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OxoScan-MS: Oxonium ion scanning mass spectrometry facilitates plasma glycoproteomics in large scale
Matthew E H White; D. Marc Jones; Joost de Folter; Simran K Aulakh; Helen R Flynn; Lynn Krüger; Vadim Demichev; Pinkus Tober-Lau; Florian Kurth; Michael Mülleder; Véronique Blanchard; Christoph B Messner; Markus Ralser.
Afiliação
  • Matthew E H White; Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, United Kingdom
  • D. Marc Jones; Bioinformatics and Computational Biology Laboratory, The Francis Crick Institute, London, United Kingdom; Department of Basic & Clinical Neuroscience, Maurice W
  • Joost de Folter; Software Engineering and Artificial Intelligence Technology Platform, The Francis Crick Institute, London, United Kingdom
  • Simran K Aulakh; Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, United Kingdom
  • Helen R Flynn; Mass Spectrometry Proteomics Science Technology Platform, The Francis Crick Institute, London, United Kingdom
  • Lynn Krüger; Department of Human Medicine, Medical School Berlin, Berlin, Germany; Institute of Diagnostic Laboratory Medicine, Charité Universitätsmedizin, Berlin, Germany
  • Vadim Demichev; Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, United Kingdom; Department of Biochemistry, Charité Universitätsmedizin Berlin,
  • Pinkus Tober-Lau; Department of Infectious Diseases and Respiratory Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
  • Florian Kurth; Department of Infectious Diseases and Respiratory Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
  • Michael Mülleder; Core Facility High-throughput Mass spectrometry, Charité Universitätsmedizin Berlin, Berlin, Germany
  • Véronique Blanchard; Department of Human Medicine, Medical School Berlin, Berlin, Germany; Institute of Diagnostic Laboratory Medicine, Charité Universitätsmedizin, Berlin, Germany
  • Christoph B Messner; Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, United Kingdom; Department of Biochemistry, Charité Universitätsmedizin Berlin,
  • Markus Ralser; Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, United Kingdom; Department of Biochemistry, Charité Universitätsmedizin Berlin,
Preprint em Inglês | bioRxiv | ID: ppbiorxiv-494393
ABSTRACT
Protein glycosylation is a complex and heterogeneous post-translational modification. Specifically, the human plasma proteome is rich in glycoproteins, and as protein glycosylation is frequently dysregulated in disease, glycoproteomics is considered an underexplored resource for biomarker discovery. Here, we present OxoScan-MS, a data-independent mass spectrometric acquisition technology and data analysis software that facilitates sensitive, fast, and cost-effective glycoproteome profiling of plasma and serum samples in large cohort studies. OxoScan-MS quantifies glycosylated peptide features by exploiting a scanning quadrupole to assign precursors to oxonium ions, glycopeptide-specific fragments. OxoScan-MS reaches a high level of sensitivity and selectivity in untargeted glycopeptide profiling, such that it can be efficiently used with fast microflow chromatography without a need for experimental enrichment of glycopeptides from neat plasma. We apply OxoScan-MS to profile the plasma glycoproteomic in an inpatient cohort hospitalised due to severe COVID-19, and obtain precise quantities for 1,002 glycopeptide features. We reveal that severe COVID-19 induces differential glycosylation in disease-relevant plasma glycoproteins, including IgA, fibrinogen and alpha-1-antitrypsin. Thus, with OxoScan-MS we present a strategy for quantitatively mapping glycoproteomes that scales to hundreds and thousands of samples, and report glycoproteomic changes in severe COVID-19.
Licença
cc_by_nc_nd
Texto completo: Disponível Coleções: Preprints Base de dados: bioRxiv Tipo de estudo: Cohort_studies / Estudo observacional / Estudo prognóstico Idioma: Inglês Ano de publicação: 2022 Tipo de documento: Preprint
Texto completo: Disponível Coleções: Preprints Base de dados: bioRxiv Tipo de estudo: Cohort_studies / Estudo observacional / Estudo prognóstico Idioma: Inglês Ano de publicação: 2022 Tipo de documento: Preprint
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