Glycan profiling, also known as glycan analysis or glycomic analysis, is a field of analysis of glycans. It provides data on the glycans present in a sample. Glycan profiling is a key method linking glycomics to biological information. Glycan profiling consists of the separation, detection and characterization of glycans and is used to give an overview of the monosaccharide composition, linkage, branching, occupancy, and the type and position of the glycan termini. Glycan profiling can be performed on either proteins or lipids. Glycan profiling has found a wide range of applications from basic glycomics to quality control of biopharmaceutical products. Glycan profiling data has been widely used to identify glycan biomarkers in a variety of diseases, including cancer and neurodegenerative diseases. In the biopharmaceutical industry, glycan structure is often considered a critical quality attribute of biotherapeutics. Glycan profiling of glycoproteins is therefore critical for monitoring glycan consistency during biopharmaceutical manufacture, to ensure batch-to-batch consistency. The glycan composition is determined after release from the protein carrier by means of enzymatic or chemical methods. The released glycans are then derivatized to add a fluorescent label. The glycan analysis is then performed, for example using hydrophilic interaction chromatography (HILIC) or capillary electrophoresis (CE) to separate the glycans. The glycans can be identified using mass spectrometry. Tandem mass spectrometry is commonly used for glycan profiling to provide structural information of glycans by fragmentation. Glycopeptide analysis also allows for site localization.
Analytical glycan profiling, often performed using mass spectrometry, has gained importance as it is now clear that carbohydrate post-translational modifications (PTMs) are an additional source of information that cannot be predicted from genome or proteome information. Glycosylation is by far more complex than other PTMs and the encoded protein sequence does not in general predict the glycan structure as it is not directly templated. Glycosylation is synthesized in a network of competing glycosyltransferases and has far greater structural complexity than nucleic acids and proteins. Glycosylation is one of the PTMs that can be used to help determine a protein's structure (e.g., folded vs. unfolded), stability, trafficking and immune fate. Glycan profiling often needs to be able to distinguish between macro-heterogeneity (site occupancy) and micro-heterogeneity (glycan structure at a single glycosylation site) of glycans attached to proteins. The analysis of glycans has a long history starting with chromatographic techniques and more recently more specialized techniques for separating and characterizing N-glycans, O-glycans, and glycolipids have been developed including mass spectrometry-based workflows for the analysis of N-glycans with high sensitivity. Glycan profiling has become an essential component in the analysis of biopharmaceutical proteins because glycosylation patterns are very sensitive to process conditions and can have a major impact on the efficacy, immunogenicity, and pharmacokinetics of the protein therapeutic. Glycan profiling can now be used to guide recombinant protein development from clone selection (based on glycoform composition) to release testing.
Fig. 1 The workflow representation of glycan analysis illustrating various techniques employed at different stages of glycomics workflow, including glycan release, purification, derivatization, and detection.1,2
Glycan profiling refers to an analytical approach to determine the structures of all glycans that are present on a particular glycoprotein in a mixture. It aims to identify and quantify all glycoforms in the mixture. Glycan profiling can involve the detection of glycans using HILIC-FLD or capillary electrophoresis with charge-to-size and polarity-based separations. To this end, glycans are first released from the peptide using either peptide-N-glycosidase F (PNGase F) to cleave N-glycans from the asparagine or through chemical release using sodium hydroxide to reduce the linkage through reductive β-elimination and release O-glycans from serine/threonine as alditols which are more stable. Released glycans are then labeled with fluorescent tags such as APTS or 2-AB. Assignment of structures is done using exoglycosidase arrays (both N and O) where digestion of terminal monosaccharides proceeds with linkage-specific enzymes and causes a shift in retention time as a given monosaccharide is removed in a step-wise manner. An orthogonal assignment can be done using mass spectrometry where MALDI-TOF MS can rapidly analyze released glycans and LC-ESI-MS/MS can fragment glycopeptides to give both glycosidic and peptide backbone fragments. This gives the site and the nature of glycan at that site and has thus evolved as a "bottom-up" glycoproteomic approach. In the case of O-glycans that do not have a universal release enzyme, the "bottom-up" glycoproteomics workflow would first use a protease to digest proteins into peptides and then use chromatography on a lectin column to enrich glycopeptides before MS analysis. The ultimate goal of glycan profiling is to combine orthogonal techniques to perform multi-attribute glycan profiling for a more complete glycan characterization. This will be necessary to differentiate isomeric glycans that only differ in linkage position or anomericity. This has also been an important issue in regulatory glycomics.
Glycan profiling's importance is highlighted in the field of therapeutic proteins where the carbohydrate moiety can directly affect the pharmacokinetics, immunogenicity and mode of action of the drug, in ways that are not readily predicted by the amino acid sequence of the protein backbone. Variations in glycosylation can introduce heterogeneity into a biopharmaceutical batch that result in undesirable batch-to-batch variation that is deleterious to clinical consistency. Asialo and afucosylated antibodies are rapidly cleared from the circulation and increased ADCC activity respectively, thus changes in the glycoform population arising during production can significantly alter the therapeutic index and bioactivity. Glycan profiling is also required as part of quality control in the development of glycoprotein therapeutics as regulatory authorities have required the demonstration that relevant glycan attributes remain within the specification ranges established with clinical lots, and out of specification batches can be rejected or further bridging studies are needed. In the context of glycomics, the use of glycan profiling provides insight into the mechanism of a given biological process, for instance, to pinpoint the glycosylation changes that are biomarkers of a certain disease state, such as cancer and inflammation, where sialylation and fucosylation patterns of serum glycoproteins are often modified. Glycan profiling is also necessary for the study of O-glycans due to their lack of an enzyme that can universally cleave all types of O-glycans and their extreme site heterogeneity. O-Glycans are important biological modifiers of the mucin function of O-glycoproteins, cell adhesion, and pathogen binding. Glycan profiling is also required in the design of vaccine glycoconjugates. The immunogenicity of the glycoconjugate is directly related to the density and presentation of the polysaccharide antigen. Profiling the glycoconjugates helps to ensure that this density is within the desired range. Glycan profiling is also an important tool for process and glyco-engineering strategies. Once the glycoform is known, researchers can begin to modify the cell line or the metabolic feeding strategy to enhance production of the glycoform of interest. Without this knowledge, structure activity relationships can not be elucidated, and biosimilars cannot be compared to originators.
In profiling applications the repertoire of glycan analysis has coalesced into an interdependent set of complementary techniques, integrating orthogonal modes of chromatographic separation and detection to address the immense complexity of glycan structures. There is no single method that can interrogate the entire glycome in one go to provide information on composition, linkage, and position isomerism; therefore, current workflows generally start with the release of glycans from the protein by enzymatic (peptide-N-glycosidase F (PNGase F) or chemical (reductive β-elimination) means, followed by derivatization. The three mainstays of glycan analysis are mass spectrometry (MS), high-performance liquid chromatography (HPLC), and capillary electrophoresis (CE), with each of these techniques providing selectivity on different dimensions. In practice, these analytical techniques are often overlaid with different derivatization reagents to further diversify the selectivity profile. Reductive amination of glycans with aromatic labels provides a fluorogenic and UV active tag for sensitive detection on a variety of platforms. Increasingly, there is an emphasis on workflows where the sample is interrogated sequentially for different attributes on the same instrument or across complementary platforms to build a glycan profile (e.g. HILIC-FLD for relative quantitation, LC-MS for structural confirmation, exoglycosidase sequencing for linkage assignment). Ion mobility spectrometry (IMS) provides a gas phase separation orthogonal to liquid phase techniques that can be used to distinguish isomeric structures based on collisional cross-section. O-glycans, in contrast to N-glycans, lack a universal release reagent, and are instead profiled based on the proteolytic release of glycopeptides, which can then be enriched using lectin affinity chromatography and sequenced using MS/MS, although this workflow requires careful optimization to achieve site-specific information.
The detection engine in glycan profiling by MS has two unique advantages compared with other separation-based methods: the exquisite sensitivity of mass spectrometers and the structural resolution by MS fragmentation. The ionization of glycans for MS analysis is mainly accomplished by two ionization techniques, matrix-assisted laser desorption/ionization (MALDI) and electrospray ionization (ESI). MALDI is a vacuum process that often causes fragmentation of sensitive glycosidic linkages, for example, of acidic monosaccharides like sialic acids and uronic acids as well as fucose, leading to underestimation of their content in the native glycans. The effect can be mitigated by derivatization through permethylation, which converts the free hydroxyl groups into methyl ethers, thus making the glycans more stable and suitable for tandem MS, at the cost of additional chemical synthesis steps and potential side reactions. The ESI source conditions are mild and operate at atmospheric pressure, which retains labile acidic monosaccharide groups and directly profiles intact native glycans. In ESI, source conditions are usually softer than the typical conditions for peptides, and excessive source parameter optimization is needed to avoid in-source fragmentation. By coupling with online chromatography, the peak capacity of MS experiments is further increased. Native or reductively aminated glycans are separated by HILIC and porous graphitized carbon (PGC) chromatography before ESI detection, leading to better detection of low-abundance glycans that may be masked during direct infusion injection analysis. For glycopeptides, reversed-phase, PGC, and HILIC chromatography may be applied. Permethylation of glycans can be analyzed by reversed-phase or PGC. For CID fragmentation to distinguish isomers, it produces glycosidic cleavages, which are informative of glycan sequence and branching information. The other fragmentation methods, such as electron-transfer dissociation (ETD) and electron-transfer/higher-energy collision dissociation (EThcD) that are often coupled, produce peptide backbone fragments that allow the site of glycan attachment to be determined, and, thus, enable site-specific glycan profiling. Linkage and positional isomers of glycans can be distinguished by high-energy CID, which can cause cross-ring cleavages. Ion mobility spectrometry (IMS) has recently been coupled to MS. IMS provides an additional gas-phase separation dimension based on the collisional cross-section of the molecule, which helps resolve isomeric structures.
The available stationary-phase chemistries and separation modes for HPLC-based glycan analysis are numerous and target different physicochemical properties of carbohydrates to provide selectivity towards various isomeric and isobaric structures. Reversed-phase chromatography, the most ubiquitous LC mode available in bioanalytical laboratories, is readily available but requires glycan derivatization with a hydrophobic tag such as 2-aminobenzamide or procainamide for retention on the non-polar stationary phase. Separation and order of elution in this mode is highly dependent on the hydrophobicity of the tag and on the individual contributions of monosaccharides to overall retention with position and linkage being significant factors in this regard, leading to resolution of isomers. However, the effect of sialic acids and fucose is less well characterized and is the subject of ongoing investigation. Porous graphitized carbon (PGC) chromatography provides an orthogonal approach to separate native and labeled glycans based on planar interactions with the graphitic surface and, similar to reversed-phase, provides excellent isomeric resolution without derivatization though the actual retention mechanism is complex and less easily understood and predictable. Hydrophilic interaction liquid chromatography (HILIC) has become the workhorse of glycan profiling. HILIC separation occurs by a partition mechanism between a hydrophilic stationary phase and a relatively hydrophobic mobile phase, i.e., acetonitrile-water gradient and volatile ammonium formate buffers. HILIC separates native and fluorescently labeled glycans based on polarity and size (the larger and more polar the analyte the later elution) and is especially well suited to resolve sialylation and antennary variants. The orthogonality between reversed-phase, PGC and HILIC modes of separation allows a comprehensive profile of the sample to be built up by using the three approaches in combination: HILIC-FLD provides a quantitative profile; PGC-MS provides a qualitative isomer profile. Resolution and analysis time have been further improved by the development of sub-2 micron particle technology and superficially porous columns, while selectivity for challenging separations can be tuned by temperature programming. Ion-pairing reagents in the mobile phase can also be used to fine-tune retention of sialylated species; however, their use is typically not compatible with MS detection and they must be removed post-column when using LC-MS.
Orthogonal techniques that can be incorporated into a multidimensional workflow are Capillary electrophoresis (CE), which affords highly complementary separation from LC. CE's basis of separation on charge to size ratio makes it extremely powerful at resolving glycans, especially those that are isomeric (same monosaccharide composition, different linkage position or anomeric configuration), which is important as many glycan isomers are not biologically distinct. N-Glycan CE separations are most commonly done with native or fluorescently labeled glycans on a background electrolyte that maximizes resolution (commonly using borate complexes for cis-diol containing sugars). Migration time indexing, with additional structural confirmation via exoglycosidase and lectin profiling provides structural identification without the need for mass spectrometry, however MS is used for definitive confirmation. The primary challenge for CE-MS interfacing has been the incompatibility of background electrolyte buffers and conditions with electrospray ionization. Sheathless interfaces, where the capillary outlet is directly connected to the ion source (generally LC MS), have been developed that minimize dead volume and preserve electrical contact between the capillary and the ion source but are generally more fragile and require careful optimization. An alternative to this is decoupled, off-line spotting of CE eluent onto MALDI plates, where matrix is delivered via the capillary inlet vial (spotting can be automated). CE labeling techniques have evolved substantially over time. Stable isotope labeling with 12C6 2-aminobenzoic acid and 13C6 2-aminobenzoic acid can be used for quantitation via twoplex comparisons. Light and heavy labeled samples are run on the same capillary at the same time and can be used to generate extracted ion electropherograms for relative quantification of glycans between two different biological states. This technique is very well suited to comparability studies of biopharmaceuticals as well as biomarker discovery due to the small injection volumes required for CE, which leaves sufficient sample for orthogonal LC-MS analysis. Other more recent techniques of note include ion mobility spectrometry coupled to mass spectrometry, which is a separation that occurs in the gas phase, based on collisional cross-section. PGC chromatography is an HPLC method, but is included here as it also separates isomeric and isobaric structures without the use of labeling.
Glycan profiling is now used throughout the biopharmaceutical development process as well as in clinical medicine. In biopharmaceutical production, glycan profiling is being used as the main quality control parameter to confirm batch to batch consistency of the glycoforms. Regulatory agencies are now requiring manufacturers to also profile products for sialylation, fucosylation, and antennary structure as critical quality attributes. Glycan mapping is also required to support biosimilar development by comparison to an originator product. Glycan profiling is also used in a predictive manner to assist in rational glyco-engineering for biotherapeutics, where correlations between specific carbohydrate structures and desired PK/pharmacodynamic (PD) behavior can be used to identify and develop cell lines and/or metabolic feeding approaches to bias the glycosylation machinery to produce those glycoforms in greatest abundance. In diagnostics, glycan profiling is used to identify disease associated glycosylation signatures that can serve as biomarkers. For example, altered sialylation and fucosylation patterns on glycoproteins shed by cells are being used as biomarkers for malignant and inflammatory conditions. Rapid profiling platforms are being used to help discover these biomarkers by comparing hundreds of clinical samples and looking for subtle glycan changes to distinguish normal versus disease states. Machine learning and AI are now also being used in combination with profiling data to predict patient response to glyco-engineered biotherapeutics.
Profiling of glycans during biopharmaceutical manufacturing is a multi-tiered quality control strategy. During the process development stage of production, it is applied as part of parameter studies to determine the impact of culture pH, dissolved oxygen, and nutrient feed composition on the resulting glycoform distribution, and to ensure that therapeutic critical attributes such as sialylation and afucosylation fall within a target range based on clinical development experience. For monoclonal antibodies, profiling can identify high-mannose species which promote clearance, and differentiate these from complex sialylated species which have extended in vivo half-lives. Optimizing the process to favor the most therapeutically beneficial glycoforms is a major goal. In development of biosimilar drugs, the glycan profile is one of several attributes for which comparability to the originator must be shown, with profiling experiments conducted under head-to-head conditions (i.e. the candidate and originator products are processed through the HILIC-FLD and mass spectrometric systems in parallel), and the resulting glycan fingerprints compared. Resolution of differences requires functional justification using binding to, and effector function of, glycan-specific receptors. Recent high-throughput methodologies using 96-well plate systems, enable profiling to be routinely performed, allowing multiple conditions to be assessed in parallel, which accelerates development. As part of quality control release testing, the batch profile now serves as one of the release criteria for therapeutic antibodies, requiring consistent ratios of G0, G1 and G2 glycoforms and triggering investigation into process drift if the profile changes. In glycan structure elucidation, the chromatographic profile can be confirmed by exoglycosidase sequencing, a series of sequential digestions and reanalyses, providing linkage-level structural confirmation of the predicted structures and ruling out artifacts.
Carbohydrate profiling can play a role in disease diagnostics and biomarker discovery. Diseases often result in specific changes in cell glycosylation, leaving glycan signatures in body fluids that can be used as early diagnostic markers. For example, there are specific changes in N-glycan branching, core fucosylation, and sialylation in cancer progression, such as increased sialylation of the metastasis-associated sialyl-Lewis X epitope, and glycan profiling of serum glycoproteins can be used to distinguish cancer patients from healthy individuals. Glycan changes have been also been associated with a number of inflammatory disorders, including rheumatoid arthritis and inflammatory bowel disease. Altered sialylation of acute-phase proteins such as immunoglobulins and haptoglobin can be monitored to track disease activity and therapy response. Serum albumin glycans can be used to predict the progression of diabetic nephropathy: hyperglycation and decreased branching have been associated with an increased risk for developing nephropathy, so glycan profiling of serum albumin can be used for early identification of high-risk individuals. High-throughput profiling is well-suited for large clinical studies, in which glycan differences that are not statistically significant in small cohorts can be identified by comparing glycan profiles of hundreds of patient samples with those of controls. Glycan profiling of serum proteins in chronic hepatitis B patients has provided insights into the disease progression, with a longitudinal study identifying significant glycan differences between the development stages of cirrhosis and cancer. Glycan biomarker discovery is in need of translation into clinical assays, a process that is gaining pace, with the development of glycobiology standard reference materials and proficiency testing programs for inter-laboratory glycan analysis standardization. Glycan profiling can be used to stratify patients according to individual susceptibility to specific diseases or to guide therapeutic decisions, so it is well-positioned to become an integral part of personalized medicine.
The data acquisition phase of glycan profiling is subject to a number of technical issues primarily derived from the structural complexity of carbohydrates and the physicochemical limitations of the analytical methods and instrumentation. There is no universal physicochemical property of carbohydrates that can be utilized for their detection (in contrast to nucleic acids or proteins), as carbohydrates are generally hydrophilic and do not retain well on reversed phase media. Additionally, multiple isomers of glycans with the same monosaccharide composition but different branching and linkage orders cannot be separated using one-dimensional separation techniques. Native glycans also do not possess a chromophore; thus, labeling or derivatization with a chromophore is required for detection but can introduce a kinetic bias against large or acid-labile glycans, and labeling efficiencies may not be equal for all glycoforms. In addition, the detection sensitivity for lower abundance O-glycans and sialylated glycans that fragment under ionization conditions may be underestimated (even if they are of potential biological significance). Ion suppression can also be problematic in mass spectrometry, where other matrix components such as peptides or salts may compete for charge with the glycans of interest and cause suppression. This generally necessitates extensive sample cleanup, which may also result in loss of minor but potentially functionally relevant glycans. Finally, the reproducibility of glycan profiling is also impacted by a lack of standardized reference materials. In contrast to synthetic peptides, isomerically pure glycans are challenging to synthesize and therefore cannot be used as standards to inter-validate methods across laboratories or to conduct proficiency testing. In light of the above challenges, it is often necessary to integrate orthogonal separation with orthogonal detection for glycan analysis, but this can complicate data management and necessitates the use of specialized bioinformatics tools for data interpretation and annotation.
The sensitivity and resolution of the glycan profiling methods are still linked problems that define the level of structure information that can be obtained from a given biological sample. The sensitivity of detection is limited by the relatively low ionization efficiency of native carbohydrates in ESI-MS, where neutral glycans have relatively low charge transfer efficiency compared to peptides, and acidic glycans (e.g. sialylated glycans) are subject to in-source fragmentation that reduces the overall signal. Fluorophore or charge tag derivatization can lead to increases in detection limits by several orders of magnitude, but it also introduces kinetic discrimination as the sterically-hindered glycoforms react slowly and are under-represented in the final analysis. CE-LIF offers excellent sensitivity for labeled glycans, but resolution can be a limiting factor in complex biological samples when isomers co-migrate, and the limited dynamic range of fluorescence detectors can lead to signal saturation of the more abundant glycans, masking the low-abundance glycans from detection. PAC-based HP/HPLC can offer excellent resolution of isomers due to planar interactions, but the resolution of sialylation variants may require a gradient optimization that can lengthen analysis times, reducing throughput for studies with large numbers of samples. Resolution in mass spectrometry is also affected by isobaric overlaps, as many glycans share the same nominal mass but have different linkages or branching, and although high-resolution instruments can resolve these, the detailed structure of the fragment ions that are necessary for linkage assignment requires specialized fragmentation modes not always available. The recent introduction of dopant-enriched nitrogen gas for ESI has led to increases in detection sensitivity by up to a hundred-fold, and the use of sub-ambient HILIC separations can offer improved resolution of positional isomers, but these methods are still limited to specialist laboratories and require significant method development to give robust, transferrable performance on different instruments and between operators.
Heterogeneous glycan mixtures with mass over the exact same value and chromatographic mobility: In a limited monosaccharide alphabet generates a large number of potential structures. Glycans on glycoproteins in serum, tissue or bioreactor harvests have a variable occupancy, with some glycosylated at all sequons, others at only some and even aglycosylated versions are present (macro-heterogeneity). This has to be considered when quantifying functional glycoforms. Glycans on one glycosylation site have several intermediates of synthesis, such as high mannose, hybrid, complex type, variably sialylated, galactosylated or fucosylated (micro-heterogeneity). These can have different biological functions. O-glycans can only be released by chemical, harsher conditions, which usually lead to degradation of the protein backbone and peeling reactions. Recovery of glycoforms is non-stoichiometric: solvent precipitation and solid-phase extraction recover them differentially. The smaller highly sialylated species can be lost during the cleanup step, while the very large and highly branched polysaccharides can be retained in the filter membranes. Glycoforms with very high abundance can saturate the detector, and other present in low abundance, often these are the pathologically important or tumor specific, aberrant glycoforms at trace levels are lost. Lectins can be used to isolate specific sub-populations of glycoforms, but these themselves show cross reactivity and inconsistent affinity. Software tools can automatically assign putative glycan compositions to the experimental masses, but cannot usually differentiate isomers and require manual interpretation of fragmentation spectra to do this, which is an expert process. Orthogonal information from HILIC for quantitation, mass for confirmation and exoglycosidase sequencing for linkage information can all be used to give a more complete picture, but this needs to be integrated, and this requires data standardization, formats and manually curated reference libraries for high confidence resolution.
Glycan profiling's integration with systems biology and precision medicine is a key development, as comprehensive biological interpretation now necessitates the inclusion of carbohydrate data within multi-omics frameworks. Glycomics is transitioning from an era of characterization to one of prediction, powered by AI and machine learning models that can discern patterns within intricate glycomic data sets. Tools like GlyCompare are leveraging deep learning to extract biosynthetic and functional insights directly from glycan profiles, edging glycomics closer to the analytical maturity of genomics and proteomics. Automation for high-throughput analysis is evolving, with microfluidic platforms and 96-well formats now permitting parallel processing of hundreds of samples, dramatically accelerating analysis from days to hours while reducing sample volumes needed from milligrams to micrograms. This scalability is vital for clinical applications where large cohorts need screening to identify disease-specific glycan signatures. An exciting nascent area involves nanopore sequencing technologies, which could potentially allow direct, label-free sequencing of oligosaccharides at the single-molecule level, possibly bypassing the biases introduced by current derivatization and separation methods. Machine learning force fields tailored to and trained on the conformational space of glycans are being developed to better predict glycan structures, overcoming the limitations of traditional additive force fields that often fail to accurately model carbohydrate flexibility. Integration with spatial omics will map glycans within the tissue microenvironment, revealing how local glycosylation influences intercellular communication and immune surveillance. These convergent technologies are setting the stage for glycan profiling to become a standard feature in clinical laboratories, facilitating real-time monitoring of disease progression and therapeutic responses through carbohydrate-based biomarkers.
The development of high-throughput methodologies is beginning to transform glycan profiling. The traditional bottleneck between sample collection and its downstream analysis can now be overcome, allowing the analysis of clinically relevant sample sizes for both biomarker validation and population studies. One of the most powerful emerging platforms is capillary electrophoresis; multicapillary analyzers based on instrumentation originally developed for DNA sequencing have been adapted to allow the analysis of up to ninety-six samples in parallel in thirty minutes with fluorophore labeling, and are capable of extremely high-throughput N-glycome screening of serum and other biofluids. The high parallelization and multiplexing reduces the cost and time required for analysis of individual samples to a level that is consistent with routine clinical screening. Microarray approaches allow thousands of different glycan structures or lectin probes to be screened for binding in a single experiment, though these platforms are currently used more for discovery than diagnostic purposes. Automation of mass spectrometry workflows is also becoming a reality with sample preparation robots that can automate enzymatic release, derivatization, and cleanup in 96-well plates, which dramatically reduces variability due to manual handling. Software for data processing has become a key element of high-throughput glycan profiling; open-source bioinformatics platforms now automate peak picking, quantitation, and structural annotation, with data processing now taking hours rather than weeks, and without significant loss of consistency. Machine learning is starting to be incorporated into processing pipelines to increase glycan identification accuracy by learning retention time and fragmentation patterns from curated libraries, though these approaches still require large amounts of training data to be robust. The remaining challenge for high-throughput methods is to connect this level of throughput with an equivalent structural depth; the majority of rapid methods still lack isomeric resolution, while high-resolution methods are still time-consuming. Future developments are likely to be focused on hybrid platforms that combine fast front-end separation with intelligent data processing to provide both.
The combination of glycan profiles with genomic and proteomic data is an exciting emerging area in the field of systems biology. Systems biology seeks to combine disparate omics modalities in an effort to create a holistic description of the molecular state of a cell. As such, current efforts focus on combining glycan profiles with genomic and proteomic datasets. A significant challenge to this effort is that glycosylation is a template free process, i.e. unlike proteins and nucleic acids, there is no linear sequence which encodes a glycan's structure. Initial steps towards this integration have begun, such as the GlyGen knowledgebase which is beginning to integrate genomic information such as single nucleotide polymorphisms and cancer mutations to glycosylation sites in order to begin to describe how mutations lead to gain/loss of attachment sequons for glycosylation and how this mechanistically may play a role in disease. These efforts allow for the identification of glycan quantitative trait loci where a variant allele can be linked to an increased/decreased abundance of a glycoform, thereby tying heritable genetic risk to specific glycosylation patterns observed in disease cohorts. Proteomic data can be combined using glycoproteomic workflows where the protein identity and site-specific glycoforms are identified. These workflows have recently been enhanced by mass spectrometry techniques which provide glycan fragment ions in addition to peptide backbone fragment ions in the same acquisition, such as stepped HCD and EThcD.
Glycan profiling plays a critical role in various applications within the field of glycobiology. It is a technique used to analyze and characterize the glycan structures present on proteins and other biomolecules. One of the key applications of glycan profiling is in the development of biopharmaceuticals. Glycoproteins, such as antibodies and hormones, are often used as therapeutic agents, and the glycan structures attached to these proteins can have a significant impact on their efficacy and safety. Glycan profiling is used to characterize and control the glycosylation patterns of these biopharmaceuticals, ensuring consistent quality and reducing variability in their therapeutic performance. In research settings, glycan profiling is used to study the structure-function relationships of glycans and their role in various biological processes. For example, changes in glycosylation patterns have been linked to various diseases, including cancer, autoimmune disorders, and infectious diseases. Glycan profiling can be used to identify disease-specific glycan biomarkers and to study the underlying mechanisms of glycan-related diseases. Another important application of glycan profiling is in the field of immunology. Glycans on the surface of cells play a crucial role in immune recognition and response. Glycan profiling can be used to analyze the glycan structures on immune cells, providing insights into their function and dysregulation in disease. Furthermore, glycan profiling is also being used in the development of personalized medicine approaches. As our understanding of the role of glycans in health and disease continues to grow, glycan profiling is becoming an important tool for identifying patient-specific glycan biomarkers and developing tailored therapies. Overall, glycan profiling is a versatile tool that has a wide range of applications in various fields of glycobiology. Its ability to provide detailed information about glycan structures and their functional roles makes it an essential technique for advancing our understanding of the roles that glycans play in health and disease and for developing novel therapeutic approaches targeting glycans.
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