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The Human Genome Project, completed in 2003, identified the sequence of the approximately 25,000 genes that comprise the human genome. Knowledge of the structure of the human genome opened the doors to studying the actual function of specific genes, which is necessary to understanding health and disease. DNA microarrays are one technique that advances understanding of the genome from structure to function.
For the most part, every cell in the body contains a complete set of identical DNA. What makes a given cell different from another is that only certain genes are active, or “expressed,” within the cell. The information contained within the DNA in a given cell is transcribed into messenger RNA (mRNA), which is subsequently translated into proteins that allow the cell to exist and function. Diseased cells may fail to work properly because they do not express necessary genes, express the wrong genes, or express inappropriate amounts of a needed gene. The ability to detect these abnormalities provides researchers with key information that may be used to diagnose and manage diseases.
DNA MICROARRAY TECHNOLOGY
Microarray technology can be used for a variety of functions, several of which are discussed below. For purposes of clarity, we focus on the use of the technique to assess gene expression levels.
Older techniques looking at gene expression were time-consuming and provided information only on relatively few genes at one time. DNA microarrays are unique in that they allow scientists to investigate the expression of thousands of genes at one time. In addition, they permit simultaneous comparisons between different cells, such as between a diseased and a normal cell.
HOW DO MICROARRAYS WORK? THE BRIEF VERSION
Various microarray systems have been developed. Some are commercially available; others are produced primarily in research laboratories. The type of solid support used (e.g., glass or filters), the surface modifications with various substrates, the type of DNA fragments used on the array (e.g., cDNA, oligonucleotides, genomic fragments), whether the transcripts are synthesized in situ or presynthesized and spotted onto the array (i.e., spot arrays), and how DNA fragments are placed on the array are all characteristics that differentiate among the available microarrays (
). There are now many commercially available microarrays that are of high quality and low cost.
The most common probes used on array platforms are complementary DNA (cDNA) or oligonucleotides. Ideal probes should be sequence-validated, unique, and representative of a significant portion of the genome, and they should have minimal cross-hybridization to related sequences. Probes for cDNA arrays are composed of cDNAs from cDNA libraries or clone collections (e.g., bacterial cDNA and bacterial artificial chromosome clone sets) that are “spotted” onto glass slides or nylon membranes at precise locations. Spotted arrays composed of a collection of cDNAs allow for increased choice of sequences incorporated in the array and may allow for gene discovery when unselected clones from cDNA libraries are used (
Oligonucleotide arrays consist of probes composed of short nucleotides (15–25 nt) or long oligonucleotides (50–120 nt) that are directly synthesized onto glass slides or silicon wafers, using either photolithography or ink-jet technology. The use of longer oligonucleotides (50–100 mers) may increase the specificity of hybridization and increase sensitivity of detection (
). Arrays fabricated by direct synthesis offer the advantage of using reproducible, high-density probe arrays containing more than 300,000 individual elements, with probes specifically designed to contain the most unique part of a transcript. This method allows for increased detection of closely related genes or splice variants.
The hybridization process
Whether cDNA or oligonucleotides are used as a probe, the test sample must be prepared. In most cases, the “target,” or that which should hybridize to the probe, is mRNA. This is extracted from the tissue, transcribed into its complementary cDNA, and labeled with either a fluorescent dye (“fluorophore”) or a radioactive isotope. The use of different fluorophores facilitates direct, parallel comparison between different tissue types; this technique could, for example, allow simultaneous comparison of known diseased versus normal tissue. Fluorescent dyes commonly used for cDNA labeling include Cy3 (which fluoresces green) and Cy5 (which fluoresces red). The labeled targets are hybridized to the probes for 16–24 hours, during which time the targets competitively bind to the corresponding array probe. The array is then washed and scanned using a laser confocal microscope. The relative fluorescence of each spot is detected and recorded. The data can then be analyzed to determine which genes are actively expressed (Figure 1). Several computing programs for image processing and data analysis are on the market, from both academic and private institutions (e.g., Biodiscovery, National Human Genome Research Institute, Spotfire, and Stanford University).
The type of data generated from microarrays depends on several factors, including the experimental design, the purity of samples, the quality of the microarray design, and data analysis. Although microarrays are capable of generating a large amount of data, microarray data do not distinguish between differences in expression that are attributable to experimental design and those that are secondary effects. Careful selection of controls and experimental samples may decrease the possibility of identifying irrelevant changes in gene expression. In addition, caution should be advised in interpreting genes that show no change in expression levels because this may reflect DNA elements on the array that fail to detect the appropriate transcript species. Verification of data generated from microarray experiments using quantitative reverse-transcriptase PCR, northern blot analysis, or RNase protection assays may be important.
USES OF MICROARRAYS
Microarray technology allows simultaneous analysis of several thousand DNA transcripts derived from experimental tissue or cells. This technique can be used to detect differences in gene expression levels, small nucleotide polymorphisms, and other sequence differences, as well as microRNA (miRNA) transcript levels (miRNAs are short, noncoding RNAs that regulate gene expression in many biologic processes). A microarray technique called comparative genomic hybridization is used to detect genomic gains or losses in the number of copies of genes in a given person’s DNA. This technique can provide clinical and prognostic information in certain tumors. Microarray technology has also been useful in biomarker determination, enabling the discovery of gene sets that correlate with disease. Other uses include changes in gene expression following exposure to medications or toxins in order to predict their effectiveness or adverse effects, respectively. Microarrays may also be used to personalize therapy in patients recently diagnosed with cancer.
APPLICATIONS IN DERMATOLOGY
Skin biology was one of the first scientific fields to benefit from microarray technology. It has been applied to a variety of dermatologic disease processes, including melanoma, cutaneous T-cell lymphoma, psoriasis, lupus erythematosus, and scleroderma. Its application in the detection of different gene expression levels and of differences in genetic sequences—including single-nucleotide polymorphisms (SNPs)—as well as in identification of new genetic targets makes microarray technology very attractive. Investigators using microarrays to determine global miRNA expression profiles identified deregulation of certain miRNAs as significantly different among melanoma lymph node metastases, melanoma cell lines, and melanocyte cultures (Caramuto et al., 2010;
). These studies identified previously unrecognized molecular targets in melanoma. Figures 2 and 3 show examples of how the data from such arrays are obtained and interpreted.
Figure 2 (Caramuto et al., 2010) shows the relative expression of a series of miRNAs (listed on the y-axis) obtained from multiple nonmelanoma (NM 1–3 on the x-axis) and melanoma (M1–M16) cell lines. Yellow indicates relatively high expression; blue indicates low expression. Detailed analysis of the data allowed researchers to identify a small number of miRNAs that were differentially expressed in melanoma versus nonmelanoma cell lines (some were underexpressed; others were overexpressed).
As shown in Figure 3 (Caramuto et al., 2010), the researchers further used microarray technology to determine whether certain miRNA expression profiles (those chosen are listed on the right side of the arrays) could be used to discriminate among BRAF mutants and BRAF and NRAS wild-type mutants. The strongest association with BRAF mutation status was shown with miR-193a, whose expression was significantly reduced in the melanoma lines.
The authors thank Julio Valencia, Staff Scientist for the Pigment Cell Biology Section, Laboratory of Cell Biology, National Cancer Institute at the National Institute of Health, and Steven Brown, University of Vermont, for their critical review of the manuscript.