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DNA microarray



  A DNA microarray (also commonly known as gene or genome chip, DNA chip, or gene array) is a collection of microscopic DNA spots, commonly representing single genes, arrayed on a solid surface by covalent attachment to a chemical matrix. DNA arrays are different from other types of microarray only in that they either measure DNA or use DNA as part of its detection system. Qualitative or quantitative measurements with DNA microarrays utilize the selective nature of DNA-DNA or DNA-RNA hybridization under high-stringency conditions and fluorophore-based detection. DNA arrays are commonly used for expression profiling, i.e., monitoring expression levels of thousands of genes simultaneously, or for comparative genomic hybridization.

Additional recommended knowledge

Contents

Introduction

Arrays of DNA can either be spatially arranged, as in the commonly known gene or genome chip, DNA chip, or gene array, or can be specific DNA sequences tagged or labelled such that they can be independently identified in solution. The traditional solid-phase array is a collection of microscopic DNA spots attached to a solid surface, such as glass, plastic or silicon chip. The affixed DNA segments are known as probes (although some sources will use different nomenclature such as reporters), thousands of which can be placed in known locations on a single DNA microarray. Microarray technology evolved from Southern blotting, whereby fragmented DNA is attached to a substrate and then probed with a known gene or fragment. DNA microarrays can be used to detect DNA (e.g., in comparative genomic hybridization); it also permits detection of RNA (most commonly as cDNA after reverse transcription) that may or may not be translated into proteins, which is referred to as "expression analysis" or expression profiling.

Since there can be tens of thousands of distinct probes on an array, each microarray experiment can potentially accomplish the equivalent number of genetic tests in parallel. Arrays have therefore dramatically accelerated many types of investigations. The use of a collection of distinct DNAs in arrays for expression profiling was first described in 1987, and the arrayed DNAs were used to identify genes whose expression is modulated by interferon. [1] These early gene arrays were made by spotting cDNAs onto filter paper with a pin-spotting device. The use of miniaturized microarrays for gene expression profiling was first reported in 1995, [2] and a complete eukaryotic genome (Saccharomyces cerevisiae) on a microarray was published in 1997. [3]

Applications of these arrays include:

Fabrication

Microarrays can by manufactured in different ways, depending on the number of probes under examination, costs, customization requirements, and the type of scientific question being asked. Arrays may have as few as 10 probes to up to 390,000 micron-scale probes from commercial vendors.

Spotted vs. oligonucleotide arrays

Microarrays can be fabricated using a variety of technologies, including printing with fine-pointed pins onto glass slides, photolithography using pre-made masks, photolithography using dynamic micromirror devices, ink-jet printing, [7] or electrochemistry on microelectrode arrays.

A DNA microarray being created
A DNA microarray being printed by a robot at the University of Delaware.

 

In spotted microarrays, the probes are oligonucleotides, cDNA or small fragments of PCR products that correspond to mRNAs. There probes are synthesized prior to deposition on the array surface and are then "spotted" onto glass. A common approach utilizes an array of fine pins or needles controlled by a robotic arm that is dipped into wells containing DNA probes and then depositing each probe at designated locations on the array surface. The resulting "grid" of probes represents the nucleic acid profiles of the prepared probes and is ready to receive complementary cDNA or cRNA "targets" derived from experimental or clinical samples.

This technique is used by research scientists around the world to produce "in-house" printed microarrays from their own labs. These arrays may be easily customized for each experiment, because researchers can choose the probes and printing locations on the arrays, synthesize the probes in their own lab (or collaborating facility), and spot the arrays. They can then generate their own labeled samples for hybridization, hybridize the samples to the array, and finally scan the arrays with their own equipment. This provides a relatively low-cost microarray that is customized for each study, and avoids the costs of purchasing often more expensive commercial arrays that may represent vast numbers of genes that are not of interest to the investigator.

Publications exist which indicate in-house spotted microarrays may not provide the same level of sensitivity compared to commercial oligonucleotide arrays, [8] possibly owing to the small batch sizes and reduced printing efficiencies when compared to industrial manufactures of oligo arrays. GE Healthcare offers a commercial array platform called the "Code Link" system where 30-mer oligonucleotide probes (sequences of 30 nucleotides in length) are piezoelectrically deposited on an acrylamide matrix without any contact being made between the depositing equipment and the array surface itself. These arrays are comparable in quality to most manufactures arrays and generally superior to in-house printed arrays.

In oligonucleotide microarrays, the probes are short sequences designed to match parts of the sequence of known or predicted open reading frames. Although oligonucleotide probes are often used in "spotted" microarrays, the term "oligonucleotide array" most often refers to a specific technique of manufacturing. Oligonucleotide arrays are produced by printing short oligonucleotide sequences designed to represent a single gene or family of gene splice-variants by synthesizing this sequence directly onto the array surface instead of depositing intact sequences. Sequences may be longer (60-mer probes such as the Agilent design) or shorter (25-mer probes produced by Affymetrix) depending on the desired purpose; longer probes are more specific to individual target genes, shorter probes may be spotted in higher density across the array and are cheaper to manufacture.

One technique used to produce oligonucleotide arrays include photolithographic synthesis (Agilent and Affymetrix) on a silica substrate where light and light-sensitive masking agents are used to "build" a sequence one nucleotide at a time across the entire array. [9] Each applicable probe is selectively "unmasked" prior to bathing the array in a solution of a single nucleotide, then a masking reaction takes place and the next set of probes are unmasked in preparation for a different nucleotide exposure. After many repetitions, the sequences of every probe become fully constructed. More recently, Maskless Array Synthesis from NimbleGen Systems has combined flexibility with large numbers of probes. [10]

Two-color vs. one-color detection

Two-Color microarrays are typically hybridized with cDNA prepared from two samples to be compared (e.g. diseased tissue versus healthy tissue) and that are labeled with two different fluorophores. [11] Fluorescent dyes commonly used for cDNA labelling include Cy3, which has a fluorescence emission wavelength of 570 nm (corresponding to the green part of the light spectrum), and Cy5 with a fluorescence emission wavelength of 670 nm (corresponding to the red part of the light spectrum). The two Cy-labelled cDNA samples are mixed and hybridized to a single microarray that is then scanned in a microarray scanner to visualize fluorescence of the two fluorophores after excitation with a laser beam of a defined wavelength. Relative intensities of each fluorophore may then be used in ratio-based analysis to identify up-regulated and down-regulated genes. [12]

Oligonucleotide microarrays often contain control probes designed to hybridize with RNA spike-ins. The degree of hybridization between the spike-ins and the control probes is used to normalize the hybridization measurements for the target probes. Although absolute levels of gene expression may be determined in the two-color array, the relative differences in expression among different spots within a sample and between samples is the preferred method of data analysis for the two-color system. Examples of providers for such microarrays includes Agilent with their Dual-Mode platform, Eppendorf with their DualChip platform, and TeleChem International with ArrayIt.

  In single-channel microarrays or one-color microarrays, the arrays are designed to give estimations of the absolute levels of gene expression. Therefore the comparison of two conditions requires two separate single-dye hybridizations. As only a single dye is used, the data collected represent absolute values of gene expression. These may be compared to other genes within a sample or to reference "normalizing" probes used to calibrate data across the entire array and across multiple arrays. Two popular single-channel systems are the Affymetrix "Gene Chip" and GE Healthcare "Code Link" arrays. One strength of the single-dye system lies in the fact that an aberrant sample cannot affect the raw data derived from other samples, because each array chip is exposed to only one sample (as opposed to a two-color system in which a single low-quality sample may drastically impinge on overall data precision even if the other sample was of high quality). Another benefit is that data are more easily compared to arrays from different experiments; the absolute values of gene expression may be compared between studies conducted months or years apart. A drawback to the one-color system is that, when compared to the two-color system, twice as many microarrays are needed to compare samples within an experiment.

Genotyping microarrays

Main article: SNP array

DNA microarrays can also be used to scan the entire sequence of a genome to identify genetic variation at certain locations.

SNP microarrays are a type of DNA microarray that are used to identify genetic variation in individuals and across populations. [6]

Standardization

The lack of standardization in arrays presents an interoperability problem in bioinformatics, which hinders the exchange of array data. Various grass-roots open-source projects are attempting to facilitate the exchange and analysis of data produced with non-proprietary chips.

  • The "Minimum Information About a Microarray Experiment" (MIAME) checklist helps define the level of detail that should exist and is being adopted by many journals as a requirement for the submission of papers incorporating microarray results. MIAME describes the minimum required information for complying experiments, but not its format. Thus, as of 2007, whilst many formats can support the MIAME requirements there is no format which permits verification of complete semantic compliance.
  • The "MicroArray Quality Control (MAQC) Project" is being conducted by the FDA to develop standards and quality control metrics which will eventually allow the use of MicroArray data in drug discovery, clinical practice and regulatory decision-making. [13]
  • The MicroArray and Gene Expression (MAGE) group is working on the standardization of the representation of gene expression data and relevant annotations.

Statistical analysis

The analysis of DNA microarrays poses a large number of statistical problems, including the normalization of the data. There are dozens of proposed normalization methods in the published literature; as in many other cases where authorities disagree, a sound conservative approach is to try a number of popular normalization methods and compare the conclusions reached: how sensitive are the main conclusions to the method chosen?

From a hypothesis-testing perspective, the large number of genes present on a single array means that the experimenter must take into account a multiple testing problem: even if the statistical P-value assigned to a given gene indicates that it is extremely unlikely that differential expression of this gene was due to random rather than treatment effects, the very high number of genes on an array makes it likely that differential expression of some genes represent false positives or false negatives. Statistical methods tailored to microarray analyses have recently become available that assess statistical power based on the variation present in the data and the number of experimental replicates, and can help minimize type I and type II errors in the analyses.[14]

A basic difference between microarray data analysis and much traditional biomedical research is the dimensionality of the data. A large clinical study might collect 100 data items per patient for thousands of patients. A medium-size microarray study will obtain many thousands of numbers per sample for perhaps a hundred samples. Many analysis techniques treat each sample as a single point in a space with thousands of dimensions, then attempt by various techniques to reduce the dimensionality of the data to something humans can visualize.

Relation between probe and gene

The relation between a probe and the mRNA that it is expected to detect is problematic. On the one hand, some mRNAs may cross-hybridize probes in the array that are supposed to detect another mRNA. On the other hand, probes that are designed to detect the mRNA of a particular gene may be relying on genomic EST information that is incorrectly associated with that gene.

Public databases of microarray data

Database Microarray Experiment Sets Sample Profiles as of Date
Gene Expression Omnibus - NCBI 5366 134669 April 1, 2007
Stanford Microarray database 12742  ? April 1, 2007
UPenn RAD database ~100 ~2500 Sept. 1, 2007
UNC Microarray database ~31 2093 April 1, 2007
MUSC database ~45 555 April 1, 2007
ArrayExpress at EBI 1643 136 April 1, 2007
caArray at NCI 41 1741 November 15, 2006
UPSC-BASE ~100  ? November 15, 2007
  • For a directory of Microarray Databases, see: Gene Expression Databases at the Open Directory Project
  • See also the Microarray databases page in Wikipedia

Online microarray data-analysis programs and tools

Several Open Directory Project categories list online microarray data analysis programs and tools:

  • Bioinformatics : Online Services : Gene Expression and Regulation at the Open Directory Project
  • Gene Expression : Databases at the Open Directory Project
  • Gene Expression : Software at the Open Directory Project
  • Data Mining : Tool Vendors at the Open Directory Project
  • Bioconductor: open source and open development software project for the analysis and comprehension of genomic data
  • Genevestigator : Web-based database and analysis tool to study gene expression across large sets of tissues, developmental stages, drugs, stimuli, and genetic modifications.
  • GeneCAT (Gene Co-expression Analysis Toolbox): Web-based database of gene expression data and expression analysis tools for Arabidopsis thaliana and barley.

Notable microarray-related articles

    • For microarray companies, see: Products and Services for Gene Expression at the Open Directory Project

References

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  3. ^ Lashkari DA, DeRisi JL, McCusker JH, Namath AF, Gentile C, Hwang SY, Brown PO, Davis RW (1997). "Yeast microarrays for genome wide parallel genetic and gene expression analysis". Proc Natl Acad Sci USA 94: 13057-13062. PMID 9371799.
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  5. ^ Moran G, Stokes C, Thewes S, Hube B, Coleman DC, Sullivan D (2004). "Comparative genomics using Candida albicans DNA microarrays reveals absence and divergence of virulence-associated genes in Candida dubliniensis". Microbiology 150: 3363-3382. PMID 15470115.
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  7. ^ http://genomebiology.com/2004/5/8/R58
  8. ^ Bammler T, Beyer RP, Bhattacharya S, Boorman GA, Boyles A, Bradford BU, Bumgarner RE, Bushel PR, Chaturvedi K, Choi D, Cunningham ML, Deng S, Dressman HK, Fannin RD, Farin FM, Freedman JH, Fry RC, Harper A, Humble MC, Hurban P, Kavanagh TJ, Kaufmann WK, Kerr KF, Jing L, Lapidus JA, Lasarev MR, Li J, Li YJ, Lobenhofer EK, Lu X, Malek RL, Milton S, Nagalla SR, O'malley JP, Palmer VS, Pattee P, Paules RS, Perou CM, Phillips K, Qin LX, Qiu Y, Quigley SD, Rodland M, Rusyn I, Samson LD, Schwartz DA, Shi Y, Shin JL, Sieber SO, Slifer S, Speer MC, Spencer PS, Sproles DI, Swenberg JA, Suk WA, Sullivan RC, Tian R, Tennant RW, Todd SA, Tucker CJ, Van Houten B, Weis BK, Xuan S, Zarbl H; Members of the Toxicogenomics Research Consortium. (2005). "Standardizing global gene expression analysis between laboratories and across platforms". Nat Methods 2: 351-356. PMID 15846362.
  9. ^ Pease AC, Solas D, Sullivan EJ, Cronin MT, Holmes CP, Fodor SP. (1994). "Light-generated oligonucleotide arrays for rapid DNA sequence analysis.". PNAS 91: 5022-5026. PMID 8197176.
  10. ^ Nuwaysir EF, Huang W, Albert TJ, Singh J, Nuwaysir K, Pitas A, Richmond T, Gorski T, Berg JP, Ballin J, McCormick M, Norton J, Pollock T, Sumwalt T, Butcher L, Porter D, Molla M, Hall C, Blattner F, Sussman MR, Wallace RL, Cerrina F, Green RD. (2002). "Gene expression analysis using oligonucleotide arrays produced by maskless photolithography.". Genome Res 12: 1749-1755. PMID 12421762.
  11. ^ Shalon D, Smith SJ, Brown PO (1996). "A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization". Genome Res 6: 639-645. PMID 8796352.
  12. ^ Tang T, François N, Glatigny A, Agier N, Mucchielli MH, Aggerbeck L, Delacroix H (2007). "Expression ratio evaluation in two-colour microarray experiments is significantly improved by correcting image misalignment". Bioinformatics 23: 2686-2691. PMID 17698492.
  13. ^ http://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/
  14. ^ Wei C, Li J, Bumgarner RE. (2004). "Sample size for detectng differentially expressed genes in microarray experiments". BMC Genomics 5: 87. PMID 15533245.
  • PLoS Biology Primer: Microarray Analysis
  • Rundown of microarray technology
  • Microarray data processing using Self-Organizing Maps tutorial: Part 1 Part 2
 
This article is licensed under the GNU Free Documentation License. It uses material from the Wikipedia article "DNA_microarray". A list of authors is available in Wikipedia.
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