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Proteomics pp 99-113 | Cite as

Multi-Lectin Affinity Chromatography for Separation, Identification, and Quantitation of Intact Protein Glycoforms in Complex Biological Mixtures

  • Sarah M. Totten
  • Majlinda Kullolli
  • Sharon J. Pitteri
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1550)

Abstract

Protein glycosylation is considered to be one of the most abundant post-translational modifications and is recognized for playing key roles in cellular functions. Aberrant N-linked glycosylation has been associated with several human diseases and has prompted the development and constant improvement of analytical tools to separate, characterize, and quantify glycoproteins in complex mixtures extracted from various biological samples (such as blood and tissue). Lectins, or carbohydrate-binding proteins, have been used as valuable tools for enriching for glycoproteins and selecting for specific types of glycosylation. Herein a method using multidimensional intact protein fractionation and LC-MS/MS analysis is described. Immunodepletion is used to remove highly abundant proteins from human plasma, followed by glycoform separation using multi-lectin affinity chromatography, in which specific lectins are chosen to capture and elute specific types of glycosylation. Reversed-phase chromatography prior to digestion is used for further fractionation, allowing for an increased number of protein identifications of moderate- to low-abundant proteins detectable in plasma. This method also incorporates isotopic labeling during alkylation for relative quantitation between two samples (such as a case and control). A bottom-up, tandem mass spectrometry-based proteomics approach is used for protein identification and quantitation, and allows for screening glycoform-specific changes across hundreds of plasma proteins.

Key words

Multi-lectin affinity chromatography Glycoproteomics Protein glycosylation Plasma proteomics 

References

  1. 1.
    Wang H, Hanash S (2011) Intact-protein analysis system for discovery of serum-based disease biomarkers. Methods Mol Biol 728:69–85CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Apweiler R, Hermjakob H, Sharon N (1999) On the frequency of protein glycosylation, as deduced from analysis of the SWISS-PROT database. Biochim Biophys Acta 1473(1):4–8CrossRefPubMedGoogle Scholar
  3. 3.
    Varki A, Cummings RD, Esko JD, et al., editors. Cold Spring Harbor (NY): Cold Spring Harbor Laboratory Press; 2009. Bookshelf ID: NBK1963 PMID: 20301279Google Scholar
  4. 4.
    Mkhikian H, Grigorian A, Li CF, Chen H-L, Newton B, Zhou RW, Beeton C, Torossian S, Tatarian GG, Lee S-U, Lau K, Walker E, Siminovitch KA, Chandy KG, Yu Z, Dennis JW, Demetriou M (2011) Genetics and the environment converge to dysregulate N-glycosylation in multiple sclerosis. Nat Commun 2:334CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Palmigiano A, Barone R, Sturiale L, Sanfilippo C, Bua RO, Romeo DA, Messina A, Capuana ML, Maci T, Le Pira F, Zappia M, Garozzo D (2016) CSF N-glycoproteomics for early diagnosis in Alzheimer’s disease. J Proteomics 131:29–37CrossRefPubMedGoogle Scholar
  6. 6.
    Theodoratou E, Campbell H, Ventham NT, Kolarich D, Pucic-Bakovic M, Zoldos V, Fernandes D, Pemberton IK, Rudan I, Kennedy NA, Wuhrer M, Nimmo E, Annese V, McGovern DPB, Satsangi J, Lauc G (2014) The role of glycosylation in IBD. Nat Rev Gastroenterol Hepatol 10:588–600, advance online publicationGoogle Scholar
  7. 7.
    Liang H-C, Russell C, Mitra V, Chung R, Hye A, Bazenet C, Lovestone S, Pike I, Ward M (2015) Glycosylation of human plasma clusterin yields a novel candidate biomarker of Alzheimer’s disease. J Proteome Res 14(12):5063–5076CrossRefPubMedGoogle Scholar
  8. 8.
    Goulabchand R, Vincent T, Batteux F, J-f E, Guilpain P (2014) Impact of autoantibody glycosylation in autoimmune diseases. Autoimmun Rev 13(7):742–750CrossRefPubMedGoogle Scholar
  9. 9.
    Lauc G, Huffman JE, Pučić M, Zgaga L, Adamczyk B, Mužinić A, Novokmet M, Polašek O, Gornik O, Krištić J, Keser T, Vitart V, Scheijen B, Uh H-W, Molokhia M, Patrick AL, McKeigue P, Kolčić I, Lukić IK, Swann O, van Leeuwen FN, Ruhaak LR, Houwing-Duistermaat JJ, Slagboom PE, Beekman M, de Craen AJM, Deelder AM, Zeng Q, Wang W, Hastie ND, Gyllensten U, Wilson JF, Wuhrer M, Wright AF, Rudd PM, Hayward C, Aulchenko Y, Campbell H, Rudan I (2013) Loci associated with N-glycosylation of human immunoglobulin G show pleiotropy with autoimmune diseases and haematological cancers. PLoS Genet 9(1):e1003225CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Zaia J (2010) Mass spectrometry and glycomics. OMICS 14(4):401–418CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Mechref Y, Hu Y, Desantos-Garcia JL, Hussein A, Tang H (2013) Quantitative glycomics strategies. Mol Cell Proteomics 12(4):874–884CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    An HJ, Kronewitter SR, de Leoz MLA, Lebrilla CB (2009) Glycomics and disease markers. Curr Opin Chem Biol 13(5-6):601–607CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Fujitani N, J-i F, Araki K, Fujioka T, Takegawa Y, Piao J, Nishioka T, Tamura T, Nikaido T, Ito M, Nakamura Y, Shinohara Y (2013) Total cellular glycomics allows characterizing cells and streamlining the discovery process for cellular biomarkers. Proc Natl Acad Sci U S A 110(6):2105–2110CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Hu Y, Zhou S, Yu C-Y, Tang H, Mechref Y (2015) Automated annotation and quantitation of Glycan by LC-ESI-MS analysis using MultiGlycan-ESI computational tool. Rapid Commun Mass Spectrom 29(1):135–142CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Ruhaak LR, Miyamoto S, Lebrilla CB (2013) Developments in the identification of glycan biomarkers for the detection of cancer. Mol Cell Proteomics 12(4):846–855CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Zhou S, Hu Y, DeSantos-Garcia JL, Mechref Y (2015) Quantitation of permethylated N-glycans through multiple-reaction monitoring (MRM) LC-MS/MS. J Am Soc Mass Spectrom 26(4):596–603CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Wu S-W, Pu T-H, Viner R, Khoo K-H (2014) Novel LC-MS2 product dependent parallel data acquisition function and data analysis workflow for sequencing and identification of intact glycopeptides. Anal Chem 86(11):5478–5486CrossRefPubMedGoogle Scholar
  18. 18.
    Saba J, Dutta S, Hemenway E, Viner R (2012) Increasing the productivity of glycopeptides analysis by using higher-energy collision dissociation-accurate mass-product-dependent electron transfer dissociation. Int J Proteomics 2012:560391CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Hong Q, Ruhaak LR, Stroble C, Parker E, Huang J, Maverakis E, Lebrilla CB (2015) A method for comprehensive glycosite-mapping and direct quantitation of serum glycoproteins. J Proteome Res 14(12):5179–5192CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Mayampurath A, Yu C-Y, Song E, Balan J, Mechref Y, Tang H (2014) Computational framework for identification of intact glycopeptides in complex samples. Anal Chem 86(1):453–463CrossRefPubMedGoogle Scholar
  21. 21.
    Hu H, Khatri K, Zaia J (2016) Algorithms and design strategies towards automated glycoproteomics analysis. Mass Spectrom Rev n/a-n/aGoogle Scholar
  22. 22.
    Mayampurath A, Song E, Mathur A, Yu C-y, Hammoud Z, Mechref Y, Tang H (2014) Label-free glycopeptide quantification for biomarker discovery in human sera. J Proteome Res 13(11):4821–4832CrossRefPubMedGoogle Scholar
  23. 23.
    Drake PM, Schilling B, Niles RK, Braten M, Johansen E, Liu H, Lerch M, Sorensen DJ, Li B, Allen S, Hall SC, Witkowska HE, Regnier FE, Gibson BW, Fisher SJ (2011) A lectin affinity workflow targeting glycosite-specific, cancer-related carbohydrate structures in trypsin-digested human plasma(). Anal Biochem 408(1):71–85CrossRefPubMedGoogle Scholar
  24. 24.
    Jung K, Cho W, Regnier FE (2009) Glycoproteomics of plasma based on narrow selectivity lectin affinity chromatography. J Proteome Res 8(2):643–650CrossRefPubMedGoogle Scholar
  25. 25.
    Gbormittah FO, Hincapie M, Hancock WS (2014) Development of an improved fractionation of the human plasma proteome by a combination of abundant proteins depletion and multi-lectin affinity chromatography. Bioanalysis 6(19):2537–2548CrossRefPubMedGoogle Scholar
  26. 26.
    Lee LY, Hincapie M, Packer N, Baker MS, Hancock WS, Fanayan S (2012) An optimized approach for enrichment of glycoproteins from cell culture lysates using native multi-lectin affinity chromatography. J Sep Sci 35(18):2445–2452CrossRefPubMedGoogle Scholar
  27. 27.
    Kullolli M, Hancock WS, Hincapie M (2008) Preparation of a high-performance multi-lectin affinity chromatography (HP-M-LAC) adsorbent for the analysis of human plasma glycoproteins. J Sep Sci 31(14):2733–2739CrossRefPubMedGoogle Scholar
  28. 28.
    Fanayan S, Hincapie M, Hancock WS (2012) Using lectins to harvest the plasma/serum glycoproteome. Electrophoresis 33(12):1746–1754CrossRefPubMedGoogle Scholar
  29. 29.
    Madera M, Mechref Y, Klouckova I, Novotny MV (2007) High-sensitivity profiling of glycoproteins from human blood serum through multiple-lectin affinity chromatography and liquid chromatography/tandem mass spectrometry. J Chromatogr B 845(1):121–137CrossRefGoogle Scholar
  30. 30.
    Song E, Zhu R, Hammoud ZT, Mechref Y (2014) LC–MS/MS quantitation of esophagus disease blood serum glycoproteins by enrichment with hydrazide chemistry and lectin affinity chromatography. J Proteome Res 13(11):4808–4820CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Mechref Y, Madera M, Novotny MV (2008) Glycoprotein enrichment through lectin affinity techniques. In: Posch A (ed) 2D PAGE: sample preparation and fractionation. Humana Press, Totowa, NJ, pp 373–396. doi: 10.1007/978-1-60327-064-9_29 CrossRefGoogle Scholar
  32. 32.
    Wu J, Xie X, Liu Y, He J, Benitez R, Buckanovich RJ, Lubman DM (2012) Identification and confirmation of differentially expressed fucosylated glycoproteins in the serum of ovarian cancer patients using a lectin array and LC-MS/MS. J Proteome Res 11(9):4541–4552CrossRefPubMedGoogle Scholar
  33. 33.
    Zhao J, Qiu W, Simeone DM, Lubman DM (2007) N-linked glycosylation profiling of pancreatic cancer serum using capillary liquid phase separation coupled with mass spectrometric analysis. J Proteome Res 6(3):1126–1138CrossRefPubMedGoogle Scholar
  34. 34.
    Kullolli M, Warren J, Arampatzidou M, Pitteri SJ (2013) Performance evaluation of affinity ligands for depletion of abundant plasma proteins. J Chromatogr B Analyt Technol Biomed Life Sci 939:10–16CrossRefPubMedGoogle Scholar
  35. 35.
    Rauch A, Bellew M, Eng J, Fitzgibbon M, Holzman T, Hussey P, Igra M, Maclean B, Lin CW, Detter A, Fang R, Faca V, Gafken P, Zhang H, Whiteaker J, States D, Hanash S, Paulovich A, McIntosh MW (2006) Computational proteomics analysis system (CPAS): an extensible, open-source analytic system for evaluating and publishing proteomic data and high throughput biological experiments. J Proteome Res 5(1):112–121CrossRefPubMedGoogle Scholar
  36. 36.
    Craig R, Beavis RC (2004) TANDEM: matching proteins with tandem mass spectra. Bioinformatics 20(9):1466–1467CrossRefPubMedGoogle Scholar
  37. 37.
    Craig R, Cortens JP, Beavis RC (2004) Open source system for analyzing, validating, and storing protein identification data. J Proteome Res 3(6):1234–1242CrossRefPubMedGoogle Scholar
  38. 38.
    Faca V, Coram M, Phanstiel D, Glukhova V, Zhang Q, Fitzgibbon M, McIntosh M, Hanash S (2006) Quantitative analysis of acrylamide labeled serum proteins by LC-MS/MS. J Proteome Res 5(8):2009–2018CrossRefPubMedGoogle Scholar
  39. 39.
    Keller A, Nesvizhskii AI, Kolker E, Aebersold R (2002) Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal Chem 74(20):5383–5392CrossRefPubMedGoogle Scholar
  40. 40.
    Nesvizhskii AI, Keller A, Kolker E, Aebersold R (2003) A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem 75(17):4646–4658CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Sarah M. Totten
    • 1
  • Majlinda Kullolli
    • 1
  • Sharon J. Pitteri
    • 1
  1. 1.Department of Radiology, Canary Center at Stanford for Cancer Early DetectionStanford University School of MedicinePalo AltoUSA

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