Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Technology Insight: microarrays—research and clinical applications

Nature Clinical Practice Endocrinology & Metabolism volume 3, pages 594–605 (2007)Cite this article

Abstract

For microarrays, the transition from research to clinical and diagnostic applications is well underway. Microarrays use a range of specific probes that are immobilized in known locations on a support matrix; this technique can measure levels of specific DNA, RNA and proteins, as well as carbohydrates and lipids. It is anticipated that analysis of these levels will lead to identification of biomarkers for the diagnosis, treatment and prognosis of a wide range of diseases. So far, this type of analysis has been particularly useful in clinical oncology, but the technology is being actively and successfully explored for diseases such as diabetes, endocrine tumors and endocrine modulators of tumors. There are now many commercial sources of microarrays, which have robust quality-control procedures in place. Progress will be enhanced when biomarkers can be established, statistical approaches can be refined and when we better understand the interactions of genes and of particular gene loci in disease progression.

Key Points

  • Microarrays provide the opportunity for analysis of large numbers of polynucleotides (DNA and mRNA) or proteins simultaneously

  • Microarrays allow genome-wide, transcriptome-wide, and proteome-wide analysis to be carried out for the identification of biomarkers and their potential applications

  • Biomarkers in the form of DNA mutations, expression profiles, and protein levels provide valuable data in basic and preclinical research, as well as diagnosis, treatment option assessment and prognostic determination

  • Microarray analysis has generated important information about the molecular basis of diabetes, implicating components of the mitochondrial ATP synthesis pathway

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Principles behind the use of microarrays for the identification and quantification of DNA and mRNA
Figure 2: Principles behind the use of microarrays for the identification and quantification of proteins
Figure 3: An example of cluster analysis after glucose-mediated stimulation of a murine β-cell line in vitro

Similar content being viewed by others

References

  1. Southern EM (2000) Blotting at 25. Trends Biochem Sci 25: 585–588

    Article  CAS  Google Scholar 

  2. Southern E et al. (1999) Molecular interactions on microarrays. Nat Genet 21 (Suppl 1): S5–S9

    Article  Google Scholar 

  3. Frei E et al. (1985) Cloning of the extra sex combs gene of Drosophila and its identification by P-element-mediated gene transfer. EMBO J 4: 979–987

    Article  CAS  Google Scholar 

  4. DeLorenzi M et al. (1988) Evidence that the Abdominal-B r element function is conferred by a trans-regulatory homeoprotein. EMBO J 7: 3223–3231

    Article  CAS  Google Scholar 

  5. Webb G et al. (1990) Location of gyrA on the physical map of the Escherichia coli chromosome. J Bacteriol 172: 6617

    Article  CAS  Google Scholar 

  6. Brown DM (1993) A brief history of oligonucleotide synthesis. Methods Mol Biol 20: 1–17

    CAS  PubMed  Google Scholar 

  7. Fodor SP et al. (1993) Multiplexed biochemical assays with biological chips. Nature 364: 555–556

    Article  CAS  Google Scholar 

  8. NCBI GenBank Overview [http://www.ncbi.nlm.nih.gov/Genbank/] (accessed 24 April 2007)

  9. Schena M et al. (1996) Parallel human genome analysis: microarray-based expression monitoring of 1000 genes. Proc Natl Acad Sci USA 93: 10614–10619

    Article  CAS  Google Scholar 

  10. White JC and Stryer L (1987) Photostability studies of phycobiliprotein fluorescent labels. Anal Biochem 161: 442–452

    Article  CAS  Google Scholar 

  11. Ernst LA et al. (1989) Cyanine dye labeling reagents for sulfhydryl groups. Cytometry 10: 3–10

    Article  CAS  Google Scholar 

  12. Minsky M (1988) Memoir on inventing the confocal scanning microscope. Scanning 10: 128–138

    Article  Google Scholar 

  13. Norris AW and Kahn CR (2006) Analysis of gene expression in pathophysiological states: balancing false discovery and false negative rates. Proc Natl Acad Sci USA 103: 649–653

    Article  CAS  Google Scholar 

  14. Kutlu B et al. (2004) New approaches for in silico identification of cytokine-modified β cell gene networks. Ann NY Acad Sci 1037: 41–58

    Article  CAS  Google Scholar 

  15. Matsunaga T and Muramatsu MA (2005) Knowledge-based computational search for genes associated with the metabolic syndrome. Bioinformatics 21: 3146–3154

    Article  CAS  Google Scholar 

  16. Dufva M and Christensen CB (2005) Diagnostic and analytical applications of protein microarrays. Expert Rev Proteomics 2: 41–48

    Article  CAS  Google Scholar 

  17. LaBaer J and Ramachandran N (2005) Protein microarrays as tools for functional proteomics. Curr Opin Chem Biol 9: 14–19

    Article  CAS  Google Scholar 

  18. Angeloni S et al. (2005) Glycoprofiling with micro-arrays of glycoconjugates and lectins. Glycobiology 15: 31–41

    Article  CAS  Google Scholar 

  19. Kanter JL et al. (2006) Lipid microarrays identify key mediators of autoimmune brain inflammation. Nat Med 12: 138–143

    Article  CAS  Google Scholar 

  20. Phizicky E et al. (2003) Protein analysis on a proteomic scale. Nature 422: 208–215

    Article  CAS  Google Scholar 

  21. Bell J (2004) Predicting disease using genomics. Nature 429: 453–456

    Article  CAS  Google Scholar 

  22. Lee TI and Young RA (2000) Transcription of eukaryotic protein-coding genes. Annu Rev Genet 34: 77–137

    Article  CAS  Google Scholar 

  23. Lemon B and Tjian R (2000) Orchestrated response: a symphony of transcription factors for gene control. Genes Dev 14: 2551–2569

    Article  CAS  Google Scholar 

  24. Emerson BM (2002) Specificity of gene regulation. Cell 109: 267–270

    Article  CAS  Google Scholar 

  25. Matsuzaki H et al. (2004) Genotyping over 100,000 SNPs on a pair of oligonucleotide arrays. Nat Methods 1: 109–111

    Article  CAS  Google Scholar 

  26. Bejjani BA et al. (2005) Array-based comparative genomic hybridization in clinical diagnosis. Expert Rev Mol Diagn 5: 421–429

    Article  CAS  Google Scholar 

  27. van Steensel B and Henikoff S (2003) Epigenomic profiling using microarrays. Biotechniques 35: 346–357

    Article  CAS  Google Scholar 

  28. Ragoussis J et al. (2006) Matrix-assisted laser desorption/ionisation, time-of-flight mass spectrometry in genomics research. PLoS Genet 2: e100

    Article  Google Scholar 

  29. Wang SM (2007) Understanding SAGE data. Trends Genet 23: 42–50

    Article  Google Scholar 

  30. Baggerman G et al. (2005) Gel-based versus gel-free proteomics: a review. Comb Chem High Throughput Screen 8: 669–677

    Article  CAS  Google Scholar 

  31. Yuan M and Kendziorski C (2006) A unified approach for simultaneous gene clustering and differential expression identification. Biometrics 62: 1089–1098

    Article  Google Scholar 

  32. Mocellin S and Rossi CR (2007) Principles of gene microarray data analysis. Adv Exp Med Biol 593: 19–30

    Article  Google Scholar 

  33. Hanai T et al. (2006) Application of bioinformatics for DNA microarray data to bioscience, bioengineering and medical fields. J Biosci Bioeng 101: 377–384

    Article  CAS  Google Scholar 

  34. Mount DW and Pandey R (2005) Using bioinformatics and genome analysis for new therapeutic interventions. Mol Cancer Ther 4: 1636–1643

    Article  CAS  Google Scholar 

  35. Carella M et al. (2003) Nanotechnologies and microchips in genetic diseases. J Nephrol 16: 597–602

    CAS  PubMed  Google Scholar 

  36. Boel E et al. (2000) Modulation of metabolism through transcriptional control has created new treatment opportunities for type 2 diabetes. Curr Pharm Biotechnol 1: 63–71

    Article  CAS  Google Scholar 

  37. Nadler ST and Attie AD (2001) Please pass the chips: genomic insights into obesity and diabetes. J Nutr 131: 2078–2081

    Article  CAS  Google Scholar 

  38. Perfetti R and D'Amico E (2003) The contribution of gene profile analysis to the design of a molecular approach to treat diabetes in humans. Diabetes Technol Ther 5: 421–423

    Article  CAS  Google Scholar 

  39. Bell GI and Polonsky KS (2001) Diabetes mellitus and genetically programmed defects in β-cell function. Nature 414: 788–791

    Article  CAS  Google Scholar 

  40. Odom DT et al. (2004) Control of pancreas and liver gene expression by HNF transcription factors. Science 303: 1378–1381

    Article  CAS  Google Scholar 

  41. Song JY et al. (2005) Diagnosis of HNF-1α mutations on a PNA zip-code microarray by single base extension. Nucleic Acids Res 33: e19

    Article  Google Scholar 

  42. Rasschaert J et al. (2003) Global profiling of double stranded RNA- and IFN-γ-induced genes in rat pancreatic β cells. Diabetologia 46: 1641–1657

    Article  CAS  Google Scholar 

  43. Webb GC et al. (2000) Expression profiling of pancreatic β cells: glucose regulation of secretory and metabolic pathway genes. Proc Natl Acad Sci USA 97: 5773–5778

    Article  CAS  Google Scholar 

  44. Scearce LM et al. (2002) Functional genomics of the endocrine pancreas: the pancreas clone set and PancChip, new resources for diabetes research. Diabetes 51: 1997–2004

    Article  CAS  Google Scholar 

  45. Bernal-Mizrachi E et al. (2003) Gene expression profiling in islet biology and diabetes research. Diabetes Metab Res Rev 19: 32–42

    Article  CAS  Google Scholar 

  46. Aune TM et al. (2003) Gene expression profiles in human autoimmune disease. Curr Pharm Des 9: 1905–1917

    Article  CAS  Google Scholar 

  47. Gunton JE et al. (2005) Loss of ARNT/HIF1β mediates altered gene expression and pancreatic-islet dysfunction in human type 2 diabetes. Cell 122: 337–349

    Article  CAS  Google Scholar 

  48. Mootha VK et al. (2003) PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 34: 267–273

    Article  CAS  Google Scholar 

  49. Subramanian A et al. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102: 15545–15550

    CAS  Google Scholar 

  50. Patti ME et al. (2003) Coordinated reduction of genes of oxidative metabolism in humans with insulin resistance and diabetes: potential role of PGC1 and NRF1. Proc Natl Acad Sci USA 100: 8466–8471

    Article  CAS  Google Scholar 

  51. Sreekumar R et al. (2002) Gene expression profile in skeletal muscle of type 2 diabetes and the effect of insulin treatment. Diabetes 51: 1913–1920

    Article  CAS  Google Scholar 

  52. Whitfield ML et al. (2006) Common markers of proliferation. Nat Rev Cancer 6: 99–106

    Article  CAS  Google Scholar 

  53. Sommer A and Haendler B (2003) Androgen receptor and prostate cancer: molecular aspects and gene expression profiling. Curr Opin Drug Discov Devel 6: 702–711

    CAS  PubMed  Google Scholar 

  54. Eigl BJ et al. (2005) Timing is everything: preclinical evidence supporting simultaneous rather than sequential chemohormonal therapy for prostate cancer. Clin Cancer Res 11: 4905–4911

    Article  CAS  Google Scholar 

  55. Novak K (2006) Where the chips fall. Nat Med 12: 158–159

    Article  CAS  Google Scholar 

  56. Xu XQ et al. (2005) Gene expression profiling to identify oncogenic determinants of autocrine human growth hormone in human mammary carcinoma. J Biol Chem 280: 23987–24003

    Article  CAS  Google Scholar 

  57. Hayashi S (2004) Prediction of hormone sensitivity by DNA microarray. Biomed Pharmacother 58: 1–9

    Article  CAS  Google Scholar 

  58. Jaeger J et al. (2005) Early diagnostic marker panel determination for microarray based clinical studies. Stat Appl Genet Mol Biol 4: Article9

  59. Carling T (2005) Multiple endocrine neoplasia syndrome: genetic basis for clinical management. Curr Opin Oncol 17: 7–12

    Article  Google Scholar 

  60. Kim IJ et al. (2002) RET oligonucleotide microarray for the detection of RET mutations in multiple endocrine neoplasia type 2 syndromes. Clin Cancer Res 8: 457–463

    CAS  PubMed  Google Scholar 

  61. Bowtell DD (1999) Options available—from start to finish—for obtaining expression data by microarray. Nat Genet 21: 25–32

    Article  CAS  Google Scholar 

  62. Cheung VG et al. (1999) Making and reading microarrays. Nat Genet 21: 15–19

    Article  CAS  Google Scholar 

  63. Haab BB et al. (2001) Protein microarrays for highly parallel detection and quantitation of specific proteins and antibodies in complex solutions. Genome Biol [doi: 10.1186/gb-2001-2-2-research0004]

  64. Hirschhorn JN and Daly MJ (2005) Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 6: 95–108

    Article  CAS  Google Scholar 

  65. Horikawa Y et al. (2000) Genetic variation in the gene encoding calpain-10 is associated with type 2 diabetes mellitus. Nat Genet 26: 163–175

    Article  CAS  Google Scholar 

  66. Morley M et al. (2004) Genetic analysis of genome-wide variation in human gene expression. Nature 430: 743–747

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. G Vlacich is an MD-PhD candidate and GC Webb is an Assistant Professor in the Department of Medicine, and C Roe is a statistician in the Department of Medical Genetics, Section of Endocrinology, Diabetes Research and Training Center, at the University of Chicago, IL, USA.,

    Gregory Vlacich, Cheryl Roe & Gene C Webb

Authors
  1. Gregory Vlacich
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cheryl Roe
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gene C Webb
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gene C Webb.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Vlacich, G., Roe, C. & Webb, G. Technology Insight: microarrays—research and clinical applications. Nat Rev Endocrinol 3, 594–605 (2007). https://doi.org/10.1038/ncpendmet0580

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ncpendmet0580

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing