introduction to dna microarrays michael f. miles, m.d., ph.d. depts. of pharmacology/toxicology and...
TRANSCRIPT
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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
225-4054
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Biological Regulation: “You are what you express”
• Levels of regulation
• Methods of measurement
• Concept of genomics
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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!
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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
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Acute Drug Use
Mesolimbic dopamine? Other
ReinforcementIntoxication
Chronic Drug Use
Compulsive Drug Use
“Addiction”
?Synaptic RemodelingPersistent Gene Exp.
ToleranceDependence
Sensitization
Altered SignalingGene Expression
?Synaptic Remodeling
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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
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Nucleic Acid Hybridization: How It Works
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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)
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High Density DNA Microarrays
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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)
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Biological Networks
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Types of Biological Networks
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Gene Regulation Network
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Examining Biological Networks: Experimental Design
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Examining Biological Networks
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PFCHIP VTA
NAC
Use of S-score in Hierarchical Clustering of Brain Regional Expression Patterns
0 +2-2
relative change
PFCHIP NAC
VTA
AvgDiff S-score
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Expression Profiling: A Non-biased, Genomic Approach to Resolving the Mechanisms of Addiction
Candidate Gene Studies
Cycles of Expression
Profiling
Merge with Biological Databases
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Utility of Expression Profiling
• Non-biased, genome-wide
• Hypothesis generating
• Gene hunting
• Pattern identification: – Insight into gene function– Molecular classification– Phenotypic mechanisms
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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
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Experimental Design with DNA Microarrays
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High Density DNA Microarrays
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Synthesis and Analysis of 2-color Spotted cDNA Arrays: “Brown Chips”
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Comparative Hybridization with Spotted cDNA Microarrays
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Synthesis of High Density Oligonucleotide Arrays by Photolithography/Photochemistry
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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
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Oligonucleotide Array Analysis
AAAA
Oligo(dT)-T7
Total RNA Rtase/Pol II
dsDNAAAAA-T7TTTT-T7
CTP-biotin
T7 polTTTT-5’5’
Biotin-cRNA
Hybridization
Steptavidin-phycoerythrin
Scanning
PM
MM
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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?
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Affymetrix Arrays: Image Analysis
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Affymetrix Arrays: Image Analysis
“.DAT” file “.CEL” file
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Affymetrix Arrays: PM-MM Difference Calculation
Probe pairs control for non-specific hybridization of oligonucleotides
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Variability and Error in DNA Microarray Hybridizations
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(a)
Variability in Ln(FC)
- 4
- 3
- 2
- 1
0
1
2
3
4
- 4 - 3 - 2 - 1 0 1 2 3 4
l n ( P F C 1 A S / V T A 1 A S )
R = 0 . 7 1
ln(FoldChange) S-score
Ln(FC1)
Ln(FC2)
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• Position Dependent Nearest Neighbor (PDNN) - 2003Zhang, 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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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
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Normalization Confounds: Non-uniform Chip Behavior
S-s
core
Gene
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Normalization Confounds: Non-linearity
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“Lowess” normalization,Pin-specific Profiles
After Print-tip Normalization
Slide Normalization: Pieces and Pins
See also: Schuchhardt, J. et al., NAR 28: e47 (2000)
http://www.ipam.ucla.edu/publications/fg2000/fgt_tspeed9.pdf
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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
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Statistical Analysis of Microarrays: “Not Your Father’s Oldsmobile”
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Normal vs. NormalNormal vs. Normal
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Normal vs. TumorNormal vs. Tumor
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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
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Secondary Analysis: Expression Patterns
• Supervised multivariate analyses– Support vector machines
• Non-supervised clustering methods– Hierarchical– K-means– SOM
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PFCHIP VTA
NAC
Use of S-score in Hierarchical Clustering of Brain Regional Expression Patterns
0 +2-2
relative change
PFCHIP NAC
VTA
AvgDiff
S-score
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Expression Networks
Expression Profiling
Behavior
Pharmacology Genetics
Prot-Prot
Interactions
OntologyHomoloGen
e
BioMed Lit
Relations
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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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Comparison of Primary Analysis Algorithms II
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Spotted cDNA Microarrays