Microarray Technique

Some background

M. Nath

Outline

Introduction

Spotting Array Technique

GeneChip Technique

Data analysis

Applications

Conclusion

Now

Blind Guess?

Functional Pathway

MicroarrayTechnique

Principle

Comprehensive functional analysis of genome

Simultaneous analysis of patterns of gene expression

Genome > Transcriptome > Proteome

Types of Microarray

cDNA Array (Brown et. al., 1995)

Genomic DNA Array (DeRisi et. al., 1997)

Oligonucleotide Array (Morton et. al., 1998)

Spotted Array Technology

LibrarySpotted Array Technology

An array of slides is printedSlides can be glass or nylon

Printing SlidesSpotted Array Technology

Spotted Array Technology

Hybridisation of Slides

Slide developer

Spotted Array Technology

Up to 48 slides are developed under uniform conditions

ScanningConfocal laser scanner is used

Two different lasers to read Red and Green dye intensities

Imaging software reads Red & Green intensity for each dot applied

A Graphic image is savedLaser Scanner

Spotted Array Technology

Results

Green = Active in Sample 1Red = Active in Sample 2Yellow = Active in both samplesBlack = Active in neither

Spotted Array Technology

GeneChip TechnologyAffymetrix chip

Oligos of 25 nt long

40 oligos for detection of each gene

11-20 oligos as Perfect Match (PM)

11-20 oligos as Mismatch (MM) at position 13

GeneChip Technology

GeneChip Technology

Spotted Array Technology

10000No. of genes / array

1Probes pair per gene

10-20 µg total RNAStarting material

RoutineFeatures

Spotted Array Technology

Laborious

Inexpensive

Moderate specificity

Moderate representation

Low Density

Cannot detect polymorphism

GeneChip Technology

4000012000No. of genes / array

420Probes pair per gene

93% identity

70-80% identity

Discrimination of related genes

1:1061:105Detection specificity

2 ng total RNA

5 µg total RNAStarting material

LimitRoutineFeatures

GeneChip Technology

Easy

Expensive

High specificity

High representation

High density

Can detect polymorphism

Data Analysis

ScalingLinearity

Data Analysis

Gene intensity Chip 1

Gen

e in

tens

ity C

hip

2

Scaling

Linear and non-linear models

Constitutively and constantly expressed Maintenance gene

More genes on chip

Data Analysis

Gene intensity Chip 1G

ene

inte

nsity

Chi

p 2

Outlier

Two chips may differ in expression for same gene

If one replicate deviates several standard deviation from mean, remove it

Data Analysis

Data Analysis

Absolute measurements

AvgDiffΣ ( PMn – MMn ) / N

Weighted AvgDiffΣ ( PMn – MMn ) φn / N

Fold Change

Log2 of ratio of intensities after being corrected for background

E.g. Log2 (Sample / Control) = Log2 (Red / Green)

=1 : unchanged; >1 : upregulated; <1 : downregulated

Affymetrix chip (AffyFold)(Sample - Control) / Min (Sample, Control)

Data Analysis

Significance Test

Test of significancet-test with unequal varianceANOVA and F testREML

Non-parametric testsWilcoxon testMann-Whitney rank sum test

Correction for multiple testingBonferroni correction

Data Analysis

Cluster AnalysisSingle array not suitable

Functional analysisCo-regulationNew gene discovery

Samples collected temporally, spatially …

Multiple array & Cluster analysis

Clustering of similarly behaving genes

Genes with similar functions generally cluster together

Data Analysis

Cluster Analysis

Cluster analysisHierarchical clusteringK-means clusteringSelf Organising MapsDistance measures

Data Analysis

Beyond Clustering

Discovery of regulatory elements in promoter regionIdentifying regulatory networks

Time series approachSteady-state approachNeural network technique

Selection of genesGene findingSelection of regions within the genesSelection of PCR primersSelection of unique oligomer probes

Data Analysis

Software Package

Affymetrix Data Mining ToolAffymetrix NetAffxBiomax Gene Expression Analysis SuiteGeneData ExpressionistInformax XpressionInvitrogen Corp. ResGen PathwaysRosetta Resolver Gene Expression SystemSilicon Genetics GeneSpringSpotfire

Data Analysis

Applications

Analysis of patterns of gene expressionFunctional relationship between genes

Expression in coregulatory gene group

Monitoring changes in genomic DNACellular pathways affected by mutation

Changes in expression profiles of mutants

Applications

Simultaneous detection of many genes

Gene discovery

Pathway analysis

Molecular basis of disease progression

Applications

Molecular signatures of pathogens

Comparative genomic studies of pathogensVirulence difference

Pathogen genetics and manifestationLife cycleReplication, translational control

Applications

Host-parasite interaction

Pathogen establishmentHost cell recognitionHost cell responseParasite response to host immune response

Applications

Constraints

Complex system of eukaryotes & multi-cellular organisms

Transcriptome analysis

Developing technology

Many stages

Design of experiment

Constraints

Array quality

Highly variable data

Analysis of data

Published experiments

Cost

From Here to Tomorrow

Recent & Powerful

More improvementProtocolHardwareExperimental designComputational technique

Integrate with other data

Reproducible, fast, sensitive & economic