Introduction to DNA Microarrays

1. Introduction to DNA Microarrays Michael F. Miles, M.D., Ph.D. Depts. of Pharmacology/Toxicology and Neurology and the Center for Study of Biological Complexity mfmiles@vcu.edu 225-4054

2. Biological Regulation: “You are what you express” • Levels of regulation • Methods of measurement • Concept of genomics

3. Regulation of Gene Expression • Transcriptional • Altered DNA binding protein complex abundance or function • Post-transcriptional • mRNA stability • mRNA processing (alternative splicing) • Translational • RNA trafficking • RNA binding proteins • Post-translational • Many forms!

4. Regulation of Gene Expression • Genes are expressed when they are transcribed into RNA • Amount of mRNA indicates gene activity • Some genes expressed in all tissues -- but are still regulated! • Some genes expressed selectively depending on tissue, disease, environment • Dynamic regulation of gene expression allows long term responses to environment

5.  Mesolimbic dopamine ? Other Reinforcement Intoxication Acute Drug Use Chronic Drug Use ?Synaptic Remodeling Persistent Gene Exp. “Addiction” Compulsive Drug Use Altered Signaling Gene Expression Tolerance Dependence Sensitization ?Synaptic Remodeling

6. Progress in Studies on Gene Regulation 1960 1970 1980 1990 2000 mRNA, tRNA discovered Nucleic acid hybridization, protein/RNA electrophoresis Molecular cloning; Southern, Northern & Western blots; 2-D gels Subtractive Hybridization, PCR, Differential Display, MALDI/TOF MS Genome Sequencing DNA/Protein Microarrays

7. Nucleic Acid Hybridization: How It Works

8. Primer on Nucleic Acid Hybridization • Hybridization rate depends on time,the concentration of nucleic acids, and the reassociation constant for the nucleic acid: C/Co = 1/(1+kCot)

9. High Density DNA Microarrays

10. A Bit of History ~1992-1996: Oligo arrays developed by Fodor, Stryer, Lockhart, others at Stanford/Affymetrix and Southern in Great Britain ~1994-1995: cDNA arrays usually attributed to Pat Brown and Dari Shalon at Stanford who first used a robot to print the arrays. In 1994, Shalon started Synteni which was bought by Incyte in 1998. However, in 1982 Augenlicht and Korbin proposed a DNA array (Cancer Research) and in 1984 they made a 4000 element array to interrogate human cancer cells. (Rejected by Science, Nature and the NIH)

11. Biological Networks

12. Types of Biological Networks

13. Gene Regulation Network

14. Examining Biological Networks: Experimental Design

15. Examining Biological Networks

16. -2 0 +2 relative change AvgDiff S-score Use of S-score in Hierarchical Clustering of Brain Regional Expression Patterns NAC NAC VTA PFC VTA PFC HIP HIP

17. Candidate Gene Studies Cycles of Expression Profiling Merge with Biological Databases Expression Profiling: A Non-biased, Genomic Approach to Resolving the Mechanisms of Addiction

18. Utility of Expression Profiling • Non-biased, genome-wide • Hypothesis generating • Gene hunting • Pattern identification: • Insight into gene function • Molecular classification • Phenotypic mechanisms

19. Comparisons (S-score, d-chip) De-noise Statistical Filtering (e.g. SAM) Filtered Gene Lists GE Database (SQL Server) Clustering Techniques Hybridization and Scanning Overlay Biological Databases (PubGen, GenMAPP, QTL, etc.) Experimental Design Provisional Gene “Patterns” Molecular Validation (RT-PCR, in situ, Western) Candidate Genes Behavioral Validation

20. Experimental Design with DNA Microarrays

21. High Density DNA Microarrays

22. Synthesis and Analysis of 2-color Spotted cDNA Arrays: “Brown Chips”

24. Comparative Hybridization with Spotted cDNA Microarrays

25. Synthesis of High Density Oligonucleotide Arrays by Photolithography/Photochemistry

26. GeneChip Features • Parallel analysis of >30K human, rat or mouse genes/EST clusters with 15-20 oligos (25 mer) per gene/EST • entire genome analysis (human, yeast, mouse) • 3-4 orders of magnitude dynamic range (1-10,000 copies/cell) • quantitative for changes >25% ?? • SNP analysis

27. Rtase/ Pol II Total RNA dsDNA Biotin-cRNA T7 pol AAAA-T7 TTTT-5’ TTTT-T7 5’ AAAA CTP-biotin Hybridization Oligo(dT)-T7 Scanning Steptavidin- phycoerythrin PM MM Oligonucleotide Array Analysis

28. Stepwise Analysis of Microarray Data • Low-level analysis -- image analysis, expression quantitation • Primary analysis -- is there a change in expression? • Secondary analysis -- what genes show correlated patterns of expression? (supervised vs. unsupervised) • Tertiary analysis -- is there a phenotypic “trace” for a given expression pattern?

29. Affymetrix Arrays: Image Analysis

30. Affymetrix Arrays: Image Analysis “.DAT” file “.CEL” file

31. Affymetrix Arrays: PM-MM Difference Calculation Probe pairs control for non-specific hybridization of oligonucleotides

32. Variability and Error in DNA Microarray Hybridizations

33. Variability in Ln(FC) Ln(FC1) (a) Ln(FC2)

35. Position Dependent Nearest Neighbor (PDNN) - 2003 • Zhang, Miles and Aldape, (2003) A model of molecular interactions on short oligogonucleotide microarrays: implications for probe design and data analysis. Nature Biotech. In Press.

37. Chip Normalization Procedures • Whole chip intensity • Assumes relatively few changes, uniform error/noise across chip and abundance classes • Spiked standards • Requires exquisite technical control, assumes uniform behavior • Internal Standards • Assumes no significant regulation • “Piece-wise” linear normalization

38. Normalization Confounds: Non-uniform Chip Behavior S-score Gene

39. Normalization Confounds: Non-linearity

40. http://www.ipam.ucla.edu/publications/fg2000/fgt_tspeed9.pdf Slide Normalization: Pieces and Pins “Lowess” normalization, Pin-specific Profiles After Print-tip Normalization See also: Schuchhardt, J. et al., NAR 28: e47 (2000)

41. Quality Assessment • Gene specific: R/G correlation, %BG, %spot • Array specific: normalization factor, % genes present, linearity, control/spike performance (e.g. 5’/3’ ratio, intensity) • Across arrays: linearity, correlation, background, normalization factors, noise

42. Statistical Analysis of Microarrays: “Not Your Father’s Oldsmobile”

43. Normal vs. Normal

44. Normal vs. Tumor

45. Sources of Variability • Target Preparation • Group target preps • Chip Run • Minor, BUT… • Be aware of processing order • Chip Lot • Stagger lots across experiment if necessary • Chip Scanning Order • Cross and block chip scanning order

46. Secondary Analysis: Expression Patterns • Supervised multivariate analyses • Support vector machines • Non-supervised clustering methods • Hierarchical • K-means • SOM

47. -2 0 +2 relative change AvgDiff S-score Use of S-score in Hierarchical Clustering of Brain Regional Expression Patterns NAC NAC VTA PFC VTA PFC HIP HIP

48. Expression Profiling Prot-Prot Interactions BioMed Lit Relations Expression Networks HomoloGene Ontology Pharmacology Genetics Behavior

49. Array Analysis: Conclusions • Be careful! Assess quality control parameters rigorously • Single arrays or experiments are of limited value • Normalization and weighting for noise are critical procedures • Across investigator/platform/species comparisons will most easily be done with relative data