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Sunday, 31 January 2016

How Microarray works ? Here is the methodology

Methodology:

A typical microarray experiment involves the hybridization of an mRNA molecule to the DNA template from which it is originated. Many DNA samples are used to construct an array. The amount of mRNA bound to each site on the array indicates the expression level of the various genes. This number may run in thousands. All the data is collected and a profile is generated for gene expression in the cell.

There are actually several different ways in which nucleic acid probes can be arrayed at high density for interrogation of labelled mRNA samples. Usually, microarray preparation is initiated by obtaining end-sequences for several thousand of the clones, and a unique set of these expressed sequence tags (ESTs), is selected for amplification. These products are robotically deposited at a density of around 30 clones per square millimetre on the end of a special glass microscope slide or filter, in batches of perhaps 100 slides. The cDNA microarray probe is then hybridized to radioactively or fluorescently labelled cDNA prepared by reverse transcription of mRNA isolated from the cells or tissues of interest. Competitive hybridization of two samples labelled with different dyes, commonly Cy3 and Cy5, allows an estimate of the ratio of transcript abundance in the two RNA samples being compared, for each spot (clone) on the microarray independently. The levels of fluorescence or radioactivity are not regarded as a reliable indicator of the absolute level of transcript, but as described below, it is possible to infer changes in gene expression from changes in the signal intensity of each clone relative to the sample mean.

The alternate oligonucleotide technology pioneered by Affymetrix GeneChips® differs in two important respects (Lockhart et al. 1996). First, the probes are a set of up to 20 short, 25 mer oligonucleotides that are specific for each gene or exon, along with the related set with single base mismatches incorporated at the middle position of each oligonucleotide. These are synthesized in situ on each silicon chip using genome sequence information to guide photolithographic deposition. Second, the arrays are hybridized to a single biotinylated amplified RNA sample, and the intensity measure for each gene is currently computed by an algorithm that shows the difference between the match and mismatch measurements and averages over each oligonucleotide. Rather than comparing ratios, inferences are drawn by contrasting differences in magnitude of these intensity scores. This technology is expensive, but has greater genome coverage than microarrays and may be more replicable and comparable across research groups, so is seeing wide application for model organisms such as yeast, Drosophila, Arabidopsis, and mice.
To date, most microarray studies have focused on fold-change in transcript abundance as the measure of interest, often employing a common reference sample as the standard against which experimental treatments are compared.
The experimental sample is competitively hybridized with a reference sample that consists of pooled RNAs from multiple treatments, and the fold-difference between two experimental samples is inferred by comparing the two ratio measurements. In many aspects microarrays resemble miniature agricultural plots (Kerr & Churchill 2001), and the data can be parsed with linear regression and mixed model analysis of variance. Replicate sample sizes of just five or six will generally be adequate to demonstrate that just a 1.5 fold-change in transcript level of a particular gene is statistically significant, while twice that number may be adequate to study differences as small as 1.2-fold. By contrasting expression relative to the sample mean, reference samples that provide no biological information can be avoided, so these quantitative microarray approaches work well with as few as twice as many replicates as the simplest duplicate experiments.

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Tuesday, 29 December 2015

Studying Gene Expression

Studying Gene Expression:

Knowing the transcriptional activity of a gene can give valuable insight to the function of the protein it encodes and to the role it plays in an organism. Gene activity in the same individual can vary from tissue to tissue, between different developmental stages, or even from morning to night time. Gene activity is influenced by the activity of other genes and the proteins they encode. Gene expression can change in response to outside factors, such as the environment or exposure of the organism to chemical substances, competitors, or pathogens.

The classical approach to measuring the activity of a gene has been to isolate messenger RNA (mRNA), design nucleic acid molecules complementary to the gene of interest, and use those to estimate the amount of mRNA of the gene of interest present at a given time in the organism. Traditionally, this has been done for one gene at a time.

Using extremely small capillaries to apply short pieces of DNA, each uniquely representing one gene. Up to 25,000 genes can be represented on a single conventional 1.5 cm x 5 cm slide. Using these microscopic arrays of DNA spots, researchers can assess the relative amount of mRNA in a sample of all 25,000 represented genes (called the target spots) in the same time that it used to take to analyse the activity of a single gene.

Such technological advances have revolutionized the way molecular bioscience is done and have sped up the rate of new discoveries. However, they have also led to the rapid acquisition of huge amounts of data that require the use of biostatistics for analysis and validation of the collected data. In practice, gene activity is assessed, by labelling mRNA that was extracted from an organism, with fluorescent dyes. The labelled mRNA, known as the “probe” is applied to the glass slide and allowed to bind to its complementary spot on the array. This process is called hybridization. Subsequently, the unbound mRNA is washed off the slide. The slide is scanned and the amount of fluorescently labelled mRNA bound to each spot is proportional to the activity of the gene it represents.

In most cases, software analysis is then used to determine how much of a signal is due to biologically relevant processes and how much is due to technical “noise”.

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Monday, 2 November 2015

Introduction to Microarray Technology

Introduction to Microarray Technology

Molecular Biology research evolves through the development of the technologies used for carrying them out. It is not possible to research on a large number of genes using traditional methods. Micro array is one such technology which enables the researchers to investigate and address issues which were once thought to be non-traceable. One can analyse the expression of many genes in a single reaction quickly and in an efficient manner. Micro-array technology has empowered the scientific community to understand the fundamental aspects underlining the growth and development of life as well as to explore the genetic causes of anomalies occurring in the functioning of the human body.

The study of gene expression profiling of cells and tissue has become a major tool for discovery in medicine. Microarray experiments allow description of genome-wide expression changes in health and disease. The results of such experiments are expected to change the methods employed in the diagnosis and prognosis of diseases. The design, analysis, and interpretation of microarray experiments require specialized knowledge that is not part of the standard curriculum of our discipline.

Whole genome sequencing projects of many species, including humans, have provided information that allows researchers to distinguish every gene in the organism. The development of microarray technology has made it possible to survey the gene expression activity of thousands of genes at the same time by using short pieces of DNA, each uniquely representing one gene, and spotting them to a solid support, such as a microscope glass slide.

“Microarray Technology” describes a set of screening tools used to study the research fields which fall under the broad term “Genomics”. These fields of research examine, in almost their entirety, a form of the genetic material or its derivatives of an organism.

History of Microarray:

The first published article to specifically use “microarrays” was Schena et al (1989) but the way in which a DNA microarray works has stemmed from the principles developed in Southern blotting techniques (Southern, 1975). These techniques use labelled nucleic acid molecules to interrogate nucleic acids attached to a solid medium via adenine-thymine and guanine-cytosine base hybridisation (Watson and Crick, 1953). For the past few years, the primary application of microarrays has been in the identification of sets of genes that respond in an extreme manner to some treatment, or that differentiate two or more tissues.

At Stanford, Dr Mark Schena initiated a new field of science - microarray technology as the first author on the Stanford team publication in the journal Science that proving that complementary DNA molecules can be immobilized on glass and used to measure gene expression in Arabidopsis thaliana.

Schena is considered the foremost authority on microarray technology. Schena was proclaimed the "Father of Microarrays" in an article written by Lloyd Dunlap, contributing editor of Drug Discovery News, in an account of Schena's pioneering work to decipher Parkinson's disease.

The methodology of microarrays was first introduced and illustrated in antibody microarrays, also referred to as antibody matrix by Tse Wen Chang in 1983 in a scientific publication. The "gene chip" industry started to grow significantly after the 1995 Science Paper by the Ron Davis and Pat Brown labs at Stanford University. With the establishment of companies, such as Affymetrix, Agilent, Applied Microarrays, Arrayit, Illumina, and others, the technology of DNA microarrays has become the most sophisticated and the most widely used, while the use of protein and peptide microarrays are expanding.

Microarrays have quickly been established as an essential tool for gene expression profiling in relation to physiology and development. When used in conjunction with classical genetic approaches and the emerging power of bioinformatics.

Definition:

Microarray is a set of DNA sequences representing the entire set of genes of an organism, arranged in a grid pattern for use in genetic testing. It is a developing technology used to study the expression of many genes at once by placing thousands of gene sequences in known locations on a glass slide called a gene chip.

It is a 2D array on a solid substrate that is usually a glass slide or silicon thin-film cell that assays large amounts of biological material using high-throughput screening miniaturized, multiplexed and parallel processing and detection methods and hence sometimes termed as a multiplex lab-on-a-chip.

Principle behind Microarray:

The principle behind microarrays is hybridization between two DNA strands, the property of complementary nucleic acid sequences to specifically pair with each other by forming hydrogen bonds between complementary nucleotide base pairs. A high number of complementary base pairs in a nucleotide sequence means tighter non-covalent bonding between the two strands. After washing off non-specific bonding sequences, only strongly paired strands will remain hybridized. Fluorescently labeled target sequences that bind to a probe sequence generate a signal that depends on the hybridization conditions (such as temperature), and washing after hybridization. Total strength of the signal, from a spot (feature), depends upon the amount of target sample binding to the probes present on that spot. Microarrays use relative quantitation in which the intensity of a feature is compared to the intensity of the same feature under a different condition, and the identity of the feature is known by its position.

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Pages

Sunday, 31 January 2016

How Microarray works ? Here is the methodology

Tuesday, 29 December 2015

Studying Gene Expression

Monday, 2 November 2015

Introduction to Microarray Technology