Microarray Principles & Applications

1. MicroarrayPrinciples & Applications

2. Overview • Technology - Differences in platforms • Utility & Applications - What will a microarray do for you? • The Future of Microarrays – Where are they heading…

3. Assays Of Biological Variation Genotype Analysis SNP Analysis Mutation Screening Proteomics Gene Expression Analysis

4. The Good Ol’ Days • Sequencing Gels • Northerns • Westerns

5. One Platform = Multiple Applications Genotyping Pharmacogenetics Diagnostics Multiplex-ELISA Diagnostics Tox Studies Expression db Microarrays

6. Microarray Development • Mainly used in gene discovery • Widely adopted • Relatively young technology

7. 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.

8. 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.

9. 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

10. 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

11. High Density Medium Density Applications Clinical PreClinical Leads • Genotyping • ADE Screens Discovery • Toxicology • Optimization • Screening • Validation • Optimization • Target Discovery • Target Validation

12. Applications in Drug Development Leads 10000 1000 10 Clinical Pre-Clinical Sample Throughput Discovery 10 1000 10000 Genes Interrogated

13. Array Technology • Array Design & Fabrication • Determine genes to be analyzed • Design DNA reagents to be arrayed • Use automated arraying instrument

14. Affymetrix Fabrication Process

15. 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

16. Oligonucleotide Microarray • Immobilized gene specific oligo probes ACUGCUAGGUUAGCUAGUCUGGACAUUAGCCAUGCGGAUGCCAUGCCGCUU GACCTGTAATCGGTACGCCTA

17. S T O R A G E V E S S E L G L A S S ARRAY S T A N D A R D 9 6 / 3 8 4 W E L L Genometrix Array Printer • Proprietary Delivery Mechanism • Fully Automated • Standard Format Compatible

18. 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

19. 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 andRT quality

20. Probe Labeling

21. Array Technology • Sample Preparation • Isolate cell, tissue, or DNA samples • Generate labeled DNA or cDNA materials • Sample Hybridization • Hybridize labeled sample to array

22. 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

23. Array Technology • Sample Analysis • CCD/ laser imaging • Rapid analysis • Highly sensitive • Fully automated

24. Image Analysis • Auto-gridding • Edge detection • Noise filtering • Background subtraction • Auto integration into database Background Adjusted Elements Element regions

25. 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….

26. Profiling Gene Expression Kidney Tumor Lung Tumor Liver Tumor

27. Normal vs. Normal

28. Normal vs. Tumor

29. Lung Tumor: Up-Regulated

30. Lung Tumor: Down-Regulated

31. Lung Tumor: Up-Regulated Signal transduction Cytoskeleton Proteases/Inhibitors Kinases

32. Lung Tumor: Up-Regulated Cyclin B1 Signal transduction Cytoskeleton Cyclin-dependent kinase Tumor expression- related protein Proteases/Inhibitors Kinases

33. Lung Tumor: Down-Regulated Cytoskeleton Signal transduction Kinases Proteases/Inhibitors

34. Genes Common to All 3 Tumors Up-regulated Down-regulated

35. 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)

36. Why would you screen more compounds? • Discovery • Manufacturability • Lower toxicity • Better mode of application • Improved efficacy

37. Optimization with Arrays Target Expression Profile

38. Optimization with Arrays Target Expression Profile

39. Optimization with Arrays Target Expression Profile

40. Optimization with Arrays Target Expression Profile From Braxton et al., Curr. Op. Biotech. 1998 (9)

41. 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

42. 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

43. TTAGCTAGTCTGGACATTAGCCATGCGGAT TTAGCTAGTCTGGACATTAGCCATGCGGAT GACCTGTAATCG GACCTATAATCG Genotyping: SNP Microarray • Immobilized allele specific oligo probes • Hybridize with labeled PCR product • Assay multiple SNPs on a single array

44. 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

45. NAT2 Polymorphisms 341 481 590 803 857 191 282 G/A C/T T/C C/T G/A A/G G/A FDA Arizona Cancer Center Validation Trial

46. NAT2/COMT 8-plex (genomic)

47. FDA/AZCC Concordance Study Sequencing of discordant samples

48. Automated Element Scoring

49. Allele Scoring GUI

50. Automation of Allele Discrimination Homozygous Allele B Heterozygous Homozygous allele A Each point is one sample and represents signal from both alleles for one SNP.