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A streamlined pipeline for multiplexed quantitative site-specific N-glycoproteomics

Author

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  • Pan Fang

    (Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry)

  • Yanlong Ji

    (Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry
    Hematology/Oncology, Department of Medicine II, Johann Wolfgang Goethe University
    Goethe University)

  • Ivan Silbern

    (Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry
    University Medical Center Göttingen)

  • Carmen Doebele

    (Hematology/Oncology, Department of Medicine II, Johann Wolfgang Goethe University
    German Cancer Consortium/German Cancer Research Center)

  • Momchil Ninov

    (Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry
    University Medical Center Göttingen)

  • Christof Lenz

    (Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry
    University Medical Center Göttingen)

  • Thomas Oellerich

    (Hematology/Oncology, Department of Medicine II, Johann Wolfgang Goethe University
    Goethe University
    German Cancer Consortium/German Cancer Research Center)

  • Kuan-Ting Pan

    (Hematology/Oncology, Department of Medicine II, Johann Wolfgang Goethe University
    Goethe University)

  • Henning Urlaub

    (Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry
    University Medical Center Göttingen)

Abstract

Regulation of protein N-glycosylation is essential in human cells. However, large-scale, accurate, and site-specific quantification of glycosylation is still technically challenging. We here introduce SugarQuant, an integrated mass spectrometry-based pipeline comprising protein aggregation capture (PAC)-based sample preparation, multi-notch MS3 acquisition (Glyco-SPS-MS3) and a data-processing tool (GlycoBinder) that enables confident identification and quantification of intact glycopeptides in complex biological samples. PAC significantly reduces sample-handling time without compromising sensitivity. Glyco-SPS-MS3 combines high-resolution MS2 and MS3 scans, resulting in enhanced reporter signals of isobaric mass tags, improved detection of N-glycopeptide fragments, and lowered interference in multiplexed quantification. GlycoBinder enables streamlined processing of Glyco-SPS-MS3 data, followed by a two-step database search, which increases the identification rates of glycopeptides by 22% compared with conventional strategies. We apply SugarQuant to identify and quantify more than 5,000 unique glycoforms in Burkitt’s lymphoma cells, and determine site-specific glycosylation changes that occurred upon inhibition of fucosylation at high confidence.

Suggested Citation

  • Pan Fang & Yanlong Ji & Ivan Silbern & Carmen Doebele & Momchil Ninov & Christof Lenz & Thomas Oellerich & Kuan-Ting Pan & Henning Urlaub, 2020. "A streamlined pipeline for multiplexed quantitative site-specific N-glycoproteomics," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19052-w
    DOI: 10.1038/s41467-020-19052-w
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    Cited by:

    1. Siyuan Kong & Pengyun Gong & Wen-Feng Zeng & Biyun Jiang & Xinhang Hou & Yang Zhang & Huanhuan Zhao & Mingqi Liu & Guoquan Yan & Xinwen Zhou & Xihua Qiao & Mengxi Wu & Pengyuan Yang & Chao Liu & Weiqi, 2022. "pGlycoQuant with a deep residual network for quantitative glycoproteomics at intact glycopeptide level," Nature Communications, Nature, vol. 13(1), pages 1-17, December.

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