Microarrays

Lecture Slides Courtesy ofDr. Tim Hughest.hughes@utoronto.ca

Outline:• Microarray experiments• Different types of microarrays•Clustering and interpretation

www.accessexcellence.org/AB/GG/nucleic.html

Nucleic Acid Hybridization

controltreatment

(drug, mutation)

updownunchangednot present

x y z

xx

x

xx

yy

yy

zz z

cDNA pools

Typical use of cDNA microarrays:“Internal” normalization using two colors

“cDNA microarrays” are essentially dot-blots on glass slides

http://arrayit.com/Products/Printing/Stealth/stealth.html

• This slide was made with 16 pins• 4.5 mm pin spacing matches 384-well plates (16 x 24)• Done with robotics• Slides usually coated with poly-lysine• Spots are usually 100-150 microns• Spot spacing is usually 200-300 microns.• Slides are 25 x 75 mm• Easy to deposit 20K spots/slide

0.45 mm

Microarray expression profiling by 2-color assay (“cDNA arrays”)

Array: PCR products6250 yeast ORFs

hybridized cDNAs:green = controlred = experiment

*Schena et al., 1995

Image processing and normalization: what is microarray data?Microarray data is summary information from image files that come out of the scanner.Image processing: line up grids, flag bad spots, quantitate.

Looking at data from a single experiment

3-AT vs.No drug

wild-type vs.wild-type

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Log10(Intensity)

Log1

0(Expression Ratio)

Slides: 11120c01 -11121c01

P-value < 0.01

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

P-value < 0.01

Log10(Intensity)

Log1

0(Expression Ratio)

Slides: 11857c01 -11858c01

log10(average intensity)

-2 -1 0 1 2

log 1

0(r

atio

)lo

g 10(r

atio

)

2

1

0

-1

-2

-2 -1 0 1 2

2

1

0

-1

-2

Other types of arrays

Photolithographic arrays (Affymetrix)

Building up oligonucleotides on a surface:

http://www.affymetrix.com/technology/manufacturing/index.affx

Photolithographic arrays (Affymetrix)

aka “GeneChip”

Arrays are typically 25-mers, with “mismatch” control for specificity

Photolithographic arrays (Affymetrix)Advantages:

Density is limited essentially by the 5 micron resolution of scanners (solution: larger arrays).

Well-developed protocols.

“Industry standard” (largely self-driven).

Disadvantages:

Not all probes work well. Affymetrix has evolved a complicated system to compensate for this, but even “believers” use at least four probes per gene, and usually more.

Single color.

Sample preparation typically requires amplification.

Single supplier; historically intellectual property issues. (i.e. comparisons)

• 25,000 oligos / 1 x 3 inches

• Sequence completely flexible

• 60-mers

G

AGTC

A

CGGG

C

TGAA

Ink-jet arrays (Agilent)

Hughes TR et al. Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer. Nat Biotechnol. 2001 Apr;19(4):342-7.

Ink-jet arrays generally agree with spotted cDNA arrays

Yeast IJS array: ~8 oligos per gene Spo vs. SC

cDNA array

mu

ltip

le o

ligos

cDNA array

sin

gle

olig

o

r = 0.96

HXT3 HXT1

HXT4

r = 0.97

Ink-jet arrays (Agilent)Advantages:

User-specified sequences; “no questions asked”

Sensitivity and specificity are defined and exceed requirement for most expression profiling applications; no amplification required

Virtually every 60-mer is functional

Data correlates well with spotted cDNA arrays

Disadvantages:

Density currently limited to ~45,000 spots per array.

Single supplier (although a protocol is in press for making your own synthesizer!)

2-D clusteringStep 1: cut experiments and transcripts

falling below P-value and ratio thresholds

-10 -5 -2 1 2 5 10

fold repression fold induction

transcript response index

exp

erim

ent

ind

ex

44 experimentsx

407 genes

2-D clustering

-10 -5 -2 1 2 5 10

fold repression fold induction

Step 2: cluster experiments and transcriptstranscript response index

exp

erim

ent

ind

ex

RHO O/XPKC O/X

ste mutants

treatment withalpha-factor

Data from Roberts et al., Science (2000)

K = 10 #1 #2 #3

There are many types of clustering. One example: K-means (must choose K)

See: Sherlock G. Analysis of large-scale gene expression data.Curr Opin Immunol. 2000 Apr;12(2):201-5.

Basics of clustering freeware: Eisen’s “Cluster” and “Treeview”

Mike Eisen's web site: rana.lbl.gov/EisenSoftware.htm

“Cluster” loads an Excel file (save as tab-delimited text) in the following format:

Cluster

Treeview

(also: “TreeArrange” - http://monod.uwaterloo.ca/downloads/treearrange/)There are also many commercial programs available.

mRNA

protein

nucleus

cell

Microarray expression data

Co-regulated groups of genes

Functional categories

Predict functions of new genes

cis, trans regulators

GO-Biological Process categories

Broad

Mid-level

Narrow eye pigment metabolism

eye morphogenesis

pigment metabolism

striated muscle contraction

ATP biosynthesis

vision

CNS development

insulin secretion

Very Broadmetabolism

163

137

21

36

25

33

34

1548

# annotated genes(mouse)

development 2341

GO-Biological Process hierarchy

eye pigment metabolism

eye morphogenesis

pigment metabolism

CNS development

metabolism

development

Other types of categorical annotations:

KEGG, EC numbers (describe biochemical “pathways”)

MIPS, YPD (yeast databases – older than GO)

Results of individual studies (localization, 2-hybrid screens, protein complexes, etc.

Sequence motifs, structural domains (pfam, SMART)

Cluster labelamino acid metabolismarginine biosynthesisarginine catabolismaromatic AA metabolismasparagine biosynthesisbranched chain AA synthlysine biosynthesismethionine biosynthesissulfur AA tnsprt, metabadenine biosynthesisaldehyde metabolismbiotin biosynthesiscitrate metabolismergosterol biosynthesisfatty acid biosynthesisgluconeogenesisNAD biosynthesisone-carbon metabolismpyridoxine metabolismthiamin biosynthesis 1thiamin biosynthesis 2hexose transportsodium ion transportpolyamine transportnucleocytoplasmic transportribosome/RNA biogenesisribosomal proteinstranslational elongationprotein foldingsecretionprotein glycosylationvesicle-mediated transportproteasomevacuole fusionmitoribosome/respirationMitochond. electron trans.iron transport/TCA cycleChromatin/transcriptionhistonesMCM2/3/6/CDC47DNA replicationmitotic cell cycleCLB1/CLB6/BBP1cytokinesisdevelopmentpheromone responseconjugationsporulation/meiosisresponse to oxidative stressstress/heat shock

Sample genesTRP4, HIS3ARG1, ARG3CAR1, CAR2ARO9, ARO10ASN1, ASN2ILV1,2,3,6LYS2, LYS9MET3,16,28MUP1, MHT1ADE1,4,8AAD4,14,16BIO3,4CIT1,2ERG1,5,11FAS1,FAS2PGK1, TDH1,2,3BNA4,6GCV1,2,3SNO1, SNZ1THI5,12THI2,20HXT4,GSY1ENA1,2,5TPO2,3KAP123,NUP100MAK16,CBF5RPS1A,RPL28TEF1,2SSA1,HSP60VTH1,KRE11ALG6,CAX4VPS5,IMH1RPN6,RPT5VTC1,3,4,PHO84MRPL1,MRPS5ATP1,COX4FRE1,FET3SNF2,CHD1,DOT6HTA1,HHF1MCM2,3,6RFA1,POL12SPC110,CIN8CLB1,6CTS1,EGT2PAM1,GIC2FUS3,FAR1CIK1,KAR3SPO11,SPO19GDH3,HYR1 HSP104,SSA4

Candidate regulatorGCN4ARG80/81ARG80/81/UME6/RPD3ARO80GCN4/HAP1/HAP2LEU3, GCN4LYS14CBF1, MET28, MET32MET31,MET32BAS1, BAS2, GCN4

RTG3ECM22/UPC2INO4GCR1

THI2/THI3THI2/THI3GCR1NRG1,MIG1HAA1RRPE-binding factorPAC/RRPE-binding factors

HAC1,ROX1RLM1XBP1

RPN4PHO4

HAP2/3/4/5MAC1/RCS1/AFT1/PDR1/3

HIR1,HIR2ECBMCBHCM1FKH1ACE2,SWI4

MATALPHA2,STE12KAR4NDT80ROX1,MSN2,MSN4MSN2,MSN4

249

gen

es1,

226

gen

esNon-overlapping yeast gene expression

clusters424 experiments

Chua et al., 2004

Analyzing clusters:

amino acid biosynthesis (p<10-

14)**amino acid metabolism (p<10-

14)**

methionine metabolism (p=1.07×10-7)

**When testing clusters against many different types of categorical annotations, should consider correcting for multiple-testing, and also consider

that categories are often not independent

**http://area51.med.utoronto.ca/FUNSPEC.html