Literature DB >> 22128873

N-Glycan profiling of dried blood spots.

L Renee Ruhaak1, Suzanne Miyamoto, Karen Kelly, Carlito B Lebrilla.   

Abstract

Serum N-glycan profiles for use as clinical biomarkers of disease(s) is of increasing scientific interest. Promising profiles have already been identified in several diseases, including cancer, Alzheimer's, and diabetes. Venipuncture is routinely performed to collect the blood necessary for this type of analysis, but blood from a fingerstick placed on filter paper (known as dried blood spots (DBS)) is more advantageous. This sampling method is less invasive than "classical" blood drawing, can be performed conveniently at home, and avoids cumbersome shipping and storage procedures. Here, we present a procedure for N-glycan profiling of DBS samples consisting of reconstitution of DBS in N-glycan release buffer, protein denaturation, enzymatic N-glycan release and PGC Solid phase extraction (SPE) for purification. Samples are then analyzed using nanoHPLC-PGC-chip-TOF-MS to generate N-glycan profiles. Using this method, ~150 N-glycan structures can be monitored, originating from 44 N-glycan compositions that can be analyzed with good repeatability (the coefficient of variation (%CV) is ~20%). To assess the stability of the N-glycans during storage, DBS samples were stored at room temperature (RT) and -80 °C. No major differences in N-glycan composition could be observed. Moreover, upon comparison of the N-glycan profile of DBS with profiles obtained from serum, which is a classical matrix for N-glycan profiling, similar patterns were observed. The method facilitates large population studies for N-glycan profiling, and is especially advantageous for children and the elderly, who have limited blood supplies, as well as animal studies in small mammals.
© 2011 American Chemical Society

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Year:  2011        PMID: 22128873      PMCID: PMC3259271          DOI: 10.1021/ac202775t

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  36 in total

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  26 in total

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5.  Multivariate two-part statistics for analysis of correlated mass spectrometry data from multiple biological specimens.

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