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Microarray Technology and Applications
Introduction to NanotechnologyFoothill College
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Outline
• Gene expression
• Microarray technology
• Microarray process
• Applications in research / medicine
• Haplotyping (SNP) genotyping projects
• Future directions– New technologies / open source science
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Several Advances in Technology Make Microarrays Possible
SurfaceChemistry
Robotics
ComputingPower & Bioinformatics
Genomics
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Gene Expression
• Cells are different because of differential gene expression.
• About 40% of human genes are expressed at any one time.
• Gene is expressed by transcribing DNA exons into single-stranded mRNA
• mRNA is later translated into a protein• Microarrays measure the level of mRNA
expression by analyzing cDNA binding
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Analysis of Gene Expression
• Examine expression during development or in different tissues
• Compare genes expressed in normal vs. diseased states
• Analyze response of cells exposed to drugs or different physiological conditions
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Monitoring Changes in Genomic DNA
• Identify mutations • Examine genomic instability such as in
certain cancers and tumors (gene amplifications, translocations, deletions)
• Identify polymorphisms (SNPs)• Diagnosis: chips have been designed to
detect mutations in p53, HIV, and the breast cancer gene BRCA-1
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Applications in Medicine
• Gene expression studies– Gene function for cell state change in
various conditions (clustering, classification)
• Disease diagnosis (classification)
• Inferring regulatory networks
• Pathogen analysis (rapid genotyping)
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Applications in Drug Discovery
• Drug Discovery– Identify appropriate molecular targets for therapeutic
intervention (small molecule / proteins)– Monitor changes in gene expression in response to
drug treatments (up / down regulation)– Analyze patient populations (SNPs) and response
• Targeted Drug Treatment– Pharmacogenomics: individualized treatments– Choosing drugs with the least probable side effects
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Many Genes Have Unknown Function
• Of the 25,498 predicted Arabidopsis genes:
• 30% have unknown function
• Only 9% are experimentally verified
The Arabidopsis Genome Initiative, Nature 2000
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Generating DNA Sequence
base callingquality clippingvector clippingcontig assembly
chromatogram files
automatedsequencer
software pipeline
>GENE01
ACCTGTCAGTGTCAACTGCTTCAATAGCTAATGCTAGGCTCGATAATCGCTGGCCTCAGCTCAGTCTAGCATTACGATTACGGAGACCTATGCTTTAGCTAGTAGGAACCTCAGCTCAGTACCTGTCAGTGTCAACTGCTTCAATAGCTAATGCTACTC
>GENE01
ACCTGTCAGTGTCAACTGCTTCAATAGCTAATGCTAGGCTCGATAATCGCTGGCCTCAGCTCAGTCTAGCATTACGATTACGGAGACCTATGCTTTAGCTAGTAGGAACCTCAGCTCAGTACCTGTCAGTGTCAACTGCTTCAATAGCTAATGCTACTC
output
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What Is Microarray Technology?
• Different ApproachesStanford/
Pat Brown
Affymetrix
How DNA sequences are laid down
Spotting Photolithography
Length of DNA sequences
cDNA(Complete sequences)
Oligonucleotides
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Oligoarrays vs. Spotted Arrays
• Oligoarrays– Shorter nucleotides– Higher feature density
• Used for SNP detection– Perfect match and mismatch (A,T,C,G)
• More expensive to prepare– Higher per unit cost production– Not manufactured in as high numbers
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Spotted Array Experiment1. Prepare sample.
Test Reference
2. Label with fluorescent dyes.
3. Combine cDNAs.
4. Print microarray.
5. Hybridize to microarray.
6. Scan.
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cDNA Array Sample Preparation
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Axon Instruments Scanner
• GenPix 4000
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Choice of Microarray System
1. cDNA arrays (Affymetrix)2. Oligonucleotide chips3. cRNA arrays (Applied
Biosystems)
4. SNP arrays Applied Biosystems)
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cDNA Arrays: Advantages
• Non-redundant clone sets are available for numerous organisms (humans, mouse, rats, drosophila, yeast, c.elegans, arabidopsis)
• Prior knowledge of gene sequence is not necessary: good choice for gene discovery
• Large cDNA size is great for hybridization• Glass or membrane spotting technology is
readily available
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Membrane cDNA microarray
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cDNA Arrays: Disadvantages
• Processing cDNAs to generate “spotting-ready” material is cumbersome
• Low density compared to oligonucleotide arrays• cDNAs may contain repetitive sequences (like
Alu in humans)• Common sequences from gene families (ex: zinc
fingers) are present in all cDNAs from these genes: potential for cross-hybridization
• Clone authentication can be difficult
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cDNA Microarray Slide
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Affymetrix gene chip
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Affymetrix Microarrays
50um
1.28cm
~107 oligonucleotides, half Perfectly Match mRNA (PM), half have one Mismatch (MM)Raw gene expression is intensity difference: PM - MM
Raw image
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Affymetrix
• Probe Array (Photolithography)– Synthesis of probe
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Affymetrix System
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Microarrays: An Example
• Leukemia: Acute Lymphoblastic (ALL) vs Acute Myeloid (AML), Golub et al, Science, v.286, 1999– 72 examples (38 train, 34 test), about 7,000 genes– well-studied (CAMDA-2000), good test example
ALL AML
Visually similar, but genetically very different
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Tumor Cell Analysis
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Heatmap Visualization of Selected Fields
ALL AML Heatmap visualizationis done by normalizing each gene to mean 0, std. 1 to get a picture like this.
Good correlation overall
AM
L-r
ela
ted
AL
L-re
late
dPossible outliers
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Analysis Tasks
• Identify up- and down-regulated genes.• Find groups of genes with similar
expression profiles (++ / -- , fold change).• Find groups of experiments (tissues) with
similar expression profiles (++ / -- genes).• Find genes that explain observed
differences among tissues (feature selection), and new pathways.
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Types of Analysis
• Unsupervised learning: learn from data only– visualization: find structure in data– clustering: find clusters / classes in data
• Supervised learning: learn from data plus prior knowledge– classification: predict discrete classes– regression: predict a real value
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Learning Gene Classes
Predictor
Learner Model
Labels
labels
experiments
experiments
Genes
Genes
Training set
Test set
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Microarray Data Life Cycle
BiologicalQuestion
SamplePreparation
MicroarrayReaction
MicroarrayDetection
Data Analysis& Modeling
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Spotted cDNA Array Production
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Hybridization Process
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Sources of Error
• Systematic
• Random
lo
g s
ign
al i
nte
nsi
ty
log RNA abundance
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Microarray Data Processing
quality & intensity
filtering
normalizationbackground correction
expression ratios (treated / control)
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Data Filtering -- Intensitye
xpt
2
expt 1
all sigs sigs > 6
sigs > 10sigs > 8
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Data Normalization Issues
• Normalization of data from different chips– MGED normalization standards –
http://www.mged.org/
• Natural biological variation is large • Technical variation is small ~ 98% auto-
correlation • MIT approach -- raw gene expression values• Stanford approach -- ratios
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Data Preparation
• Thresholding: usually min 20, max 16,000– For older Affy chips (new Affy chips do not have
negative values)
• Filtering - remove genes with insufficient variation– e.g. MaxVal - MinVal < 500 and MaxVal/MinVal < 5– biological reasons– feature reduction for algorithmic
• For clustering, normalize each gene separately to – Mean = 0, Std. Dev = 1
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Clustering
Goals
• Find natural classes in the data
• Identify new classes / gene correlations
• Refine existing taxonomies
• Support biological analysis / discovery
• Different Methods– Hierarchical clustering, SOM's, etc
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Identifying Co-Expressed Genes
• Cluster
• Treeview
• Eisen et al., PNAS 1998
• http://rana.lbl.gov
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SOM Clustering
• SOM - self organizing maps• Preprocessing
– filter away genes with insufficient biological variation
– normalize gene expression (across samples) to mean 0, st. dev 1, for each gene separately.
• Run SOM for many iterations• Plot the results
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Microarray Analysis
• Gene discovery • Pattern discovery• Inferences about biological processes• Classification of biological processes
What genes are up-regulated, down-regulated, co-regulated,
not-regulated?
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Identifying Differential Expression
SAMSignificance
Analysis of
Microarrays
Tusher et al., PNAS 2001
http://www-stat.stanford.edu/~tibs/SAM/index.html
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• Diauxic shiftrespiration <- fermentation
Monitoring Cell Differentiation
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Gene Prediction and Annotation
...TTCTCCTAGTTCTAGAGCGTTCGGCACGAGCAAATCTTAAACGTTTTACTTTGTGCT...raw sequence
predicted gene
predicted mRNAor EST
protein sequence database
presumed identity/function
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Functional Genomics: High Throughput Platforms
• Microarrays– spotted cDNAs/ESTs– spotted oligonucleotides– in situ synthesized oligos
• SAGE• GeneCalling® www.curagen.com
• Megasort www.lynxgen.com
• MAPs (High Throughput Genomics, Tucson)
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Advanced Questions in Microarray Analysis
• e.g. can we correlate patterns in other types of data with the microarray results?
- cis-elements– protein domains– protein-protein interactions– orthologous genes– gene ontologies– textual associations
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Procedures
• Preparation– Target DNA (reference and test samples)– Slides
• Reaction(Droplet or Pin Spotting)– Hybridization
• Scanning• Analysis
– Image processing– Data mining– Modeling
Arrayer
ScannerHardware
Software
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The Basic System Computational Tasks
• Gene set selection
• Probe design
• Image analysis
• Normalization of chip, sample….
• …more….
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A Typical GeneChip Array Experiment
Target Preparation
Isolate total RNA from tissue
Synthesis of cDNA & addition of T7 promoter
Biotin-labeling of cRNA & purification
Hybridization to GeneChip
Deposition of oligo probeson GeneChip
Image acquisition& analysis
Data MiningInteresting genes
Pattern Recognition
Pathways
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Probe Design System
• A method of hybridization of short synthetic oligonucleotide probes to cloned DNA sequences to derive genetic sequence information.
• Application field – oligonucleotide array– Polymerase Chain Reaction : primer design
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Probe Design System
• Probe design issue– Unique probe for each gene– Different probe sets for each genes :
fingerprints– Probes (or a unique probe) of each gene
should have the ability of representing the gene.
– The similarities between probes should very low.
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The Analysis - Computational Tasks
• Clustering genes: which genes seem to be regulated together
• Classifying genes: which functional class does a given gene fall into
• Classifying cell samples: does this patient have ALL or AML
• Inferring regulatory networks: what is the “circuitry” of the cell
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Oligo Probe Pairs (SNPs)
• Oligos are selected from a region of the gene that has low similarity to other genes.
Perfect match: ATGTTTGACGCAGCGTAGATCCGAGMismatch: ATGTTTGACGCAACGTAGATCCGAG
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SNPs – Single Nucleotide Polymorphisms
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A GeneChip “Spot” is Composed of Numerous Cells and Includes Controls
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SNPs and Alleles
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SNPs and Intervals
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Haplotype Mapping of Chromosome 21
Seven haplotypes make up 93% of the genetic sampling
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Summary
• Microarray process
• Microarray applications
• From genes to pathways
• Haplotyping and SNP mapping
• Using microarrays and medicine