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Magnetic carbon nanocomposites as a MALDI co-matrix enhancing MS-based glycomics

Banazadeh, Alireza, Williamson, Seth, Zabet, Masoud, Hussien, Ahmed, Mechref, Yehia
Analytical and bioanalytical chemistry 2018 v.410 no.28 pp. 7395-7404
blood serum, breasts, carbon, desorption, glycomics, glycoproteins, glycosylation, human diseases, humans, ionization, magnetism, models, nanocomposites, polysaccharides, protein folding, signal transduction, spectroscopy
More than 50% of all known proteins are glycosylated, which is critical for many biological processes such as protein folding and signal transduction. Glycosylation has proven to be associated with different mammalian diseases such as breast and liver cancers. Therefore, characterization of glycans is highly important to facilitate a better understanding of the development and progression of many human diseases. Although matrix-assisted laser desorption ionization-mass spectrometry (MALDI-MS) offers several advantages such as ease of operation and short analysis times, however, due to the complexity of glycan structures and their low ionization efficiency, there are still challenges that need to be addressed to achieve sensitive glycan analysis. Here, magnetic carbon nanocomposites (CNPs@Fe₃O₄ NCs) were used as a new MALDI matrix or co-matrix for the analysis of glycans derived from different model glycoproteins and human blood serum samples. The addition of CNPs@Fe₃O₄ NCs to the matrix significantly enhanced glycan signal intensity by several orders of magnitude, and effectively controlled/reduced/eliminated in-source decay (ISD) fragmentation. The latter was attained by modulating CNPs@Fe₃O₄ NCs concentrations and allowed the simultaneous study of intact and fragmented glycans, and pseudo-MS³ analysis. Moreover, CNPs@Fe₃O₄ NCs was also effectively employed to desalt samples directly on MALDI plate, thus enabling direct MALDI-MS analysis of unpurified permethylated glycans derived from both model glycoproteins and biological samples. On-plate desalting enhanced sensitivity by reducing sample loss. Graphical abstract ᅟ