Introduction to Microarrays

The Central Dogma

Life - a recipe for making proteins

DNA proteinRNATranslationTranscription

ATCTTTTTCGGCTTTTTTTAGTATCCACAGAGGTTATCGACAACATTTTCACATTACCAACCCCTGTGGACAAGGTTTTTTCAACAGGTTGTCCGCTTTGTGGATAAGATTGTGACAACCATTGCAAGCTCTCGTTTATTTTGGTATTATATTTGTGTTTTAACTCTTGATTACTAATCCTACCTTTCCTCTTTATCCACAAAGTGTGGATAAGTTGTGGATTGATTTCACACAGCTTGTGTAGAAGGTTGTCCACAAGTTGTGAAATTTGTCGAAAAGCTATTTATCTACTATATTATATGTTTTCAACATTTAATGTGTACGAATGGTAAGCGCCATTTGCTCTTTTTTTGTGTTCTATAACAGAGAAAGACGCCATTTTCTAAGAAAAGGAGGGACGTGCCGGAAGATGGAAAATATATTAGACCTGTGGAACCAAGCCCTTGCTCAAATCGAAAAAAAGTTGAGCAAACCGAGTTTTGAGACTTGGATGAAGTCAACCAAAGCCCACTCACTGCAAGGCGATACATTAACAATCACGGCTCCCAATGAATTTGCCAGAGACTGGCTGGAGTCCAGATACTTGCATCTGATTGCAGATACTATATATGAATTAACCGGGGAAGAATTGAGCATTAAGTTTGTCATTCCTCAAAATCAAGATGTTGAGGACTTTATGCCGAAACCGCAAGTCAAAAAAGCGGTCAAAGAAGATACATCTGATTTTCCTCAAAATATGCTCAATCCAAAATATACTTTTGATACTTTTGTCATCGGATCTGGAAACCGATTTGCACATGCTGCTTCCCTCGCAGTAGCGGAAGCGCCCGCGAAAGCTTACAACCCTTTATTTATCTATGGGGGCGTCGGCTTAGGGAAAACACACTTAATGCATGCGATCGGCCATTATGTAATAGATCATAATCCTTCTGCCAAAGTGGTTTATCTGTCTTCTGAGAAATTTACAAACGAATTCATCAACTCTATCCGAGATAATAAAGCCGTCGACTTCCGCAATCGCTATCGAAATGTTGATGTGCTTTTGATAGATGATATTCAATTTTTAGCGGGGAAAGAACAAACCCAGGAAGAATTTTTCCATACATTTAACACATTACACGAAGAAAGCAAACAAATCGTCATTTCAAGTGACCGGCCGCCAAAGGAAATTCCGACACTTGAAGACAGATTGCGCTCACGTTTTGAATGGGGACTTATTACAGATATCACACCGCCTGATCTAGAAACGAGAATTGCAATTTTAAGAAAAAAGGCCAAAGCAGAGGGCCTCGATATTCCGAACGAGGTTATGCTTTACATCGCGAATCAAATCGACAGCAATATTCGGGAACTCGAAGGAGCATTAATCAGAGTTGTCGCTTATTCATCTTTAATTAATAAAGATATTAATGCTGATCTGGCCGCTGAGGCGTTGAAAGATATTATTCCTTCCTCAAAACCGAAAGTCATTACGATAAAAGAAATTCAGAGGGTAGTAGGCCAGCAATTTAATATTAAACTCGAGGATTTCAAAGCAAAAAAACGGACAAAGTCAGTAGCTTTTCCGCGTCAAATCGCCATGTACTTATCAAGGGAAATGACTGATTCCTCTCTTCCTAAAATCGGTGAAGAGTTTGGAGGACGTGATCATACGACCGTTATTCATGCGCATGAAAAAATTTCAAAACTGCTGGCAGATGATGAACAGCTTCAGCAGCATGTAAAAGAAATTAAAGAACAGCTTAAATAGCAGGACCGGGGATCAATCGGGGAAAGTGTGAATAACTTTTCGGAAGTCATACACAGTCTGTCCACATGTGGATAGGCTGTGTTTCCTGTCTTTTTCACAACTTATCCACAAATCCACAGGCCCTACTATTACTTCTACTATTTTTTATAAATATATATATTAATACATTATCCGTTAGGAGGATAAAAATGAAATTCACGATTCAAAAAGATCGTCTTGTTGAAAGTGTCCAAGATGTATTAAAAGCAGTTTCATCCAGAACCACGATTCCCATTCTGACTGGTATTAAAATTGTTGCATCAGATGATGGAGTATCCTTTACAGGGAGTGACTCAGATATTTCTATTGAATCCTTCATTCCAAAAGAAGAAGGAGATAAAGAAATCGTCACTATTGAACAGCCCGGAAGCATCGTTTTACAGGCTCGCTTTTTTAGTGAAATTGTAAAAAAATTGCCGATGGCAACTGTAGAAATTGAAGTCCAAAATCAGTATTTGACGATTATCCGTTCTGGTAAAGCTGAATTTAATCTAAACGGACTGGATGCTGATGAATATCCGCACTTGCCGCAGATTGAAGAGCATCATGCGATTCAGATCCCAACTGATTTGTTAAAAAATCTAATCAGACAAACAGTATTTGCAGTGTCCACCTCAGAAACACGCCCTATCTTGACAGGTGTAAACTGGAAAGTGGAGCAAAGTGAATTATTATGCACTGCAACGGATAGCCACCGTCTTGCATTAAGAAAGGCGAAACTTGATATTCCAGAAGACAGATCTTATAACGTCGTGATTCCGGGAAAAAGTTTAACTGAACTCAGCAAGATTTTAGATGACAACCAGGAACTTGTAGATATCGTCATCACAGAAACCCAAGTTCTGTTTAAAGCGAAAAACGTCTTGTTCTTCTCACGGCTTCTGGACGGGAATTATCCAGACACAACCAGCCTGATTCCGCAAGACAGCAAAACAGAAATCATTGTGAACACAAAAGAATTCCTTCAGGCCATTGATCGTGCATCTCTTTTAGCTAGAGAGGGACGCAACAAATTGCCGATGGCAACTGTAGAAATTGAAGTCCAAAATCAGTATTTGACGATTATCCGTTCTGGTAAAGCTGAATTTAATCTAAACGGACTGGATGCTGATGAATATCCGCACTTGCCGCAGATTGAAGAGCATCATGCGATTCAGATCCCAACTGATTTGTTAAAAAATCTAATCAGACAAACAGTATTTGCAGTGTCCACCTCAGAAACACGCCCTATCTTGACAGGTGTAAACTGGAAAGTGGAGCAAAGTGAATTATTATGCACTGCAACGGATAGCCACCGTCTTGCATTAAGAAAGGCGAAACTTGATATTCCAGAAGACAGATCTTATAACGTCGTGATTCCGGGAAAAAGTTTAACTGAACTCAGCAAGATTTTAGATGACAACCAGGAACTTGTAGATATCGTCATCACAGAAACCCAAGTTCTGTTTAAAGCGAAAAACGTCTTGTTCTTCTCACGGCTTCTGGACGGGAATTATCCAGACACAACCAGCCTGATTCCGCAAGACAGCAAAACAGAAATCATTGTGAACACAAAAGAATTCCTTCAGGCCATTGATCGTGCATCTCTTTTAGCTAGAGAGGGACGCAACACAGACACAACCAGCCTGATTCCGCAAGACAGCAAAACAGAAATCATTGTGAACACAAAAGAATTCCTTCAGGCCATTGATCGTGCATCTCTTTTAGCTAGAGAGGGACGCAACAAAGAATTCCTTCAGGCCATTGATCGTGCATCTCTTTTAGCTAGAGAGGGACGCAACATTGTGA

DNA

The Central Dogma

Hybridization

A

A

A

T

T

G

G

C

C

T

A

T

G

A

T

G

C

C

A

A

A

T

T

G

G

C

C

T

A

T

G

A

T

G

C

C

Introduction to Microarrays

Microarrays - The Concept

Measure the level of transcript from a

very large number of genes in one go

CELL

RNA

Microarrays - The Technologies

Stanford Microarrays

High-density

Why?

RNA

How?

gene specific DNA probeslabeled target

gene

mRNA

Stanford-type Microarrays

Stanford-type Microarrays

Coating glass slides

Deposition of probes

Post-processing

Hybridization

Making Microarrays

1. Produce probes

2. Print by the use of a robot

• oligos• cDNA library• PCR products

Spotting - Mechanical deposition of probes

16-pin microarrayer

Microarrayer

Making Microarrays

1. Produce probes

2. Print by the use of a robot

• oligos• cDNA library• PCR products

3. Post-process:

• rehydrate• snap dry• UV-cross link• block surface

Sample preparation

1. Design experimentQuestion?Replicates?Test?

2. Perform experiment

4. Label RNAAmplification?Direct or indirect?Label?

wild typemutant

3. Precipitate RNAEukaryote/prokaryote?Cell wall?

mRNAmRNA

cDNAcDNA

Cy3-cDNACy5-cDNA

SAMPLE CONTROL

Stanford

microarrays

DESIGN

and ORDER

PROBES

Affymetrix GeneChip® oligonucleotide array

• 11 to 20 oligonucleotide probes for each gene

• On-chip synthesis of 25 mers

• ~20.000 genes per chip

• good quality data – low variance

Catalog Arrays

HumanMouseRat ArabidopsisC. elegansCanineDrosophila

E. coliP. aeruginosaPlasmodium/AnophelesVitis vinifera (Grape) Xenopus laevisYeastZebrafish

NimbleExpress™ Array Program

Fluidic Station and Scanner

The Affymetrix Genechip®

TTT

T

T

TT

T

T

T

A

A

AA

A

A

A

AAA

Photolithographyin situ synthesis

Spacers bound to surface with photolabile protection groups

Mask #1Mask #2

Photolithography - Micromirrors

NimbleExpress™ Array Program

manufactured on Iceland by NimbleGen Systems Inc.

The Affymetrix GeneChip®

A gene is represented like this:

- Perfect Match (PM)- MisMatch (MM)

PMMM

PM: CGATCAATTGCACTATGTCATTTCT MM: CGATCAATTGCAGTATGTCATTTCT

NimbleGen Systems Inc.

~380.000 probes/array

They do most of the practical work

The Technologies - Flexibility

Stanford microarrays: Are flexible, but

new probes must be ordered each time

High-density: Are not flexible, ....unless you

order the NimbleExpress™ chip or use the

NimbleGen Systems

Analysis of Data

Normalization:Linear or non-linear

Is it worth it?

Nu

mb

er

of

kn

ow

n p

osit

ives

Number of significantly affected genes

Qspline normalization

Linear normalization

Known positives versus the total number of significantly affected genes at 5 different cutoffs in the

TnrA experiment

Analysis of Data

Normalization:Linear or non-linear

Statistical test:student’s t-testANalysis Of VAriance

(ANOVA)Analysis:

Principle Component Analysis (PCA) Clustering and visualization