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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 [email protected] 225-4054 Biological Regulation: “You are what you express” • Levels of regulation • Methods of measurement • Concept of genomics 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! 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 Mesolimbic dopamine ? Other Acute Drug Use Reinforcement Intoxication Altered Signaling Gene Expression Tolerance Dependence ?Synaptic Remodeling Sensitization Chronic Drug Use ?Synaptic Remodeling Persistent Gene Exp. Compulsive Drug Use “Addiction” 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 Nucleic Acid Hybridization: How It Works 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) High Density DNA Microarrays 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) Biological Networks Types of Biological Networks Gene Regulation Network Examining Biological Networks: Experimental Design Examining Biological Networks AvgDiff Use of Sscore in Hierarchical Clustering of Brain Regional Expression Patterns S-score -2 0 +2 relative change Expression Profiling: A Non-biased, Genomic Approach to Resolving the Mechanisms of Addiction Candidate Gene Studies Cycles of Expression Profiling Merge with Biological Databases Utility of Expression Profiling • • • • Non-biased, genome-wide Hypothesis generating Gene hunting Pattern identification: – Insight into gene function – Molecular classification – Phenotypic mechanisms Comparisons (S-score, dchip) De-noise GE Database (SQL Server) Statistical Filtering (e.g. SAM) Hybridization and Scanning Clustering Techniques Experimental Design Behavioral Validation Provisional Gene “Patterns” Molecular Validation (RT-PCR, in situ, Western) Candidate Genes Filtered Gene Lists Overlay Biological Databases (PubGen, GenMAPP, QTL, etc.) Experimental Design with DNA Microarrays High Density DNA Microarrays Synthesis and Analysis of 2-color Spotted cDNA Arrays: “Brown Chips” Comparative Hybridization with Spotted cDNA Microarrays Synthesis of High Density Oligonucleotide Arrays by Photolithography/Photochemistry 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 Oligonucleotide Array Analysis Total RNA 5’ AAAA Rtase/ Pol II dsDNA AAAA-T7 TTTT-T7 T7 pol Biotin-cRNA TTTT-5’ CTP-biotin Oligo(dT)-T7 Hybridization Scanning PM MM Steptavidinphycoerythrin 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? Affymetrix Arrays: Image Analysis Affymetrix Arrays: Image Analysis “.DAT” file “.CEL” file Affymetrix Arrays: PM-MM Difference Calculation Probe pairs control for non-specific hybridization of oligonucleotides Variability and Error in DNA Microarray Hybridizations Variability in Ln(FC) Ln(FC1) (a) Ln(FC2) • 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. 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 S-score Normalization Confounds: Non-uniform Chip Behavior Gene Normalization Confounds: Non-linearity Slide Normalization: Pieces and Pins “Lowess” normalization, Pin-specific Profiles After Print-tip Normalization http://www.ipam.ucla.edu/publications/fg2000/fgt_tspeed9.pdf See also: Schuchhardt, J. et al., NAR 28: e47 (2000) 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 Statistical Analysis of Microarrays: “Not Your Father’s Oldsmobile” Normal vs. Normal Normal vs. Tumor 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 Secondary Analysis: Expression Patterns • Supervised multivariate analyses – Support vector machines • Non-supervised clustering methods – Hierarchical – K-means – SOM AvgDif f Use of Sscore in Hierarchica l Clustering of Brain Regional Expression Patterns Sscore -2 0 +2 relative change Expression Profiling Prot-Prot Interactions BioMed Lit Relations Expression Networks HomoloGen e Ontology Pharmacology Genetics Behavior 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 Comparison of Primary Analysis Algorithms II Spotted cDNA Microarrays