Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Glyco-DIA: a method for quantitative O-glycoproteomics with in silico-boosted glycopeptide libraries

Nature Methods volume 16, pages 902–910 (2019)Cite this article

Subjects

Abstract

We report a liquid chromatography coupled to tandem mass spectrometry O-glycoproteomics strategy using data-independent acquisition (DIA) mode for direct analysis of O-glycoproteins. This approach enables characterization of glycopeptides and structures of O-glycans on a proteome-wide scale with quantification of stoichiometries (though it does not allow for direct unambiguous glycosite identification). The method relies on a spectral library of O-glycopeptides; the Glyco-DIA library contains sublibraries obtained from human cell lines and human serum, and it currently covers 2,076 O-glycoproteins (11,452 unique glycopeptide sequences) and the 5 most common core1 O-glycan structures. Applying the Glyco-DIA library to human serum without enrichment for glycopeptides enabled us to identify and quantify 269 distinct glycopeptide sequences bearing up to 5 different core1 O-glycans from 159 glycoproteins in a SingleShot analysis.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Graphic depiction of the O-GalNAc glycosylation pathway and Glyco-DIA libraries design.
Fig. 2: Sensitive identification and accurate quantification with the Glyco-DIA method.
Fig. 3: Expanding the Tn/T-DIA libraries in silico.
Fig. 4: Error-rate control in Glyco-DIA analysis.
Fig. 5: Application of Glyco-DIA to human blood serum.
Fig. 6: Site-specific Glyco-DIA.

Similar content being viewed by others

Data availability

All of the Glyco-DIA libraries are available on our O-Glycoproteome database (http://glycoproteomics.somee.com) and https://github.com/CCGMS/Glyco-DIA, and can be directly imported to Spectronaut. All of the raw data including DDA and DIA runs have been deposited to the ProteomeXchange Consortium35, via the PRIDE partner repository, with the data set identifier PXD011063.

Code availability

All of the R scripts are available at https://github.com/CCGMS/Glyco-DIA.

References

  1. Goth, C. K. et al. A systematic study of modulation of ADAM-mediated ectodomain shedding by site-specific O-glycosylation. Proc. Natl Acad. Sci. USA 112, 14623–14628 (2015).

    Article  CAS  Google Scholar 

  2. Ohtsubo, K. & Marth, J. D. Glycosylation in cellular mechanisms of health and disease. Cell 126, 855–867 (2006).

    Article  CAS  Google Scholar 

  3. Levery, S. B. et al. Advances in mass spectrometry driven O-glycoproteomics. Biochim. Biophys. Acta 1850, 33–42 (2015).

    Article  CAS  Google Scholar 

  4. Darula, Z., Sherman, J. & Medzihradszky, K. F. How to dig deeper? Improved enrichment methods for mucin core-1 type glycopeptides. Mol. Cell. Proteom. 11, 016774 (2012). mcp. O111.

    Article  Google Scholar 

  5. King, S. L. et al. Characterizing the O-glycosylation landscape of human plasma, platelets, and endothelial cells. Blood Adv. 1, 429–442 (2017).

    Article  CAS  Google Scholar 

  6. Medzihradszky, K. F., Kaasik, K. & Chalkley, R. J. Tissue-specific glycosylation at the glycopeptide level. Mol. Cell. Proteom. 14, 050393 (2015). mcp. M115.

    Article  Google Scholar 

  7. Hoffmann, M., Marx, K., Reichl, U., Wuhrer, M. & Rapp, E. Site-specific O-glycosylation analysis of human blood plasma proteins. Mol. Cell. Proteom. 15, 624–641 (2016).

    Article  CAS  Google Scholar 

  8. Steentoft, C. et al. Precision mapping of the human O-GalNAc glycoproteome through simple cell technology. EMBO J. 32, 1478–1488 (2013).

    Article  CAS  Google Scholar 

  9. Hintze, J. et al. Probing the contribution of individual polypeptide GalNAc-transferase isoforms to the O-glycoproteome by inducible expression in isogenic cell lines. J. Biol. Chem. 293, 19064–19077 (2018).

    Article  CAS  Google Scholar 

  10. Venable, J. D., Dong, M.-Q., Wohlschlegel, J., Dillin, A. & Yates, J. R. III Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat. Methods 1, 39 (2004).

    Article  CAS  Google Scholar 

  11. Chapman, J. D., Goodlett, D. R. & Masselon, C. D. Multiplexed and data-independent tandem mass spectrometry for global proteome profiling. Mass Spectrom. Rev. 33, 452–470 (2014).

    Article  CAS  Google Scholar 

  12. Gillet, L. C. 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. Proteom. 11, O111.016717 (2012).

    Article  Google Scholar 

  13. Ludwig, C. et al. Data‐independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. Mol. Syst. Biol. 14, e8126 (2018).

    Article  Google Scholar 

  14. Liu, Y. et al. Glycoproteomic analysis of prostate cancer tissues by SWATH mass spectrometry discovers N-acylethanolamine acid amidase and protein tyrosine kinase 7 as signatures for tumor aggressiveness. Mol. Cell. Proteom. 13, 1753–1768 (2014).

    Article  CAS  Google Scholar 

  15. Pan, K.-T., Chen, C.-C., Urlaub, H. & Khoo, K.-H. Adapting data-independent acquisition for mass spectrometry-based protein site-specific N-glycosylation analysis. Anal. Chem. 89, 4532–4539 (2017).

    Article  CAS  Google Scholar 

  16. Couto, N., Davlyatova, L., Evans, C. A. & Wright, P. C. Application of the broadband collision-induced dissociation (bbCID) mass spectrometry approach for protein glycosylation and phosphorylation analysis. Rapid Commun. Mass Spectrom. 32, 75–85 (2018).

    Article  CAS  Google Scholar 

  17. Zacchi, L. F. & Schulz, B. L. SWATH-MS glycoproteomics reveals consequences of defects in the glycosylation machinery. Mol. Cell. Proteom. 15, 2435–2447 (2016).

    Article  CAS  Google Scholar 

  18. Lin, C.-H., Krisp, C., Packer, N. H. & Molloy, M. P. Development of a data independent acquisition mass spectrometry workflow to enable glycopeptide analysis without predefined glycan compositional knowledge. J. Proteom. 172, 68–75 (2018).

    Article  CAS  Google Scholar 

  19. Steentoft, C. et al. Mining the O-glycoproteome using zinc-finger nuclease-glycoengineered simple cell lines. Nat. Methods 8, 977–982 (2011).

    Article  CAS  Google Scholar 

  20. Bruderer, R. et al. Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues. Mol. Cell. Proteom. 14, 1400–1410 (2015).

    Article  CAS  Google Scholar 

  21. Furukawa, J.-i et al. Quantitative O-glycomics by microwave-assisted β-elimination in the presence of pyrazolone analogues. Anal. Chem. 87, 7524–7528 (2015).

    Article  CAS  Google Scholar 

  22. Fujitani, N. et al. Total cellular glycomics allows characterizing cells and streamlining the discovery process for cellular biomarkers. Proc. Natl Acad. Sci. USA 110, 2105–2110 (2013).

    Article  CAS  Google Scholar 

  23. Wuhrer, M., Catalina, M. I., Deelder, A. M. & Hokke, C. H. Glycoproteomics based on tandem mass spectrometry of glycopeptides. J. Chromatogr. B 849, 115–128 (2007).

    Article  CAS  Google Scholar 

  24. Nilsson, J. Liquid chromatography-tandem mass spectrometry-based fragmentation analysis of glycopeptides. Glycoconj. J. 33, 261–272 (2016).

    Article  CAS  Google Scholar 

  25. Rosenberger, G. et al. Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses. Nat. Methods 14, 921–927 (2017).

    Article  CAS  Google Scholar 

  26. Chalkley, R. J., Thalhammer, A., Schoepfer, R. & Burlingame, A. Identification of protein O-GlcNAcylation sites using electron transfer dissociation mass spectrometry on native peptides. Proc. Natl Acad. Sci. USA 106, 8894–8899 (2009).

    Article  CAS  Google Scholar 

  27. Liu, Y. et al. Quantitative variability of 342 plasma proteins in a human twin population. Mol. Syst. Biol. 11, 786 (2015).

    Article  Google Scholar 

  28. Ting, Y. S. et al. PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data. Nat. Methods 14, 903 (2017).

    Article  CAS  Google Scholar 

  29. Darula, Z. & Medzihradszky, K. F. Carbamidomethylation side reactions may lead to glycan misassignments in glycopeptide analysis. Anal. Chem. 87, 6297–6302 (2015).

    Article  CAS  Google Scholar 

  30. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

  31. Katrine, T.-B. S. et al. Probing isoform-specific functions of polypeptide GalNAc-transferases using zinc finger nuclease glycoengineered SimpleCells. Proc. Natl Acad. Sci. USA 109, 9893–9898 (2012).

    Article  Google Scholar 

  32. R Core Team. R: A language and environment for statistical computing v3.5.1 (R, 2013); https://www.r-project.org/

  33. Taus, T. et al. Universal and confident phosphorylation site localization using phosphoRS. J. Proteome Res. 10, 5354–5362 (2011).

    Article  CAS  Google Scholar 

  34. Reiter, L. et al. mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nat. Methods 8, 430 (2011).

    Article  CAS  Google Scholar 

  35. Vizcaíno, J. A. et al. ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nat. Biotechnol. 32, 223 (2014).

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by The Lundbeck Foundation, The Mizutani Foundation and the Danish National Research Foundation (DNRF107). We thank L. Kristensen and C. Dauly (Thermo Fisher Scientific) for their help with this study.

Author information

Author notes

  1. Yang Mao

    Present address: School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China

Authors and Affiliations

  1. Copenhagen Center for Glycomics, Departments of Cellular and Molecular Medicine and School of Dentistry, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark

    Zilu Ye, Yang Mao, Henrik Clausen & Sergey Y. Vakhrushev

Authors
  1. Zilu Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yang Mao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Henrik Clausen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sergey Y. Vakhrushev
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Z.Y., Y.M., H.C. and S.Y.V. conceived and designed the study. Z.Y. and S.Y.V. contributed with experimental data and interpretation. Z.Y., H.C. and S.Y.V. wrote the manuscript. All authors approved the final version.

Corresponding author

Correspondence to Sergey Y. Vakhrushev.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information: Allison Doerr was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Comparison between Glyco-DIA libraries.

(a) Number of glycoproteins in Tn-DIA libraries from HEK293, HepG2, OVCAR3 and M3T4 SC lines. (b) Number of glycopeptides in Tn-DIA libraries from HEK293, HepG2, OVCAR3 and M3T4 SC lines. (c) Number of glycoproteins in T-DIA libraries from HEK293 WT and HepG2 WT cell lines and human blood serum. (d) Number of glycopeptides in T-DIA libraries from HEK293 WT and HepG2 WT cell lines and human blood serum.

Supplementary Figure 2 Comparison of glycopeptides in serum library with and without depletion of most abundant proteins.

Top 12 abundant proteins were depleted with spin columns (85164, Pierce, Thermo Scientific), including α1-acid glycoprotein, α1-antitrypsin, α1-macroglubulin, albumin, apolipoprotein A-I, apolipoprotein A-II, fibrinogen, haptoglobin, IgA, IgG, IgM, transferrin.

Supplementary Figure 3 Window width distribution of DIA method in Glyco-DIA.

The DIA method was designed based on the precursor distribution in HEK293 WT T-DIA library (histogram in the background). It covers precursor range from m/z 400 to m/z 1,200 and a total of 41 windows are listed in the method, including 3 windows of 100 m/z isolation width, 12 windows of 20 m/z isolation width, and 26 windows of 10 m/z isolation width.

Supplementary Figure 4

Comparison of glycopeptides in the HepG2 WT SSL and the comprehensive HepG2 T-DIA library.

Supplementary Figure 5

Comparison of identified glycopeptides in the HepG2 WT Jacalin enriched non-diluted sample with SSL and the HepG2 T-DIA library.

Supplementary Figure 6 ShapQualityScore distributions of sLWAC enriched HepG2 WT samples processed with the HepG2 T-DIA library.

A total of 541 T-glycopeptides were identified and 279 with ShapeQualityScore higher than 0.7.

Supplementary information

Supplementary Information

Supplementary Figs. 1–6 and Supplementary Notes 1–8.

Reporting Summary

Supplementary Table 1

Overview of the generated spectral libraries and the figures they were used in

Supplementary Table 2

Combined list of all the identifications in the SingleShot analysis

Supplementary Data Set 1

ETD-MS2 spectra and extracted ion chromatograms of all the positional isoforms from the ETD/HCD DDA run for the site-specific library.

Supplementary Data Set 2

Comparison of serum T-DIA library with published data, related to Supplementary Note 3.

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ye, Z., Mao, Y., Clausen, H. et al. Glyco-DIA: a method for quantitative O-glycoproteomics with in silico-boosted glycopeptide libraries. Nat Methods 16, 902–910 (2019). https://doi.org/10.1038/s41592-019-0504-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41592-019-0504-x

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research