Glycomics, an emerging \"omics\" technology that was developed after genomics and proteomics, is a discipline that studies the composition, structure, and functions of glycomes in cells, tissues, and organisms. Glycomics plays key roles in understanding the laws of major life activities, disease prevention and treatment, and drug quality control and development. At present, the structural analysis of glycans relies mainly on mass spectrometry. However, glycans have low abundance in biological samples. In addition, factors such as variable monosaccharide compositions, differences in glycosidic bond positions and modes, diverse branching structures, contribute to the complexity of the compositions and structures of glycans, posing great challenges to glycomics research. Liquid chromatography can effectively remove matrix interferences and enhance glycan separation to improve the mass spectrometric response of glycans. Thus, liquid chromatography and liquid chromatography coupled with mass spectrometry are important technical tools that have been actively applied to solve these problems; these technologies play indispensable roles in glycomics research. Different studies have highlighted similarities and differences in the applications of various types of liquid chromatography, which also reflects the versatility and flexibility of this technology. In this review, we first discuss the enrichment methods for glycans and their applications in glycomics research from the perspective of chromatographic separation mechanisms. We then compare the advantages and disadvantages of these methods. Some glycan-enrichment modes include affinity, hydrophilic interactions, size exclusion, and porous graphitized carbon adsorption. A number of newly developed materials exhibit excellent glycan-enrichment ability. We enumerate the separation mechanisms of reversed-phase high performance liquid chromatography (RP-HPLC), high performance anion-exchange chromatography (HPAEC), hydrophilic interaction chromatography (HILIC), and porous graphitic carbon (PGC) chromatography in the separation and analysis of glycans, and describe the applications of these methods in the separation of glycans, glycoconjugates, and glyco-derivatives. Among these methods, HILIC and PGC chromatography are the most widely used, whereas HPAEC and RP-HPLC are less commonly used. The HILIC and RP-HPLC modes are often used for the separation of derived glycans. The ionization efficiency and detectability of glycans are significantly improved after derivatization. However, the derivatization process is relatively cumbersome, and byproducts inevitably affect the accuracy and completeness of the detection results. HPAEC and PGC chromatography exhibit good separation effects on nonderivative glycans, but issues related to the detection integrity of low-abundance glycans owing to their poor detection effect continue to persist. Therefore, the appropriate analytical method for a specific sample or target analyte or mutual verification must be selected. Finally, we highlight the research progress in various chromatographic methods coupled with mass spectrometry for glycomics analysis. Significant progress has been made in glycomics research in recent years owing to advancements in the development of chromatographic separation techniques. However, several significant challenges remain. As the development of novel separation materials and methods continues, chromatographic techniques may be expected to play a critical role in future glycomics research.

Nasopharyngeal carcinoma (NPC) is closely related to Epstein-Barr virus (EBV) infection, and glycosylation of proteins is associated with precancerous lesions and carcinogenesis of NPC, and viral glycoproteins mediates the fusion of viruses with B cells or epithelial cells in the infection stage, promoting the conversion of normal epithelial cells into cancer cells. In the process of occurrence and development of NPC, various glycoproteins in the body promote or inhibit the proliferation, invasion, metastasis, and drug resistance of tumor cells, such as the tumor inhibitory effect of NGX6 and inhibin B (INHBB); the cancer-promoting effect of tenascin-C (TNC), fibronectin 1 (FN1), insulin-like growth factor binding protein-3 (IGFBP3), serglycin, and its core protein; and some effects of glycosylation of immune proteins on immunotherapy in NPC. This article provides an overview of the research progress on the interaction of glycoproteins associated with EBV infection with the occurrence and development of NPC.

Idiopathic membranous nephropathy (IMN) is an antibody-mediated and kidney-specific autoimmune disease, with the antigen phospholipase A2 receptor 1 (PLA2R1) accounting for approximately 70% of IMN cases. Although a variety of new podocyte target antigens and their autoantibodies have been identified, they are still of limited diagnostic and therapeutic value due to lack of high specificity and sensitivity. N-glycans play vital roles in renal system and their pathobiological relevance has become increasingly recognized in many kidney diseases, but not fully explored in IMN. To find possible glyco-signatures for PLA2R1-related IMN diagnosis, we herein established a comprehensive workflow for total serum N-glycome analysis based on our recently developed mass spectrometry (MS)-based N-glycan purification method, named Ultrafast Glycoprotein Immobilization for Glycan extraction (UltraGIG). A total of 191 N-glycans were identified from IMN patients, representing the largest N-glycome dataset in IMN. Compared to healthy controls, up-regulation of sialylation and core-fucosylation as well as down-regulation of galactosylation were observed in PLA2R1-positive IMN patients, and up-regulation of hyper-galactosylation was specific for PLA2R1-negative IMN patients. A six-glycan marker panel consisting of H4N3S1, H4N3F1, H6N4S2, H6H5F1S2, H6N5 and H6N6F1S1, was proposed to aid in the accurate diagnosis of PLA2R1-related IMN, which provided new insights into IMN biomarker study. SIGNIFICANCE: PLA2R1-related IMN is a kidney-specific autoimmune disease with a high risk of developing end-stage renal disease (ESRD) and even kidney failure. Current biomarkers are still of limited diagnostic and therapeutic value due to lack of high specificity and sensitivity. An in-depth MS analysis of total serum N-glycome of PLA2R1-related IMN patients was conducted for the first time. We generated the largest dataset of serum N-glycome for IMN to date, and proposed a novel six-glycan marker panel that may help the accurate diagnosis of PLA2R1-related IMN.

Ganglioside glycans are ubiquitous and complex biomolecules that are involved in a wide range of biological functions and disease processes. Variations in sialylation and sulfation render the structural complexity and diversity of ganglioside glycans, and influence protein-carbohydrate interactions. Structural and functional insights into the biological roles of these glycans are impeded due to the limited accessibility of well-defined structures. Here we report an integrated chemoenzymatic strategy for expeditious and systematic synthesis of a comprehensive 65-membered ganglioside glycan library covering all possible patterns of sulfation and sialylation. This strategy relies on the streamlined modular assembly of three common sialylated precursors by highly stereoselective iterative sialylation, modular site-specific sulfation through flexible orthogonal protecting-group manipulations and enzymatic-catalysed diversification using three sialyltransferase modules and a galactosidase module. These diverse ganglioside glycans enable exploration into their structure-function relationships using high-throughput glycan microarray technology, which reveals that different patterns of sulfation and sialylation on these glycans mediate their unique binding specificities.

Quantitative glycosylation analysis serves as an effective tool for detecting changes in glycosylation patterns in cancer and various diseases. However, compared with N-glycans, O-glycans present challenges in both qualitative and quantitative mass spectrometry analysis due to their low abundance, ease of peeling, lack of a universal enzyme, and difficult accessibility. To address this challenge, we developed O-GlycoIsoQuant, a novel O-glycome quantitative approach utilizing superbase release and isotopic Girard\'s P labeling. This method facilitates rapid and efficient nonreducing β-elimination to dissociate O-glycans from proteins using the organic superbase, 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU), combined with light and heavy isotopic Girard\'s reagent P (GP) labeling for relative quantification of O-glycans by mass spectrometry. Employing this method, labeled O-glycans exhibit a double peak with a mass difference of 5 Da, suitable for stable relative quantification. The O-GlycoIsoQuant method is characterized by its high labeling efficiency, excellent reproducibility (CV < 20%), and good linearity (R2 > 0.99), across a dynamic range spanning a 100-fold range. This method was applied to various complex sample types, including human serum, porcine spermatozoa, human saliva, and urinary extracellular vesicles, detecting 33, 39, 49, and 37 O-glycans, respectively, thereby demonstrating its broad applicability.

Glycosylation, a fundamental biological process, involves the attachment of glycans to proteins, lipids, and RNA, and it plays a crucial role in various biological pathways. It is of great significance to obtain the precise spatial distribution of glycosylation modifications at the cellular and tissue levels. Here, we introduce LectoScape, an innovative method enabling detailed imaging of tissue glycomes with up to 1 μm resolution through image mass cytometry (IMC). This method utilizes 12 distinct, nonoverlapping lectins selected via microarray technology, enabling the multiplexed detection of a wide array of glycans. Furthermore, we developed an efficient labeling strategy for these lectins. Crucially, our approach facilitates the concurrent imaging of diverse glycan motifs, including N-glycan and O-glycan, surpassing the capabilities of existing technologies. Using LectoScape, we have successfully delineated unique glycan structures in various cell types, enhancing our understanding of the glycan distribution across human tissues. Our method has identified specific glycan markers, such as α2,3-sialylated Galβ1, 3GalNAc in O-glycan, and terminal GalNAc, as diagnostic indicators for cervical intraepithelial neoplasia. This highlights the potential of LectoScape in cancer diagnostics through the detection of abnormal glycosylation patterns.

Mapping the N-glycome of porcine sperm before and after sperm capacitation is important for understanding the rearrangement of glycoconjugates during capacitation. In this work, we characterized the N-glycome on the membranes of 18 pairs of fresh porcine sperm before capacitation and porcine sperm after capacitation by MALDI-MS (Matrix-assisted laser desorption/ionization-mass spectrometry). A total of 377 N-glycans were detected and a comprehensive N-glycome map of porcine sperm membranes before and after capacitation was generated, which presents the largest N-glycome dataset of porcine sperm cell membranes. Statistical analysis revealed a significantly higher level of high mannose glycosylation and a significantly lower level of fucosylation, galactosylation, and α-2,6-NeuAc after capacitation, which is further verified by flow cytometry and lectin blotting. This research reveals new insights into the relationship between N-glycosylation variations and sperm capacitation, including the underlying mechanisms of the capacitation process.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, with a 5-year survival rate of less than 10%, owing to its late-stage diagnosis. Early detection of pancreatic cancer (PC) can significantly increase survival rates.
OBJECTIVE: To identify the serum biomarker signatures associated with early-stage PDAC by serum N-glycan analysis.
METHODS: An extensive patient cohort was used to determine a biomarker signature, including patients with PDAC that was well-defined at an early stage (stages I and II). The biomarker signature was derived from a case-control study using a case-cohort design consisting of 29 patients with stage I, 22 with stage II, 4 with stage III, 16 with stage IV PDAC, and 88 controls. We used multiparametric analysis to identify early-stage PDAC N-glycan signatures and developed an N-glycan signature-based diagnosis model called the \"Glyco-model\".
RESULTS: The biomarker signature was created to discriminate samples derived from patients with PC from those of controls, with a receiver operating characteristic area under the curve of 0.86. In addition, the biomarker signature combined with cancer antigen 19-9 could discriminate patients with PDAC from controls, with a receiver operating characteristic area under the curve of 0.919. Glyco-model demonstrated favorable diagnostic performance in all stages of PC. The diagnostic sensitivity for stage I PDAC was 89.66%.
CONCLUSIONS: In a prospective validation study, this serum biomarker signature may offer a viable method for detecting early-stage PDAC.

BACKGROUND: Hepatocellular carcinoma (HCC) ranks as the third most common cause of cancer related death globally, representing a substantial challenge to global healthcare systems. In China, the primary risk factor for HCC is the hepatitis B virus (HBV). Aberrant serum glycoconjugate levels have long been linked to the progression of HBV-associated HCC (HBV-HCC). Nevertheless, few study systematically explored the dysregulation of glycoconjugates in the progression of HBV-associated HCC and their potency as the diagnostic and prognostic biomarker.
METHODS: An integrated strategy that combined transcriptomics, glycomics, and glycoproteomics was employed to comprehensively investigate the dynamic alterations in glyco-genes, N-glycans, and glycoproteins in the progression of HBV- HCC.
RESULTS: Bioinformatic analysis of Gene Expression Omnibus (GEO) datasets uncovered dysregulation of fucosyltransferases (FUTs) in liver tissues from HCC patients compared to adjacent tissues. Glycomic analysis indicated an elevated level of fucosylated N-glycans, especially a progressive increase in fucosylation levels on IgA1 and IgG2 determined by glycoproteomic analysis.
CONCLUSIONS: The findings indicate that the abnormal fucosylation plays a pivotal role in the progression of HBV-HCC. Systematic and integrative multi-omic analysis is anticipated to facilitate the discovery of aberrant glycoconjugates in tumor progression.

Protein glycosylation is associated with the pathogenesis of various cancers. The utilization of certain glycans in cancer diagnosis models holds promise, yet their accuracy is not always guaranteed. Here, we investigated the utility of deep learning techniques, specifically random forests combined with transfer learning, in enhancing serum glycome\'s discriminative power for cancer diagnosis (including ovarian cancer, non-small cell lung cancer, gastric cancer, and esophageal cancer). We started with ovarian cancer and demonstrated that transfer learning can achieve superior performance in data-disadvantaged cohorts (AUROC >0.9), outperforming the approach of PLS-DA. We identified a serum glycan-biomarker panel including 18 serum N-glycans and 4 glycan derived traits, most of which were featured with sialylation. Furthermore, we validated advantage of the transfer learning scheme across other cancer groups. These findings highlighted the superiority of transfer learning in improving the performance of glycans-based cancer diagnosis model and identifying cancer biomarkers, providing a new high-fidelity cancer diagnosis venue.