Analytical and Bioanalytical Chemistry

, Volume 409, Issue 2, pp 619–627 | Cite as

Site-specific analysis of changes in the glycosylation of proteins in liver cirrhosis using data-independent workflow with soft fragmentation

  • Miloslav Sanda
  • Lihua Zhang
  • Nathan J. Edwards
  • Radoslav Goldman
Research Paper
Part of the following topical collections:
  1. Glycomics, Glycoproteomics and Allied Topics


Cirrhosis of the liver is associated with increased fucosylation of proteins in the plasma. We describe a data-independent (DIA) strategy for comparative analysis of the site-specific glycoforms of plasma glycoproteins. A library of 161 glycoforms of 25 N-glycopeptides was established by data-dependent LC-MS/MS analysis of a tryptic digest of 14 human protein groups retained on a multiple affinity removal column. The collision-induced dissociation conditions were adjusted to maximize the yield of selective Y-ions which were quantified by a data-independent mass spectrometry workflow using a 10-Da acquisition window. Using this workflow, we quantified 125 glycoforms of 25 glycopeptides, covering 10 of the 14 proteins, without any further glycopeptide enrichment. Comparison of the proteins in the plasma of healthy controls and cirrhotic patients shows an average 1.5-fold increase in the fucosylation of bi-antennary glycoforms and 3-fold increase in the fucosylation of tri- and tetra- antennary glycoforms. These results show that the adjusted glycopeptide DIA workflow using soft collision-induced fragmentation of glycopeptides is suitable for site-specific analysis of protein glycosylation in complex mixtures of analytes without glycopeptide enrichment.


Data-independent analysis N-glycopeptide Fucosylation GP-SWATH 


Compliance with ethical standards


This work was supported by National Institutes of Health Grants UO1 CA168926, UO1 CA171146, and RO1 CA135069 (to R.G.) and CCSG Grant P30 CA51008 (to Lombardi Comprehensive Cancer Center supporting the Proteomics and Metabolomics Shared Resource).

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval and consent to participate

All the participants were enrolled and signed informed consent under protocols approved by the MedStar Health Research Institute—Georgetown University Oncology Institutional Review Board (IRB) under IRB # 2014-0804. All bio-specimens are de-identified and may not be linked to identifiable individual private information.

Supplementary material

216_2016_41_MOESM1_ESM.pdf (153 kb)
ESM 1 (PDF 153 kb)


  1. 1.
    Haltiwanger RS, Lowe JB. Role of glycosylation in development. Annu Rev Biochem. 2004;73:491–537.CrossRefGoogle Scholar
  2. 2.
    Freeze HH. Understanding human glycosylation disorders: biochemistry leads the charge. J Biol Chem. 2013;288(10):6936–45.CrossRefGoogle Scholar
  3. 3.
    Cummings RD. The repertoire of glycan determinants in the human glycome. Mol Biosyst. 2009;5(10):1087–104.CrossRefGoogle Scholar
  4. 4.
    Moremen KW, Tiemeyer M, Nairn AV. Vertebrate protein glycosylation: diversity, synthesis and function. Nat Rev Mol Cell Biol. 2012;13(7):448–62.CrossRefGoogle Scholar
  5. 5.
    Canis K, McKinnon TA, Nowak A, Haslam SM, Panico M, Morris HR, et al. Mapping the N-glycome of human von Willebrand factor. Biochem J. 2012;447(2):217–28.CrossRefGoogle Scholar
  6. 6.
    Alley Jr WR, Mann BF, Novotny MV. High-sensitivity analytical approaches for the structural characterization of glycoproteins. Chem Rev. 2013;113(4):2668–732.CrossRefGoogle Scholar
  7. 7.
    Thaysen-Andersen M, Packer NH, Schulz BL. Maturing glycoproteomics technologies provide unique structural insights into the N-glycoproteome and its regulation in health and disease. Mol Cell Proteomics. 2016;15(6):1773–90.CrossRefGoogle Scholar
  8. 8.
    Wuhrer M. Glycomics using mass spectrometry. Glycoconj J. 2013;30(1):11–22.CrossRefGoogle Scholar
  9. 9.
    Goldman R, Sanda M. Targeted methods for quantitative analysis of protein glycosylation. Proteomics Clin Appl. 2015;9(1-2):17–32.CrossRefGoogle Scholar
  10. 10.
    Harvey DJ. Derivatization of carbohydrates for analysis by chromatography; electrophoresis and mass spectrometry. J Chromatogr B Anal Technol Biomed Life Sci. 2011;879(17-18):1196–225.CrossRefGoogle Scholar
  11. 11.
    Kailemia MJ, Ruhaak LR, Lebrilla CB, Amster IJ. Oligosaccharide analysis by mass spectrometry: a review of recent developments. Anal Chem. 2014;86(1):196–212.CrossRefGoogle Scholar
  12. 12.
    Ashline DJ, Hanneman AJ, Zhang H, Reinhold VN. Structural documentation of glycan epitopes: sequential mass spectrometry and spectral matching. J Am Soc Mass Spectrom. 2014;25(3):444–53.CrossRefGoogle Scholar
  13. 13.
    Liu T, Qian WJ, Gritsenko MA, Camp DG, Monroe ME, Moore RJ, et al. Human plasma N-glycoproteome analysis by immunoaffinity subtraction, hydrazide chemistry, and mass spectrometry. J Proteome Res. 2005;4(6):2070–80.CrossRefGoogle Scholar
  14. 14.
    Stahl-Zeng J, Lange V, Ossola R, Aebersold R, Domon B. High sensitivity detection of plasma proteins by multiple reaction monitoring of N-glycosites. Mol Cell Proteomics. 2007;6:1809–17.CrossRefGoogle Scholar
  15. 15.
    Zielinska DF, Gnad F, Wisniewski JR, Mann M. Precision mapping of an in vivo N-glycoproteome reveals rigid topological and sequence constraints. Cell. 2010;141(5):897–907.CrossRefGoogle Scholar
  16. 16.
    Rudd PM, Dwek RA. Glycosylation: heterogeneity and the 3D structure of proteins. Crit Rev Biochem Mol Biol. 1997;32(1):1–100.CrossRefGoogle Scholar
  17. 17.
    Thaysen-Andersen M, Packer NH. Site-specific glycoproteomics confirms that protein structure dictates formation of N-glycan type, core fucosylation and branching. Glycobiology. 2012;22:1440–52.CrossRefGoogle Scholar
  18. 18.
    Chandler K, Goldman R. Glycoprotein disease markers and single protein-omics. Mol Cell Proteomics. 2013;12(4):836–45.CrossRefGoogle Scholar
  19. 19.
    Plomp R, Hensbergen PJ, Rombouts Y, Zauner G, Dragan I, Koeleman CA, et al. Site-specific N-glycosylation analysis of human immunoglobulin E. J Proteome Res. 2014;13(2):536–46.CrossRefGoogle Scholar
  20. 20.
    Gillet LC, Navarro P, Tate S, Roest H, Selevsek N, Reiter L, et al. Targeted data extraction of the MS/MS spectra generated by data independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics. 2012;11:O111.016717.CrossRefGoogle Scholar
  21. 21.
    Ting YS, Egertson JD, Payne SH, Kim S, MacLean B, Kall L, et al. Peptide-centric proteome analysis: an alternative strategy for the analysis of tandem mass spectrometry data. Mol Cell Proteomics. 2015;14(9):2301–7.CrossRefGoogle Scholar
  22. 22.
    Keller A, Bader SL, Kusebauch U, Shteynberg D, Hood L, Moritz RL. Opening a SWATH window on posttranslational modifications: automated pursuit of modified peptides. Mol Cell Proteomics. 2016;15(3):1151–63.CrossRefGoogle Scholar
  23. 23.
    Benicky J, Sanda M, Pompach P, Wu J, Goldman R. Quantification of fucosylated hemopexin and complement factor H in plasma of patients with liver disease. Anal Chem. 2014;86(21):10716–23.CrossRefGoogle Scholar
  24. 24.
    Szabo Z, Guttman A, Karger BL. Rapid release of N-linked glycans from glycoproteins by pressure-cycling technology. Anal Chem. 2010;82(6):2588–93.CrossRefGoogle Scholar
  25. 25.
    Pompach P, Brnakova Z, Sanda M, Wu J, Edwards N, Goldman R. Site-specific glycoforms of haptoglobin in liver cirrhosis and hepatocellular carcinoma. Mol Cell Proteomics. 2013;12(5):1281–93.CrossRefGoogle Scholar
  26. 26.
    Selman MH, Derks RJ, Bondt A, Palmblad M, Schoenmaker B, Koeleman CA, et al. Fc specific IgG glycosylation profiling by robust nano-reverse phase HPLC-MS using a sheath-flow ESI sprayer interface. J Proteomics. 2012;75(4):1318–29.CrossRefGoogle Scholar
  27. 27.
    Wang B, Tsybovsky Y, Palczewski K, Chance MR. Reliable determination of site-specific in vivo protein N-glycosylation based on collision-induced MS/MS and chromatographic retention time. J Am Soc Mass Spectrom. 2014;25(5):729–41.CrossRefGoogle Scholar
  28. 28.
    Yuan W, Sanda M, Wu J, Koomen J, Goldman R. Quantitative analysis of immunoglobulin subclasses and subclass specific glycosylation by LC-MS-MRM in liver disease. J Proteomics. 2015;116:24–33.CrossRefGoogle Scholar
  29. 29.
    Mehta AS, Norton P, Liang H, Comunale MA, Wang M, Rodemich-Betesh L, et al. Increased levels of tetra-antennary N-linked glycan but not core fucosylation are associated with hepatocellular carcinoma tissue. Cancer Epidemiol Biomarkers Prev. 2012;21:925–33.CrossRefGoogle Scholar
  30. 30.
    Sanda M, Pompach P, Brnakova Z, Wu J, Makambi K, Goldman R. Quantitative liquid chromatography-mass spectrometry-multiple reaction monitoring (LC-MS-MRM) analysis of site-specific glycoforms of haptoglobin in liver disease. Mol Cell Proteomics. 2013;12(5):1294–305.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Miloslav Sanda
    • 1
  • Lihua Zhang
    • 1
  • Nathan J. Edwards
    • 2
  • Radoslav Goldman
    • 1
    • 2
  1. 1.Department of Oncology, Lombardi Comprehensive Cancer Center PSB GD9Georgetown UniversityWashingtonUSA
  2. 2.Department of Biochemistry and Molecular & Cellular BiologyGeorgetown UniversityWashingtonUSA

Personalised recommendations