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The “Reverse Capture” Autoantibody Microarray:

An Innovative Approach to Profiling the Autoantibody Response to Tissue-Derived Native Antigens

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Part of the book series: Methods in Molecular Biology™ ((MIMB,volume 441))

Summary

Recently, we reported the development and use of a “reverse capture” antibody microarray for the purpose of investigating antigen-autoantibody profiling. This platform was developed to allow researchers to characterize and compare the autoantibody profiles of normal and diseased patients. Our “reverse capture” protocol is based on the dual-antibody sandwich immunoassay of enzyme-linked immunosorbent assay (ELISA), and we have previously reported its use to detect autoimmunity to epitopes found on native antigens derived from tumor cell lines. In this protocol, we used ovarian cancer as a model system to adapt the “reverse capture” procedure for use with native antigens derived from frozen tissue samples. The use of this platform in studies of autoimmunity is valuable because it allows for the detection of autoantibody reactivity with epitopes found on the post-translational modifications (PTMs) of native antigens, a feature not present with other protein array platforms. In the first step in the “reverse capture” process, tissue-derived native antigens are immobilized onto the 500 monoclonal antibodies that are spotted in duplicate on the array surface. Using the captured antigens as “baits,” we then incubate the array with labeled IgG from test and control samples, and perform a two-slide dye-swap to account for any dye effects. Here, we present a detailed description of the “reverse capture” autoantibody microarray for use with tissue-derived native antigens.

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References

  1. Prehn, R.T. (2006) An adaptive immune reaction may be necessary for cancer development. Theor. Biol. Med. Model 3(1), 6.

    Article  Google Scholar 

  2. Dunn, G.P., Old, L.J., and Shreiber, R.D. (2004) The immunobiology of cancer immunosurveillance and immunoediting. Immunity 21,137–148.

    Article  CAS  PubMed  Google Scholar 

  3. Madrid, F.F. (2005) Autoantibodies in breast cancer sera: Candidate biomarkers and reporters of tumorigenesis. Cancer Lett. 230,187–198.

    Article  Google Scholar 

  4. Mintz, P.J., Kim, J., Do, K.A., Wang, X., Zinner, R.G., Cristofanilli, M., Arap, M.A., Hong, W.K., Troncoso, P., Logothetis, C.J., Pasqualini, R., and Arap, W. (2003) Finger printing the circulating repertoire of antibodies from cancer patients. Nat Biotechnol. 21, 57–63.

    Article  CAS  PubMed  Google Scholar 

  5. Wang, X., Yu, J., Sreekumar, A., Varambally, S., Shen, R., Gia cherio, D., Mehra, R., Montie, J.E., Pienta, K.J., Sanda, M.G., Kantoff, P.W., Rubin, M.A., Wei, J.T., Ghosh, D., and Chinnaiyan, A.M. (2005) Autoantibody signatures in prostate cancer. N. Engl. J. Med. 353, 1224–1235.

    Article  CAS  PubMed  Google Scholar 

  6. Hueber, W. (2005) Antigen microarray profiling in rheumatoid arthritis. Arthritis Rheum. 52, 2645–2655.

    Article  CAS  PubMed  Google Scholar 

  7. Robinson, W.H., Steinman, L., and Utz, P.J. (2002) Protein and peptide array analysis of autoimmune disease. BioTechniques 33, S66–S69.

    Google Scholar 

  8. Qin, S., Qui, W., Ehrlich, J.R., Ferdinand, A.S., Richie, J.P., O’Leary, M.P., Lee, M.L.T., and Liu, B.C.-S. (2006) Development of a “reverse capture” autoantibody microarray for studies of antigen-autoantibody profiling. Proteomics 6, 3199–3209.

    Article  CAS  PubMed  Google Scholar 

  9. Ehrlich, J.R., Qin, S., and Liu, B.C-S. (2006) The ‘reverse capture’ autoantibody microarray: A native antigen-based platform for autoantibody profiling. Nat. Protocols 1, 452–460.

    Article  CAS  Google Scholar 

  10. Liu, B.C-S. and Ehrlich, J.R. (2006) Proteomics approaches to urologic diseases. Expert Rev. Proteomics 3, 283–296.

    Article  CAS  PubMed  Google Scholar 

  11. Quackenbush, J. (2001) Computational analysis of microarray data. Nat. Rev. Genet. 2, 418–427.

    Article  CAS  PubMed  Google Scholar 

  12. Hess, K.R., Zhang, W., Baggerly, K.A., Stivers, D.N., and Coombes, K.R. (2001) Microarrays: handling the deluge of data and extracting reliable information. Trends Biotechnol. 19, 463–468.

    Article  CAS  PubMed  Google Scholar 

  13. McLachlan, G.J., Do, K.A., and Ambroise, C. (2004) Analyzing Microarray Gene Expression Data. Hoboken, New Jersey: Wiley Interscience.

    Google Scholar 

  14. Quackenbush, J. (2002) Microarray data normalization and transformation. Nat. Genet. 32, 496–501.

    Article  CAS  PubMed  Google Scholar 

  15. Schuchhardt, J., Beule, D., Malik, A., Wolski, E., Eickhoff, H., Lehrach, H., and Herzel, H. (2000) Normalization strategies for cDNA microarrays. Nucleic Acids Res. 28 (article e47).

    Article  Google Scholar 

  16. Yang, Y.H., Dudoit, S., Luu, P., Lin, D.M., Peng, V., Ngai, J., and Speed, T.P. (2002) Normalization for cDNA microarray data: A robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 30 (article e15).

    Google Scholar 

  17. Kerr, K., Martin, M., and Churchill, G. (2000) Analysis of variance for gene expression microarray data. J. Comput. Biol. 7, 819–837.

    Article  CAS  PubMed  Google Scholar 

  18. Wolfinger, R.D., Gibson, G., Wolfinger, E.D., Bennett, L., Hamadeh, H., Bushel, P., Afshari, C., and Paules, R.S. (2001) Assessing gene significance from cDNA microarray expression data via mixed models. J. Comput. Biol. 8, 625–637.

    Article  CAS  PubMed  Google Scholar 

  19. Cleveland, W.S. and Devlin, S.J. (1988) Locally weighted regression: An approach to regression analysis by local fitting. J. Am. Stat. Assoc. 83, 596–610.

    Article  Google Scholar 

  20. Kepler, T.B., Crosby, L., and Morgan, K.T. (2002) Normalization and analysis of DNA microarray data by self-consistency and local regression. Genome Biol. 3 (article 0037.1–0037.12).

    Article  Google Scholar 

  21. Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., and Altman, R.B. (2001) Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520–525.

    Article  CAS  PubMed  Google Scholar 

  22. Boutros, P.C. and Okey, A.B. (2005) Unsupervised pattern recognition: an introduction to the whys and wherefores of clustering microarray data. Brief. Bioinform. 6, 331–343.

    Article  CAS  PubMed  Google Scholar 

  23. Eisen, M.B., Spellmann, P.T., Brown, P.O., and Botstein, D. (1998) Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. U.S.A. 95, 14863–14868.

    Article  CAS  PubMed  Google Scholar 

  24. Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E. Lander, E.S., and Golub, T.R. (1999) Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. U.S.A. 96, 2907–2912.

    Article  CAS  PubMed  Google Scholar 

  25. McLachlan, G.J., Bean, R.W., and Peel, D. (2002) A mixture model-based approach to the clustering of microarray expression data. Bioinformatics 18, 413–422.

    Article  CAS  PubMed  Google Scholar 

  26. Yeung, K.Y., Fraley, C., Murua, A., Raftery, A.E., and Ruzzo, W.L. (2001) Model-based clustering and data transformations for gene expression data. Bioinformatics 17, 977–987.

    Article  CAS  PubMed  Google Scholar 

  27. Ng, S.K., McLachlan, G.J., Wang, K., Ben-Tovim Jones, L., and Ng, S.-W. (2006) A mixture model with random-effects components for clustering correlated gene-expression profiles. Bioinformatics 22, 1745–1752.

    Article  CAS  PubMed  Google Scholar 

  28. Luan, Y. and Li, H. (2003) Clustering of time-course gene expression data using a mixed-effects model with B-splines. Bioinformatics 19, 474–482.

    Article  CAS  PubMed  Google Scholar 

  29. Yeung, K.Y., Medvedovic, M., and Bumgarner, R.E. (2003) Clustering gene-expression data with repeated measurements. Genome Biol. 4 (article R34).

    Google Scholar 

  30. Guyon, L., Weston, J., Barnhill, S., and Vapnik, V. (2002) Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422.

    Article  Google Scholar 

  31. Ramaswamy, S., Tamayo, P., Rifkin, R., Mukherjee, S., Yeang, C.-H., Angelo, M., Ladd, C., Reich, M., Latulippe, E., Mesirov, J.P., Poggio, T., Gerald, W., Loda, M., Lander, E.S., and Golub, T.R. (2001) Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl. Acad. Sci. U.S.A. 98, 15149–15154.

    Article  CAS  PubMed  Google Scholar 

  32. Ambroise, C. and McLachlan, G.J. (2002) Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Natl. Acad. Sci. U.S.A. 99, 6562–6566.

    Article  CAS  PubMed  Google Scholar 

  33. Bair, E. and Tibshirani, R. (2004) Semi-supervised methods to predict patient survival from gene expression data. PloS Biol. 2, 511–522.

    Article  CAS  Google Scholar 

  34. Beer, D.J., Kardia, S.L.R., Huang, C.-C., Giordano, T.J., Levin, A.M., Misek, D.E., Lin, L., Chen, G., Gharib, T.G., Thomas, D.G., Lizyness, M.L., Kuick, R., Hayasaka, S., Taylor, J.M.G., Iannettoni, M.D., Orringer, M.B., and Hanash, S. (2002) Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat. Med. 8, 816–824.

    CAS  PubMed  Google Scholar 

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Acknowledgments

This work was supported, in part, by Grant U01DK063665 from the National Institutes of Health to B.C.-S. Liu. Liangdan Tang is supported by a fellowship from China Scholarship Council (CSC). Shu-Wing Ng is partially supported by a Clinical Innovator Award from the Flight Attendant Medical Research Institute (FAMRI).

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International patent protection is currently pending for the “reverse capture” autoantibody microarray platform.

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© 2008 Humana Press, a part of Springer Science+Business Media, LLC

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Ehrlich, J.R. et al. (2008). The “Reverse Capture” Autoantibody Microarray:. In: Liu, B.CS., Ehrlich, J.R. (eds) Tissue Proteomics. Methods in Molecular Biology™, vol 441. Humana Press. https://doi.org/10.1007/978-1-60327-047-2_12

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  • DOI: https://doi.org/10.1007/978-1-60327-047-2_12

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-58829-679-5

  • Online ISBN: 978-1-60327-047-2

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