Native N-glycome profiling of single cells and ng-level blood isolates using label-free capillary electrophoresis-mass spectrometry.

The development of reliable single-cell dispensers and substantial sensitivity improvement in mass spectrometry made proteomic profiling of individual cells achievable. Yet, there are no established methods for single-cell glycome analysis due to the inability to amplify glycans and sample losses associated with sample processing and glycan labeling. In this work, we present an integrated platform coupling online in-capillary sample processing with high-sensitivity label-free capillary electrophoresis-mass spectrometry for N-glycan profiling of single mammalian cells. Direct and unbiased quantitative characterization of single-cell surface N-glycomes are demonstrated for HeLa and U87 cells, with the detection of up to 100 N-glycans per single cell. Interestingly, N-glycome alterations are unequivocally detected at the single-cell level in HeLa and U87 cells stimulated with lipopolysaccharide. The developed workflow is also applied to the profiling of ng-level amounts (5-500 ng) of blood-derived protein, extracellular vesicle, and total plasma isolates, resulting in over 170, 220, and 370 quantitated N-glycans, respectively.

Glycoproteomics: Charting new territory in mass spectrometry and glycobiology.

Glycosylation is an incredibly common and diverse post-translational modification that contributes widely to cellular health and disease. Mass spectrometry is the premier technique to study glycoproteins; however, glycoproteomics has lagged behind traditional proteomics due to the challenges associated with studying glycosylation. For instance, glycans dissociate by collision-based fragmentation, thus necessitating electron-based fragmentation for site-localization. The vast glycan heterogeneity leads to lower overall abundance of each glycopeptide, and often, ion suppression is observed. One of the biggest issues facing glycoproteomics is the lack of reliable software for analysis, which necessitates manual validation and serves as a massive bottleneck in data processing. Here, I will discuss each of these challenges and some ways in which the field is attempting to address them, along with perspectives on how I believe we should move forward.

O-GlycoIsoQuant: A Novel O-Glycome Quantitative Approach through Superbase Release and Isotopic Girard's P Labeling.

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 ß-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.

LectoScape: A Highly Multiplexed Imaging Platform for Glycome Analysis and Biomedical Diagnosis.

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 Galß1, 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.

CSF N-Glycomics Using High-Throughput UPLC-ESI Techniques in Alzheimer's Disease.

In this chapter, we will present a high-throughput method applied in our laboratory for the structural elucidation of the cerebrospinal fluid (CSF) N-glycome. This methodology is based on a commercial equipment developed by WATERS™ to speed up N-deglycosylation and N-glycan labeling of glycoproteins of pharmaceutical and biological interest such as monoclonal antibodies. This analytical kit is sold under the trade name of RapiFluor-MS (RFMS). We have slightly modified the methodology, increasing the glycosylation time and using a high-resolution mass analyzer for the analysis of CSF N-glycans, thus obtaining a high-throughput method (up to 96 samples simultaneously), mass accuracy better than 5 ppm, and the ability to separate and identify isomers.

CSF N-Glycomics Using MALDI MS Techniques.

In this chapter, we will present the methodology currently applied in our laboratory for the structural elucidation of the cerebrospinal fluid (CSF) N-glycome. N-glycans are released from denatured carboxymethylated glycoproteins by digestion with peptide-N-glycosidase F (PNGase F) and purified using both C18 Sep-Pak® and porous graphitized carbon (PGC) HyperSep™ Hypercarb™ solid phase extraction (SPE) cartridges. The glycan pool is subsequently permethylated to increase mass spectrometry sensitivity. Molecular assignments are performed through matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI TOF MS) analysis considering either the protein N-linked glycosylation pathway or MALDI TOF MS/MS data. Each stage has been optimized to obtain high-quality mass spectra in reflector mode with an optimal signal-to-noise ratio up to m/z 4800. This method has been successfully adopted to associate specific N-glycome profiles to the early and the advanced phases of Alzheimer's disease (AD).

Analysis of Native and Permethylated N-Glycan Isomers Using MGC-LC-MS Techniques.

Glycosylation is an important post-translational modification that affects many critical cellular functions such as adhesion, signaling, protein stability, and function, among others. Abnormal glycosylation has been linked to many diseases. As such, the investigation of glycans and their roles in disease pathway and progression is important. Glycan analysis can be challenging, however, due to such factors as the heterogeneity of glycans and isomers as well as the poor ionization efficiency provided by mass spectrometry analyses. This chapter presents efficient methods that overcome these and other challenges for the analysis of native and permethylated N-glycan isomers in biological samples. Instructions regarding the packing of the MGC column, the N-glycan sample prep, and the LC-MS conditions are also provided.

Targeted Glycoproteomics Analysis Using MRM/PRM Approaches.

MS-target analyses are frequently utilized to analyze and validate structural changes of biomolecules across diverse fields of study such as proteomics, glycoproteomics, glycomics, lipidomics, and metabolomics. Targeted studies are commonly conducted using multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM) techniques. A reliable glycoproteomics analysis in intricate biological matrices is possible with these techniques, which streamline the analytical workflow, lower background interference, and enhance selectivity and specificity.

Droplet-Based Glycan and RNA Sequencing for Profiling the Distinct Cellular Glyco-States in Single Cells.

Plate-based single-cell glycan and RNA sequencing (scGR-seq) is previously developed to realize the integrated analysis of glycome and transcriptome in single cells. However, the sample size is limited to only a few hundred cells. Here, a droplet-based scGR-seq is developed to address this issue by adopting a 10x Chromium platform to simultaneously profile ten thousand cells' glycome and transcriptome in single cells. To establish droplet-based scGR-seq, a comparative analysis of two distinct cell lines is performed: pancreatic ductal adenocarcinoma cells and normal pancreatic duct cells. Droplet-based scGR-seq revealed distinct glycan profiles between the two cell lines that showed a strong correlation with the results obtained by flow cytometry. Next, droplet-based scGR-seq is applied to a more complex sample: peripheral blood mononuclear cells (PBMC) containing various immune cells. The method can systematically map the glycan signature for each immune cell in PBMC as well as glycan alterations by cell lineage. Prediction of the association between the glycan expression and the gene expression using regression analysis ultimately leads to the identification of a glycan epitope that impacts cellular functions. In conclusion, the droplet-based scGR-seq realizes the high-throughput profiling of the distinct cellular glyco-states in single cells.

Integrated analysis of natural glycans using a versatile pyrazolone-type heterobifunctional tag ANPMP.

Glycans mediate various biological processes through carbohydrate-protein interactions, and glycan microarrays have become indispensable tools for understanding these mechanisms. However, advances in functional glycomics are hindered by the absence of convenient and universal methods for obtaining natural glycan libraries with diverse structures from glycoconjugates. To address this challenge, we have developed an integrative approach that enables one-pot release and simultaneously capture, separation, structural characterization, and functional analysis of N/O-glycans. Using this approach, glycoconjugates are incubated with a pyrazolone-type heterobifunctional tag-ANPMP to obtain glycan-2ANPMP conjugates, which are then converted to glycan-AEPMP conjugates. We prepared a tagged glycan library from porcine gastric mucin, soy protein, human milk oligosaccharides, etc. Following derivatization by N-acetylation and permethylation, glycans were subjected to detailed structural characterization by ESI-MSn analysis, which revealed >83 highly pure glycan-AEPMPs containing various natural glycan epitopes. A shotgun microarray is constructed to study the fine details of glycan-bindings by proteins and antisera.

Comprehensive Comparison of Bioactive N-Glycans among Seven Species of Livestock and Poultry Plasma Using a Relative Quantification Strategy.

Glycans have been proven to play special roles in keeping human health as a class of nutritional and bioactive ingredients in many food materials. However, their broad use in the food industry is hindered by the lack of comprehensive analytical methods for high-quality food glycomics studies and large-quantity raw materials for their production. This study focuses on structural identification and quantitative comparison of bioactive N-glycans in seven species of livestock and poultry plasma as potential natural glycan resources by a novel comprehensive relative quantification strategy based on stable isotope labeling with nondeuterated and deuterated 4-methyl-1-(2-hydrazino-2-oxoethyl)-pyridinium bromide (d0/d7-HMP) in combination with linkage-specific derivatization of sialic acid residues. Methodological validation of the method in terms of detection sensitivity, signal resolution, quantification linearity, precision, and accuracy on model neutral and complicated sialylated glycans demonstrated its advantages over the existing methods. Based on this method, a series of bioactive N-glycans were found in seven species of livestock and poultry plasma, and their differences in structure, abundance percentages, and relative contents of N-glycans were revealed, demonstrating their excellent applicability for comprehensive food glycomics analysis and great exploitation potential of these plasma samples as large-quantity raw materials in producing bioactive N-glycans for application in food and pharmaceutical industries.

Decoding glycomics with a suite of methods for differential expression analysis.

Glycomics, the comprehensive profiling of all glycan structures in samples, is rapidly expanding to enable insights into physiology and disease mechanisms. However, glycan structure complexity and glycomics data interpretation present challenges, especially for differential expression analysis. Here, we present a framework for differential glycomics expression analysis. Our methodology encompasses specialized and domain-informed methods for data normalization and imputation, glycan motif extraction and quantification, differential expression analysis, motif enrichment analysis, time series analysis, and meta-analytic capabilities, synthesizing results across multiple studies. All methods are integrated into our open-source glycowork package, facilitating performant workflows and user-friendly access. We demonstrate these methods using dedicated simulations and glycomics datasets of N-, O-, lipid-linked, and free glycans. Differential expression tests here focus on human datasets and cancer vs. healthy tissue comparisons. Our rigorous approach allows for robust, reliable, and comprehensive differential expression analyses in glycomics, contributing to advancing glycomics research and its translation to clinical and diagnostic applications.

6-Plex mdSUGAR Isobaric-Labeling Guide Fingerprint Embedding for Glycomics Analysis.

Glycans are vital biomolecules with diverse functions in biological processes. Mass spectrometry (MS) has become the most widely employed technology for glycomics studies. However, in the traditional data-dependent acquisition mode, only a subset of the abundant ions during MS1 scans are isolated and fragmented in subsequent MS2 events, which reduces reproducibility and prevents the measurement of low-abundance glycan species. Here, we reported a new method termed 6-plex mdSUGAR isobaric-labeling guide fingerprint embedding (MAGNI), to achieve multiplexed, quantitative, and targeted glycan analysis. The glycan peak signature was embedded by a triplicate-labeling strategy with a 6-plex mdSUGAR tag, and using ultrahigh-resolution mass spectrometers, the low-abundance glycans that carry the mass fingerprints can be recognized on the MS1 spectra through an in-house developed software tool, MAGNIFinder. These embedded unique fingerprints can guide the selection and fragmentation of targeted precursor ions and further provide rich information on glycan structures. Quantitative analysis of two standard glycoproteins demonstrated the accuracy and precision of MAGNI. Using this approach, we identified 304 N-glycans in two ovarian cancer cell lines. Among them, 65 unique N-glycans were found differentially expressed, which indicates a distinct glycosylation pattern for each cell line. Remarkably, 31 N-glycans can be quantified in only 1 × 103 cells, demonstrating the high sensitivity of our method. Taken together, our MAGNI method offers a useful tool for low-abundance N-glycan characterization and is capable of determining small quantitative differences in N-glycan profiling. Therefore, it will be beneficial to the field of glycobiology and will expand our understanding of glycosylation.

Glycan/Protein-Stable Isotope Labeling in Cell Culture for Enabling Concurrent Quantitative Glycomics/Proteomics/Glycoproteomics.

The complexity and heterogeneity of protein glycosylation present an analytical challenge to the studies of characterization and quantitation. Various LC-MS-based quantitation strategies have emerged in recent decades. Metabolic stable isotope labeling has been developed to enhance the accurate LC/MS-based quantitation between different cell lines. Stable isotope labeling by amino acids in a cell culture (SILAC) is the most widely used metabolic labeling method in proteomic analysis. However, it can only label the peptide backbone and is thus limited in glycomic studies. Here, we present a metabolic isotope labeling strategy, named GlyProSILC (Glycan Protein Stable Isotope Labeling in Cell Culture), that can label both the glycan motif and peptide backbone from the same batch of cells. It was performed by feeding cells with a heavy medium containing amide-15N-glutamine, 13C6-arginine (Arg6), and 13C6-15N2-lysine (Lys8). No significant change of cell line metabolism after GlyProSILC labeling was observed based on transcriptomic, glycomic, and proteomic data. The labeling conditions, labeling efficiency, and quantitation accuracy were investigated. After quantitation correction, we simultaneously quantified 62 N-glycans, 574 proteins, and 344 glycopeptides using the same batch of mixed 231BR/231 cell lines. So far, GlyProSILC provides an accurate and effective quantitation approach for glycomics, proteomics, and glycoproteomics in a cell culture system.

Mouse brain glycomics - Insights from exploring the Allen Brain Atlas and the implications for the neuroimmune brain.

The Allen Institute Mouse Brain Atlas, with visualisation using the Brain Explorer software, offers a 3-dimensional view of region-specific RNA expression of thousands of mouse genes. In this Viewpoint, we focused on the region-specific expression of genes related to cellular glycosylation, and discuss their relevance towards psychoneuroimmunology. Using specific examples, we show that the Atlas validates existing observations reported by others, identifies previously unknown potential region-specific glycan features, and highlights the need to promote collaborations between glycobiology and psychoneuroimmunology researchers.

A Holistic 4D Approach to Optimize Intrinsic and Extrinsic Factors Contributing to Variability in Microarray Biosensing in Glycomics.

Protein-carbohydrate interactions happen to be a crucial facet of biology, discharging a myriad of functions. Microarrays have become a premier choice to discern the selectivity, sensitivity and breadth of these interactions in a high-throughput manner. The precise recognition of target glycan ligands among the plethora of others is central for any glycan-targeting probe being tested by microarray analyses. Ever since the introduction of the microarray as an elemental tool for high-throughput glycoprofiling, numerous distinct array platforms possessing different customizations and assemblies have been developed. Accompanying these customizations are various factors ushering variances across array platforms. In this primer, we investigate the influence of various extrinsic factors, namely printing parameters, incubation procedures, analyses and array storage conditions on the protein-carbohydrate interactions and evaluate these factors for the optimal performance of microarray glycomics analysis. We hereby propose a 4D approach (Design-Dispense-Detect-Deduce) to minimize the effect of these extrinsic factors on glycomics microarray analyses and thereby streamline cross-platform analyses and comparisons. This work will aid in optimizing microarray analyses for glycomics, minimize cross-platform disparities and bolster the further development of this technology.

Characterization of aberrant glycosylation associated with osteoarthritis based on integrated glycomics methods.

BACKGROUND: Osteoarthritis (OA) is the most common form of arthritis, affecting millions of aging people. Investigation of abnormal glycosylation is essential for the understanding of pathological mechanisms of OA. METHODS: The total protein was isolated from OA (n = 13) and control (n = 11) cartilages. Subsequently, glycosylation alterations of glycoproteins in OA cartilage were investigated by lectin microarrays and intact glycopeptides analysis. Finally, the expression of glycosyltransferases involved in the synthesis of altered glycosylation was assessed by qPCR and GEO database. RESULTS: Our findings revealed that several glycopatterns, such as α-1,3/6 fucosylation and high-mannose type of N-glycans were altered in OA cartilages. Notably, over 27% of identified glycopeptides (109 glycopeptides derived from 47 glycoproteins mainly located in the extracellular region) disappeared or decreased in OA cartilages, which is related to the cartilage matrix degradation. Interestingly, the microheterogeneity of N-glycans on fibronectin and aggrecan core protein was observed in OA cartilage. Our results combined with GEO data indicated that the pro-inflammatory cytokines altered the expression of glycosyltransferases (ALG3, ALG5, MGAT4C, and MGAT5) which may contribute to the alterations in glycosylation. CONCLUSION: Our study revealed the abnormal glycopatterns and heterogeneities of site-specific glycosylation associated with OA. To our knowledge, it is the first time that the heterogeneity of site-specific N-glycans was reported in OA cartilage. The results of gene expression analysis suggested that the expression of glycosyltransferases was impacted by pro-inflammatory cytokines, which may facilitate the degradation of protein and accelerate the process of OA. Our findings provide valuable information for the understanding of molecular mechanisms in the pathogenesis of OA.

The Role of Clinical Glyco(proteo)mics in Precision Medicine.

Glycoproteomics reveals site-specific O- and N-glycosylation that may influence protein properties including binding, activity, and half-life. The increasingly mature toolbox with glycomic and glycoproteomic strategies is applied for the development of biopharmaceuticals and the discovery and clinical evaluation of glycobiomarkers in various disease fields. Notwithstanding the contributions of glycoscience in identifying new drug targets, the current report is focused on the biomarker modality that is of interest for diagnostic and monitoring purposes. To this end, it is noted that the identification of biomarkers has received more attention than the corresponding quantification. Most analytical methods are very efficient in detecting large numbers of analytes, but developments to accurately quantify these have so far been limited. In this perspective, a parallel is made with earlier proposed tiers for protein quantification using mass spectrometry. Moreover, the foreseen reporting of multimarker readouts is discussed to describe an individual's health or disease state and their role in clinical decision-making. The potential of longitudinal sampling and monitoring of glycomic features for diagnosis and treatment monitoring is emphasized. Finally, different strategies that address the quantification of a multimarker panel are discussed.

GlycoNote with Iterative Decoy Searching and Open-Search Component Analysis for High-Throughput and Reliable Glycan Spectral Interpretation.

Mass spectrometry-based glycome analysis is a viable strategy for the compositional and functional exploration of glycosylation. However, the lack of generic tools for high-throughput and reliable glycan spectral interpretation largely hampers the broad usability of glycomic research. Here, we developed a generic and reliable glycomic tool, GlycoNote, for comprehensive and precise glycome analysis. GlycoNote supports interpretation of tandem-mass spectrometry glycomic data from any sample source, uses a novel target-decoy method with iterative decoy searching for highly reliable result output, and embeds an open-search component analysis mode for heterogeneity analysis of monosaccharides and modifications. We tested GlycoNote on several different large-scale glycomic datasets, including human milk oligosaccharides, N- and O-glycome from human cell lines, plant polysaccharides, and atypical glycans from Caenorhabditis elegans, demonstrating its high capacity for glycome analysis. An application of GlycoNote to the analysis of labeled and derived glycans further demonstrates its broad usability in glycomic studies. By enabling generic characterization of various glycan types and elucidation of component heterogeneity in glycomic samples, the freely available GlycoNote is a promising tool for facilitating glycomics in glycobiology research.