Label-Free Carbohydrate Expression Profiling

DESCRIPTION (provided by applicant): New methods for studying gene and protein expression, such as DNA and protein microarrays, have revolutionized the study of biological processes. Carbohydrate expression profiling could have a huge impact; however, carbohydrates are exceptionally difficult to analyze. Biopraxis proposes to develop a new, label-free microarray technology for rapidly profiling complex mixtures of glycans. The analysis will produce a three dimensional (3D) profile of sample glycans comprising (1) the specificities of the biomolecules to which the glycans bind; (2) spectral fingerprints of the biomolecule-glycan complexes; and (3) the intensities of those fingerprints. The 3D profiles from different samples can be compared, to detect differences in carbohydrate expression. Ultimately, this new technology will be developed as a platform tool for glycobiology, comprising a 'microarray reader' that can analyze microarrays designed for a variety of applications. For example, 'global' microarrays comprising thousands of different biomolecules can be used to detect differences in carbohydrate expression, and identify carbohydrate biomarkers associated with disease. The known specificities of the biomolecules to which the glycans bind, coupled with the information on glycan structure found in the spectral fingerprints, can be used to help elucidate the reasons for the differences in carbohydrate expression (e.g., the types of enzymes that were involved in carbohydrate synthesis.) Global arrays can also used to rapidly screen carbohydrates or carbohydrate mimetics for binding specificities. 'Content' microarrays comprising a few biomolecule pixels can be designed, e.g., for diagnostics based on multiple carbohydrate biomarkers; or to confirm that recombinant therapeutics are properly glycosylated during manufacture. Analysis will be very rapid, since the fingerprints can be collected in seconds. And because spectra can be collected from micron-sized pixels, traces of biomolecules will be consumed per chip, extremely small samples can be analyzed, and very little waste will be produced. Hence, life cycle costs will be very low.