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A streamlined pipeline for multiplexed quantitative site-specific N-glycoproteomics
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 (...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7572468/ https://www.ncbi.nlm.nih.gov/pubmed/33077710 http://dx.doi.org/10.1038/s41467-020-19052-w |
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author | Fang, Pan Ji, Yanlong Silbern, Ivan Doebele, Carmen Ninov, Momchil Lenz, Christof Oellerich, Thomas Pan, Kuan-Ting Urlaub, Henning |
author_facet | Fang, Pan Ji, Yanlong Silbern, Ivan Doebele, Carmen Ninov, Momchil Lenz, Christof Oellerich, Thomas Pan, Kuan-Ting Urlaub, Henning |
author_sort | Fang, Pan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7572468 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75724682020-10-21 A streamlined pipeline for multiplexed quantitative site-specific N-glycoproteomics Fang, Pan Ji, Yanlong Silbern, Ivan Doebele, Carmen Ninov, Momchil Lenz, Christof Oellerich, Thomas Pan, Kuan-Ting Urlaub, Henning Nat Commun Article 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. Nature Publishing Group UK 2020-10-19 /pmc/articles/PMC7572468/ /pubmed/33077710 http://dx.doi.org/10.1038/s41467-020-19052-w Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Fang, Pan Ji, Yanlong Silbern, Ivan Doebele, Carmen Ninov, Momchil Lenz, Christof Oellerich, Thomas Pan, Kuan-Ting Urlaub, Henning A streamlined pipeline for multiplexed quantitative site-specific N-glycoproteomics |
title | A streamlined pipeline for multiplexed quantitative site-specific N-glycoproteomics |
title_full | A streamlined pipeline for multiplexed quantitative site-specific N-glycoproteomics |
title_fullStr | A streamlined pipeline for multiplexed quantitative site-specific N-glycoproteomics |
title_full_unstemmed | A streamlined pipeline for multiplexed quantitative site-specific N-glycoproteomics |
title_short | A streamlined pipeline for multiplexed quantitative site-specific N-glycoproteomics |
title_sort | streamlined pipeline for multiplexed quantitative site-specific n-glycoproteomics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7572468/ https://www.ncbi.nlm.nih.gov/pubmed/33077710 http://dx.doi.org/10.1038/s41467-020-19052-w |
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