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Targeted glycomics by selected reaction monitoring for highly sensitive glycan compositional analysis
Author(s) -
Zhang Hongquan,
Wang Zhaohui,
Stupak Jacek,
Ghribi Othman,
Geiger Jonathan D.,
Liu Qing Yan,
Li Jianjun
Publication year - 2012
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201100567
Subject(s) - glycomics , glycan , biomarker , detection limit , biomarker discovery , computational biology , biology , cancer detection , cancer , biochemistry , chemistry , chromatography , proteomics , glycoprotein , gene , genetics
The development of glycomics increasingly requires the detection and quantification of large numbers of glycans, which is only partially achieved by current glycomics approaches. Taking advantage of selected reaction monitoring to enhance both sensitivity and selectivity, we report here a strategy termed targeted glycomics that enables highly sensitive and consistent identification and quantification of diverse glycans across multiple samples at the same time. In this proof‐of‐principle study, we validated the method by analyzing global N ‐glycans expressed in different systems: single proteins, cancer cells, and serum samples. A dynamic range of three orders of magnitude was obtained for the detection of all five glycans released from ribonuclease B . The limit of detection of 80 attomole for M an 9 G lc NA c 2 demonstrated the excellent sensitivity of the method. The capability of the strategy to identify diverse glycans was demonstrated by identification and detection of 162 different glycans and isomers from pancreatic cancer cells. The sensitivity of the method was illustrated further by the ability to detect eight glycans from 250 cancer cells and five glycans released from 100 cancer cells. In serum obtained from rabbits fed control diet or diet enriched with 2% cholesterol, differences to 42 glycans were accurately measured and this indicates that this strategy might find use in studies of biomarker discovery and validation.