Automated Analysis of Tissue Microarrays
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The analysis of protein expression in tissue by immunohistochemistry (IHC) presents three significant challenges. They are (1) the time-consuming nature of pathologist-based scoring of slides; (2) the need for objective quantification and localization of protein expression; and (3) the need for a highly reproducible measurement to limit intra- and inter-observer variability. While there are a variety of commercially available platforms for automated chromagen-based and fluorescence-based image acquisition of tissue microarrays, this chapter is focused on the analysis of fluorescent images by AQUA® analysis (Automated QUantitative Analysis) and the solutions offered by such a method for research and diagnostics. AQUA analysis is a method for molecularly defining regions of interest or “compartments” within a tissue section. The methodology can be utilized with tissue microarrays to provide rapid, quantitative, localized, and reproducible protein expression data that can then be used to identify statistically relevant correlations in populations. Ultimately this allows for a multiplexed, objective and standardized quantitative approach for biomarker research and diagnostic assay development for protein expression in tissue.
Key wordsImmunohistochemistry Automated analysis AQUA Tissue microarrays Quantitative analysis Biomarkers Immunofluorescence
RLC is supported by grants from the NIH/NCI including R21 CA 125277 and R21 CA 116265. DLR is supported by grants from the NIH including RO-1 CA 114277, R33 CA 106709 and R33 CA 110511.
- 7.Rao, J., D. Seligson, and G.P. Hemstreet (2002) Biotechniques. 32(4) 924–6, 928–30, 932.Google Scholar
- 10.Wolff, A.C., M.E. Hammond, J.N. Schwartz, K.L. Hagerty, D.C. Allred, R.J. Cote, M. Dowsett, P.L. Fitzgibbons, W.M. Hanna, A. Langer, L.M. McShane, S. Paik, M.D. Pegram, E.A. Perez, M.F. Press, A. Rhodes, C. Sturgeon, S.E. Taube, R. Tubbs, G.H. Vance, M. van de Vijver, T.M. Wheeler, and D.F. Hayes (2007) J Clin Oncol. 25(1) 118–45.PubMedCrossRefGoogle Scholar
- 17.Garraway, L.A., H.R. Widlund, M.A. Rubin, G. Getz, A.J. Berger, S. Ramaswamy, R. Beroukhim, D.A. Milner, S.R. Granter, J. Du, C. Lee, S.N. Wagner, C. Li, T.R. Golub, D.L. Rimm, M.L. Meyerson, D.E. Fisher, and W.R. Sellers (2005) Nature. 436(7047) 117–22.Google Scholar
- 26.Moeder, C., J. Giltnane, S. Pozner-Moulis, and D.L. Rimm, Quantitative, Fluorescence-based In-Situ Assessment of Protein Expression. Methods in Molecular Biology: Tumor Marker Discovery. in press, Totowa New Jersey: Humana Press.Google Scholar
- 27.Gustavson, M.D., B. Bourke-Martin, D.M. Reilly, M. Cregger, C. Williams, J. Mayotte, M. Zerkowski, G. Tedeschi, R. Pinard, and J. Christiansen (2009) Arch Pathol Lab Med. 133:1413–19.Google Scholar
- 30.Dolled-Filhart, M., R. Pinard, D. Waldron, A. Ang, L. Goodrich, S. Myrand, D. Thornton, J. Graff, and B. Mullaney. Clustering of phospho-proteins targeted by enzastaurin identifies significant biomarker association with patient outcome and novel associations between biomarker groupings in a glioblastoma multiforme cohort. in AACR Annual Meeting. 2008. San Deigo, CA.Google Scholar