SIVQ-aided laser capture microdissection: A tool for high-throughput expression profiling

Jason Hipp1, Jerome Cheng1, Jeffrey C. Hanson2, Wusheng Yan2, Phil Taylor3, Nan Hu2, Jaime
Rodriguez-Canales2, Jennifer Hipp2, Michael A. Tangrea2, Michael R. Emmert-Buck2, Ulysses Balis1

1Department of Pathology, University of Michigan School of Medicine, Division of Pathology Informatics, M4233 Med Sci I, 1301 Catherine, Ann Arbor, MI 48109-0602.

2National Institutes of Health, National Cancer Institute, Laboratory of Pathology, Center for Cancer Research, Advanced Technology Center, Room 109, 8717 Grovemont Circle, Gaithersburg, MD 20877.

3Division of Cancer Epidemiology and Genetics, Executive Plaza South, Room 7006, Rockville, MD 20892.

Abstract
Introduction: Laser capture microdissection (LCM) facilitates procurement of defined cell populations for study in the context of histopathology. The morphologic assessment step in the LCM procedure is time consuming and tedious, thus restricting the utility of the technology for large applications.

Results: Here, we describe the use of Spatially Invariant Vector Quantization (SIVQ) for histological analysis within LCM. Using SIVQ, we selected vectors as morphologic predicates that were representative of normal epithelial or cancer cells and then searched for phenotypically similar cells across entire tissue sections. The selected cells were subsequently auto-microdissected and the recovered RNA was analyzed by expression microarray. Gene expression profiles from SIVQ–LCM and standard LCM-derived samples demonstrated highly congruous signatures, confirming the equivalence of the differing microdissection methods.

Conclusion: SIVQ–LCM improves the workflow of microdissection in two significant ways. First, the process is transformative in that it shifts the pathologist’s role from technical execution of the entire microdissection to a limited-contact supervisory role, enabling large-scale extraction of tissue by expediting subsequent semi-autonomous identification of target cell populations. Second, this work-flow model provides an opportunity to systematically identify highly constrained cell populations and morphologically consistent regions within tissue sections. Integrating SIVQ with LCM in a single environment provides advanced capabilities for efficient and high-throughput histological-based molecular studies.

Key words: Laser capture microdissection, microarray, Spatially Invariant Vector Quantization

E-mail: *Ulysses J. Balis – ulysses@umich.edu
*Corresponding author
Received: 26 January 11 Accepted: 22 February 11 Published: 31 March 11
DOI: 10.4103/2153-3539.78500 J Pathol Inform 2011, 2:19
This article is available from: http://www.jpathinformatics.org/content/2/1/19
Copyright: © 2011 Hipp J. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and
reproduction in any medium, provided the original author and source are credited.

This article may be cited as:
Hipp J, Cheng J, Hanson JC, Yan W, Taylor P, Hu N, Rodriguez-Canales J, Hipp J, Tangrea MA, Emmert-Buck MR, Balis U. SIVQ-aided laser capture microdissection: A tool for high-throughput
expression profiling. J Pathol Inform 2011;2:19