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

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

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

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

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

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

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

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

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

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

(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)

• 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.

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

Normalization Confounds: Non-uniform Chip Behavior

S-s

core

Gene

Normalization Confounds: Non-linearity

“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

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. NormalNormal vs. Normal

Normal vs. TumorNormal 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

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

Expression Networks

Expression Profiling

Behavior

Pharmacology Genetics

Prot-Prot

Interactions

OntologyHomoloGen

e

BioMed Lit

Relations

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