Advertisement

Complementary microarray technologies

  • Bernhard Gerstmayer
Part of the Progress in Inflammation Research book series (PIR)

Abstract

As outlined in the previous chapters of this book, the main microarray applications in inflammation rely on mRNA expression profiling based on either Oligo or cDNA microarray platforms. By virtue of measuring mRNA transcript levels, the activity of genes in inflammatory lesions can be analyzed. However, we have not focused solely on mRNA expression via microarray analysis over the last decade. Different disciplines from the genomics, glycomics, proteomics and metabolomics area have been combined in order to get an “all-inclusive” picture of the disease to be analysed. This combined approach has led to the creation of a new discipline named “systems biology”. Novel microarray-based technologies have been developed that enable the analysis of “messenger” molecules other than mRNA. The focus of the current chapter is on those microarray platforms which either already play or are expected to play an important role in our understanding of the pathogenesis of inflammation. In addition, all described microarray platforms are meant to speed up drug discovery in future research and/or serve as prognostic or diagnostic tools in inflammatory diseases. This overview will concentrate on microarray platforms developed for promoter or CpG methylation as well as Chromatin Immunoprecipitation on chip analysis (ChIP on Chip), array comparative genomic hybridization (aCGH), carbohydrate microarrays, protein microarrays and microarrays for the detection and analysis of microRNAs.

Keywords

Comparative Genomic Hybridization cDNA Microarray Chromatin Immunoprecipitation Genomic Hybridization MICROARRAY Technology 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baylin, S.B., J.E. Ohm, Epigenetic gene silencing in cancer — a mechanism for early oncogenic pathway addiction? Nature reviews. Cancer, 2006. 6(2): 107–16PubMedGoogle Scholar
  2. 2.
    Rauch, T et al., MIRA-assisted microarray analysis, a new technology for the determination of DNA methylation patterns, identifies frequent methylation of homeodomaincontaining genes in lung cancer cells. Cancer Res, 2006. 66(16): 7939–47PubMedCrossRefGoogle Scholar
  3. 3.
    Boyer, L.A. et al., Core transcriptional regulatory circuitry in human embryonic stem cells. Cell, 2005. 122(6): 947–56PubMedCrossRefGoogle Scholar
  4. 4.
    Barrett, M.T. et al., Comparative genomic hybridization using oligonucleotide microarrays and total genomic DNA. Proc Nat Acad Sci USA, 2004. 101(51): 17765–70. Epub 2004 Dec 10PubMedCrossRefGoogle Scholar
  5. 5.
    Gall, J.G., M.L. Pardue, Formation and detection of RNA-DNA hybrid molecules in cytological preparations. Proc Nat Acad Sci USA, 1969. 63(2): 378–83PubMedCrossRefGoogle Scholar
  6. 6.
    Pardue, M.L.,J.G. Gall, Molecular hybridization of radioactive DNA to the DNA of cytological preparations. Proc Nat Acad Sci USA, 1969. 64(2): 600–4PubMedCrossRefGoogle Scholar
  7. 7.
    Kallioniemi, A. et al., Comparative genomic hybridization for molecular cytogenetic analysis of solid tumors. Science (New York, N.Y.), 1992. 258(5083): 818–21PubMedCrossRefGoogle Scholar
  8. 8.
    Pinkel, D. et al.,High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays. Nature Genetics, 1998. 20(2): 207–11PubMedCrossRefGoogle Scholar
  9. 9.
    Greshock, J. et al., 1-Mb resolution array-based comparative genomic hybridization using a BAC clone set optimized for cancer gene analysis. Genome Research, 2004. 14(1): 179–87. Epub 2003 Dec 12PubMedCrossRefGoogle Scholar
  10. 10.
    Drickamer, K., M.E. Taylor, Glycan arrays for functional glycomics. Genome Biology, 2002. 3(12): REVIEWS1034. Epub 2002 Nov 27Google Scholar
  11. 11.
    Kulig, P., J. Cichy, Acute phase mediator oncostatin M regulates affinity of alpha1-protease inhibitor for concanavalin A in hepatoma-derived but not lung-derived epithelial cells. Cytokine, 2005. 30(5): 269–74. Epub 2005 Mar 25PubMedCrossRefGoogle Scholar
  12. 12.
    Renkonen, J. et al., Glycosylation might provide endothelial zip codes for organ-specific leukocyte traffic into inflammatory sites. Am J Pathol, 2002. 161(2): 543–50PubMedGoogle Scholar
  13. 13.
    Rosen, S.D, Endothelial ligands for L-selectin: from lymphocyte recirculation to allograft rejection. Am J Pathol, 1999. 155(4): 1013–20PubMedGoogle Scholar
  14. 14.
    Dube, D.H., C.R. Bertozzi, Glycans in cancer and inflammation — potential for therapeutics and diagnostics. Nature reviews. Drug Discovery, 2005. 4(6): 477–88PubMedCrossRefGoogle Scholar
  15. 15.
    Wang, D. et al., Carbohydrate microarrays for the recognition of cross-reactive molecular markers of microbes and host cells. Nature Biotechnology, 2002. 20(3): 275–81PubMedCrossRefGoogle Scholar
  16. 16.
    Blixt, O. et al., Printed covalent glycan array for ligand profiling of diverse glycan binding proteins. Proc Nat Acad Sci USA, 2004. 101(49): 17033–8. Epub 2004 Nov 24PubMedCrossRefGoogle Scholar
  17. 17.
    Khan, I, D.V. Desai, A. Kumar, Carbochips: a new energy for old biobuilders. J Biosci-Bioeng, 2004. 98(5): 331–7PubMedGoogle Scholar
  18. 18.
    Miyamoto, S., Clinical applications of glycomic approaches for the detection of cancer and other diseases. Curr Op Mol Ther, 2006. 8(6): 507–13Google Scholar
  19. 19.
    Horlacher, T., P.H. Seeberger, The utility of carbohydrate microarrays in glycomics. Omics: a Journal of Integrative Biology, 2006. 10(4): 490–8CrossRefGoogle Scholar
  20. 20.
    Hall, D.A. et al.,Regulation of gene expression by a metabolic enzyme. Science (New York, N.Y.), 2004. 306(5695): 482–4Google Scholar
  21. 21.
    Zhu, H., M. Snyder, Protein arrays and microarrays. Curr Op Chem Biol, 2001. 5(1): 40–5CrossRefGoogle Scholar
  22. 22.
    Ptacek, J. et al., Global analysis of protein phosphorylation in yeast. Nature, 2005. 438(7068): 679–84PubMedCrossRefGoogle Scholar
  23. 23.
    Sreekumar, A. et al., Profiling of cancer cells using protein microarrays: discovery of novel radiation-regulated proteins. Cancer Res, 2001. 61(20): 7585–93PubMedGoogle Scholar
  24. 24.
    Hall, D.A, J. Ptacek, M. Snyder, Protein microarray technology. Mechanisms of Ageing and Development, 2007. 128(1): 161–7. Epub 2006 Nov 28PubMedCrossRefGoogle Scholar
  25. 25.
    Zhu, H., M. Snyder, Protein chip technology. Curr Op Chem Biol, 2003. 7(1): 55–63CrossRefGoogle Scholar
  26. 26.
    Speer, R. et al., Reverse-phase protein microarrays for tissue-based analysis. Curr Op Mol Ther, 2005. 7(3): 240–5Google Scholar
  27. 27.
    Ramachandran, N. et al., Self-assembling protein microarrays. Science (New York, N.Y.), 2004. 305(5680): 86–90Google Scholar
  28. 28.
    Ramachandran, N. et al., On-chip protein synthesis for making microarrays. Methods Mol Biol (Clifton, N.J.), 2006. 328: 1–14PubMedGoogle Scholar
  29. 29.
    Kusnezow, W., J.D. Hoheisel, Solid supports for microarray immunoassays. Journal of Molecular Recognition: JMR, 2003. 16(4): 165–76PubMedCrossRefGoogle Scholar
  30. 30.
    Ramachandran, N. et al., Emerging tools for real-time label-free detection of interactions on functional protein microarrays. FEBS, 2005. 272(21): 5412–25CrossRefGoogle Scholar
  31. 31.
    Pinto-Plata, V. et al., Profiling serum biomarkers in patients with COPD: associations with clinical parameters. Thorax, 2007. 62(7): 595–601. Epub 2007 Mar 13PubMedCrossRefGoogle Scholar
  32. 32.
    Tabibiazar, R. et al., Proteomic profiles of serum inflammatory markers accurately predict atherosclerosis in mice. Physiological Genomics, 2006. 25(2): 194–202. Epub 2006 Jan 17PubMedCrossRefGoogle Scholar
  33. 33.
    Landgraf, P. et al., A mammalian microRNA expression atlas based on small RNA library sequencing. Cell, 2007. 129(7): 1401–14PubMedCrossRefGoogle Scholar
  34. 34.
    Liu, C.G. et al., Expression profiling of microRNA using oligo DNA arrays. Methods (San Diego, Calif.), 2008. 44(1): 22–30Google Scholar
  35. 35.
    Castoldi, M. et al., miChip: an array-based method for microRNA expression profiling using locked nucleic acid capture probes. Nature Protocols, 2008. 3(2): 321–9PubMedCrossRefGoogle Scholar
  36. 36.
    Wienholds, E.et al., MicroRNA expression in zebrafish embryonic development. Science (New York, N.Y.), 2005. 309(5732): 310–1. Epub 2005 May 26Google Scholar
  37. 37.
    Dai, Y. et al., Microarray analysis of microRNA expression in peripheral blood cells of systemic lupus erythematosus patients. Lupus, 2007. 16(12): 939–46PubMedCrossRefGoogle Scholar

Copyright information

© Birkhäuser Verlag Basel/Switzerland 2008

Authors and Affiliations

  • Bernhard Gerstmayer
    • 1
  1. 1.Miltenyi Biotec GmbHBergisch GladbachGermany

Personalised recommendations