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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 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 • • • • 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 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 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 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 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 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 SNP14 A G SNP16 A G SNPB A G 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 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 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 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 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 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?