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Home » Biology Articles » Biochemistry » Carbohydrate Biochemistry » Large-scale approaches for glycobiology » Development of high-throughput technologies for glycomics

Development of high-throughput technologies for glycomics
- Large-scale approaches for glycobiology

The success of DNA microarrays, on which thousands of discrete interactions are observed at once, has spawned array-based methods for confronting almost every problem. Carbohydrate analysis is no exception, and two array-based strategies are now being pursued. The more mature approach - which has reached the point of using robotic microspotting - involves attaching hundreds of different oligosaccharides of known composition to a surface, and is used to identify binding partners (Figure 3) [24-26]. This approach reproduces the 'glycocode' found on the cell surface and helps determine how biological systems decode the vast information-carrying capacity of carbohydrates [27]. In a second type of array, carbohydrate-binding proteins such as lectins are arrayed on the surface. This technique, made possible by protein-array printing techniques that avoid altering the recognition capacity of proteins, has recently been demonstrated in concept for a modestly sized lectin array [20]. In the future, when the hundreds of lectins now available, as well as the growing number of antibodies that bind specific glycan structures, are incorporated, such arrays will facilitate the rapid profiling of cellular glycosylation states.

Conventional methods, including chromatography or two-dimensional gel electrophoresis, used in proteomics to separate proteins isolated from a cell or tissue (Figure 2), are rapidly and effectively being adapted for oligosaccharide characterization [28]. In contrast to microarrays, identification is not inherent in these techniques, necessitating a reliance on mass spectrometry for identification of glycoconjugates after separation; mass spectrometry is extremely sensitive, allowing minute amounts of samples isolated from biological samples or purified by capillary electrophoresis or two-dimensional gels to be identified successfully [29]. Unfortunately, the need to isolate individual oligosaccharides by chromatography or electrophoresis prior to mass spectrometry, and the lack of automated identification algorithms, limits the throughput of these methods, leading to techniques such as fluorescence differential gel electrophoresis (DIGE [30]), that do not characterize all products and settle for the less ambitious goal of identifying a limited number of molecules that differ between two samples (for example, healthy versus diseased tissue) [31]. To overcome the bottleneck of identification, much effort is being put into developing automated, high-throughput computational tools for the interpretation of glycoconjugate mass spectra [23,32].

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