Molecular Biotechnology

, Volume 36, Issue 3, pp 205–219 | Cite as

Microarray analysis: basic strategies for successful experiments

Original Paper

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.

Keywords

Microarrays Affymetrix GeneChips Genomics Gene expression Transcription Clustering Normalization Data analysis Hybridization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 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.PubMedCrossRefGoogle Scholar
  2. 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.PubMedCrossRefGoogle Scholar
  3. 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.PubMedCrossRefGoogle Scholar
  4. 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.PubMedCrossRefGoogle Scholar
  5. 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.PubMedCrossRefGoogle Scholar
  6. 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.PubMedCrossRefGoogle Scholar
  7. 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.PubMedCrossRefGoogle Scholar
  8. 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.CrossRefGoogle Scholar
  9. 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.PubMedCrossRefGoogle Scholar
  10. 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.PubMedCrossRefGoogle Scholar
  11. 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.PubMedCrossRefGoogle Scholar
  12. 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.PubMedCrossRefGoogle Scholar
  13. 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.CrossRefGoogle Scholar
  14. 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.PubMedCrossRefGoogle Scholar
  15. 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.PubMedCrossRefGoogle Scholar
  16. 16.
    Segal, M. R., Dahlquist, K. D., & Conklin, B. R. (2003). Regression approaches for microarray data analysis. Journal of Computational Biology, 10, 961–980.PubMedCrossRefGoogle Scholar
  17. 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.PubMedCrossRefGoogle Scholar
  18. 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.PubMedGoogle Scholar
  19. 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.PubMedCrossRefGoogle Scholar
  20. 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.PubMedCrossRefGoogle Scholar
  21. 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.PubMedCrossRefGoogle Scholar
  22. 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.PubMedCrossRefGoogle Scholar
  23. 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.PubMedCrossRefGoogle Scholar

Copyright information

© Humana Press Inc. 2007

Authors and Affiliations

  1. 1.Department of Molecular Genetics & Microbiology, Keck-UNM Genomics Resource and UNM Cancer Research and Treatment Center, MSC08 4660University of New Mexico HSCAlbuquerqueUSA

Personalised recommendations