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

Pan Fang, Yanlong Ji, Ivan Silbern, Carmen Doebele, Momchil Ninov, Christof Lenz, Thomas Oellerich, Kuan-Ting Pan () and Henning Urlaub ()
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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
Ivan Silbern: Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry
Carmen Doebele: Hematology/Oncology, Department of Medicine II, Johann Wolfgang Goethe University
Momchil Ninov: Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry
Christof Lenz: Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry
Thomas Oellerich: Hematology/Oncology, Department of Medicine II, Johann Wolfgang Goethe University
Kuan-Ting Pan: Hematology/Oncology, Department of Medicine II, Johann Wolfgang Goethe University
Henning Urlaub: Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-19052-w

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