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An improved comprehensive strategy for deep and quantitative N-glycomics based on optimization of sample preparation, isotope-based data quality control and quantification, new N-glycan libraries and new algorithms

By Yudong Guan, Jiaxiang Hu, Weiqian Cao, Wencong Cui, Fan Yang, Christoph Krisp, Ling Lin, Min Zhang, Hannah Voss, Raphael Schuster, Guoquan Yan, Marceline Manka Fuh, Morten Thaysen-Andersen, Nicolle H. Packer, Huali Shen, Pengyuan Yang, Hartmut Schl├╝ter

Posted 15 Oct 2020
bioRxiv DOI: 10.1101/2020.10.15.340638

Global in-depth analysis of N-glycosylation, as the most complex post-translational modification of proteins, is requiring methods being as sensitive, selective and reliable as possible. Here, an enhanced strategy for N-glycomics is presented comprising optimized sample preparation yielding enhanced glycoprotein recovery and permethylation efficiency, isotopic labelling for data quality control and relative quantification, integration of new N-glycan libraries (human and mouse), newly developed R-scripts matching experimental MS1 data to theoretical N-glycan compositions and bundled sequencing algorithms for MS2-based structural identification to ultimately enhance the coverage and accuracy of N-glycans. With this strategy the numbers of identified N-glycans are more than doubled compared with previous studies, exemplified by etanercept (more than 3-fold) and chicken ovalbumin (more than 2-fold) at nanogram level. The power of this strategy and applicability to biological samples is further demonstrated by comparative N-glycomics of human acute promyelocytic leukemia cells before and after treatment with all-trans retinoic acid, showing that N-glycan biosynthesis is slowed down and 57 species are significantly altered in response to the treatment. This improved analytical platform enables deep and accurate N-glycomics for glycobiological research and biomarker discovery. ### Competing Interest Statement The authors have declared no competing interest.

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