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JMD 2007, Vol. 9, No. 1
Copyright © 2007 American Society for Investigative Pathology & Association for Molecular Pathology

Technical Advance

Semiautomated Multiplexed Quantum Dot-Based in Situ Hybridization and Spectral Deconvolution

Richard J. Byers*, Dolores Di Vizio{dagger}, Fionnuala O’Connell{dagger}{ddagger}, Eleni Tholouli§, Richard M. Levenson, Kirk Gossard, David Twomey||, Yu Yang{dagger}{ddagger}, Elisa Benedettini{dagger}, Joshua Rose{dagger}, Keith L. Ligon{dagger}{ddagger}, Stephen P. Finn{dagger}{ddagger}, Todd R. Golub{dagger}|| and Massimo Loda{dagger}{ddagger}

From the Division of Laboratory and Regenerative Medicine, * University of Manchester, Manchester, United Kingdom; the Department of Medical Oncology, {dagger} Dana-Farber Cancer Institute, and the Department of Pathology, {ddagger} Brigham and Women’s Hospital, Harvard Medical School, Cambridge, Massachusetts; the University Department of Hematology, § Manchester Royal Infirmary, Manchester, United Kingdom; Cambridge Research and Instrumentation, Woburn, Massachusetts; and the Broad Institute of Massachusetts Institute of Technology and Harvard, || Cambridge, Massachusetts

Abstract

Gene expression profiling has identified several potentially useful gene signatures for predicting outcome or for selecting targeted therapy. However, these signatures have been developed in fresh or frozen tissue, and there is a need to apply them to routinely processed samples. Here, we demonstrate the feasibility of a potentially high-throughput methodology combining automated in situ hybridization with quantum dot-labeled oligonucleotide probes followed by spectral imaging for the detection and subsequent deconvolution of multiple signals. This method is semiautomated and quantitative and can be applied to formalin-fixed, paraffin-embedded tissues. We have combined dual in situ hybridization with immunohistochemistry, enabling simultaneous measurement of gene expression and cell lineage determination. The technique achieves levels of sensitivity and specificity sufficient for the potential application of known expression signatures to biopsy specimens in a semiquantitative way, and the semiautomated nature of the method enables application to high-throughput studies.




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