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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