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Open Access Highly Accessed Methodology article

A highly sensitive and specific system for large-scale gene expression profiling

Guohong Hu, Qifeng Yang, Xiangfeng Cui, Gang Yue, Marco A Azaro, Hui-Yun Wang and Honghua Li*

  • * Corresponding author: Honghua Li holi@umdnj.edu

  • † Equal contributors

Author Affiliations

Department of Molecular Genetics, Microbiology and Immunology/The Cancer Institute of New Jersey, University of Medicine and Dentistry of New Jersey Robert Wood Johnson Medical School, Piscataway, New Jersey 08854, USA

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BMC Genomics 2008, 9:9  doi:10.1186/1471-2164-9-9

Published: 10 January 2008

Abstract

Background

Rapid progress in the field of gene expression-based molecular network integration has generated strong demand on enhancing the sensitivity and data accuracy of experimental systems. To meet the need, a high-throughput gene profiling system of high specificity and sensitivity has been developed.

Results

By using specially designed primers, the new system amplifies sequences in neighboring exons separated by big introns so that mRNA sequences may be effectively discriminated from other highly related sequences including their genes, unprocessed transcripts, pseudogenes and pseudogene transcripts. Probes used for microarray detection consist of sequences in the two neighboring exons amplified by the primers. In conjunction with a newly developed high-throughput multiplex amplification system and highly simplified experimental procedures, the system can be used to analyze >1,000 mRNA species in a single assay. It may also be used for gene expression profiling of very few (n = 100) or single cells. Highly reproducible results were obtained from duplicate samples with the same number of cells, and from those with a small number (100) and a large number (10,000) of cells. The specificity of the system was demonstrated by comparing results from a breast cancer cell line, MCF-7, and an ovarian cancer cell line, NCI/ADR-RES, and by using genomic DNA as starting material.

Conclusion

Our approach may greatly facilitate the analysis of combinatorial expression of known genes in many important applications, especially when the amount of RNA is limited.