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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 mfmiles@vcu.edu 225-4054. Biological Regulation: “You are what you express”. Levels of regulation Methods of measurement

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introduction to dna microarrays

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
Biological Regulation: “You are what you express”
  • Levels of regulation
  • Methods of measurement
  • Concept of genomics
regulation of gene expression
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 expression1
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
slide5

 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

progress in studies on gene regulation
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

primer on nucleic acid hybridization
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)

a bit of history
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)

slide16

-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

slide17

Candidate Gene Studies

Cycles of Expression Profiling

Merge with Biological Databases

Expression Profiling: A Non-biased, Genomic Approach to Resolving the Mechanisms of Addiction

utility of expression profiling
Utility of Expression Profiling
  • Non-biased, genome-wide
  • Hypothesis generating
  • Gene hunting
  • Pattern identification:
    • Insight into gene function
    • Molecular classification
    • Phenotypic mechanisms
slide19

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

genechip features
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
slide27

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

stepwise analysis of microarray data
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?
slide30

Affymetrix Arrays: Image Analysis

“.DAT” file

“.CEL” file

affymetrix arrays pm mm difference calculation
Affymetrix Arrays: PM-MM Difference Calculation

Probe pairs control for non-specific hybridization of oligonucleotides

variability in ln fc
Variability in Ln(FC)

Ln(FC1)

(a)

Ln(FC2)

slide35

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
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
slide normalization pieces and pins

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)

quality assessment
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
sources of variability
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
Secondary Analysis: Expression Patterns
  • Supervised multivariate analyses
    • Support vector machines
  • Non-supervised clustering methods
    • Hierarchical
    • K-means
    • SOM
slide47

-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

slide48

Expression Profiling

Prot-Prot

Interactions

BioMed Lit

Relations

Expression Networks

HomoloGene

Ontology

Pharmacology

Genetics

Behavior

array analysis conclusions
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