microarray principles & applications. overview technology - differences in platforms utility...

MicroarrayPrinciples & Applications

Overview

Technology - Differences in platforms Utility & Applications - What will a microarray

do for you? The Future of Microarrays – Where are they

heading…

Genotype AnalysisSNP AnalysisMutation Screening

Proteomics

Gene Expression Analysis

Assays Of Biological Variation

The Good Ol’ Days

Sequencing Gels Northerns Westerns

GenotypingPharmacogenetics

Diagnostics

Multiplex-ELISADiagnostics

Tox StudiesExpression db

Microarrays

One Platform = Multiple Applications

Mainly used in gene discovery

Microarray Development

Widely adopted

Relatively young technology

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

DiscoveryLeads

PreClinicalClinical

Target Discovery

Target Validation

Screening Validation Optimization

Toxicology Optimization

Genotyping ADE Screens

Medium Density

High Density

Applications in Drug Development

Sam

ple

Thr

ough

put

Genes Interrogated

10000

1000

10

10 1000 10000

Leads

Discovery

Pre-Clinical

Clinical

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

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

STORAGE

VESSEL

STANDARD 96/384 W ELL

G AL S S

ARRAY

• 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

• 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

CCD/ laser imaging Rapid analysis Highly sensitive Fully automated

Image Analysis

Element regionsBackground

Adjusted Elements

Auto-gridding Edge detection Noise filtering

Background subtraction Auto integration into

database

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

LungTumor

LiverTumor

KidneyTumor

Normal vs. Normal

Normal vs. Tumor

Lung Tumor: Up-Regulated

Lung Tumor: Down-Regulated


Signal transduction Cytoskeleton

Proteases/Inhibitors Kinases


Signal transduction Cytoskeleton


Cyclin B1

Cyclin-dependentkinase

Tumor expression-related protein

Lung Tumor: Down-RegulatedSignal transduction Cytoskeleton


Genes Common to All 3 Tumors

Up-regulated

Down-regulated

Microarrays and Lead Validation and Optimization

May alleviate current bottlenecks 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?

Discovery Manufacturability Lower toxicity Better mode of application Improved efficacy

Optimization with Arrays

-10

-5

0

5

10

15

Competition Lead Optimized Toxin Best Drug

Gene Index

Dif

fere

nti

al E

xp

ress

ion

Ex

pre

ss

ion

Pro

file

Target

Optimization with Arrays

-10

-5

0

5

10

15

Competition Lead Optimized Toxin Best Drug

Gene Index

Dif

fere

nti

al E

xp

ress

ion

Ex

pre

ss

ion

Pro

file

Target

From Braxton et al., Curr. Op. Biotech. 1998 (9)

Classical Microarray Experiments Normal vs Disease

Example: Analysis of GE patterns in cancer

- DeRisi et. Al (1996)- Pattern of gene expression-networks- Novel gene association/discovery

Molecular Classification Example:Comparison of Breast Tumors

- Perou et. Al (2000)

- Samples classified into subtypes Genome-Wide Analysis

Example: Genome-wide expression in S. cerevisiae

- Wodicka et. Al (1997) 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

341 481 590 803 857

T/C C/T G/A A/G G/A

282

C/T

191

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%

Gene

Genometrix Accurate Call Overall % Accuracy

NAT2 481 6/7 99.86%

NAT2 590 5/6 99.85%

Sequencing of discordant samples

Automated Element Scoring

Allele Scoring GUI

Automation of Allele Discrimination

Each point is one sample and represents signal from both alleles for one SNP.

Homozygous Allele B

Homozygous allele A

Heterozygous

0 2000 4000 6000 8000 10000

Allele A

0

4000

8000

12000

Alle

le B

Allele Scoring – Sample Output

A G A G A G A G A G

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

SNPBNationality Sex

SNP1 SNP9 SNP14 SNP16

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

Cy5/Cy3 fluorescence ratio calculated at each antigen concentration and plotted against actual ratios

Antigen concentration (ng/ml)

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

marker protein

cytokine

VEGFIL-10IL-6IL-1 MIX

BIOTINYLATED MAB

CAPTURE MAB

ANTIGEN

Detection system

Cytokine Specific Microarray ELISA

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

How to analyze thousands of genes? Linear Plots Clustering Principal Components Analysis

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 FofanovVictor Polinger

U. Of Nottingham

Microarray Future


Inexpensive, high-throughput, genome-wide scan is the end game for research applications

Microarray Future



Protein microarrays beginning to be used Fundamentally change experimental design Will enhance protein dB construction

Microarray Future



Protein microarrays being used Publications are now being focused on

biology rather than technology

Microarray Future



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





SNP analysis-population surveys, SNP map Chemicogenomics

Dissection of pathways by compound application Fundamental change to lead validation

Microarray Future





SNP analysis-population surveys, SNP map Chemicogenomics Diagnostics

Tumor classification Patient stratification Intervention therapeutics

Microarray Future





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?

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