Introduction to DNA Microarray

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Introduction to DNA Microarray

• Neha Jain
• Lecturer
• School of Biotechnology
• Devi Ahilya University, Indore

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Genes can be regulated at many levels
Usually, when we speak of gene regulation, we are
referring to transcriptional regulation. The
complete set of all genes being transcribed are
referred to as the transcriptome.

• .

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• In the last dozen years, it has become possible
  to look at the entire transcriptome in a single
  experiment!
• High Throughput - Simultaneous analysis of all
  genes in a genome.
• The high throughput analysis of all expressed
  genes is termed as Transcriptome analysis. The
  expression analysis of the full set of RNA
  molecules produced by a cell under a given set of
  conditions.
• Transcriptome analysis facilitates our
  understand-ing of how sets of genes work together
  to form metabolic, regulatory, and signalling
  pathways within the cell.

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Genomic analysis of gene expression

• Methods capable of giving a snapshot of RNA
  expression of all genes
• Can be used as diagnostic profile
• Example cancer diagnosis
• Can show how RNA levels change during
  development, after exposure to stimulus, during
  cell cycle, etc.
• Provides large amounts of data
• Can help us start to understand how whole systems
  function

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Types of Gene Expression Analysis

• While there are a number of variations, there are
  essentially two basic ways of doing expressed
  gene analysisusing sequencing-based methods and
  microarrays.
• These have largely replaced older methods such as
  subtractive hybridization and differential
  display.
• Sequencing-based methods are very powerful but
  have typically been prohibitively expensive.
• However, with recent advances in low-cost,
  high-throughput next generation sequencing, these
  methodsreferred to as RNA-seqare becoming
  more common and may soon be dominant.

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RNA-seq

• Although details of the methods vary, the concept
  behind RNA-seq is simple
• Isolate all mRNA
• Convert to cDNA using reverse transcriptase
• Sequence the cDNA
• Map sequences to the genome
• The more times a given sequence is detected, the
  more abundantly transcribed it is.
• If enough sequences are generated, a
  comprehensive and quantitative view of the entire
  transcriptome of an organism or tissue can be
  obtained.

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DNA microarrays

• .

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DNA microarrays

• Microarrays may eventually be eclipsed by
  sequence-based methods, but meanwhile have become
  incredibly popular since their inception in 1995
  (Schena et al. (1995) Science 270467-70).
• DNA microarrays rely on the hybridization
  properties of nucleic acids to monitor DNA or RNA
  abundance on a genomic scale in different types
  of cells
• In other words, the principle behind microarray
  is the ability of complementary strands of DNA
  (or DNA and RNA) to hybridize to one another in
  solution with high specificity.

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Nucleic acid hybridization
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Introduction

• A microarray (or gene chip) is a slide attached
  with a high-density array of immobilized DNA
  oligomers (sometimes cDNAs) representing the
  entire genome of the species under study.
• Each DNA is attached to solid support
• Glass, plastic, or nylon
• Oligomer is spotted on the slide and serves as a
  probe for binding to a unique, complementary
  cDNA.
• The cDNA population, labelled with fluorescent
  dyes or radioisotopes, is allowed to hybridize
  with the oligo probes on the chip.
• The amount of fluorescent or radiolabels at each
  spot position reflects the amount of
  corresponding mRNA in the cell.
• Sets of genes involved in the same regulatory or
  metabolic pathways can potentially be identified.

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The Process
Building the chip
MASSIVE PCR
PCR PURIFICATION AND PREPARATION
PREPARING SLIDES
PRINTING
DNA/RNA preparation
Hybing the chip
POST PROCESSING
CELL CULTURE AND HARVEST
ARRAY HYBRIDIZATION
RNA ISOLATION
cDNA PRODUCTION
DATA ANALYSIS
PROBE LABELING
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• For each spot on the microarray, red and green
  fluorescence signals are recorded.
• The two fluorescence images from the scanner are
  then overlaid to create a composite image, which
  indicates the relative expression levels of each
  gene.
• Thus, the measurement from the composite image
  reflects the ratio of the two color intensities.
• If a gene is expressed at a higher level in the
  experimental condition (red) than in the control
  (green), the spot displays a reddish color. I
• f the gene is expressed at a lower level than the
  control, the spot appears greenish.
• Unchanged gene expression, having equal amount
  of green and red fluorescence, results in a
  yellow spot.
• The colored image is stored as a computer file
  (in TIFF format) for further processing.

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Microarray life cyle
Biological Question
Data Analysis
Sample Preparation
Microarray Detection
Taken from Schena Davis
Microarray Reaction
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Steps of Microarray Experiment

• A typical DNA microarray experiment involves a
  multistep procedure
• Fabrication of microarrays by fixing properly
  designed oligonucleotides representing specific
  genes
• Hybridization of cDNA populations onto the
  microarray Scanning hybridization signals and
  image analysis
• Transformation and normalization of data
• Analyzing data to identify differentially
  expressed genes as well as sets of genes that are
  co regulated

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Some Important Points about Microarray

• DNA microarrays are generated by fixing
  oligonucleotides onto a solid
• support such as a glass slide using a
  robotic device
• The probes should be specific enough to minimize
  cross-hybridization
• with non-specific genes.
• This requires BLAST searches against genome
  databases to find
• sequence regions with least sequence
  similarity with non target
• genes.
• The probes should be sensitive and devoid of
  low-complexity regions
• (a string of identical Nucleotides)
• The oligonucleotide sequences should not form
  stable internal
• secondary structures.
• Number of programs have been developed for
  designing probe
• sequences for microarrays spotting.
• OligoWiz
• OligoArray

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Image Processing

• Image processing is to locate and quantitate
  hybridization spots
• and to separate true hybridization signals from
  background noise.
• The background noise and artifacts produced in
  this step include nonspecific hybridization,
  unevenness of the slide surface, and the presence
  of contaminants such as dust on the surface of
  the slide.
• Computer programs are used to correctly locate
  the boundaries of the spots and measure the
  intensities of the spot images after subtracting
  the background pixels.
• After subtracting the background noise, the
  array signals are converted into numbers and
  reported as ratios between Cy5 and Cy3 for each
  spot.

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ArrayDB(http//genome.nhgri.nih.gov/arraydb/) Sc
anAlyze(http//rana.lbl.gov/EisenSoftware.htm) T
IGR Spotfinder (http//www.tigr.org/softlab/) are
Windows program for microarray image processing
using the TIFF image format.
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Data Transformation and Normalization

• Following image processing, the digitized gene
  expression
• data need to be further processed before
  differentially
• expressed genes can be identified.
• This processing is referred to as data
  normalization and is
• designed to correct bias owing to variations in
  microarray
• data collection rather than intrinsic biological
  differences.
• When the raw fluorescence intensity Cy5 is
  plotted against
• Cy3, most of the data are clustered near the
  bottom left of
• the plot, showing a non-normal distribution of
  the raw data.
• one way to improve the data discrimination is to
  transform
• Raw Cy5 and Cy3 values by taking the logarithm to
  the base of 2.

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• This has the major advantage that it treats
  differential up-regulation and down-regulation
  equally, and also has a continuous mapping space.
  
• For example, if the expression ratio is 1, then
  log2(1) equals 0 represents no change in
  expression. If the expression ratio is 4, then
  log2 (4) equals 2 and for expression ratio of
  log2(1/4) equals -2.
• Thus, in this transformation the mapping space
  is continuous and up-regulation and
  down-regulation are comparable.
• Normalization -When one compares the expression
  levels of genes that should not change in the two
  conditions (say, housekeeping genes), what one
  quite often finds is that an average expression
  ratio of such genes deviates from 1. This may be
  due to various reasons, for example, variation
  caused by differential labelling efficiency of
  the two fluorescent dyes or different amounts of
  starting mRNA material in the two samples. Thus,
  in the case of microarray experiments, as for any
  large-scale experiments, there are many sources
  of systematic variation that affect measurements
  of gene expression levels.
• Normalization is a term that is used to describe
  the process of eliminating such variations to
  allow appropriate comparison of data obtained
  from the two samples.

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• A method to normalize the data is by using Lowess
  (locally weighted scatter plot smoother)regression
  method.
• The following two software programs that are
  freely available are specialized in image
  analysis and data normalization.
• Arrayplot
• SNOMAD

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Statistical Analysis to Identify Differentially
Expressed Genes

• One of the reasons to carry out a microarray
  experiment is to monitor the expression level of
  genes at a genome scale. The processed data,
  after the normalization procedure, can then be
  represented in the form of a matrix, often called
  gene expression matrix Each row in the matrix
  corresponds to a particular gene and each column
  could either correspond to an experimental
  condition or a specific time point at which
  expression of the genes has been measured. Once
  we have obtained the gene expression matrix
  additional levels of annotation can be added
  either to the gene or to the sample. For example,
  the function of the genes can be provided, or the
  additional details on the biology of the sample
  may be provided, such as ?disease state'or
  ?normal state'.
• Depending on whether the annotation is used or
  not, analysis of gene expression data can be
  classified into two different types,
• Supervised learning, we do use the annotation
  of either the gene or the sample, and create
  clusters of genes or samples in order to identify
  patterns that are characteristic for the cluster.
  
• Unsupervised learning, the expression data is
  analysed to identify patterns that can group
  genes or samples into clusters without the use of
  any form of annotation. For example, genes with
  similar expression profi les can be clustered
  together without the use of any annotation.

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Statistical Analysis to Identify Differentially
Expressed Genes

• To separate genes that are differentially
  expressed, a normalization cut off of twofold as
  a criterion
• . But a data point above or below the cut off
  line could simply be there by chance or because
  of error.
• The only way to ensure that a gene that appears
  to be differentially expressed is truly
  differentially expressed is to perform multiple
  replicate experiments and to perform statistical
  testing.
• The repeat experiments provide replicate data
  points that offer information about the
  variability of the expression data at a
  particular condition.
• The main hindrance to obtaining multiple
  replicate datasets is often the cost microarray
  experiments are extremely expensive for regular
  research laboratories.
• To do the statistical analysis two test are used
  ANOVA (analysis of variance) and T-Test
• Softwares
• MA-ANOVA
• Cyber-T

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Microarray Data Clustering

• One of the goals of microarray data analysis is
  to cluster genes or samples with similar
  expression profiles together, to make meaningful
  biological inference about the set of genes or
  samples.
• The similar expression patterns are often a
  result of the fact that the genes involved are in
  the same metabolic pathway and have similar
  functions.
• The genetic basis of the co regulation could be
  the result of common promoters and regulatory
  regions.

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• Clustering is one of the unsupervised approaches
  to classify data into groups of genes or samples
  with similar patterns that are characteristic to
  the group.
• Clustering methods can be
• Hierarchical (grouping objects into clusters and
  specifying relationships among objects in a
  cluster, resembling a phylogenetic tree)This can
  be of 2 types
• Agglomerative (starting with the assumption
  that each object is a cluster and grouping
  similar objects into bigger clusters)
• Divisive (starting from grouping all objects
  into one cluster and subsequently breaking the
  big cluster into smaller clusters with similar
  properties)
• Non-hierarchical (grouping into clusters without
  specifying relationships between objects in a
  cluster).Non-hierarchical clustering requires
  predetermination of the number of clusters.
  Non-hierarchical clustering then groups existing
  objects into these predefined
• clusters rather than organizing them into a
  hierarchical structure.

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• Experimental Design for Microarrays
• There are a number of important experimental
  design considerations for a microarray
  experiment
• Technical vs biological replicates
• Amplification of RNA
• Dye swaps

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• Experimental Design for Microarrays
• Technical vs biological replicates
• Technical replicates are repeat hybridizations
  using the same RNA isolate
• Biological replicates use RNA isolated from
  separate experiments/experimental organisms
• Although technical replicates can be useful for
  reducing variation due to hybridization, imaging,
  etc., biological replicates are necessary for a
  properly controlled experiment

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• Experimental Design for Microarrays
• Amplification of RNA
• Linear amplification methods can be used to
  increase the amount of RNA so that microarray
  experiments can be performed using very small
  numbers of cells. Its not clear to what degree
  this affects results, especially with respect to
  rare transcripts, but seems to be generally OK if
  done correctly

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Experimental Design for Microarrays Dye
swaps When using 2-color arrays, its important
to hybridize replicates using a dye-swap strategy
in which the colors (labels) are reversed between
the two replicates. This is because there can be
biases in hybridization intensity due to which
dye is used (even when the sequence is the same).
Normally 2 dyes Cy5(Red Florescence for
infected/experimental samples) and Cy3 (Green
florescence for Samples)
S1
S2
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Thanks