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Sequential phosphoproteomics and N-glycoproteomics of plasma-derived extracellular vesicles

Nature Protocols volume 15, pages 161–180 (2020)Cite this article

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Abstract

Extracellular vesicles (EVs) are increasingly being recognized as important vehicles for intercellular communication and as promising sources for biomarker discovery. Because the state of protein post-translational modifications (PTMs) such as phosphorylation and glycosylation can be a key determinant of cellular physiology, comprehensive characterization of protein PTMs in EVs can be particularly valuable for early-stage diagnostics and monitoring of disease status. However, the analysis of PTMs in EVs has been complicated by limited amounts of purified EVs, low-abundance PTM proteins, and interference from proteins and metabolites in biofluids. Recently, we developed an approach to isolate phosphoproteins and glycoproteins in EVs from small volumes of human plasma that enabled us to identify nearly 10,000 unique phosphopeptides and 1,500 unique N-glycopeptides. The approach demonstrated the feasibility of using these data to identify potential markers to differentiate disease from healthy states. Here we present an updated workflow to sequentially isolate phosphopeptides and N-glycopeptides, enabling multiple PTM analyses of the same clinical samples. In this updated workflow, we have improved the reproducibility and efficiency of EV isolation, protein extraction, and phosphopeptide/N-glycopeptide enrichment to achieve sensitive analyses of low-abundance PTMs in EVs isolated from 1 mL of plasma. The modularity of the workflow also allows for the characterization of phospho- or glycopeptides only and enables additional analysis of total proteomes and other PTMs of interest. After blood collection, the protocol takes 2 d, including EV isolation, PTM/peptide enrichment, mass spectrometry analysis, and data quantification.

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Fig. 1: Workflow for sequential EV phosphoproteomics and glycoproteomics.
Fig. 2: Schematic of experimental setup for sequential and separate phosphopeptide and glycopeptide enrichments.
Fig. 3: Venn diagrams showing the phosphopeptide and phosphoprotein overlap between replicates and the overlap between the separate and sequential workflows.
Fig. 4: Venn diagrams showing the glycopeptide and glycoprotein overlap between replicates and the overlap between the separate and sequential workflows.
Fig. 5: Quantitation results from MaxQuant and Perseus showing Pearson correlations across each condition and replicate.

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Data availability

The raw MS data from this study have been deposited into the ProteomeXchange Consortium via the PRIDE Archive with the identifier PXD013893 (https://www.ebi.ac.uk/pride/archive/projects/PXD013893).

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Acknowledgements

This project was funded by NIH grants 5R01GM088317 and 1R01GM111788 and NSF grant 1506752.

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Authors and Affiliations

  1. Department of Chemistry, Purdue University, West Lafayette, IN, USA

    Hillary Andaluz Aguilar & W. Andy Tao

  2. Tymora Analytical Operations, West Lafayette, IN, USA

    Anton B. Iliuk & W. Andy Tao

  3. Department of Biochemistry, Purdue University, West Lafayette, IN, USA

    Anton B. Iliuk, I-Hsuan Chen & W. Andy Tao

  4. Purdue Center for Cancer Research, Purdue University, West Lafayette, IN, USA

    W. Andy Tao

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Contributions

H.A.A., A.B.I. and W.A.T. designed the studies. H.A.A., A.B.I. and I.-H.C. developed methods. H.A.A. performed the experiments and analyzed the data. H.A.A. and W.A.T. wrote the manuscript.

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Correspondence to W. Andy Tao.

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A.B.I and W.A.T. are the co-founders of Tymora Analytical Operations, which commercialized a polyMAC kit used in parts of the described protocol.

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Key references using this protocol

Chen, I.-H. et al. Proc. Natl. Acad. Sci. USA 114, 3175–3180 (2017): https://www.pnas.org/content/114/12/3175

Chen, I.-H. et al. Anal. Chem. 90, 6307–6313 (2018): https://pubs.acs.org/doi/10.1021/acs.analchem.8b01090

Wu X., Li, L., Iliuk, A. & Tao, W. A. J. Proteome Res. 17, 3308–3316 (2018): https://pubs.acs.org/doi/10.1021/acs.jproteome.8b00459

Integrated supplementary information

Supplementary Figure 1 Venn diagrams showing the overlap between the EV database from Vesiclepedia and EV data from control and breast cancer.

The Venn diagrams show a 77% overlap between both, the EV database and control EV proteins, and the EV database and EV breast cancer proteins. Overlap of the proteome from the control and the breast cancer EV data is also shown.

Supplementary Figure 2 Quantitation results from MaxQuant and Perseus showing Pearson correlations from proteome analysis across each condition and replicate.

Scatterplots and Pearson correlation coefficients depicting the log2-transformed intensities from proteome analysis of control and breast cancer samples, each in triplicate.

Supplementary Figure 3 Scatterplots representing the targeted proteomic (PRM) analysis.

Raw intensities from five individual control samples corresponding to five phosphopeptides from five EV proteins were plotted. See Table S1 for details.

Supplementary information

Supplementary Information

Supplementary Figs. 1–3 and Supplementary Tables 1 and 2.

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Andaluz Aguilar, H., Iliuk, A.B., Chen, IH. et al. Sequential phosphoproteomics and N-glycoproteomics of plasma-derived extracellular vesicles. Nat Protoc 15, 161–180 (2020). https://doi.org/10.1038/s41596-019-0260-5

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