Abstract
The enormous amount of clinical, pathological, and staining data to be linked, analyzed, and correlated in a tissue microarray (TMA) project makes digital slides ideal to be integrated into TMA database systems. With the help of a computer and dedicated software tools, digital slides offer dynamic access to microscopic information at any magnification with easy navigation, annotation, measurement, and archiving features. Advanced slide scanners work both in transmitted light and fluorescent modes to support biomarker testing with immunohistochemistry, immunofluorescence or fluorescence in situ hybridization (FISH). Currently, computer-driven integrated systems are available for creating TMAs, digitalizing TMA slides, linking sample and staining data, and analyzing their results. Digital signals permit image segmentation along color, intensity, and size for automated object quantification where digital slides offer superior imaging features and batch processing. In this chapter, the workflow and the advantages of digital TMA projects are demonstrated through the project-based MIRAX system developed by 3DHISTECH and supported by Zeiss.
The enhanced features of digital slides compared with those of still images can boost integration and intelligence in TMA database management systems, offering essential support for high-throughput biomarker testing, for example, in tumor progression/prognosis, drug discovery, and target therapy research.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kallioniemi, O-P., Wagner, U., Kononen, J., Sauter, G. (2001) Tissue microarray technology for high-throughput molecular profiling of cancer. Hum. Mol. Gen. 10:657–662.
Kononen, J., Bubendorf, L., Kallioniemi, A., Barlund, M., Schraml, P., Leighton, S., Torhorst, J., Mihatsch, M.J., Sauter, G., Kallioniemi, O.P. (1998) Tissue microarrays for high through-put molecular profiling of tumor specimens. Nat. Med. 4:844–847.
Kayser, K., Molnar, B., and Weinstein, R.S. (2006) Virtual slides technology. In: K. Kayser, B. Molnar, and R.S. Weinstein (eds.), Virtual microscopy: fundamentals, applications, perspectives of electronic tissue-based diagnosis. VSV Publlication, Berlin, pp. 103–123.
Marinelli, R.J., Montgomery, K., Liu, C.L., Shah, N.H., Prapong, W., Nitzberg, M., Zachariah, Z.K., Sherlock, G.J., Natkunam, Y., West, R.B., van de Rijn, M., Brown, P.O., Ball, C.A. (2008) The Stanford tissue microarray database. Nucleic Acids Res. 36:D871–D817
Faith, D.A., Isaacs, W.B., Morgan, J.D., Fedor, H.L., Hicks, J.L., Mangold, L.A., Walsh, P.C., Partin, A.W., Platz, E.A., Luo, J., De Marzo, A.M. (2004) Trefoil factor 3 over-expression in prostatic carcinoma: prognostic importance using tissue microarrays. Prostate. 61:215–227.
Thallinger, G.G., Baumgartner, K., Pirklbauer, M., Uray, M., Pauritsch, E., Mehes, G., Buck, C.R., Zatloukal, K., Trajanoski, Z. (2007) TAMEE: data management and analysis for tissue microarrays. BMC Bioinformatics. 8:81.
Kajdacsy-Balla, A., Geynisman, J.M., Macias, V., Setty, S., Nanaji, N.M., Berman, J.J., Dobbin, K., Melamed, J., Kong, X., Bosland, M., Orenstein, J., Bayerl, J., Becich, M.J., Dhir, R., Datta, M.W. (2007) Practical aspects of planning, building, and interpreting tissue microarrays: the Cooperative Prostate Cancer Tissue Resource experience. J. Mol. Histol. 38:113–121.
Lee, H.W., Park, Y.R., Sim, J., Park, R.W., Kim, W.H., Kim, J.H. (2006) The tissue microarray object model: a data model for storage, analysis, and exchange of tissue microarray experimental data. Arch. Pathol. Lab. Med.130:1004–1013.
Berman, J.J., Datta, M., Kajdacsy-Balla, A., Melamed, J., Orenstein, J., Dobbin, K., Patel, A., Dhir, R., Becich, M.J. (2004) The tissue microarray data exchange specification: implementation by the Cooperative Prostate Cancer Tissue Resource. BMC Bioinformatics. 27:5–19.
Galon, J., Costes, A., Sanchez-Cabo, F., Kirilovsky, A., Mlecnik, B., Lagorce-Page, C., Tosolini, M., Camus, M., Berger, A., Wind, P., Zinzindohoue, F., Bruneval, P., Cugnenc, P-H., Trajanoski, Z., Fridman, W-H., Page, F. (2006) Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313:1960–1965.
Hober, S. and Uhlen, M. (2008) Human protein atlas and the use of microarray technologies. Curr. Opin. Biotechnol. 19:30–35. DOI 10.1016/j.copbio. 2007.11.006.
Stromberg, S., Bjorklund, M. G., Asplund, C., Skollermo, A., Persson, A., Wester, K., Kampf, C., Nilsson, P., Andersson, A. C., Uhlen, M., Kononen, J., Ponten, F., Asplund, A. (2007) A high-throughput strategy for protein profiling in cell microarrays using automated image analysis. Proteomics 7:2142–2150
Papay, J., Krenacs, T., Moldvay, J., Stelkovics, E., Furak, J., Molnar, B., Kopper, L. (2007) Immunophenotypic profiling of non-small cell lung cancer progression using the tissue microarray approach. Appl. Immunohistochem. Mol. Morphol. 15:19–30, 2007
Stelkovics, E., Korom, I., Marczinovits, I., Molnar, J., Rasky, K., Raso, E., Ficsor, E., Molnar, B., Kopper, L., Krenacs, T. (2008) Collagen XVII/BP180 protein expression in squamous cell carcinoma of the skin detected with novel monoclonal antibodies in archived tissues using tissue microarrays and digital microscopy. Appl. Immunohistochem. Mol. Morphol. 16:433–441
Anderson, W.F., Luo, S., Chatterjee, N., Rosenberg, P.S., Matsuno, R.K., Goodman, M.T., Hernandez, B.Y., Reichman, M., Dolled-Filhart, M.P., O’Regan, R.M., Garcia-Closas, M., Perou, C.M., Jatoi, I., Cartun, R,W., Sherman, M.E. (2008) Human epidermal growth factor receptor-2 and estrogen receptor expression, a demonstration project using the residual tissue repository of the Surveillance, Epidemiology, and End Results (SEER) program. Breast Cancer Res. Treat. 113:189–196
Turbin, D.A., Leung, S., Cheang, M.C., Kennecke, H.A., Montgomery, K.D., McKinney, S., Treaba, D.O., Boyd, N., Goldstein, L.C., Badve, S., Gown, A.M., van de Rijn, M., Nielsen, T.O., Gilks, C.B., Huntsman, D.G. (2007) Automated quantitative analysis of estrogen receptor expression in breast carcinoma does not differ from expert pathologist scoring: a tissue microarray study of 3,484 cases. Breast Cancer Res. Treat. 110:417–426
Rubin, M.A., Dunn, R., Strawderman, M., Pienta, K.J. (2002) Tissue microarray sampling strategy for prostate cancer biomarker analysis. Am. J. Surg. Pathol. 26:312–319.
Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D, (1998) Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95:14863–14868
Liu, C.L., Prapong, W., Natkunam, Y., Alizadeh, A., Montgomery, K., Gilks, C.B., Rijn, M. (2002) Software tools for high-throughput analysis and archiving of immunohistochemistry staining data obtained with tissue microarrays. Am. J. Pathol. 161:1557–1565
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media, LLC
About this protocol
Cite this protocol
Krenacs, T., Ficsor, L., Varga, S.V., Angeli, V., Molnar, B. (2010). Digital Microscopy for Boosting Database Integration and Analysis in TMA Studies. In: Simon, R. (eds) Tissue Microarrays. Methods in Molecular Biology, vol 664. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60761-806-5_16
Download citation
DOI: https://doi.org/10.1007/978-1-60761-806-5_16
Published:
Publisher Name: Humana Press, Totowa, NJ
Print ISBN: 978-1-60761-805-8
Online ISBN: 978-1-60761-806-5
eBook Packages: Springer Protocols