Techniques for Gene Expression Profiling

  • Mark P. Richards
Part of the Springer Protocols Handbooks book series (SPH)


Now that the human genome and the genomes of a growing number of important model eukaryotic organisms such as yeast and mouse have been sequenced, research emphasis in the “postgenomic” era is beginning to shift to events downstream of the whole genome. Interest is now focused on the identification and characterization of individual genes and gene networks to better understand gene function at the cell, tissue and organ levels in different states of health and disease. This new approach to studying the genome has been termed “functional genomics” because efforts are directed toward understanding the connections between the expression of individual genes or groups of genes and their unique biological functions. Although every cell in the body contains the same complement of genetic material, each is distinguished by the level and the spectrum of activation or expression of a specific set of genes. Determining which genes are active in different cells and tissues under different conditions (i.e., physiological, developmental, environmental, stress, disease, etc.) aids researchers in understanding cellular and tissue function at the molecular level. Moreover, it also allows them to relate this information to a general set of characteristics observed at the cell, tissue, and organism levels, commonly referred to as the phenotype.


Ribonuclease Protection Assay Polymerase Chain Reaction Amplicon Entire Transcriptome Differential Display Polymerase Chain Reaction Understand Gene Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Baak, J. P. A., Path, F. R. C., Hermsen, M. A. J. A., Meijer, G., Schmidt, J., and Janssen, E. A. M. (2003) Genomics and proteomics in cancer. Eur. J. Cancer 39, 1199–1215.PubMedCrossRefGoogle Scholar
  2. 2.
    Sabelli, P. A. (1998) Northern blot analysis, in Molecular Biomethods Handbook (Rapley, R. and Walker, J. M., eds.), Humana, Totowa, NJ, pp. 89–93.CrossRefGoogle Scholar
  3. 3.
    Einspanier, R. and Plath, A. (1998) Detecting mRNA by use of the ribonuclease protection assay (RPA), in Molecular Biomethods Handbook (Rapley, R. and Walker, J. M., eds.), Humana, Totowa, NJ, pp. 51–57.CrossRefGoogle Scholar
  4. 4.
    Bustin, S. A. (2002) Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. J. Mol. Endocrinol. 29, 23–39.PubMedCrossRefGoogle Scholar
  5. 5.
    Richards, M. P. and Poch, S. M. (2002) Quantitative analysis of gene expression by reverse transcription polymerase chain reaction and capillary electrophoresis with laser-induced fluorescence detection. Mol. Biotechnol. 21, 19–37.PubMedCrossRefGoogle Scholar
  6. 6.
    duSart, D. and Choo, K. H. A. (1998) The technique of in situ hybridization, in Molecular Biomethods Handbook (Rapley, R. and Walker, J. M., eds.), Humana, Totowa, NJ, pp. 697–720.CrossRefGoogle Scholar
  7. 7.
    Kononen, J., Bubendorf, L., Kallioniemi, A., et al. (1998) Tissue microarrays for high-throughput molecular profiling of tumor specimens. Nature Med. 4, 844–847.PubMedCrossRefGoogle Scholar
  8. 8.
    Adams, M. D., Kelley, J. M., Gocayne, J. D., et al. (1991) Complementary DNA sequencing: expressed sequence tags and human genome project. Science 252, 1651–1656.PubMedCrossRefGoogle Scholar
  9. 9.
    Velculescu, V. E., Zhang, L., Vogelstein, B., and Kinzler, K. W. (1995) Serial analysis of gene expression. Science 270, 484–487.PubMedCrossRefGoogle Scholar
  10. 10.
    Schena, M., Shalon, D., Davis, R. W., and Brown, P. O. (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470.PubMedCrossRefGoogle Scholar
  11. 11.
    Lipshutz, R. J., Fodor, S. P. A., Gingeras, T. R., and Lockhart, D. J. (1999) High density synthetic oligonucleotide arrays. Nature Genet. 21(Suppl. 1), 20–24.PubMedCrossRefGoogle Scholar
  12. 12.
    Duggan, D. J., Bittner, M., Chen, Y., Meltzer, P., and Trent, J. M. (1999) Expression profiling using cDNA microarrays. Nature Genet. 21(Suppl. 1), 10–14.PubMedCrossRefGoogle Scholar
  13. 13.
    Hood, L. (2002) A personal view of molecular technology and how it has changed biology. J. Proteome Res. 1, 399–409.PubMedCrossRefGoogle Scholar
  14. 14.
    Liotta, L. and Petricoin, E. (2000) Molecular profiling of human cancer. Nature Rev. Genet. 1, 48–56.PubMedCrossRefGoogle Scholar
  15. 15.
    Missler, M. and Sudhof, T. C. (1998) Neurexins: three genes and 1001 products. Trends Genet. 14, 20–26.PubMedCrossRefGoogle Scholar
  16. 16.
    Patterson, S. D. and Aebersold, R. H. (2003) Proteomics: the first decade and beyond. Nature Genet. 33(Suppl. 1), 311–323.PubMedCrossRefGoogle Scholar
  17. 17.
    Haab, B. B., Dunham, M. J., and Brown, P. O. (2001) Protein microarrays for highly parallel detection and quantitation of specific proteins and antibodies in complex solutions. Genome Biol. 2, 0004.1–0004.13.CrossRefGoogle Scholar
  18. 18.
    MacBeath, G. (2002) Protein microarrays and proteomics. Nature Genet. 32(Suppl. 2), 526–532.PubMedCrossRefGoogle Scholar
  19. 19.
    Sydor, J. R. and Nock, S. (2003) Protein expression profiling arrays: tools for the multiplexed highthroughput analysis of proteins. Proteome Sci. 1, 1–7.CrossRefGoogle Scholar
  20. 20.
    Brown, P. O. and Botstein, D. (1999) Exploring the world of the genome with DNA microarrays. Nature Genet. 21(Suppl. 1), 33–37.PubMedCrossRefGoogle Scholar
  21. 21.
    Patterson, S.D. (2003) Proteomics: evolution of the technology. Biotechniques 35, 440–444.PubMedGoogle Scholar
  22. 22.
    Hanash, S. (2003) Disease proteomics. Nature 422, 226–232.PubMedCrossRefGoogle Scholar
  23. 23.
    Petricoin, E. and Liotta, L. A. (2003) Clinical applications of proteomics. J. Nutr. 133, 2476S–2484S.PubMedGoogle Scholar
  24. 24.
    Debouck, C. and Goodfellow, P. N. (1999) DNA microarrays in drug discovery and development. Nature Genet. 21(Suppl. 1), 48–50.PubMedCrossRefGoogle Scholar
  25. 25.
    Gerhold, D. L., Jensen, R. V., and Gullans, S. R. (2002) Better therapeutics through microarrays. Nature Genet. 32(Suppl. 2), 547–552.PubMedCrossRefGoogle Scholar
  26. 26.
    Petricoin, E. F., Hackett, J. L., Lesko, L. J., Puri, R. K., Gutman, S. I., Chumakov, K., Woodcock, J., Feigal, D. W., Zoon, K. C., and Sistare, F. D. (2002) Medical applications of microarray technologies: a regulatory science perspective. Nature Genet. 32(Suppl. 2) 474–479.PubMedCrossRefGoogle Scholar
  27. 27.
    Chen, G., Gharib, T. G., Huang, C.-C., et al. (2002) Proteomic analysis of lung adenocarcinoma: identification of a highly expressed set of proteins in tumors. Clin. Cancer Res. 8, 2298–2305.PubMedGoogle Scholar
  28. 28.
    Constans, A. (2003) Beyond Sanger: toward the $1,000 genome. The Scientist 17, 36–38.Google Scholar
  29. 29.
    Pennisi, E. (2003) The ultimate gene gizmo: humanity on a chip. Science 302, 211.PubMedCrossRefGoogle Scholar

Copyright information

© Humana Press Inc., Totowa, NJ 2005

Authors and Affiliations

  • Mark P. Richards
    • 1
  1. 1.US Department of AgricultureAgricultural Research Service, Animal and Natural Resources InstituteBeltsville

Personalised recommendations