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Microarray analysis: basic strategies for successful experiments

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Abstract

Microarrays offer a powerful approach to the analysis of gene expression that can be used for a wide variety of experimental purposes. However, there are several types of microarray platforms that are available. In addition, microarray experiments are expensive and generate complicated data sets that can be difficult to interpret. Success with microarray approaches requires a sound experimental design and a coordinated and appropriate use of statistical tools. Here, the advantages and pitfalls of utilizing microarrays are discussed, as are practical strategies to help novice users succeed with this method that can empower them with the ability to assay changes in gene expression at the whole genome level.

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References

  1. Benito, M., Parker, J., Du, Q., Wu, J., Xiang, D., Perou, C. M., & Marron, J. S. (2004). Adjustment of systematic microarray data biases. Bioinformatics, 20, 105–114.

    Article  PubMed  CAS  Google Scholar 

  2. Calin, G. A., Dumitru, C. D., Shimizu, M., Bichi, R., Zupo, S., Noch, E., Aldler, H., Rattan, S., Keating, M., Rai, K., Rassenti, L., Kipps, T., Negrini, M., Bullrich, F, & Croce, C. M. (2002). Frequent deletions and down-regulation of micro- RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences of the United States of America, 99, 15524–15529.

    Article  PubMed  CAS  Google Scholar 

  3. Ferrando, A. A., Neuberg, D. S., Staunton, J., Loh, M. L., Huard, C., Raimondi, S. C., Behm, F. G., Pui, C. H., Downing, J. R., Gilliland, D. G., Lander, E. S., Golub, T. R., Look, A. T. (2002). Gene expression signatures define novel oncogenic pathways in T cell acute lymphoblastic leukemia. Cancer Cell, 1, 75–87.

    Article  PubMed  CAS  Google Scholar 

  4. Frueh, F. W., Hayashibara, K. C., Brown, P. O., & Whitlock, J. P. J. (2001) Use of cDNA microarrays to analyze dioxin-induced changes in human liver gene expression. Toxicology Letters, 122, 189–203.

    Article  PubMed  CAS  Google Scholar 

  5. Helman, P., Veroff, R., Atlas, S. R., & Willman, C. (2004) A Bayesian network classification methodology for gene expression data. Journal of Computational Biology, 11(4), 581–615.

    Article  PubMed  CAS  Google Scholar 

  6. Iorio, M. V., Ferracin, M., Liu, C. G., Veronese, A., Spizzo, R., Sabbioni, S., Magri, E., Pedriali, M., Fabbri, M., Campiglio, M., Menard, S., Palazzo, J. P., Rosenberg, A., Musiani, P., Volinia, S., Nenci, I., Calin, G. A., Querzoli, P., Negrini, M, & Croce, C. M. (2005). MicroRNA gene expression deregulation in human breast cancer. Cancer Research, 65, 7065–7070.

    Article  PubMed  CAS  Google Scholar 

  7. Ishida, N., Hara, T., Kamura, T., Yoshida, M., Nakayama, K, & Nakayama, K. I. (2002). Phosphorylation of p27Kip1 on serine 10 is required for its binding to CRM1 and nuclear export. Journal of Biological Chemistry, 277, 14355–14358.

    Article  PubMed  CAS  Google Scholar 

  8. Karyala, S., Guo, J., Sartor, M., Medvedovic, M., Kann, S., Puga, A., Ryan, P, Tomlinson, C. R. (2004). Different global gene expression profiles in benzo[a]pyrene- and dioxin-treated vascular smooth muscle cells of AHR-knockout and wild-type mice. Cardiovascular and Toxicology, 4, 47–73.

    Article  CAS  Google Scholar 

  9. Lei, W., Rushton, J. J., Davis, L. M., Liu, F, Ness, S. A. (2004). Positive and negative determinants of target gene specificity in Myb transcription factors. Journal of Biological Chemistry, 279, 29519–29527.

    Article  PubMed  CAS  Google Scholar 

  10. Liang, G., Gonzales, F. A., Jones, P. A., Orntoft, T. F., & Thykjaer, T. (2002). Analysis of gene induction in human fibroblasts and bladder cancer cells exposed to the methylation inhibitor 5-aza-2′-deoxycytidine. Cancer Research, 62, 961–966.

    Article  PubMed  CAS  Google Scholar 

  11. Liu, F., Lei, W., O’Rourke, J. P., & Ness, S. A. (2006). Oncogenic mutations cause dramatic, qualitative changes in the transcriptional activity of c-Myb. Oncogene, 25, 795–805.

    Article  PubMed  CAS  Google Scholar 

  12. Monks, A., Harris, E., Hose, C., Connelly, J., & Sausville, E. A. (2003). Genotoxic profiling of MCF-7 breast cancer cell line elucidates gene expression modifications underlying toxicity of the anticancer drug 2-(4-amino-3-methylphenyl)-5-fluorobenzothiazole. Molecular Pharmacology, 63, 766–772.

    Article  PubMed  CAS  Google Scholar 

  13. Pan, Q., Saltzman, A. L., Kim, Y. K., Misquitta, C., Shai, O., Maquat, L. E., Frey, B. J., & Blencowe, B. J. (2006). Quantitative microarray profiling provides evidence against widespread coupling of alternative splicing with nonsense-mediated mRNA decay to control gene expression. Genes & Development, 20, 153–158.

    Article  CAS  Google Scholar 

  14. Pan, Q., Shai, O., Misquitta, C., Zhang, W., Saltzman, A. L., Mohammad, N., Babak, T., Siu, H., Hughes, T. R., Morris, Q. D., Frey, B. J., & Blencowe, B. J. (2004). Revealing global regulatory features of mammalian alternative splicing using a quantitative microarray platform. Molecular Cell, 16, 929–941.

    Article  PubMed  CAS  Google Scholar 

  15. Rushton, J. J., Davis, L. M., Lei, W., Mo, X., Leutz, A, & Ness, S. A. (2003). Distinct changes in gene expression induced by A-Myb, B-Myb and c-Myb proteins. Oncogene, 22, 308–313.

    Article  PubMed  CAS  Google Scholar 

  16. Segal, M. R., Dahlquist, K. D., & Conklin, B. R. (2003). Regression approaches for microarray data analysis. Journal of Computational Biology, 10, 961–980.

    Article  PubMed  CAS  Google Scholar 

  17. Sotiriou, C., Neo, S. Y., McShane, L. M., Korn, E. L., Long, P. M., Jazaeri, A., Martiat, P., Fox, S. B., Harris, A. L., & Liu, E. T. (2003). Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proceedings of the National Academy of Sciences of the United States of America, 100, 10393–10398.

    Article  PubMed  CAS  Google Scholar 

  18. Spellman, P. T., Sherlock, G., Zhang, M. Q., Iyer, V. R., Anders, K., Eisen, M. B., Brown, P. O., Botstein, D., & Futcher, B. (1998). Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Molecular Biology of the Cell, 9, 3273–3297.

    PubMed  CAS  Google Scholar 

  19. Teh, M. T., Blaydon, D., Chaplin, T., Foot, N. J., Skoulakis, S., Raghavan, M., Harwood, C. A., Proby, C. M., Philpott, M. P., Young, B. D., & Kelsell, D. P. (2005). Genomewide single nucleotide polymorphism microarray mapping in basal cell carcinomas unveils uniparental disomy as a key somatic event. Cancer Research, 65, 8597–8603.

    Article  PubMed  CAS  Google Scholar 

  20. Valk, P. J., Verhaak, R. G., Beijen, M. A., Erpelinck, C. A., Barjesteh van Waalwijk van Doorn-Khosrovani, S., Boer, J. M., Beverloo, H. B., Moorhouse, M. J., van der Spek, P. J., Lowenberg, B., & Delwel, R. (2004). Prognostically useful gene-expression profiles in acute myeloid leukemia. New England Journal of Medicine, 350, 1617–1628.

    Article  PubMed  CAS  Google Scholar 

  21. Verheyen, G. R., Nuijten, J. M., Van Hummelen, P., & Schoeters, G. R. (2004). Microarray analysis of the effect of diesel exhaust particles on in␣vitro cultured macrophages. Toxicology In Vitro, 18, 377–391.

    Article  PubMed  CAS  Google Scholar 

  22. Wilson, C. S., Davidson, G. S., Martin, S. B., Andries, E., Potter, J., Harvey, R., Ar, K., Xu, Y., Kopecky, K. J., Ankerst, D. P., Gundacker, H., Slovak, M. L., Mosquera-Caro, M., Chen, I. M., Stirewalt, D. L., Murphy, M., Schultz, F. A., Kang, H., Wang, X., Radich, J. P., Appelbaum, F. R., Atlas, S. R., Godwin, J., & Willman, C. L. (2006). Gene expression profiling of adult acute myeloid leukemia identifies novel biologic clusters for risk classification and outcome prediction. Blood, 108, 685–696.

    Article  PubMed  CAS  Google Scholar 

  23. Yang, H. C., Liang, Y. J., Huang, M. C., Li, L. H., Lin, C. H., Wu, J. Y., Chen, Y. T., & Fann, C. S. (2006). A genome-wide study of preferential amplification/hybridization in microarray-based pooled DNA experiments. Nucleic Acids Research, 34, e106.

    Article  PubMed  CAS  Google Scholar 

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Acknowledgments

S.A.N. is supported by grants from the USPHS/National Cancer Institute (#RO1 CA058443 and #R01 CA105257) and by institutional support from the University of New Mexico Health Sciences Center and is Director of the Keck-UNM Genomics Resource, a microarray and gene expression analysis facility supported by a grant from the W.M. Keck Foundation as well as the State of New Mexico and the UNM Cancer Research and Treatment Center. The author thanks Dr. Gavin G. Pickett for helpful discussions and comments on the manuscript.

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  1. Department of Molecular Genetics & Microbiology, Keck-UNM Genomics Resource and UNM Cancer Research and Treatment Center, MSC08 4660, University of New Mexico HSC, Albuquerque, NM, 87131-0001, USA

    Scott A. Ness

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Correspondence to Scott A. Ness.

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Ness, S.A. Microarray analysis: basic strategies for successful experiments. Mol Biotechnol 36, 205–219 (2007). https://doi.org/10.1007/s12033-007-0012-6

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  • DOI: https://doi.org/10.1007/s12033-007-0012-6

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