Microarray Technology and Applications

Introduction to NanotechnologyFoothill College

Outline

• Gene expression

• Microarray technology

• Microarray process

• Applications in research / medicine

• Haplotyping (SNP) genotyping projects

• Future directions– New technologies / open source science

Several Advances in Technology Make Microarrays Possible

SurfaceChemistry

Robotics

ComputingPower & Bioinformatics

Genomics

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

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

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

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)

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

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

Generating DNA Sequence

base callingquality clippingvector clippingcontig assembly

chromatogram files

automatedsequencer

software pipeline

>GENE01

ACCTGTCAGTGTCAACTGCTTCAATAGCTAATGCTAGGCTCGATAATCGCTGGCCTCAGCTCAGTCTAGCATTACGATTACGGAGACCTATGCTTTAGCTAGTAGGAACCTCAGCTCAGTACCTGTCAGTGTCAACTGCTTCAATAGCTAATGCTACTC

>GENE01

ACCTGTCAGTGTCAACTGCTTCAATAGCTAATGCTAGGCTCGATAATCGCTGGCCTCAGCTCAGTCTAGCATTACGATTACGGAGACCTATGCTTTAGCTAGTAGGAACCTCAGCTCAGTACCTGTCAGTGTCAACTGCTTCAATAGCTAATGCTACTC

output

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

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

Spotted Array Experiment1. Prepare sample.

Test Reference

2. Label with fluorescent dyes.

3. Combine cDNAs.

4. Print microarray.

5. Hybridize to microarray.

6. Scan.

cDNA Array Sample Preparation

Axon Instruments Scanner

• GenPix 4000

Choice of Microarray System

1. cDNA arrays (Affymetrix)2. Oligonucleotide chips3. cRNA arrays (Applied

Biosystems)

4. SNP arrays Applied Biosystems)

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

Membrane cDNA microarray

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

cDNA Microarray Slide

Affymetrix gene chip

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

Affymetrix

• Probe Array (Photolithography)– Synthesis of probe

Affymetrix System

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

Tumor Cell Analysis

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

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.

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

Learning Gene Classes

Predictor

Learner Model

Labels

labels

experiments

experiments

Genes

Genes

Training set

Test set

Microarray Data Life Cycle

BiologicalQuestion

SamplePreparation

MicroarrayReaction

MicroarrayDetection

Data Analysis& Modeling

Spotted cDNA Array Production

Hybridization Process

Sources of Error

• Systematic

• Random

lo

g s

ign

al i

nte

nsi

ty

log RNA abundance

Microarray Data Processing

quality & intensity

filtering

normalizationbackground correction

expression ratios (treated / control)

Data Filtering -- Intensitye

xpt

2

expt 1

all sigs sigs > 6

sigs > 10sigs > 8

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

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

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

Identifying Co-Expressed Genes

• Cluster

• Treeview

• Eisen et al., PNAS 1998

• http://rana.lbl.gov

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

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?

Identifying Differential Expression

SAMSignificance

Analysis of

Microarrays

Tusher et al., PNAS 2001

http://www-stat.stanford.edu/~tibs/SAM/index.html

• Diauxic shiftrespiration <- fermentation

Monitoring Cell Differentiation

Gene Prediction and Annotation

...TTCTCCTAGTTCTAGAGCGTTCGGCACGAGCAAATCTTAAACGTTTTACTTTGTGCT...raw sequence

predicted gene

predicted mRNAor EST

protein sequence database

presumed identity/function

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)

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

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

The Basic System Computational Tasks

• Gene set selection

• Probe design

• Image analysis

• Normalization of chip, sample….

• …more….

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

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

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.

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

Oligo Probe Pairs (SNPs)

• Oligos are selected from a region of the gene that has low similarity to other genes.

Perfect match: ATGTTTGACGCAGCGTAGATCCGAGMismatch: ATGTTTGACGCAACGTAGATCCGAG

SNPs – Single Nucleotide Polymorphisms

A GeneChip “Spot” is Composed of Numerous Cells and Includes Controls

SNPs and Alleles

SNPs and Intervals

Haplotype Mapping of Chromosome 21

Seven haplotypes make up 93% of the genetic sampling

Summary

• Microarray process

• Microarray applications

• From genes to pathways

• Haplotyping and SNP mapping

• Using microarrays and medicine