Carbohydrates are the most abundant and diverse class of organic compounds in nature, chemically they consist of carbon, hydrogen and oxygen. Carbohydrates constitute the main source of energy for all bodily functions and are necessary for the metabolism of other nutrients. They can be classified according to their chemical structure as monosaccharides, disaccharides, oligosaccharides, polysaccharides, sugar alcohols, and conjugated carbohydrates.
Many traditional analytical tools, including GC/MS for monosaccharide and linkage analysis, matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) for glycan composition analysis, tandem mass spectrometry for sequence analysis, near-infrared spectroscopy (NIRS) and NMR spectroscopy have been used with great success for carbohydrate analysis. Chromatography is the most powerful analytical technique for the analysis of monosaccharides and oligosaccharides. Carbohydrates can be separated based on their partition coefficient, polarity or size.
Most chemical methods are based on the fact that monosaccharides and oligosaccharides are reducing agents that produce precipitates or colored products when reacted with other components and can be quantified. The same applies to non-reducing carbohydrates, which first need to be converted into reducing molecules by hydrolysis. Chemical methods for carbohydrate analysis include titration, gravimetric and colorimetric methods.
These methods rely on the ability of the enzyme to catalyze a specific reaction, and they are rapid, highly specific, and sensitive to low concentrations. The two most common methods used to determine carbohydrate concentrations are (i) allowing the reaction to complete and measuring the concentration of the product, which is proportional to the initial substrate concentration, and (ii) measuring the initial rate of the enzyme-catalyzed reaction as this rate is proportional to the substrate concentration.
Carbohydrates are involved in a variety of biochemical events, including signal transduction, intercellular adhesion, and molecular recognition. The important biological role of carbohydrates in life processes has made carbohydrate-mediated processes a target for drug development. In addition, carbohydrates have been increasingly used to design novel drugs, including pure carbohydrate drugs, carbohydrate conjugates, and carbohydrate vaccines.
The surfaces of bacterial pathogens are covered with densely arranged complex glycans, such as the lipopolysaccharides of Gram-negative bacteria, which are capable of interacting with the immune system to induce carbohydrate-specific antibody production. The development of glycoconjugate vaccines based on polysaccharide antigens becomes an attractive target. However, the isolation and purification of the carbohydrates required in glycoconjugate vaccines remains a great challenge. Natural glycans may suffer from heterogeneous composition and batch-to-batch variation. Therefore, carbohydrate analysis helps to elucidate the clear composition of natural glycans, the minimal structure of microbial polysaccharides (called epitopes or antigenic determinant) to ensure the production of sufficient amounts of antibodies to confer long-term protective immunity to the host.
Fig. 1 The development of polysaccharide-based vaccines (Ihssen, 2021)
A large proportion of the therapeutic biologics under development are glycoproteins. Therapeutic glycoproteins are produced as recombinant products in cell culture systems and can easily change glycosylation patterns if there is a lack of control in the biological process. Changes in carbohydrate structure can lead to differences in protein stability and solubility, half-life in circulation, and possibly bioactivity, folding and aggregation. To ensure that glycosylation proceeds as expected, thus providing reproducible carbohydrate structures and glycoprotein structures, glycosylation patterns can be monitored using carbohydrate analysis.
For example, monosaccharide composition analysis can provide qualitative information on protein glycosylation by processing glycoproteins with glycolytic enzymes and electrophoresis followed by carbohydrate staining or lectin blotting. In addition, a number of methods have been developed to analyze oligosaccharide structures. Oligosaccharide analysis methods include cutting the glycan chain from the protein backbone, separating the resulting glycans, detecting each oligosaccharide, and establishing the signal relative ratio; oligosaccharide mixtures can also be analyzed directly by MALDI-MS.
Fig. 2 MALDI-MS for oligosaccharide analysis (Huang, 2019)
Carbohydrate analysis helps define glycan composition, structure, and heterogeneity, which are critical for ensuring product consistency. It also supports structure–function studies and enables better control of process variability.
Glycans often show structural complexity, branching, and isomer diversity, which can complicate identification and quantification. Advanced analytical workflows improve sensitivity and resolution to overcome these limitations.
Mass spectrometry enables detailed structural analysis, including composition, sequence, and linkage information. It also allows high-throughput screening of glycan patterns across different samples.
Chromatographic methods are preferred for separating monosaccharides, oligosaccharides, and complex glycan mixtures based on polarity or size. These approaches provide accurate quantification and reproducible profiling.
Monitoring glycosylation patterns helps maintain batch consistency and supports evaluation of stability, solubility, and aggregation behavior. It also enables optimization of upstream and downstream processing strategies.
Enzymatic methods offer high specificity and sensitivity for detecting target carbohydrate structures. They are widely used for rapid screening and validation of analytical workflows.
It enables identification of key glycan motifs and minimal antigenic structures in polysaccharides. This supports rational design and quality assessment of carbohydrate-based conjugates.
Innovations such as multi-omics integration, automation, and AI-driven data analysis are improving glycan annotation and interpretation. These tools enhance scalability and reproducibility in complex glycan research.
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