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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.

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Presentation on theme: "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."— Presentation transcript:

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

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

6 Progress in Studies on Gene Regulation 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 ~ : Oligo arrays developed by Fodor, Stryer, Lockhart, others at Stanford/Affymetrix and Southern in Great Britain ~ : 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 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 PFC HIP VTA NAC Use of S- score in Hierarchical Clustering of Brain Regional Expression Patterns relative change PFC HIP NAC VTA AvgDiffS-score

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

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

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

20 Experimental Design with DNA Microarrays

21 High Density DNA Microarrays

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

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

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 “.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 (a) Variability in Ln(FC) Ln(FC 1 ) Ln(FC 2 )

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35 Position Dependent Nearest Neighbor (PDNN) 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.

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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 “Lowess” normalization, Pin-specific Profiles After Print-tip Normalization Slide Normalization: Pieces and Pins 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 PFC HIP VTA NAC Use of S- score in Hierarchica l Clustering of Brain Regional Expression Patterns relative change PFC HIP NAC VTA AvgDif f S- score

48 Expression Networks Expression Profiling Behavior PharmacologyGenetics Prot-Prot Interactions Ontology HomoloGen e BioMed Lit Relations

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

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54 Comparison of Primary Analysis Algorithms II

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56 Spotted cDNA Microarrays


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