Microarray technology - Enhanced versatility, persistent challenge

Current Opinion in Biotechnology

Volumn 11, Issue 1, 2000, Pages 36-41

  Epstein, Charles B ^a   Butow, Ronald A ^a  

^a UNIVERSITY OF TEXAS SOUTHWESTERN MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DNA; NUCLEIC ACID;

BIOTECHNOLOGY; CANCER; CLUSTER ANALYSIS; DATA ANALYSIS; DATA BASE; EVOLUTION; GENE DOSAGE; GENE MAPPING; GENETIC TRANSCRIPTION; HUMAN; MEIOSIS; MITOSIS; NONHUMAN; PHYLOGENY; PRIORITY JOURNAL; REVIEW; TECHNIQUE;

EID: 0033967014     PISSN: 09581669     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0958-1669(99)00065-8     Document Type: Review

References (50)

1. Eisen M.B., Brown P.O. DNA arrays for analysis of gene expression. Methods Enzymol. 303:1999;179-205.
2. Lipshutz R.J., Fodor S.P., Gingeras T.R., Lockhart D.J. High density synthetic oligonucleotide arrays. Nat Genet. 21:1999;20-24.
3. The Chipping Forecast. Nat Genet. 21:1999;1-60. (suppl 1).
4. Schena M. DNA Microarrays - a Practical Approach. 1999;Oxford University Press, Oxford.
5. Bowtell D.D. Options available - from start to finish - for obtaining expression data by microarray. Nat Genet. 21:1999;25-32.
6. Sinclair B. Everything's great when it sits on a chip: a bright future for DNA arrays. The Scientist. 17:1999;18-20.
7. Worm Chip Directory on World Wide Web URL: http://cmgm.stanford.edu/~kimlab/wmdirectorybig.html.
8. Bryant Z., Subrahmanyan L., Tworoger M., LaTray L., Liu C.R., Li M.J., van den Engh G., Ruohola-Baker H. Characterization of differentially expressed genes in purified Drosophila follicle cells: toward a general strategy for cell type-specific developmental analysis. Proc Natl Acad Sci USA. 96:1999;5559-5564.
9. Drosophila DNA Microarray Homepage on World Wide Web URL: http://cmgm.stanford.edu/~kpwhite/.
10. Somerville C., Somerville S. Plant functional genomics. Science. 285:1999;380-383.
11. Braxton S., Bedilion T. The integration of microarray information in the drug development process. Curr Opin Biotechnol. 9:1998;643-649.
12. Watson A., Mazumder A., Stewart M., Balasubramanian S. Technology for microarray analysis of gene expression. Curr Opin Biotechnol. 9:1998;609-614.
13. Perou C.M., Jeffrey S.S., van de Rijn M., Rees C.A., Eisen M.B., Ross D.T., Pergamenschikov A., Williams C.F., Zhu S.X., Lee J.C.et al. Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc Natl Acad Sci USA. 96:1999;9212-9217. This is a landmark study applying gene cluster analysis to the molecular comparison of several primary breast tumors. The authors emphasize that they are able to discern patterns of gene expression (across tissues) that are characteristic of tumor cells, as well as distinct patterns that are characteristic of contaminating lymphocytes and tumor-associated stromal cells. They intend to use immunocytochemistry to associate a number of genes identified by microarray analysis with the cell type of origin.
14. ••] and is available as free software within the academic community.
15. Khan J., Simon R., Bittner M., Chen Y., Leighton S.B., Pohida T., Smith P.D., Jiang Y., Gooden G.C., Trent J.M., Meltzer P.S. Gene expression profiling of alveolar rhabdomyosarcoma with cDNA microarrays. Cancer Res. 58:1998;5009-5013.
16. Chen Y., Dougherty E.R., Bittner M.L. Ratio-based decisions and the quantitative analysis of cDNA microarray images. J Biomedical Optics. 2:1997;364-374.
17. Welford S.M., Gregg J., Chen E., Garrison D., Sorensen P.H., Denny C.T., Nelson S.F. Detection of differentially expressed genes in primary tumor tissues using representational differences analysis coupled to microarray hybridization. Nucleic Acids Res. 26:1998;3059-3065.
18. Lisitsyn N., Wigler M. Cloning the differences between two complex genomes. Science. 259:1993;946-951.
19. Yang G.P., Ross D.T., Kuang W.W., Brown P.O., Weigel R.J. Combining SSH and cDNA microarrays for rapid identification of differentially expressed genes. Nucleic Acids Res. 27:1999;1517-1523.
20. Diatchenko L., Lukyanov S., Lau Y.F., Siebert P.D. Suppression subtractive hybridization: a versatile method for identifying differentially expressed genes. Methods Enzymol. 303:1999;349-380.
21. Wang K., Gan L., Jeffery E., Gayle M., Gown A.M., Skelly M., Nelson P.S., Ng W.V., Schummer M., Hood L., Mulligan J. Monitoring gene expression profile changes in ovarian carcinomas using cDNA microarray. Gene. 229:1999;101-108.
22. Cho R.J., Campbell M.J., Winzeler E.A., Steinmetz L., Conway A., Wodicka L., Wolfsberg T.G., Gabrielian A.E., Landsman D., Lockhart D.J., Davis R.W. A genome-wide transcriptional analysis of the mitotic cell cycle. Mol Cell. 2:1998;65-73.
23. Spellman P.T., Sherlock G., Zhang M.Q., Iyer V.R., Anders K., Eisen M.B., Brown P.O., Botstein D., Futcher B. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell. 9:1998;3273-3297. Yeast cultures were synchronized for their passage through the cell cycle and a prodigious catalog of cell cycle periodic transcripts was derived using a cDNA array. This paper is notable for its use of Fourier analysis of transcript ratios to assign variable genes to periodic or aperiodic classes.
24. ••] as well as an unpublished 'self organizing map' method. Overall, 1,116 out of ~6,200 genes were found to change expression in response to the induction of sporulation.
25. Jelinsky S.A., Samson L.D. Global response of Saccharomyces cerevisiae to an alkylating agent. Proc Natl Acad Sci USA. 96:1999;1486-1491.
26. Amundson S.A., Bittner M., Chen Y., Trent J., Meltzer P., Fornace A.J. Jr. Fluorescent, cDNA microarray hybridization reveals complexity and heterogeneity of cellular genotoxic stress responses. Oncogene. 18:1999;3666-3672.
27. Lee C.K., Klopp R.G., Weindruch R., Prolla T.A. Gene expression profile of aging and its retardation by caloric restriction. Science. 285:1999;1390-1393. This paper brings the study of changes in gene transcription as a function of age under the scope of microarray technology. Transcripts derived from the calf muscles of young and old mice were compared, using Affymetrix chips. Additionally, caloric-restricted old mice were compared to free-fed old mice. The authors found changes in genes related to stress response, energy metabolism, protein turnover, and neuronal factors. Caloric restriction, which is known to extend life span in some organisms, reversed many of the aging-related changes, but was associated overall with other unique responses affecting genes involved in synthesis of fatty acids, glutamine, steroids and nucleotides.
28. Fambrough D., McClure K., Kazlauskas A., Lander E.S. Diverse signaling pathways activated by growth factor receptors induce broadly overlapping, rather than independent, sets of genes. Cell. 97:1999;727-741. The authors examined immediate early gene induction mediated by the cytoplasmic domain of the PDGF receptor, a receptor tyrosine kinase. After constructing a series of distinct cytoplasmic Tyr→Phe mutations, they used Affymetrix chips specifying 6000 mouse genes and ESTs to evaluate the dependence of immediate early genes on distinct signaling pathways. Surprisingly, they found that individual signaling pathways make a quantitative rather than a qualitative contribution to the transcriptional response to ligand. A mutant receptor defective for signaling via at least three pathways still induced 64 of the normal 66 genes in response to ligand, but the average level of induction was 59% of wild type.
29. Holstege F.C., Jennings E.G., Wyrick J.J., Lee T.I., Hengartner C.J., Green M.R., Golub T.R., Lander E.S., Young R.A. Dissecting the regulatory circuitry of a eukaryotic genome. Cell. 95:1998;717-728. Working with Affymetrix chips for the detection of yeast transcripts, the authors examined the dependence of yeast transcription on components of the general transcription machinery. Evaluations were based on comparisons between wild-type yeast and deletion mutants (in the case of non-essential components) or conditional mutations (in the case of essential components). Different perturbations had differing impacts on global transcription, ranging from nearly absolute inhibition (in strains depleted for RNA polymerase II large subunit) to 3% inhibition (in strains deleted for SRB10, a regulatory protein kinase).
30. Kallioniemi A., Kallioniemi O.P., Sudar D., Rutovitz D., Gray J.W., Waldman F., Pinkel D. Comparative genomic hybridization for molecular cytogenetic analysis of solid tumors. Science. 258:1992;818-821.
31. Solinas-Toldo S., Lampel S., Stilgenbauer S., Nickolenko J., Benner A., Dohner H., Cremer T., Lichter P. Matrix-based comparative genomic hybridization: biochips to screen for genomic imbalances. Genes Chromosomes Cancer. 20:1997;399-407.
32. Pinkel D., Segraves R., Sudar D., Clark S., Poole I., Kowbel D., Collins C., Kuo W.L., Chen C., Zhai Y.et al. High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays. Nat Genet. 20:1998;207-211.
33. Pollack J.R., Perou C.M., Alizadeh A.A., Eisen M.B., Pergamenschikov A., Williams C.F., Jeffrey S.S., Botstein D., Brown P.O. Genome-wide analysis of DNA copy-number changes using cDNA microarrays. Nat Genet. 23:1999;41-46. This paper further extends the technique of array-based comparative genomic hybridization, using arrays of PCR products under 2 kb in length. When the method was applied to samples known to have a single copy difference in chromosome copy numbers (i.e. XX versus X0 females), 98% sensitivity and specificity were achieved (i.e. false negative and false positive rates were both 2%). To achieve this sensitivity, data were analyzed as a moving average of three adjacent genes.
34. Geschwind D.H., Gregg J., Boone K., Karrim J., Pawlikowska-Haddal A., Rao E., Ellison J., Ciccodicola A., D'Urso M., Woods R.et al. Klinefelter's syndrome as a model of anomalous cerebral laterality: testing gene dosage in the X chromosome pseudoautosomal region using a DNA microarray. Dev Genet. 23:1998;215-229.
35. Ferea T.L., Botstein D., Brown P.O., Rosenzweig R.F. Systematic changes in gene expression patterns following adaptive evolution in yeast. Proc Natl Acad Sci USA. 96:1999;9721-9726.
36. Behr M.A., Wilson M.A., Gill W.P., Salamon H., Schoolnik G.K., Rane S., Small P.M. Comparative genomics of BCG vaccines by whole-genome DNA microarray. Science. 284:1999;1520-1523.
37. Cargill M., Altshuler D., Ireland J., Sklar P., Ardlie K., Patil N., Lane C.R., Lim E.P., Kalayanaraman N., Nemesh J.et al. Characterization of single-nucleotide polymorphisms in coding regions of human genes. Nat Genet. 22:1999;231-238.
38. Hacia J.G., Fan J.B., Ryder O., Jin L., Edgemon K., Ghandour G., Mayer R.A., Sun B., Hsie L., Robbins C.M.et al. Determination of ancestral alleles for human single-nucleotide polymorphisms using high-density oligonucleotide arrays. Nat Genet. 22:1999;164-167.
39. Bassett D.E. Jr., Eisen M.B., Boguski M.S. Gene expression informatics - it's all in your mine. Nat Genet. 21:1999;51-55.
40. ••] to reveal 10 distinct response sets. 517 genes out of 8,613 showed at least a 2.2-fold change in at least two experiments. The study illuminates the molecular overlap between the regulation of cell division and the wound healing response.
41. Hilsenbeck S.G., Friedrichs W.E., Schiff R., O'Connell P., Hansen R.K., Osborne C.K., Fuqua S.A. Statistical analysis of array expression data as applied to the problem of tamoxifen resistance. J Natl Cancer Inst. 91:1999;453-459.
42. Wittes J., Friedman H.P. Searching for evidence of altered gene expression: a comment on statistical analysis of microarray data. J Natl Cancer Inst. 91:1999;400-401.
43. Tavazoie S., Hughes J.D., Campbell M.J., Cho R.J., Church G.M. Systematic determination of genetic network architecture. Nat Genet. 22:1999;281-285.
44. Tamayo P., Slonim D., Mesirov J., Zhu Q., Kitareewan S., Dmitrovsky E., Lander E.S., Golub T.R. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA. 96:1999;2907-2912. This paper describes the method for clustering genes based on their expression profiles using self-organizing maps (SOMs). The associated software can be applied to either intensity data or ratiometric data, and displays the mean behavior of the genes in a cluster, rather than displaying the patterns of variation of the individual genes forming a cluster.
45. Alon U., Barkai N., Notterman D.A., Gish K., Ybarra S., Mack D., Levine A.J. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci USA. 96:1999;6745-6750.
46. Toronen P., Kolehmainen M., Wong G., Castren E. Analysis of gene expression data using self-organizing maps. FEBS Lett. 451:1999;142-146.
47. Norman T.C., Smith D.L., Sorger P.K., Drees B.L., O'Rourke S.M., Hughes T.R., Roberts C.J., Friend S.H., Fields S., Murray A.W. Genetic selection of peptide inhibitors of biological pathways. Science. 285:1999;591-595. Twenty peptides were identified that allowed yeast cells to proliferate in the presence of mating pheromone. By genetic criteria, the peptides were hypothesized to function by reversing silencing of a silenced mating-type locus, converting the haploid yeast into functional a/α diploids, which are pheromone insensitive. To test their hypothesis, the authors used a microarray to compare the effect of the peptides with the effect of a known mutation that destroys silencing. They found a significant degree of correlation, confirming the specificity of the action of the peptides.
48. Marton M.J., DeRisi J.L., Bennett H.A., Iyer V.R., Meyer M.R., Roberts C.J., Stoughton R., Burchard J., Slade D., Dai H.et al. Drug target validation and identification of secondary drug target effects using DNA microarrays. Nat Med. 4:1998;1293-1301. This paper offers a creative application of transcription profiling to the challenge of identifying useful drugs. Typically, drugs have partially known effects and poorly known targets. The authors use transcript profiles to determine 'signatures' for the effects of both drugs and mutations. When a drug and a mutation have similar signatures, it suggests the drug targets the gene involved in the mutation, but confirmation is necessary. A second tier experiment is done, wherein the mutant strain itself is exposed to the drug, to evaluate residual response. Finding uncorrelated drug responses between wild-type and mutant strains suggests that the drug specifically targets the gene affected by the mutation.
49. Galitski T., Saldanha A.J., Styles C.A., Lander E.S., Fink G.R. Ploidy regulation of gene expression. Science. 285:1999;251-254. The authors derived 11 closely related yeast strains whose ploidies spanned the range from haploid to tetraploid. Working with Affymetrix chips, they determined expression intensities for all genes. For each gene in each strain (1N, 2N, 3N and 4N), they correlated expression intensities to the reference series 1, 2, 3, 4 and 1, 0.5, 0.33, 0.25. They found 17 genes showing significant correlation to the reference series. One gene showing repression at higher ploidies (CLN1) was already known to have a disruption phenotype of increased cell volume. The inverse relation between ploidy and expression for CLN1 may contribute to the larger cell volume of higher ploidy cells.
50. Singh-Gasson S., Green R.D., Yue Y., Nelson C., Blattner F., Sussman M.R., Cerrina F. Maskless fabrication of light-directed oligonucleotide microarrays using a digital micromirror array. Nat Biotechnol. 17:1999;974-978. These authors describe a novel approach to the in situ synthesis of oligonucleotide arrays using light-directed chemistry, but without the use of photolithographic masks. Rather, they use a computer-controlled array of micro-mirrors to focus light on specific regions of the reaction chamber during oligonucleotide synthesis. A similar approach was taken by HR Garner (HR Garner, personal communication).