. Line 116, page 6\n\"likely due to more extensive mass spectrometry coverage\"\nThe authors mainly attribute the high number of identified glycosylation sites in the kidney to differences in MS coverage. However, several publications have demonstrated that the kidney is inherently rich in glycoproteins, and this biological background should also be considered. Moreover, if differences in the number of MS runs or fractionation conditions significantly affect the number of identifications, then comparable MS conditions (e.g., injection amount, number of runs, number of fractions) should be applied when comparing different organs. This would require a reconsideration of the overall experimental design of this study.\nQ8. Line 180, page 8 (Table S4)\nPlease provide the selection criteria for the 209 glycosyltransferases (GTs). For example, ST6GALNAC1 shows a maximum value of only 0.05 across all tissues. If, as in Ref.4, the data are expressed in TPM (log10), this gene would not display clear differential expression across tissues. If the total number of GTs included in the dataset happens to be 209, this should also be clearly stated.\nQ9. Line 190, page 8\nIt should be clearly stated that the annotations of subcellular localization are based on predicted cellular localization (e.g., GO terms) rather than experimental validation. Additionally, it would be advisable to note somewhere in the manuscript that predictions of localization based on GO annotations have inherent limitations.\nQ10. Line 182, page 8 (Fig. 2A, F–H)\nThe authors suggest that high expression of Mgat3 in brain tissues explains the elevated levels of bisected N-glycans (Core III/IV). However, in Figure 2A, bisected structures also appear abundant in intestinal tissues, where Mgat3 expression is not particularly high. This raises the question of whether mRNA expression alone can sufficiently explain the observed glycan patterns across tissues. Are there additional lines of evidence—such as lectin array results, that support the proposed correlation? Moreover, is there any broader correlation with other glycosyltransferases beyond Mgat3 and Fut9?\nQ11. Line 230, page 10\nThe order of glycan structures differs between Figure 3B and Figure S6, making it visually difficult to confirm reproducibility. Unifying the order of glycans or providing a quantitative measure of similarity between them could improve the clarity of interpretation.\nMoreover, the authors emphasized that glycosylation patterns are strongly influenced by subcellular localization. Therefore, it is important to verify whether the 24 glycoproteins compared across tissues are biased toward specific cellular compartments (e.g., the endoplasmic reticulum). It is recommend that the authors indicate the subcellular localization of these proteins and discuss whether any bias might affect the observed reproducibility.\nQ12. Line 242, page 10\nThe authors refer to \"functional demands,\" but the manuscript does not present direct evidence or analyses clarifying what specific functional requirements drive organ-specific glycosylation. Observing correlations alone is insufficient to support causality. Therefore, if the term \"functional demands\" is used, it would be helpful to include supporting data or discussion—such as references to the functional roles of glycan structures in specific localizations or analyses of associations with functionally relevant genes. Otherwise, it is suggested carefully revising expressions that imply causation.\nQ13. Line 248, page 10\nThe authors discuss the biological significance of organ-specific glycan remodeling by using the example of glycosylation at Asn-84 of M6pr. However, it is unclear whether differences in protein abundance of M6pr across tissues were considered. Since detection frequency and structural diversity of glycan modifications can depend on protein expression levels, it is recommend providing supporting data (e.g., proteomics or RNA-seq data) to examine whether expression differences might influence glycosylation patterns. This would help clarify whether the differences in glycan structures reflect true remodeling rather than biases from protein abundance.\nQ14. Line 257, page 11\nIn Figure 4E, the majority of glycans in the seminal vesicle show zero sialylation. Therefore, the corresponding explanation in the manuscript should be reviewed for consistency.\nQ15. Line 290, page 12 (paragraph 6):\nRegarding the expression \"prefer to perform specialized functions,\" although GO analysis results are presented to suggest functional roles of glycosylation, no causal relationship between glycan structures and functional outcomes is demonstrated in the manuscript. The current evidence only supports correlations. It is recommend modifying \"prefer to perform specialized functions\" to a more cautious expression such as \"are suggested to have functional relevance.\"\nFurthermore, to strengthen the validity of the claimed link between glycan structures and function, it would be beneficial to include references or experimental data supporting the functional importance of specific structural features, such as Lewis antigens or LacdiNAc modifications, in the relevant tissues.\nQ16. Discussion\nThroughout the discussion, there are several statements suggesting causal relationships between glycan structures and their functions. Since the study does not directly test functional outcomes, it would be more appropriate to phrase these descriptions in terms of associations rather than causation.\nQ17. Line 464, page 18\nThe statement that glycosylation \"adapts to extracellular functional demands\" is not directly supported by the data presented in this study. Rather, the observed glycan patterns are more likely due to structural constraints or the localization of biosynthetic enzymes. Therefore, this expression should be revised.\nPlease note that while Ref.56 is relevant for the first part of the sentence about biosynthetic pathways, it does not provide any information to support the second part of the sentence regarding glycosylation adapting to functional demands.\nQ18. Line 486, page 19\nThe statement on the co-occurrence of polySia and Lewisx/a structures is interesting. However, without experimental evidence of spatial proximity (e.g., whether these modifications occur on the same molecule) or functional interactions, it seems premature to conclude \"functional synergy.\"\nQ19. In methodology, what’s the reason for further High-pH HPLC fractionation after HILIC column enrichment?\nQ20. Do the authors find any N-glycans with polyLacNAc extensions? If not, what could be the reason?\nQ21. In Figure 2E, what does the fifth glycan from the right represent? (Does it mean non-branching?)\nQ22. In Figure 3, It would be helpful if the figure legend were more detailed. It’s hard to know which tissue was used for this analysis.\nQ23. Figure 3D\nIn Figure 2A, bisecting glycans were presented as a distinguishing feature of nervous system tissues. However, in Figure 3D, bisected structures do not appear particularly enriched in the plasma membrane fraction. This discrepancy may warrant further explanation\nQ24. Figure S1A\nIt is difficult to understand how the figure corresponds to the main text. Please consider revising the figure or adding clarifications in the caption.\nQ25. Table S3\nIt is unclear what the numbers in this table represent. The authors appear to indicate the percentage of each subtype or structure in each organ, but please clarify this point explicitly.\nQ26. Table S6\nAlthough Lamc1 is included as an example, it is not mentioned in the main text.\nTypographical or formatting issues\nQ27. Line 307, page 12\n\"Ploy\" → should be corrected to \"Poly.\"\nQ28. \"Gall bladder\" should be \"Gallbladder\" in:\nFig. 1A, H, J; Fig. 2A, C, D, E; Fig. 3D; Fig. 4A, B, D, E; Fig. 5A, B, D; Fig. 6A, E; Fig. 7A.\nQ29. In Fig. 2A, there’s a red line under Liver/Serum.\nQ30. In Fig. 2F: \"Olfactorybulb\" → should be \"Olfactory bulb\"\nQ31. In Fig. 3E and Fig. 4C: \"Celluar\" → should be \"Cellular\"\nIn a study where the key contribution is biological rather than technical, structural credibility becomes paramount. This is especially important given ongoing global efforts, such as the HUPO Glycoproteomics Initiative, which stress not only data quantity but interpretability. According to HUPO's cross-platform evaluation, less than 25% of glycopeptides overlap between tools, with glycan composition being the main point of disagreement (https://doi.org/10.6084/m9.figshare.27095782.v3). Hence, rigorous structural validation is essential.\nUnfortunately, the current manuscript lacks sufficient evidence to support the glycan assignments. Several assigned glycans appear biosynthetically implausible, prompting us to examine representative MS/MS spectra. In multiple cases, the fragment patterns did not support the claimed structures, suggesting misassignments and undermining confidence in the overall dataset. In addition, the claimed isomer resolution and motif detection (e.g., bisecting GlcNAc) are not convincingly demonstrated. Without such evidence, the claim of “High-Resolution N-Glycoproteome” in the title is premature.\nHowever, should the authors validate the structure carefully, a substantially revised manuscript could be reconsidered as a new manuscript. To ensure structural reliability, novel glycan assignments should not rely solely on the developed de novo interpretation tool. Robust validation—such as (1) manual MS/MS annotation, (2) cross-database comparison, (3) reproducibility accross samples or datasets, (4) cross-tool evaluation, (5) biosynthetic plausibility assessments, (6) targeted enrichment and structural characterization—is essential to avoid misassignments and overinterpretation.\nRecommended Criteria for Validation (for reference)\nTo enhance the reliability of glycan structural assignments, it is strongly recommended that the authors clearly distinguish between previously reported structures and novel assignments. For known glycans, validation should be performed by cross-referencing established databases such as UniCarb-DB or GlyTouCan. For novel structures, extensive evidence more than the developed de novo tool is required to avoid misassignments or overinterpretation. The following validation strategies are recommended:\n1. Manual MS/MS annotation\nManual annotation of representative and structurally complex examples is required. This includes identification of diagnostic ions that clearly support the proposed isomer assignments. Resolving isomeric glycan structures at the same glycosylation site on an identical peptide backbone provides more convincing evidence than comparisons made between different peptides. Such examples should be fully presented to support the claim of \"high-resolution glycoproteomics.\"\n2. Cross-database comparison\nVerify whether each assigned structure is registered in international databases such as GlyTouCan, and report matches/mismatches against UniCarb-DB.\n3. Reproducibility Across Samples or Datasets\nConfirm whether the identified structures are consistently observed in other tissue batches, independent datasets, or biological replicates. If structures are observed in only one sample, provide supporting data to establish their validity.\n4. Cross-tool evaluation\nFor glycan structures uniquely identified by StrucGP, explain why they are not detected by other tools (e.g., pGlyco, Byonic). Provide MS/MS-based evidence to support that the structures are not tool-specific false positives.\n5. Biosynthetic plausibility assessments\nInvestigate whether the assigned structures are consistent with known glycosyltransferase specificities and established biosynthetic pathways. If not, provide direct literature evidence or in vitro data to support the biological plausibility.\n6. Targeted enrichment and structural characterization\nFor particularly novel structures, further biochemical validation is recommended, including glycopeptide isolation, specific enzymatic digestion, PMAA linkage analysis, and LC-MSⁿ analysis.\nReviewer’s expertise\nI have extensive experience in using lectin arrays and mass spectrometry to investigate inter-tissue glycan heterogeneity in mouse models (e.g., Analytical and Bioanalytical Chemistry (2025) 417:973–988). The content of the current manuscript falls well within my area of expertise.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response: We thank the reviewer for his/her overall positive evaluations on our work, and appreciate the professional and constructive comments and suggestions. Below we have addressed each comment in a point- by-point manner and made corresponding revisions in the manuscript accordingly.\n- Including the data summary of what different search algorithms generated is important, but more details for how the different glycopeptide search algorithm comparisons were done would be helpful. Is it known why each algorithm identified unique glycopeptides relative to each other?\nResponse: We thank the reviewer for this thoughtful comment. Based on previous studies and our own analysis, the differences in glycopeptide identifications among various search algorithms can be primarily attributed to the following four factors:\n1. Scoring and filtering differences:\nR1\nEach algorithm uses different scoring rules for glycopeptide-spectrum matches. For example, pGlyco requires strong glycan fragment evidence, Byonic allows partial glycan matches, and MSFragger-Glyco uses an open search approach that increases sensitivity but may reduce specificity.\n2. Glycan database differences:\nThe glycan search space varies across tools. pGlyco uses a full glycan structure database, while StrucGP uses a branch-based glycan library. Byonic and MSFragger-Glyco allow semi-combinatorial glycan generation. These differences affect what types of glycans and glycopeptides can be identified.\n3. Fragmentation model differences:\nTools like StrucGP and pGlyco consider more detailed glycan fragmentation patterns and linkage-specific ions, which improves accuracy but may also filter out lower-confidence matches.\n4. FDR control strategies:\nDifferent tools use different methods to estimate and control FDR at both the peptide and glycan levels, leading to different thresholds for accepting results. Specifically, although both pGlyco and StrucGP follow a glycan-first identification strategy, their decoy construction methods differ. As far as we understand, pGlyco generates decoy spectra by adding random mass shifts to Y ions within each spectrum. This approach primarily evaluates the correctness of the peptide mass or the Y1 ion. However, such decoys often lack the conserved N-glycan pentasaccharide core and differ significantly from real N-glycopeptide spectra, which may limit their utility in accurately assessing glycan-level FDR. In contrast, our decoy strategy introduces random mass shifts at the precursor ion level for each spectrum. This generates decoy spectra that retain the conserved pentasaccharide core but differ only in glycan branching structures. As a result, our decoys more closely resemble true N-glycopeptide spectra and allow for more realistic and reliable estimation of glycan-level FDR, thereby improving confidence in glycan assignments.\nIn summary, although different algorithms report partly overlapping and partly unique results, combining them can increase glycopeptide coverage. Our dataset from 24 tissues provides a valuable resource for such integration. However, how to merge results with different confidence levels remains a challenge. Recent studies (Nat Commun. 2025 Jul 1;16(1):5568.) suggest that AI models trained on multi-confidence data may help address this.\nIn the revised manuscript, we re-searched the gallbladder dataset using other tools (pGlyco, Byonic and MSFragger-Glyco) and added the results to Supplementary Figure 1, with related updates in Supplementary Table 2. We added a comparative description in the Results section (Page 5), highlighting that StrucGP uniquely identifies a larger number of glycoproteins and glycosites, demonstrating its sensitivity for distinct\nR2\nglycopeptide features. In the Discussion (Page 18), we acknowledged that different tools yield partially distinct identifications and emphasized that consensus results across multiple engines can improve data coverage, underscoring the dataset’s value for such cross-platform analyses.\n- There does not appear to be any sulfated or phosphorylated N-glycans glycopeptides reported by any algorithm. Is the reason for this known?\nResponse: We appreciate the valuable comments. In general, the low detection rate of sulfated and phosphorylated N-glycans can be attributed to several technical and biological factors. First, sulfation of N- glycans occurs at very low abundance in mammals and is often masked by highly abundant unmodified glycopeptides in MS spectra, making it more difficult to identify. Second, sulfate groups are labile and prone to dissociation during fragmentation, leading to the loss of key diagnostic ions and further complicating confident identification. Compared to sulfation, phosphorylation is relatively more stable and easier to detect. However, increasing the identification rate of both modifications, especially sulfation, typically requires specialized sample preparation workflows. For instance, phospho-glycopeptides often benefit from enrichment strategies such as IMAC to enhance specificity, whereas for sulfated glycopeptides, there is currently no well-established enrichment method to selectively improve detection.\nIn our study, sulfated and phosphorylated N-glycans were not reported in the dataset primarily because our glycopeptide search algorithms rely on a predefined glycan branch library that must explicitly include such modified structures (with defined compositions and masses) for successful spectral matching. According to current literature, sulfation of N-glycans in mammals occurs at extremely low levels. Therefore, to avoid inflating the search space and increasing the risk of false-positive identifications, we deliberately excluded sulfated and phosphorylated glycans from the initial search library used in this study.\nAccurate identification of these rare modifications remains technically challenging and requires carefully curated search libraries along with rigorous false discovery rate (FDR) control and optimized collision energy settings. We are currently developing and validating an expanded glycan branch library that includes sulfated and phosphorylated structures, coupled with corresponding analytical workflows. In this context, we have established a lower-energy fragmentation strategy to better preserve diagnostic B ions, although the associated technology is still under development. Until this strategy is fully established and benchmarked, incorporating these modifications into routine searches could compromise data quality by introducing spurious matches.\nNevertheless, the high-coverage glycopeptide dataset derived from 24 mouse tissues in this study provides a solid foundation and can be leveraged in future targeted efforts to identify and validate such rare or regulatory\nR3\nglycan modifications under optimized conditions.\nIn the revised manuscript, we have added a statement in the Discussion (Page 18) emphasizing that our mouse tissue dataset can support not only existing tools but also the development of new algorithms for identifying diverse glycan features, including rare modifications (e.g., sulfation, phosphorylation, O- acetylation), uncommon glycan structures or monosaccharides, and even O-glycans.\n- In the Discussion, limitations, mentioning the differences in mouse relative to human (like NeuGc, alpha- Gal) could be helpful. Most mouse models do not replicate human disease in many aspects, and it has been known for a long time in the glycobiology community that the glycan differences are major factors.\nResponse: We appreciate this insightful comment. We agree that species-specific glycosylation patterns, such as the expression of Neu5Gc and α-Gal in mice but not in humans, are important considerations when interpreting the translational relevance of our findings.\nIn the revised manuscript, we have discussed the limitations of directly extrapolating mouse glycosylation patterns to humans due to species-specific differences in the Discussion section (Page 21).\n- Demonstrating that the same glycoprotein has different glycans dependent on organ location is powerful, and highlights the type of comparisons that could only be done after generating the data. The authors should consider highlighting more examples of this, like glycoproteins detected in at least half of the tissues, in a supplementary table perhaps? Are there examples that are essentially invariant for site N-glycan composition across the tissues?\nResponse: We appreciate the reviewer’s insightful suggestion. To better illustrate tissue-dependent heterogeneity and conservation of N-glycan structures at specific glycosites, we performed a new analysis by calculating a similarity score matrix for 49 glycosites derived from 38 commonly expressed glycoproteins across 23 tissues (excluding serum). In this matrix, a higher score indicates greater similarity of glycan structures at the same site across tissues, while a lower score reflects more site-specific heterogeneity. The scoring criteria and statistical methods are detailed in the revised Methods section (Page 24-25).\nIn the revised manuscript, we have added incorporated the new analysis into Supplementary Table 7 (updated from the original Supplementary Table 6), which provides the full list of site-specific glycan similarity scores across tissues, ranked from high to low. Additionally, we have added Supplementary Figure 8, which illustrates representative examples of (1) glycosites exhibiting high glycan heterogeneity across tissues (Lamc1, Asn-1221) and (2) glycosites with conserved glycan compositions (Cemip2, Asn-914). The\nR4\ncorresponding descriptions, which highlight the tissue-dependent heterogeneity observed at Lamc1 Asn-1221 and the conserved glycosylation pattern at Cemip2 Asn-914, have also been added in the Results section of the revised manuscript (Page 11).\n- In several comparisons, it would be helpful to include some additional summary tables (supplemental) of the specific identities of the shared glycopeptides. A list of the top 20 glycoproteins for each organ, for example. In particular a list of the protein identities of the 62 ubiquitous glycoproteins detected, the 24 expressed in all tissues and the 38 in all samples except serum. And the 51 proteins detected only in neuronal tissues.\nResponse: We thank the reviewer for this constructive suggestion. In the revised supplementary materials, we have now included several new summary tables to facilitate deeper comparison across tissues. Specifically, we provide: (1) A list of the top 20 most abundant glycoproteins identified in each tissue (Supplementary Table S8); (2) A detailed list of the 24 proteins detected in all tissues, the 38 proteins identified in all tissues except serum, and the 51 glycoproteins uniquely detected in neuronal tissues (Supplementary Table S6).\n- Data for the brain and kidney were highlighted throughout as they have quite distinct N-glycomes relative to other organs, something that many other groups have noted in previous studies. While it is not possible or relevant to cite every previous study, there are a few suggestions below for the authors to consider addressing, in the context of data comparison for glycopeptides and spatial glycomics (just for mouse brain and kidney). Co-occurrence of N-glycans has been reported in multiple mouse tissues using spatial glycomic mass spectrometry imaging methods.\n- Spatial Glycomic references:\nGustafsson OJ, et al. MALDI imaging mass spectrometry of N‐linked glycans on formalin‐fixed paraffin‐ embedded murine kidney. Anal Bioanal Chem. 2015;407(8):2127‐2139.\nClarke HA, et al. Spatial mapping of the brain metabolome lipidome and glycome. Nat Commun. 2025 May 12;16(1):4373. doi: 10.1038/s41467-025-59487-7.\n- Murine brain and kidney glycopeptide studies:\nKawade H, et al. Bisecting GlcNAc modification of angiotensin-related glycoproteins in mouse kidney. J Biochem. 2025 Jun 13:mvaf033.\nBradberry MM, et al. N-glycoproteomics of brain synapses and synaptic vesicles. Cell Rep. 2023 Apr 25;42(4):112368.\nResponse: We thank the reviewer for these valuable references and suggestions. We agree that the brain and\nR5\nkidney exhibit highly distinct N-glycosylation features, which are consistent with previous spatial glycomics and glycopeptide profiling studies. Notably, the cited studies further validate the accuracy of our overall glycan structure assignments by independently supporting key tissue-specific patterns observed in our dataset.\nIn the revised manuscript, we have now cited the suggested publications in the Result section (Page 8) to better contextualize our findings and highlight their consistency with established tissue-specific glycosylation patterns reported in the literature.\nMinor comments:\n- The NeuGc and NeuAc comparisons in Figure 5 are very useful. Were any glycan species with both NeuAc and NeuGc on the same glycopeptide detected.\nResponse: We thank the reviewer for this insightful question. Upon re-examining our dataset, we found that glycan containing both NeuAc and NeuGc on the same structure were indeed present and accounted for approximately 2%–25% of all unique glycans identified in each tissue. The five mouse tissues with the highest proportions of such dual-sialylated glycans were heart (25%), liver (18.4%), muscle (17.1%), testis (17.0%), and spleen (15.5%). These results suggest that co-occurrence of NeuAc and NeuGc, while not dominant, is not uncommon in certain tissues.\nIn the revised manuscript, we have added this result on Page 13, and included the corresponding spectra and statistics in a new supplementary figure (Supplementary Figure 9A and 9B).\n- Were male versus female comparisons possible for some of the most abundant tissue types?\nResponse: We thank the reviewer for this valuable suggestion. Given that the kidney yielded the highest number of glycoprotein and glycopeptide identifications in our dataset, we had indeed performed a comparative analysis of the renal N-glycoproteome between male and female mice. While the overall profiles—such as the numbers and identities of glycopeptides, glycoproteins, and glycosites—were highly similar between sexes, with only subtle structural differences observed.\nSpecifically, we observed subtle but significant differences between male and female kidneys: female kidneys showed enriched LacdiNAc structures and predominantly Neu5Ac-containing glycosylated glycans, whereas male kidneys showed a higher prevalence of Neu5Gc-modified N-glycans. In addition, female- enriched glycans were functionally associated with reproductive and immune processes, whereas male- enriched glycans were associated with protein localization and metabolic functions. These findings provide new insights into gender-related glycosylation profiles and underscore the importance of considering gender\nR6\nas a biological variable in glycoproteomics studies.\nIn the revised manuscript, we have added a summary of the sex-specific differences in the Results section (Page 7), and included detailed data and visualization in Supplementary Figure 6.\n- Can a mouse specific glycan list be provided (as compared to humans for example)?\nResponse: We appreciate the reviewer’s suggestion regarding a mouse-specific glycan list. Currently, we do not possess a human glycan structural database generated under identical experimental conditions as our mouse dataset. Moreover, existing public glycan databases may not fully correspond to the glycan structures identified in our mouse samples.\nIn the revised manuscript, we have expanded the Discussion section to include a brief comparison highlighting key differences in N-glycosylation between humans and mice (Page 20), which we hope will help clarify species-specific glycosylation features and assist readers in interpreting our findings. Importantly, this dataset also provides a foundation for future efforts aimed at systematically defining human–mouse glycan differences, which will be critical for translational research and comparative glycobiology.\n- Ref 7 and 43 are the same paper\n- Table S4, cite the source of the RNA seq data (it was cited in the main text)\n- Check again for misspelled words and a few occurrences of grammatical errors\nResponse: We thank the reviewer for the careful reading of our manuscript. We have corrected the duplicated citation by removing the redundant entry and updating the reference list accordingly. In Table S4, we have now clearly cited the source of the RNA-seq dataset, consistent with the citation already provided in the manuscript. Additionally, we have carefully rechecked the manuscript for typographical and grammatical errors and made necessary corrections throughout the manuscript.\nWe sincerely thank the reviewers again for their constructive and insightful comments. These suggestions have greatly helped us improve the quality and clarity of the manuscript.\nR7"}, {"round": 2, "reviewer_comments": {"reviewer_1": "In this revised manuscript, the authors very effectively addressed questions and concerns from my previous critique. I disagree with them only in one of their rebuttal statements indicating that sulfation of N-glycans are rare, but there is no need to further address that relative to this manuscript.", "reviewer_2": "We thank the authors for the substantial effort in revising the manuscript and providing detailed responses to our previous comments. While some our earlier concerns have been addressed, several key points remain unresolved and prevent us from recommending acceptance of the manuscript in its current form.\nOur main concern is the lack of clarity regarding structural confidence across the 3,045 reported N-glycan structures. The authors acknowledge several misassignments we identified and state that these account for less than 0.5% of all structures. However, there is still no dataset-wide, systematic quantification of structural confidence. Figures present all structures as fully determined, without visual or quantitative distinction between high- and low-confidence assignments. Additionally, a glycan-level false discovery rate supported by a realistic decoy strategy has not been demonstrated, leaving the global structure-level error rate unclear.\nIn our previous review, we outlined six validation criteria. The current revision addresses these only partially:\n(1) Manual annotation was performed for a limited set of examples, without extending to a statistically meaningful proportion of the dataset, and without providing a clear basis for the stated misassignment rate of less than 0.5%.\n(2) Cross-database matching was restricted to GlyConnect, without systematic reporting of matches/mismatches to other major databases or explicit differentiation between known and novel structures.\n(3) Reproducibility was shown for selected tissues/legacy data, but not quantified across the dataset.\n(4) Cross-tool analysis appears limited to selected subsets and largely at the glycan composition level; no structure-level concordance metric or spectrum-based adjudication of discrepancies is provided.\n(5) Biosynthetic plausibility is discussed qualitatively, but the number/proportion of pathway-inconsistent calls is unquantified.\n(6) Orthogonal validation (e.g., stepwise exoglycosidase assays, binding assays with anti-glycan probes such as antibodies and lectins) for novel/critical features is not provided.\nWe fully appreciate the challenges inherent to N-glycan structural analysis, particularly in backbone classification, antenna number determination, and algorithmic assignment. However, given that these dataset-level validation steps remain incomplete, further work is necessary before the study can provide the level of reliability and interpretability expected for publication in Nature Communications.\nWe encourage the authors to consider a systematic, dataset-wide validation—including quantitative structural confidence measures, expanded cross-database matching, structure-level cross-tool concordance, and targeted orthogonal experiments—before resubmission. Such steps would greatly strengthen the impact and reliability of this valuable dataset.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response: Thank you for your careful review of our revised manuscript. We are pleased to hear that you think we have effectively addressed the previous concerns and improved the manuscript. Regarding your comments on the sulfation of N-glycans, we appreciate your valuable feedback. In fact, we are developing a new method to enhance the detection of these type of chemical modifications and some special glycan structures such as di- or poly-LacNAc by using much lower HCD energies, as we found that the characteristic B ions for sulfated glycans or polyLacNAc were easily lost under HCD energy of 20%. We will investigate the sulfated glycopeptides and some other chemical modifications from these datasets once the method is developed.\nOnce again, we sincerely thank you for your constructive and insightful suggestions, which have contributed significantly to enhancing the quality of our manuscript.\nR1\nResponse: We sincerely thank the reviewer for their thorough and thoughtful evaluation of our revised manuscript, as well as for acknowledging the improvements we made in response to your previous comments. We greatly appreciate your concerns regarding the accuracy of the glycan structures, and we agree with your emphasis on structural confidence. In addressing this concern, as well as the six validation criteria you outlined, we have added a new Supplementary Table (Table S2; with subsequent tables renumbered), including the detailed annotation of the 3,045 N-glycans. These revisions are as follows:\n1. Validation of Glycan Structures: In response to Comments 1 and 5, we have provided more detailed annotations for glycans that do not align with known biosynthetic pathways in order to highlight the potential misassignments. To systematically evaluate such cases, we applied predefined filtering criteria for glycans unlikely to arise from canonical biosynthetic pathways. These criteria were derived from established knowledge of N-glycan biosynthesis (Essentials of Glycobiology, 4th edition; Nat Rev Mol Cell Biol. 2020 Dec;21(12):729-749; Annu Rev Biochem. 1985:54:631-64; Glycobiology. 2003 Jul;13(7):41R-53R; Zoology (Jena). 2004;107(1):49-64), and included the following thresholds: N < 2 or N > 8; H < 3 or\nR3\nH > 9; S > 4; F > 3 (especially F > 4, which rarely conforms to canonical pathways); structures with disproportionately high sialylation relative to N/H (e.g., N2H3S5); and G > 2. It should be emphasized that these criteria are not “absolute exclusion rules” but rather serve as “red-flag indicators.” Certain atypical structures (e.g., highly sialylated or heavily fucosylated glycans) may occur under extreme conditions, such as in tumors or specific tissues (Mol Cell Proteomics, 2022, 21(4):100214; J Proteome Res. 2022, 21(7):1664-1674), and many such cases have also been reported in public databases such as GlyTouCan. For transparency, we have annotated all such glycans in Supplementary Table 2, together with explanations for why they were flagged.\nImportantly, their overall frequency in the dataset remains relatively low, supporting the robustness of our identifications.\nAdditionally, we apologize for not providing more detailed justification for the misassignment rate of less than 0.5% in our initial response. Specifically, based on the identification statistics of unique glycopeptides across 24 tissues (Supplementary Table 1), we identified a total of 71,190 high-mannose glycopeptides. Among these, the glycan N2H4F1 appeared 352 times (0.49%), N2H9F1 appeared 24 times (0.03%), N2H8F1 appeared 102 times (0.14%), and N3H8 appeared 61 times (0.08%).\n2. Cross-database Matching: In response to Comment 2, we expanded our database search beyond GlyConnect and included other widely-used glycan databases. We found that most of these databases (including GlyConnect, Glygen, and Glycosmos) are linked via the GlyToucan ID, which serves as a universal token across these platforms. We utilized the GlyToucan API to annotate all glycan compositions and clearly indicated whether each glycan has been reported in these public databases. For the UniCarb-DB database, we found that it had not been updated since 2016 and contained primarily common structures, constrained by the technologies available at that time. Since UniCarb-DB is also linked to GlyToucan, we relied mainly on GlyToucan for our annotations. Accordingly, we added results in the revised manuscript (Page 5), indicating that 71% of the identified glycan compositions have been reported in GlyToucan (since a large number of glycan structures were incomplete in the\nR4\nGlyToucan database, it is still difficult to compare our results with databases at the glycan structural level).\n3. Glycan identification and reproducibility across the dataset: In response to Comment 3, we conducted a comprehensive statistical analysis of glycan identifications across the entire dataset, including information on the corresponding tissues, glycoproteins, glycosites, and PSMs for each glycan. This ensures that the reproducibility of glycan identifications is evaluated comprehensively across all tissues, rather than being limited to a few selected examples.\n4. Structural confidence: Since all glycan structures reported in this study are the combinations of 4 known core and 17 known branch structures with three major glycan types, there should not be any fundamental distinction between known and novel structures in our dataset. In addition, as our false discovery rate (FDR) is controlled at the PSM level, transforming it to the unique glycopeptide level might lead to a relatively higher FDR. However, based on our observations, even a spectrum has been identified as a decoy peptide, the matched glycan structure can still be a common one in lots of cases. And therefore the estimation of FDR at the glycan structure level is quiet difficult. Instead, we have provided the highest score for each glycan structure for facilitating other researchers’ judgement when using our datasets. All four points above constitute the main annotations have been added in Supplementary Table 2.\nIn response to Comment 4, we would like to clarify that almost all other search algorithms provide only composition-level data rather than structural information, which restricts cross-tool comparisons mainly to glycan compositions. For instance, MSFragger-Glyco and Glyco Decipher infer only possible glycan masses to assign compositions, while pGlyco introduces artificial masses on Y1–Y5 ions to judge Y1 correctness. However, this strategy does not represent a genuine glycan-level FDR, and our benchmarking during StrucGP development showed that such approaches fail to remove false positives. Indeed, relaxing glycan-level FDR control can nearly double the number of identified PSMs, but at the expense of reliability. These methodological limitations explain why cross-tool evaluation is inherently challenging, especially at the\nR5\nstructural level. Even when putative structures are reported (e.g., pGlyco3 assigning the most common structure for each composition), substantial discrepancies in glycopeptide identifications remain across tools. This issue has also been highlighted in recent studies (Nat Commun. 2025 Jul 1;16(1):5568.), which discusses the difficulty of merging results obtained with different FDR thresholds across tools. As we mentioned in our initial response, the discrepancies in the identification of unique glycopeptides across different software tools may be attributed to four major factors:\n(1) Scoring and Filtering Differences;\n(2) Glycan Database Differences;\n(3) Fragmentation Model Differences;\n(4) FDR Control Strategies. Together, these factors explain the lack of consistency in glycan identifications across tools and underscore the challenges of cross-tool evaluation, especially at the structural level.\nIn response to Comment 6, we fully agree that targeted orthogonal experiments are necessary before performing more in-depth functional analysis. It should be noted that the main conclusions in our manuscript—such as tissue-specific glycan patterns and glycan heterogeneity on shared proteins—are based on glycans that were repeatedly identified across multiple tissues. Glycans with few identifications, which may include potential misassignments, do not affect these conclusions. Furthermore, some observations, such as the enrichment of bisected glycans in brain and kidney tissues and the prevalence of diantennary glycans with terminal Neu5Gc modifications in liver and serum, have been confirmed in previous glycomics studies, supporting the reliability of the majority of glycan structures reported here. In addition, some rare structures, such as LacdiNAc, have been validated in our other projects (Redox Biol. 2025 Apr:81:103584; Adv Sci (Weinh). 2025 Jul 24: e02013.) using lectin-based assays (e.g., WFA-Cy5), indicating that the identification of these less common glycans is also relatively trustworthy. For the numerous other low-abundance glycans mentioned in the manuscript, their verification would be best performed by biological experts prior to functional studies. It should also be noticed that all glycan structures reported in this study are actually the various combinations of 4 known core and 17 known branch structures with three known glycan types, and therefore it may be quite difficult to\nR6\nvalidate these special glycan structures especially at the glycosite-specific level using lectins or antibodies (this information has been included in the manuscript, Page 5).\nAccordingly, we have carefully revised the manuscript to emphasize that researchers should perform appropriate experimental validation before conducting in-depth functional and mechanical studies based on our dataset. These points have been added to the Discussion section (Page 20).\nOnce again, we sincerely thank the reviewer for their constructive suggestions. We hope the additional supplementary tables and revisions we have made will address your concerns regarding the structural accuracy of the glycans.\nR7\nResponse: We sincerely thank the Reviewer #3 for his/ her valuable evaluation on our manuscript, and appreciate his/ her professional comments and constructive suggestions, which has helped us improve the quality and clarity of our study.\nResponse: We sincerely thank the Reviewer #3 for his/ her valuable evaluation on our manuscript, and appreciate his/ her professional comments and constructive suggestions, which has helped us improve the quality and clarity of our study."}, {"round": 3, "reviewer_comments": {"reviewer_2": "We thank the authors for their continued efforts in revising the manuscript and for adding materials such as Supplementary Table S2 and expanded database annotations. However, our central concern remains unresolved: among the 3,045 reported N-glycan structures, which are high- versus low-confidence, and in what proportions? The manuscript still does not provide dataset-wide, quantitative measures of structural confidence, and figures continue to present all glycans as if fully determined, without distinguishing levels of certainty.\nNote. Items 3 and 4 are considered addressed (within current practical limits). Items 1, 2, 5, and 6 remain outstanding.\nThe six validation criteria remain only partially addressed:\n1. Manual annotation and misassignment rate – The reported “<0.5% error rate” is derived only from high-mannose glycans that we had highlighted as potential misassignments, and does not reflect more ambiguous features. In addition, the Highest_GlycanScore is not interpretable as a confidence metric, since no thresholds or categories are provided.\n2. Cross-database matching – Expanded to GlyTouCan, but still restricted to composition-level comparisons. Structural-level confirmation remains absent.\n3. Reproducibility [Addressed] – We acknowledge that the authors have provided reproducibility statistics across the dataset, including replicate-level analyses.\n4. Cross-tool analysis [Addressed within current software limits] – The authors have performed cross-tool comparisons at the glycan composition level. Given the current limitations of available software, we agree that structure-level comparisons are not feasible, and we consider this point addressed as far as is reasonably possible.\n5. Biosynthetic plausibility – New “red-flag” criteria were applied, but flagged glycans remain included in the overall figures and statistics. As a result, readers cannot distinguish between high- and low-confidence structures. These criteria are heuristic rather than a quantitative confidence framework.\n6. Orthogonal validation – No systematic validation (e.g., exoglycosidase digests, lectin/antibody assays) is presented for novel or critical structures.\nIn conclusion, while transparency has improved through supplementary annotations, the fundamental issue of dataset-wide structural confidence remains to be fully addressed.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response: Thank you for your careful review of our revised manuscript. We are pleased to hear that we have effectively addressed some of your concerns and improved the manuscript. In response to additional comments on glycan confidence classification, we further compared our data with the GlyTouCan database at the glycan level and added key information to Supplementary Table 2, including the classification into high-, intermediate-, and low-confidence categories as well as the detailed scoring criteria. These revisions are as follows:\nResponse to Comment 1, 2, and 5 — Confidence-level assignment of glycans\nwe have established a systematic confidence framework for all 3,045 reported N-glycan structures based on five criteria, integrating both biological and statistical considerations:\n1) Cross-database matching at the structural level (GlyTouCan, 30 points; highest weight): Any glycan matched to GlyTouCan was automatically assigned the full 30 points.\n2) Biosynthetic plausibility (30 points; highest weight): Glycans consistent with known pathways were given 30 points, and inconsistent glycans were given 0 points.\n3) Reproducibility across biological replicates (20 points; moderate weight): Glycans detected in ≥12 tissues out of 24 received 20 points; glycans detected in 2–11 tissues received 10 points; glycans found in only one tissue or a single site received 0 points.\n4) Highest_GlycanScore (10 points; auxiliary weight): Log-transformed GlycanScores (base 10) were classified using the three-sigma rule (μ ± 3σ): glycans with log (GlycanScore) > 3.70 received 10 points; glycans within 1.04–3.70 received 5 points; glycans <1.04 received 0 points.\n5) Probability of GlycanType and All Branches (10 points; auxiliary weight): Glycans with both probabilities >0.99 received 10 points; those with either probability >0.99 received 5 points; glycans with neither >0.99 received 0 points.\nIt should be noted that for high-mannose glycans, since they lack branching structures, the branch probability is inherently zero. In such cases, the confidence evaluation relied solely on the probability of GlycanType assignment.\nBased on these five criteria (total 100 points), glycans were classified into three confidence categories: high confidence (≥75 points), intermediate confidence (40–74 points), and low confidence (< 40 points). To ensure robustness, glycans structurally matched to GlyTouCan were automatically assigned at least intermediate confidence.\nApplying this classification, 67% of glycans (2,041/ 3,045) were high-confidence, 27.5% (837/ 3,045) were intermediate-confidence, and 5.5% (167/ 3,045) were low-confidence. To ensure reliability, we manually interpreted 1–3 spectra for each low-confidence glycan as well as for intermediate-confidence glycans that did not conform to canonical biosynthetic pathways, totaling 352 glycans and more than 700 spectra. Manual interpretation showed that 4.3% of glycans (131/ 3,045) remained structurally ambiguous. The results of these manual annotations, labeled as either “structurally reasonable” or “structurally ambiguous,” have been added to Supplementary Table 2.\nIn addition, we manually interpreted all glycans (106 spectra) mentioned in the manuscript. The results confirm that all glycans presented in this paper are sufficiently supported by characteristic B and Y ions. We have compiled all corresponding spectra into Supplementary Figure 2, with characteristic ions highlighted in red boxes.\nIn the revised manuscript, the confidence classification results for all 3,045 glycans are now described in the Results section (Page 5-6), while the detailed criteria are presented in the Methods section (Page 25-26). The corresponding statistical summary has been also added as Supplementary Figure 1F. To provide readers with an intuitive overview, all figures presenting glycan structures (Figure 1, 2, 6, 7, and S12) have been updated with color gradients to denote confidence levels, with corresponding explanations in the legends. In addition, we added new Supplementary Figure 2 (with subsequent tables renumbered), which presents representative spectra for all glycans displayed in the main and supplementary figures. It should be noted that although some glycans did not conform to canonical biosynthetic pathways, the results of manual interpretation were reasonable (e.g., the spectra of the glycans in Figure 6D, as shown in Supplementary Figure 2).\nResponse to Comment 2 — Cross-database matching (structural level)\nWe performed composition-level and structure-level matching against GlyTouCan (covering major resources including GlyConnect, GlyGen, and GlyCosmos). Overall, 76.3% (2,324/ 3,045) at the composition levels and 34.2% (1,024/ 3,045) at the structural levels have exact matches registered in GlyTouCan. The relatively lower proportion of structural-level matches may be explained by two factors. First, StrucGP does not rely on pre-existing glycan databases, and is therefore more capable of identifying novel glycans. Second, all glycan structures reported in this study are in fact combinatorial assemblies of four known core structures and seventeen known branches structures within three N-glycan types. Based on these considerations, and in line with the reviewer’s suggestion, we have now classified all identified glycans into high, medium, and low confidence categories according to a set of stringent scoring criteria.\nTo further validate these assignments, we manually annotated over 800 spectra, explicitly marking each structure as either “reasonable” or “ambiguous.” Importantly, we extended this manual verification to every glycan structure discussed in the manuscript, thereby reinforcing the reliability of conclusions derived from structural-level analyses. These overlap rates have been added to the Results section (Page 5), and per-glycan GlyTouCan IDs and match status are listed in Supplementary Table S2.\nResponse to Comment 6 — Orthogonal validation\nWe agree that targeted orthogonal experiments are necessary before performing more in-depth functional analyses. However, verifying such a large number of structures individually is challenging, and all glycan structures reported in this study are combinatorial assemblies of four known cores and seventeen known branches within three N-glycan types; therefore, glycosite-specific validation using lectins or antibodies is inherently difficult (this information is included in the manuscript, Page 5).\nFortunately, the main conclusions in our manuscript—such as tissue-specific glycan patterns and glycan heterogeneity on shared proteins—are based on glycans that were repeatedly identified across multiple tissues. The newly added confidence classification together with manual spectral interpretation further supports that these glycans fall into the intermediate-to-high confidence categories, with sufficient diagnostic B- and Y-ions corroborating their structural assignments (Supplementary Figure 2). Furthermore, most of the conclusions in the manuscript result from proportion-based differential analyses of substructural features, such as different core and branch structures, rather than whole glycan structures. This means that rare erroneous identifications exert minimal influence on the trends underpinning biological interpretation. In addition, observations such as the enrichment of bisected glycans in brain and kidney tissues and the prevalence of diantennary glycans with terminal Neu5Gc modifications in liver and serum have been confirmed in previous glycomics studies, supporting the reliability of the majority of glycan structures reported here. Some rare structures, such as LacdiNAc, have been validated in our other projects (Redox Biol. 2025 Apr:81:103584; Adv Sci (Weinh). 2025 Jul 24: e02013.) using lectin-based assays (e.g., WFA-Cy5), indicating that the identification of these less common glycans is also relatively trustworthy. For the numerous other low-abundance glycans mentioned in the manuscript, their verification would be best performed by biological experts prior to functional studies. It should also be noticed that all glycan structures reported in this study are actually the various combinations of 4 known core and 17 known branch structures with three known glycan types, and therefore it may be quite difficult to validate these special glycan structures especially at the glycosite-specific level using lectins or antibodies (this information has been included in the manuscript, Page 5).\nAccordingly, these limitations have been added to the Discussion section (Page 20-21).\nOnce again, we sincerely thank you for your constructive and insightful suggestions, which have contributed significantly to enhancing the quality of our manuscript. Based on these comments, we will also be actively improving our StrucGP software."}, {"round": 4, "reviewer_comments": {"reviewer_2": "We thank the authors for their continued efforts on this third revision and for substantially improving transparency by introducing a five-criterion confidence framework. However, the scientific justification of the framework remains insufficient, and the headline claim of “high-resolution characterization of 3,045 N-glycan structures” is not yet supported as written. Only 34.2% (1,024/3,045) match GlyTouCan at the structure level, and the current High/Intermediate/Low distribution (67.0%/27.5%/5.5%) requires a rigorously justified framework and systematic validation to substantiate whole-structure identification across all entries.\nMore specifically: (i) the five-criterion weighting lacks scientific rationale; (ii) direct MS/MS evidence (GlycanScore) contributes only 10/100 relative to largely indirect criteria; (iii) GlycanScore is under-specified (inputs, scoring function, interpretation); and (iv) the ~700 manual MS2 verifications are not propagated into confidence assignments or any auditable misassignment/FDR estimate.\nIn addition, analyses should be limited to structures above the defined confidence threshold; otherwise, the revision improves transparency but does not amount to validation.\nRevisions requested\nThis revision substantially improves transparency and represents a valuable large-scale data resource. To strengthen the manuscript within the current scope, please address the items below.\n1. Reframe the main focus and narrative\nShift from “identification of 3,045 structures” to a resource article centered on sub-structure–based biological insights beyond monosaccharide composition. When citing counts, report the confidence distribution (High/Intermediate/Low).\n2. Provide a scientifically justified confidence framework (the most important)\nProvide a scientifically justified and fully specified confidence framework.\nRedefine and clearly document the framework: state the criteria, weights, thresholds, and class boundaries with rationale; mathematically specify GlycanScore (inputs, scoring function, interpretation); spell out the decision rules/data flow from evidence to confidence assignment; link the ~700 manual MS2 verifications to per-call confidence; provide dataset-level misassignment/FDR and sensitivity analyses showing that indirect criteria cannot outweigh direct MS2 evidence.\n3. Apply a confidence cut-off for analyses\nRestrict biological comparisons to High-confidence calls; inclusion of Intermediate may be allowed only if justified by the revised framework. Known or likely misassignments (e.g., sulfation, O-glycan co-occurrence, poly-LacNAc ambiguities) should be flagged and excluded from primary analyses where feasible.\n4. State methodological limits\nExplicitly connect current constraints (search space, fragmentation limits, branch number ambiguity) to what cannot be concluded at whole-structure resolution.\nStatus of the six validation criteria of the current manuscript\n1. Manual annotation and misassignment rate [Not adequately addressed]–\nAs noted above, the framework’s scientific justification remains insufficient.\n2. Cross-database matching [Addressed]\n3. Reproducibility [Addressed]\n4. Cross-tool analysis [Addressed within current software limits]\n5. Biosynthetic plausibility [Addressed]\n6. Orthogonal validation [Not addressed, but acceptable as a limitation]–\nIf the manuscript reframed as a sub-structure–centric resource, this can be stated in the limitation section.\nReviewer #3: I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\nReviewer #4: I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response: We thank Reviewer #2 for the thorough and constructive evaluation of our third revision. We greatly appreciate the reviewer’s recognition of the improved transparency brought by the introduction of the five-criterion confidence framework and for acknowledging the value of this large-scale N-glycan dataset. In response to the reviewer's suggestions, we have made the following adjustments:\nResponse to Request 2.1-Provide a scientifically justified confidence framework (most important)\nIn this revision, we have completely redefined the confidence assignment system based on a data-driven and quantitatively validated model that integrates both direct MS/MS evidence and indirect structural criteria (as the reviewer suggested, we used manually verified spectra as a gold-standard reference to calibrate the discrimination power of each criterion, thereby establishing a quantifiable and interpretable confidence system).\n1. Gold-standard dataset construction\nTo calibrate the framework, we used manually annotated spectra as a gold-standard subset containing both positive and negative examples. In total, 472 N-glycan structures were manually interpreted and labeled as either structurally reasonable (1) or structurally ambiguous (0). The subset included 352 spectra from the previous revision (mainly medium/low-confidence spectra) and an additional ~200 randomly selected spectra (to avoid sampling bias in the gold subset and ensure a balanced distribution of positive and negative examples).\n2. Feature selection and preprocessing\nWe used six quantitative indicators derived from the five-criterion system:\n(1) Highest_GlycanScore (continuous),\n(2) GlyTouCan match (binary),\n(3) GlycanType_probability (continuous),\n(4) BranchesOverall_probability (continuous),\n(5) Pathway_status (binary), and\n(6) Reproducibility (excluded in this revision).\nWe explicitly excluded Reproducibility across biological replicates as an input variable, because manual inspection revealed that repeated identifications do not necessarily indicate correctness—for example, the misassigned structure N2H9F1 appeared 24 times but remained incorrect, whereas many structures identified only once were still accurate (77% (115/149) were “structurally reasonable” through manual inspection), which demonstrates that repeated identification frequency cannot effectively discriminate between correct and incorrect structures, and thus should not be included as a confidence criterion. Moreover, several glycans that were identified only once exhibited clear and well-matched B- and Y-ion fragmentation patterns, as shown in Figure R1.\nAll continuous features (Highest_GlycanScore, GlycanType_probability, BranchesOverall_probability) were z-score–standardized before model fitting to eliminate scale effects.\n3. Model training and performance evaluation\nWe trained a logistic regression model to predict structural validity, using five-fold cross-validation (5-fold CV) combined with grid search (GridSearchCV) for hyperparameter optimization. The final configuration was:\nLogisticRegression(max_iter=100, C=1, class_weight='balanced', penalty='l1', solver='saga').\nThe model achieved stable and satisfactory performance across folds: AUC = 0.79; Accuracy = 0.69; Precision = 0.77; Recall = 0.69; F1 score = 0.70. These results indicate that the model provides good discrimination and robustness in distinguishing reasonable from ambiguous structures (demonstrating solid and reproducible discriminative performance).\nTo ensure probabilistic interpretability, we further calibrated the output probabilities using isotonic regression (CalibratedClassifierCV, method='isotonic') with five-fold cross-validation. (Note: logistic regression is already well-calibrated by design; therefore, calibration caused negligible change in model performance. For brevity, we do not report post-calibration metrics separately but confirm that the probabilities can be directly interpreted as true confidence levels.)\n4. Threshold determination and class boundaries\nTo define interpretable confidence categories, we systematically scanned the calibrated probability outputs and calculated Positive Predictive Value (PPV) and Negative Predictive Value (NPV) at each threshold. High confidence was defined as PPV ≥ 0.95. Low confidence was defined as NPV ≥ 0.95. The automatically determined thresholds were: t_high = 0.82; t_low = 0.46. Since no threshold achieved NPV ≥ 0.95, we adopted the cutoff that maximized NPV (t_low = 0.46) (ensuring that the classification boundaries are statistically optimal and interpretable).\nWe then applied the calibrated model to the 2,573 N-glycan structures that had not undergone manual MS2 verification, and automatically assigned confidence levels according to the optimized probability thresholds. The resulting confidence distribution was: High-confidence (probability ≥ 0.82): 2,355 glycans; Intermediate-confidence (0.46 ≤ probability < 0.82): 218 glycans; Low-confidence (probability < 0.46): none (Consistent with the previous scoring system, these 2,573 glycans were previously classified as medium- to high-confidence structures.). The minimum predicted probability among all glycans was 0.33, indicating that all entries exhibit reasonably high modeled confidence. This is consistent with manual MS2 verification, confirming that the model-based confidence stratification aligns with biological plausibility and expert evaluation.\nResponse to Request 2.2-Mathematically specify GlycanScore\nThe specific calculation method for GlycanScore has been detailed in a previous article (Nature Methods, 2021) and is briefly supplemented in the revised Methods section. Briefly, it is calculated as a function of matched ion ratios and precursor accuracy: GlycanScore=(Ratioi)0.6×(RatioY)1.6, where RatioY is the ratio of identified Y5–Yn ions to theoretical Y5–Yn ions, Ratioi is the intensity of Y ions relative to the total ion intensity in the corresponding m/z region, and Errp denotes the precursor mass error (included in the search tolerance Tolp). The formula and exponents were optimized using a parameter-learning strategy previously applied in StrucGP glycopeptide identification, based on 24 MS datasets generated from mouse brain and validated via target–decoy estimation at 1% FDR. We confirmed that small perturbations (±0.2 in exponents) change total identifications by < 2%, indicating robustness.\nIn the revised manuscript, the newly confidence classification results for all 3,045 glycans are now described in the Results section (Page 5), while the detailed criteria are presented in the Methods section (Page 25-26). The machine learning–based confidence framework has been updated in Supplementary Table 2.\nResponse to Request 1, 3, and 4-Reframing, Confidence Cut-off, and Methodological Limits\nSince these three comments are all derived from the implementation of the revised, data-driven confidence framework described in Request 2, we address them together below.\nIn this revision, we have thoroughly updated both the manuscript and figures to reflect the implementation of the new, data-driven confidence framework. The title has been adjusted from the original “High-Resolution” to “Comprehensive N-- glycoproteome atlas” to ensure rigorous scientific narration. The distribution of high-, medium-, and low-confidence glycans was clearly reported in the results and legend upon the first occurrence of 3,045 glycans. The model construction process and evaluation metrics are described in detail in the revised Methods.\nAll downstream biological analyses based on complete glycan structures are now derived exclusively from high-confidence glycans. Medium- and low-confidence glycans-including previously mentioned cases such as sulfated forms, O-glycan co-occurrence, or poly-LacNAc ambiguities-were excluded from these analyses. Conversely, analyses based on substructures were retained, as all substructures were identified through clearly matched B- and Y-ion fragment features. In addition, statistical results and figures have been updated accordingly, and figure legends have been revised to reflect the new data scope and confidence classifications. Importantly, despite the stringent filtering criteria applied, 88.2% (2,687/3,045) of glycans were still classified as high confidence, indicating that the capability of StrucGP to resolve complete glycan structures should not be ignored. Therefore, this study should not be narrowly characterized as limited to substructure analysis. Recognizing the importance of structural accuracy, we have carefully removed all biological conclusions derived from medium- and low-confidence glycans in the previous version and retained only analyses supported by high-confidence structures.\nFinally, the Discussion section has been expanded to describe the methodological limitations of the current workflow, including constraints related to the glycan search space, fragmentation coverage, and branch-number ambiguity. Collectively, these revisions ensure that all conclusions are supported by reproducibly validated data and that the scope of the study accurately reflects the achievable structural resolution of the current MS/MS-based approach.\nIn the revised manuscript, the results based solely on high-confidence glycans have been updated in the Results section (Page 6, 12, 13, 14). The corresponding figures and legends have also been revised accordingly, including Figures 1, 2, 4, 5, 6, 7, S1, S4, S6, S9, S11, S12 and S13, and the associated supplementary tables (Table S1, S2, S3, S8, S10, and S11) have been updated to reflect the new high-confidence statistics. The technical limitations of the current approach have been described in detail in the Discussion section (Page 21).\nOnce again, we sincerely thank you for your constructive and insightful suggestions, which have contributed significantly to enhancing the quality of our manuscript."}, {"round": 5, "reviewer_comments": {"reviewer_2": "Comments to the Authors\nWe appreciate the authors’ efforts to introduce a systematic confidence framework using supervised learning, which clearly improved reliability. While the manuscript has progressed, a bit more clarification would further strengthen the scientific soundness of the framework and its alignment with the paper’s overall narrative. In particular, please use confidence-aware (High/Intermediate/Low) wording for structure counts in the Abstract.\nBelow are two items that remain only partially addressed:\n(1) Manual annotation and misassignment rate — Partially addressed\nThe authors describe a logistic regression model based on five features trained on 472 manually annotated examples. To further improve scientific soundness:\n•Data selection criteria for the gold-standard dataset: Briefly state the criteria by which ~700 previously reported manual annotations were narrowed to 472 (e.g., spectrum quality, structure categories), so readers can assess representativeness.\n•Class counts: Report the numbers of structurally reasonable vs structurally ambiguous examples used for training.\n•Feature contribution: Present regression coefficients (with signs) or feature importance so readers can intuitively understand the relative influence of the five criteria on confidence estimation.\n•GlycanScore interpretation: Add a short, qualitative sentence connecting the score to structural confidence and indicating how higher scores related to the tier boundaries (no formal proof needed).\n•Model positioning: State explicitly that the model is not a true/false classifier, but an integrative framework that combines MS2 evidence and database consistency to assist confidence estimation.\n(6) Orthogonal validation — Not addressed, acceptable as a limitation\nSystematic orthogonal validation was not performed; state this explicitly as a limitation in the Discussion. Because the primary conclusions rest on sub-structure-level comparisons, make that focus explicit in the Discussion and clarify why full orthogonal validation is not required for the main claims. To avoid over-interpretation, use confidence-aware (High/Intermediate/Low) wording for structure counts in the Abstract. For example: “assigned 3,045 candidate N-glycan structures; among these, 2,687 (88.2%) met the high-confidence threshold.” This wording maintains transparency without diminishing the value of the work.\nMinor comment\nIn Fig. 5E, the confidence-level annotation appears missing—please verify and add if needed.\nVisualization of confidence: In figures where color already represents tissue/system (e.g., Fig. 2A), consider using a different visual channel for confidence (e.g., color + hatching or line style).\nA few typos (e.g., microheterogeneitie (line 135), Ploy-sialic/ploySia (lines 340, 348, 437, ), co-existance (line 1005), thw white matter (line 558)); please proofread.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response: We thank Reviewer #2 for the thorough and constructive evaluation of our revised manuscript. We have now revised the Abstract to adopt confidence-aware wording for reported structure counts and added additional methodological details to improve transparency:\nResponse to Comment 1- Manual annotation and misassignment rate\n1. Data selection criteria for the gold-standard dataset\nWe thank the reviewer for pointing this out and apologize for the confusion caused by our previous wording. To clarify, the gold-standard dataset was not narrowed down from ~700 glycans to 472. Instead, the number of glycan structures represented in the manually annotated set actually expanded from 352 to 472. The previously mentioned “~700 spectra” referred to the fact that, for the original set of 352 glycans, we manually interpreted 1–3 spectra per glycan to ensure structural reliability. Because many of these glycans were originally assigned low confidence in the scoring framework, multiple independent manual interpretations were necessary to support the correctness of the structures. As stated in our prior response:\n“To ensure reliability, we manually interpreted 1–3 spectra for each low- confidence glycan as well as for intermediate-confidence glycans that did not conform to canonical biosynthetic pathways, totaling 352 glycans and more than 700 spectra.”\nIn addition, to avoid sampling bias and to ensure a balanced representation of positive and negative examples for the logistic regression model, we subsequently added ~200 randomly selected spectra from the medium-to-high confidence portion of the original scoring results. This led to a final training set comprising 472 glycans and their associated spectra, as previously summarized:\n“The subset included 352 spectra from the previous revision (mainly medium/low- confidence spectra) and an additional ~200 randomly selected spectra (to avoid sampling bias in the gold subset and ensure a balanced distribution of positive and negative examples).”\nTo address the reviewer’s request and further improve transparency, we have now added a more explicit description of the data selection criteria in the Methods section (Page 26) of the revised manuscript.\n2. Class counts\nSpecifically, among the 472 manually annotated N-glycan structures used for model training, 332 were classified as structurally reasonable and 140 as structurally ambiguous. This information has been added to the Methods section (Page 26) to improve transparency.\n3. Feature contribution\nIn the revised manuscript, we now report the contribution of each feature to the logistic regression classifier. Specifically, the model coefficients show the following order of contribution: Pathway (1.25) > All Branches probability (0.49) > GlyTouCan match (0.45) > Highest_GlycanScore (0.41) > GlycanType probability (0.03). These values have been added to the Methods section (Page 26) to provide a clearer understanding of how each feature contributes to the overall confidence prediction.\n4. GlycanScore interpretation\nWe thank the reviewer for this helpful suggestion. In the revised manuscript, we have added a brief explanation to clarify the interpretation of GlycanScore in Methods section (Page 26). Although GlycanScore is only one of several features contributing to the integrative confidence model, it reflects the overall quality of MS/MS spectral evidence supporting a glycan structure. Higher GlycanScores correspond to spectra with more complete and higher-quality Y-ion series, and therefore are generally associated with higher structural confidence.\n5. Model positioning\nIn the revised manuscript, we have now explicitly clarified that the model is not intended to function as a binary true/false classifier, but rather as an integrative confidence-estimation framework. This clarification has been added to the Results section (Page 5) of the revised manuscript.\nResponse to Comment 2- Orthogonal validation\nWe thank the reviewer for this thoughtful comment. In the revised manuscript, we have explicitly acknowledged the lack of systematic orthogonal validation as a limitation in the Discussion (Page 21). We now clarify that all statistical analyses involving glycan structures are restricted to high-confidence assignments, whereas the other biological conclusions of the study rely on sub-structure–level comparisons. As suggested, we have also revised the Abstract (Page 2) by incorporating confidence-aware wording and explicitly reporting the number of high-confidence glycans.\nMinor Comments\nIn the revised manuscript, we have added the missing confidence-level annotation to Fig. 5E and improved the visualization of confidence levels in figures where color is already used to denote tissue or system (e.g., Fig. 2A) by replacing color coding with alternative visual channels, specifically double solid lines, single solid lines, and dashed lines. In addition, all noted typographical errors have been corrected, and the entire manuscript has been thoroughly re-proofread for accuracy and consistency.\nOnce again, we sincerely thank you for your constructive and insightful suggestions, which have contributed significantly to enhancing the quality of our manuscript."}]

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