DNA MicroarraysDNA Microarrays

M. Ahmad Chaudhry, Ph. D.M. Ahmad Chaudhry, Ph. D.

Outline of the lectureOutline of the lecture

• Overview of Micoarray Technology• Types of Microarrays• Manufacturing

• Instrumentation and Softwares• Data analysis

• Applications

• Mainly used in gene discovery

Microarray DevelopmentMicroarray Development

• Widely adopted

• Relatively young technology

Evolution & IndustrializationEvolution & Industrialization• 1994- First cDNAs arrays are

developed at Stanford.• 1995- Quantitative Monitoring

of Gene Expression Patterns with a cDNA Microarray

• 1996- Commercialization of arrays

• 1996-Accessing Genetic Information with High Density DNA Arrays

• 1997-Genome-wide Expression Monitoring in S. cerevisiae

ApproachesApproaches• What genes are Present/Absent in a tissue?

• What genes are Present/Absent in the experiment vs. control?

• Which genes have increased/decreased expression in experiment vs. control?

• Which genes have biological significance based on my knowledge of the biological system under investigation?

• Microarrays are simply small glass or silicon slides upon the surface of which are arrayed thousands of genes (usually between 500-20,000)

• Via a conventional DNA hybridization process, the level of expression/activity of those genes is measured

• Data are read using laser-activated fluorescence readers

• The process is “ultra-high throughput”

What are Microarrays?What are Microarrays?

GENE EXPRESSION ANALYSIS WITH MICROARRAYS

DNA Chips

Miniaturized, high density arrays of oligos (Affymetrix Inc.)

Printed cDNA or Oligonucleotide Arrays Robotically spotted cDNAs or Oligonucleotides • Printed on Nylon, Plastic or Glass surface

Affymetrix MicroarraysAffymetrix Microarrays

Involves Fluorescently tagged cRNA • One chip per sample• One for control• One for each experiment

Glass Slide Microarrays Involves two dyes/one chip

• Red dye• Green dye• Control and experiment on same chip

Gene Chip Technology Affymetrix Inc

Miniaturized, high density arrays of oligos 1.28-cm by 1.28-cm (409,000 oligos)

Manufacturing Process

Solid-phase chemical synthesis and Photolithographic fabrication techniques employed in semiconductor industry

Selection of Expression ProbesSelection of Expression ProbesSet of oligos to be synthesized is defined, based on its ability to Set of oligos to be synthesized is defined, based on its ability to hybridize to the target genes of interesthybridize to the target genes of interest

Probes

Sequence

Perfect Match

MismatchChip

5’ 3’

Computer algorithms are used to design photolithographic masks for use in manufacturing

Each gene is represented on the probe array by multiple probe pairsEach probe pair consists of a perfect match and a mismatch oligonucleotide.

Photolithographic SynthesisPhotolithographic Synthesis

Manufacturing ProcessManufacturing ProcessProbe arrays are manufactured by light-directed chemical Probe arrays are manufactured by light-directed chemical synthesis process which enables the synthesis of hundreds of synthesis process which enables the synthesis of hundreds of thousands of discrete compounds in precise locationsthousands of discrete compounds in precise locations

Lamp

Mask Chip

Click here to launch the movie file

Affymetrix Wafer and Chip FormatAffymetrix Wafer and Chip Format

1.28cm

20 - 50 µm

20 - 50 µm

Millions of identical oligonucleotide

probes per feature

49 - 400 chips/wafer

up to ~ 400,000 features/chip

RNA-DNA HybridizationRNA-DNA Hybridization

probe setsDNA

(25 base oligonucleotides of known sequence)

TargetsRNA

Non-Hybridized Targets are Washed AwayNon-Hybridized Targets are Washed Away

“probe sets” (oligo’s)

Targets(fluorescently tagged)

Non-bound ones are washed away

Target PreparationTarget Preparation

cDNA

Wash & Stain

Scan

Hybridize

(16 hours)

mRNAAAAA

B B B B

Biotin-labeled transcripts Fragment

(heat, Mg2+)

Fragmented cRNA

B B

B

B

IVT(Biotin-UTPBiotin-CTP)

GeneChipGeneChip®® Expression Analysis Expression Analysis

Hybridization and StainingHybridization and Staining

Array

cRNA Target

Hybridized Array

Streptravidin-phycoerythrinconjugate

Instrumentation for Gene Chip

Affymetrix Gene ChipsAffymetrix Gene Chips• Human Genome U133 Chip Set

• 33,000 genes, 2 chip set• uses recent draft of human genome

•Arabidopsis Genome Chip: 24,000 genes• Murine Genome Chip: 36,000 genes• E. coli Genome Chip: 4,200 genes• C. elegans Genome Chip: 22,500 genes

Affymetrix Gene ChipsAffymetrix Gene Chips• Rat Toxicology Chip: 850 genes

• CYP450’s, Heat Shock proteins• Drug transporters• Stress-activated kinases

• Rat Neurobiology Chip: 1,200 genes• Synuclein 1, prion protein, Huntington’s disease

• Syntaxin, Neurexin, neurotransmitters

• Drosophila Genome Chip: 13,500 genes • Yeast Genome Chip: 6,400 genes

Quality Control IssuesQuality Control Issues

• RNA purity and integrity• cDNA synthesis efficiency• Efficient cRNA synthesis, labeling and

fragmentation• Target evaluation with Test Chips

GENE EXPRESSION ANALYSIS WITH MICROARRAYS

DNA Chips

Miniaturized, high density arrays of oligos (Affymetrix Inc.)

Printed cDNA or Oligonucleotide Arrays Robotically spotted cDNAs or Oligonucleotides • Printed on Nylon, Plastic or Glass surface

Microarray of thousands of genes on a glass slide

Spotted arraysSpotted arrays

1 nanolitre spots90-120 um diameter

384 well source plate

chemically modified slides

steel

spotting pin

Spotted cDNA microarraysSpotted cDNA microarraysAdvantages• Lower price and flexibility• Simultaneous comparison of two related

biological samples (tumor versus normal, treated versus untreated cells)

• ESTs allow discovery of new genes

Disadvantages• Needs sequence verification• Measures the relative level of expression

between 2 samples

Gene D Over-expressed in normal tissue

Gene E Over-expressed in tumour

• Biomarkersof prognosis

• Genes affecting Treatment

Response

The challenges of microarraysThe challenges of microarrays

• Acquisition of high quality clinical samples, tumor and normal tissues

• High Quality RNA• Experimental design: what to compare to what?• Data analysis -1: what to do with the data? • Data analysis -2: How to do it?

– Very large number of data points

– Size of data files

– Choice of data analysis strategy/algorithm/software

Experimental DesignExperimental Design

• Choice of reference: Common (non-biologically relevant) reference, or paired samples?

• Number of replicates: How many are needed? (How many are affordable?). Are the replicate results going to be

averaged or treated independently?• Choice of data base: Where and how to

store the data?

Data Pre-processingData Pre-processing

Filtering – Background subtraction – Low intensity spots– Saturated spots – Low quality spots (ghost spots, dust

spots etc)

Normalization– Housekeeping genes/ control genes

Affymetrix Software for Microarray Data Analysis

• Microarray Suite 5 • Micro DB • Data Mining Tool (DMT)• NetAffx

Affymetrix Microarray Suite - Data AnalysisAffymetrix Microarray Suite - Data Analysis

Absolute Analysis – used to determine whether transcripts represented on the probe array are detected or not within one sample (uses data from one probe array experiment).

Comparison Analysis – used to determine the relative change in abundance for each transcript between a baseline and an experimental sample (uses data from two probe array experiments). Intensities for each experiment are compared to a baseline/control.

Microarray data analysisMicroarray data analysis

Scatter plots

• Intensities of experimental samples versus normal samples

• Quick look at the changes and overall quality of microarray

log/log

scatter plot

UP

DOWN

Intesities scatter plot for tumor sample OCA21BReference: Ambion normal ovary

Normalized

10

100

1000

10000

100000

10 100 1000 10000 100000

Cy3 intensity (OCA21B)

Cy5

in

ten

sity

(A

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ion

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rmal

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Intensities scatter plot for normal sample StratageneReference: Ambion normal ovary

Normalized

10

100

1000

10000

100000

10 100 1000 10000 100000

Cy3 intensity (Stratagene normal)

Cy5

in

ten

sity

(A

mb

ion

no

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)Normal ovary #1 versus normal ovary #2

Tumor ovary versus normal ovary #1

Microarray data analysisMicroarray data analysis

Supervised versus unsupervised analysis

– Clustering: organization of genes that are similar to each other and samples that are similar to each other using clustering algorithms

– Statistical analysis: how significant are the results?

Two dimensional hierarchical clusteringTwo dimensional hierarchical clustering (Eisen (Eisen et alet al, PNAS (1998) , PNAS (1998) 9595, p. 14863), p. 14863)

• Unsupervised: no assumption on samples

• The algorithm successively joins gene expression profiles to form a dendrogram based on their pair-wise similarities.

• Two-dimensional hierarchical clustering first reorders genes and then reorders tumors based on similarities of gene expression between samples.

Two dimensional hierarchical Two dimensional hierarchical (“Eisen”) Clustering(“Eisen”) Clustering

Cluster analysis of genes in G1 and G2

Chaudhry et. al., 2002

Publicly Available SoftwaresPublicly Available Softwares

CLUSTER and TREEVIEWCLUSTER and TREEVIEW

• Hierarchical Clustering

• K means Clustering

• Self Organizing Maps

Publicly Available Softwares

GenMAPP

Visualize gene expression data on maps representing biological pathways and groupings of genes.

Other Softwares

Extraction of information from DNA-chip with the technology of promoter analysis

Genomatix Software GmbH

Microarray Applications (some)Microarray Applications (some)• Identify new genes implicated in disease progression and

treatment response (90% of our genes have yet to be ascribed a function)

• Assess side-effects or drug reaction profiles

• Extract prognostic information, e.g. classify tumors based on hundreds of parameters rather than 2 or 3.

• Detect gene copy number changes in cancer (array CGH)

• Identify new drug targets and accelerate drug discovery and testing

• ???

ApplicationsApplications

Discovery

Leads

PreClinical

Clinical

• Target Discovery

• Target Validation

• Screening• Validation• Optimization

• Toxicology• Optimization

• Genotyping• ADE Screens

Microarray Technology - ApplicationsMicroarray Technology - Applications

• Gene Discovery-– Assigning function to sequence– Discovery of disease genes and drug targets– Target validation

• Genotyping– Patient stratification (pharmacogenomics)– Adverse drug effects (ADE)

• Microbial ID

The List Continues To Grow….

Profiling Gene ExpressionProfiling Gene Expression

LungTumor

LiverTumor

KidneyTumor

Normal vs. NormalNormal vs. Normal

Normal vs. TumorNormal vs. Tumor

Lung Tumor: Up-RegulatedLung Tumor: Up-Regulated

Lung Tumor: Down-RegulatedLung Tumor: Down-Regulated

Microarray FutureMicroarray Future

• Must go beyond describing differentially expressed genes

• Inexpensive, high-throughput, genome- wide scan is the end game for research applications

• Protein microarrays beginning to be used–Fundamentally change experimental design–Will enhance protein dB construction

Microarray FutureMicroarray Future

• Publications are now being focused on biology rather than technology

• SNP analysis –Faster, cheaper, as accurate as sequencing–Disease association studies–Population surveys

• Chemicogenomics–Dissection of pathways by compound application–Fundamental change to lead validation

Microarray FutureMicroarray Future

• Diagnostics– Tumor classification– Patient stratification– Intervention therapeutics

ConclusionConclusion

• Technology is evolving rapidly.• Blending of biology, automation, and

informatics.• New applications are being pursued

– Beyond gene discovery into screening, validation, clinical genotyping, etc.

• Microarrays are becoming more broadly available and accepted.– Protein Arrays– Diagnostic Applications