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Microarray
Principles & Applications
Overview
 Technology - Differences in platforms
 Utility & Applications - What will a microarray
do for you?
 The Future of Microarrays – Where are they
heading…
Assays Of Biological Variation
Genotype Analysis
SNP Analysis
Mutation Screening
Proteomics
Gene Expression Analysis
The Good Ol’ Days
 Sequencing Gels
 Northerns
 Westerns
One Platform = Multiple Applications
Genotyping
Pharmacogenetics
Diagnostics
Multiplex-ELISA
Diagnostics
Tox Studies
Expression db
Microarrays
Microarray Development
 Relatively young technology
 Widely adopted
 Mainly used in gene discovery
Evolution & Industrialization
 1994- First cDNAs are
developed at Stanford.
 1995- Quantitative Monitoring
of Gene Expression Patterns
with a Complementary DNA
Microarray- Schena et. al.
 1996- Commercialization of
arrays
 1996-Accessing Genetic
Information with High Density
DNA Arrays-Chee et. al.
 1997-Genome-wide
Expression Monitoring in S.
cerevisiae-Wodicka et. al.
Technology
 Definition
 Microarray- A substrate with bound capture probes
 Capture probe
 An oligonucleotide/DNA with gene/polymorphism of
interest
 Fabrication
 Photolithography-Affymetrix
 Printing-Incyte, Genometrix
 Target Generation
 One color
 Two color
 Analysis
 “Scanning” of array
 Amount of hybridized target is assessed.
Background of Microarrays
 Basic Types of Fabrication
 Photolithographic
» Affymetrix
» Oligonucleotide capture probe
 Mechanical deposition
» Incyte, Molecular Dynamics, Genometrix
» cDNA or oligonucleotide capture probes
» Ink jets, capillaries, tips
 Target Preparation
 RT of RNA to cDNA
 RNA amplification
Array Advantages
 Efficient use of reagents
 Small volume deposition
 Minimal wasted materials
 High-throughput capability
 Assess many genes simultaneously
 Examine many samples quickly
 Can be automated
Applications
Clinical
PreClinical
Leads
Discovery
 Target
Discovery
 Target
Validation
High Density
Medium Density
 Screening
 Validation
 Optimization
 Toxicology
 Optimization
 Genotyping
 ADE Screens
Applications in Drug Development
Leads
10000
Sample Throughput
Clinical
Pre-Clinical
1000
Discovery
10
10
1000
Genes Interrogated
10000
Array Technology
 Array Design & Fabrication
 Determine genes to be analyzed
 Design DNA reagents to be arrayed
 Use automated arraying instrument
Affymetrix Fabrication Process
cDNA Microarray Fabrication
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Up to 10,000 elements per array
Elements 500 to 5000 bases in length
Proprietary surface chemistry
Reduced background
Cleanroom fabrication facility
 Scalable operation
Oligonucleotide Microarray
 Immobilized gene specific oligo probes
ACUGCUAGGUUAGCUAGUCUGGACAUUAGCCAUGCGGAUGCCAUGCCGCUU
GACCTGTAATCGGTACGCCTA
Genometrix Array Printer
ST ORAG E
VESSEL
ARRAY
GL A S S
STANDARD 96/384 W ELL
• Proprietary Delivery Mechanism
• Fully Automated
• Standard Format Compatible
VistaArray Microarrays
 Medium density-up to 250 elements
 Preselect genes based on high-density arrays
 Can be easily customized
 Cost effective
High-throughput capability
Hundreds of samples
Automatable
Probe Labeling
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Optimized one-step fluorescent labeling protocol
No amplification of RNA
Starting material 200 ng of polyA mRNA
Built in controls for sensitivity, ratios and RT
quality
Probe Labeling
Array Technology
 Sample Preparation
 Isolate cell, tissue, or DNA samples
 Generate labeled DNA or cDNA materials
 Sample Hybridization
 Hybridize labeled sample to array
Microarray Hybridization
 Two probe populations competitively hybridized
 1/100,000 sensitivity across most genes in 200 ng
mRNA
 Routinely detects two-fold changes in expression
Array Technology
 Sample Analysis
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CCD/ laser imaging
Rapid analysis
Highly sensitive
Fully automated
Image Analysis
 Auto-gridding
 Edge detection
 Noise filtering
 Background subtraction
 Auto integration into
database
Element regions
Background
Adjusted Elements
Applications…
 Gene Discovery Assigning function to sequence
 Discovery of disease genes and drug targets
 Target validation
 Genotyping
 Patient stratification (pharmacogenomics)
 Adverse drug effects (ADE)
 Microbial ID
The List Continues To Grow….
Profiling Gene Expression
Kidney
Tumor
Lung
Tumor
Liver
Tumor
Normal vs. Normal
Normal vs. Tumor
Lung Tumor: Up-Regulated
Lung Tumor: Down-Regulated
Lung Tumor: Up-Regulated
Signal transduction
Proteases/Inhibitors
Cytoskeleton
Kinases
Lung Tumor: Up-Regulated
Cyclin
Signal transduction
B1
Cytoskeleton
Cyclin-dependent
kinase
Tumor expressionProteases/Inhibitors
related protein
Kinases
Lung Tumor: Down-Regulated
Signal transduction
Proteases/Inhibitors
Cytoskeleton
Kinases
Genes Common to All 3 Tumors
Up-regulated
Down-regulated
Microarrays and Lead Validation
and Optimization
 May alleviate current bottlenecks
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High-throughput
Biological relevance (e.g. primary cell lines)
Validate more than one target per compound
Easy and quick assay to develop (no cell engineering)
 Generate toxicity data on compound
 Database correlation to compound structure
 Determine mode(s) of compound/target
interaction.
 Broad functionality to a compound (e.g. ion channel
mod, cell cycle regulator, membrane receptor)
Why would you screen more
compounds?
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Discovery
Manufacturability
Lower toxicity
Better mode of application
Improved efficacy
Optimization with Arrays
Competition
Lead
Optimized
15
Target
5
0
Expression Profile
Differential Expression
10
-5
-10
Gene Index
Toxin
Best Drug
Optimization with Arrays
Competition
Lead
Optimized
15
Target
5
0
Expression Profile
Differential Expression
10
-5
-10
Gene Index
Toxin
Best Drug
Optimization with Arrays
Competition
Lead
Optimized
15
Target
5
0
Expression Profile
Differential Expression
10
-5
-10
Gene Index
Toxin
Best Drug
Optimization with Arrays
Competition
Lead
Optimized
Toxin
15
Target
5
0
Expression Profile
Differential Expression
10
-5
-10
Gene Index
From Braxton et al., Curr. Op. Biotech. 1998 (9)
Best Drug
Classical Microarray Experiments
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Normal vs Disease
 Example: Analysis of GE patterns in cancer
- DeRisi et. Al (1996)
- Pattern of gene expression-networks
- Novel gene association/discovery
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Molecular Classification
 Example:Comparison of Breast Tumors
- Perou et. Al (2000)
- Samples classified into subtypes
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Genome-Wide Analysis
 Example: Genome-wide expression in S. cerevisiae
- Wodicka et. Al (1997)
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Cross-species comparisons
Arrays for SNP and Mutation Analysis
 Analyze many samples on hypothesis-driven
array configurations to derive genetic information
critical to pharmacogenetic evaluation of drug
response or disease risk assessment.
 Target analytes are derived by multiplex PCR.
 All steps from sample preparation to image
analysis can be automated.
DNA
Genotyping: SNP Microarray
 Immobilized allele specific oligo probes
 Hybridize with labeled PCR product
 Assay multiple SNPs on a single array
TTAGCTAGTCTGGACATTAGCCATGCGGAT
GACCTGTAATCG
TTAGCTAGTCTGGACATTAGCCATGCGGAT
GACCTATAATCG
Genotyping Validation Study
 NAT2 polymorphisms
 N-acetyltransferase enzyme
 Phase II metabolic pathway for converting
hydrophobic compounds into water-soluble
metabolites
 NAT2 polymorphisms associated with differences
in response to drug therapy
 Concordance
 ~740 colon cancer patient samples
 NAT2 genotyping by PCR/RFLP
NAT2 Polymorphisms
191
282
341
481
590
803 857
G/A
C/T
T/C
C/T
G/A
A/G G/A
FDA Arizona Cancer Center Validation Trial
NAT2/COMT 8-plex (genomic)
FDA/AZCC Concordance Study
Gene
# Concordant
with RFLP
% Concordance
NAT2 481
685/692
99.0%
NAT2 590
676/682
99.1%
NAT2 857
660/660
100%
sCOMT
16/16
100%
Sequencing of discordant samples
Gene
Genometrix
Accurate Call
Overall % Accuracy
NAT2 481
6/7
99.86%
NAT2 590
5/6
99.85%
Automated Element Scoring
Allele Scoring GUI
Automation of Allele Discrimination
12000
Homozygous
Allele B
Heterozygous
Allele B
8000
4000
Homozygous
allele A
0
0
2000
4000
6000
Allele A
8000
10000
Each point is one sample and
represents signal from both
alleles for one SNP.
Allele Scoring – Sample Output
Nationality
Sex
Utah (father)
Male
Utah (mother)
Female
Utah (child)
Male
Utah (child)
Male
Utah (child)
Male
Utah (child)
Female
Utah (child)
Male
Utah (child)
Male
Utah (child)
Male
Utah (child)
Male
Utah (pat G)
Male
Utah (pat G)
Female
Utah (mat G)
Male
Utah (mat G)
Female
Utah (child)
Female
Caucasian
Female
Dutch
Female
German
Male
German/Danish
Female
SNP1
A
G
SNP9
A
G
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SNP14
A
G
SNP16
A
G
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SNPB
A
G
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Protein Based Microarrays
 Platform may support micro-ELISA format or large
scale proteomics projects.
 Protein levels may be correlated with mRNA
expression profiles.
 ELISA reagents already developed and approved
in the diagnostic field.
Protein
Proteomics
 Microarrays
 Mendoza et al (1998)
» Sandwich assay for 7 antigens
 High-density arrays
 Holt et al (2000)
» Screened 27K human fetal brain proteins on
membrane
 McBeath and Schreiber (2000)
» Arrayed 0ver 10,000 proteins and screened for
small molecule binding
 Haab et al (2001)
» Competitive hybridization of proteins on
antibody arrays
High- throughput proteomic analysis
High-density Antibody array
 Six to twelve replicates of 114 different antibodies spotted
 Protein mixes at different concentrations labeled and
detected
Haab et al (2001)
Actual vs observed ratios
Antigen concentration (ng/ml)
Cy5/Cy3 fluorescence ratio calculated at each antigen concentration and
plotted against actual ratios
Haab et al (2001)
Applications of Protein arrays
Applications
 Screening for Small molecule
targets
Post-translational
modifications
 Protein-protein
interactions
Protein-DNA
interactions
 Enzyme assays
 Epitope mapping
Cytokine Specific Microarray ELISA
IL-1 
IL-6
IL-10
marker protein
cytokine
Detection system
BIOTINYLATED MAB
ANTIGEN
CAPTURE MAB
VEGF
MIX
Competing Technologies
 Bead-based approaches
 Illumina-fiber optics
 Luminex-flow cytometry
 Mass spectrometry
 Ciphergen-protein chips
 Sequenom-SNP detection
 Gel-based
 Sequencing
Conclusion
 Technology is evolving rapidly.
 Blending of biology, automation, and
informatics.
 New applications are being pursued
 Beyond gene discovery into screening, validation,
clinical genotyping, etc.
 Microarrays are becoming more broadly
available and accepted.
 Protein Arrays
 Diagnostic Applications…
Analysis Tools
 How to analyze thousands of genes?
 Linear Plots
 Clustering
 Principal Components Analysis
Analysis Tools
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How to analyze thousands of genes?
 Linear Plots
 Clustering
 Principal Components Analysis
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How to handle error bars across array/sample
normalization?
How to analyze thousands of genes across a
distribution of time?
How to analyze thousands of genes across a
distribution of time and a distribution of samples?
How does a user visualize genetic networks?
Microarray Future
 Must go beyond describing differentially
expressed genes
Potential Visualization Tools for Time Series
•Regular and extended
clusters (combining
genes interrelated at the
same time)
•Causally related
genes (combining
genes interrelated at
different times)
Yuriy Fofanov
Victor Polinger
U. Of Nottingham
Microarray Future
 Must go beyond describing differentially
expressed genes
 Inexpensive, high-throughput, genome-wide
scan is the end game for research
applications
Microarray Future
 Must go beyond describing differentially
expressed genes
 Inexpensive, high-throughput, genome-wide
scan is the end game for research
applications
 Protein microarrays beginning to be used
 Fundamentally change experimental design
 Will enhance protein dB construction
Microarray Future
 Must go beyond describing differentially
expressed genes
 Inexpensive, high-throughput, genome-wide
scan is the end game for research
applications
 Protein microarrays being used
 Publications are now being focused on
biology rather than technology
Microarray Future
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Must go beyond describing differentially expressed
genes
Inexpensive, high-throughput, genome-wide scan is
the end game for research applications
Protein microarrays will be deployed within the next
year
Publications are now being focused on biology rather
than technology
SNP analysis
 Faster, cheaper, as accurate as sequencing
 Disease association studies
 Population surveys
Microarray Future
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Must go beyond describing differentially expressed
genes
Inexpensive, high-throughput, genome-wide scan is
the end game for research applications
Protein microarrays will be deployed within the next
year
Publications are now being focused on biology rather
than technology
SNP analysis-population surveys, SNP map
Chemicogenomics
 Dissection of pathways by compound application
 Fundamental change to lead validation
Microarray Future
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Must go beyond describing differentially expressed
genes
Inexpensive, high-throughput, genome-wide scan is
the end game for research applications
Protein microarrays will be deployed within the next
year
Publications are now being focused on biology rather
than technology
SNP analysis-population surveys, SNP map
Chemicogenomics
Diagnostics
 Tumor classification
 Patient stratification
 Intervention therapeutics
Microarray Future
 Must go beyond describing differentially
expressed genes
 Inexpensive, high-throughput, genome-wide
scan is the end game for research
applications
 Protein microarrays will be deployed within
the next year
 Publications are now being focused on
biology rather than technology
 SNP analysis-population surveys, SNP map
 Chemicogenomics
 Diagnostics
Industrialized Biology
 Rapid replacement of single-gene
experiments
 Human genome project ushered in
production line sequencing
 Biologists in industry-what
background is appropriate?