DNA Microarray /Expression

안성환

(주)지노믹트리

Genomictree GeneTrack Human DNA microarray

DNA Microarray

Intron

Exon: 4만gene- PCR product- Oligo DNA

. Methylation assay

AAAA

RNAProtein

CGCGAT

GCGCTACGCGATCG

GCGCTAAC

CH3 SNP증폭

결실 C

DNAChromosome

LOH

자극전달

반응

MSI

expression

Methylation

Genetic and Epigenetic alteration through second hitaffects on gene expression

miRNA

환경

유전자1

유전자2

유전자3

Central Dogma ?Central Dogma ?

DNAmRNA Protein

근육세포 피부세포 신경세포

유전자1

유전자2

유전자3

Normal Cell Cancer Cell어떻게 차이를구분할 것인가?

정상세포와 암세포 : 유전자 활동차이

RNA identification and differential detectionin molecular level

Northern blotPrimer extensionRNase ProtectionRT-PCRDD-PCR

Real time –PCR

Microarray

Profile of Differential Expression

Single gene

To

Genomic level

P32* Probe (gene)

Target(total RNA)

C T C T

C T

Test

Control

2

6

Probe (gene)

Cy3 Cy5

Target(total RNA)

C T

Test

Control

6

23X

6

23X

Northern Blot Array-based Blot

2 6

N

N

N

N

N

N

Individual AmplificationRT-PCR

Individual detectionGel-based assay Microarray-based Assay

Hybridization

RNA

From low to High throughput AssayMultiplex amplificationRT or PCR

Gelelectrophoresis

Human 35KRow 27 x Column 27 x 48 blocks = 34,992genesPrinting Area : 54 mm x 18 mm

Hybridization image 2.5 cm

7.5 cm

Printing area: 5.4 X 1.8 cm

Genomictree

Each gene in each well of plate

Robotic machine

Pin head

Open slotPin : capillary

DNA microarray

-Total RNA

Target labeled

Reference(N) Test(T)

Cy5-dUTPCy3-dUTP

Scan

microarray experiment

=Hybridization

Level ⇒ Cy5

Cy3

NormalTissue

cancerTissue

Competitive hybridization

Equal mix

Reverse transcription

DATA MINING

control Test

Probe

Up regulated gene Down regulated geneNo change

Competitive hybridization

Ratio of intensity

Noncompetitive hybridization

Control RNA Test RNA

1. Scanning 30분전에 scanner를 power-on하여 pre-warming.

2. GT-microarray hybridization method에 의해 실험이 수행된 microarray를GenePix 4000(Axon) scanner로 가지고 온다.

4. Scanner의 덮개를 좌측으로 미끄러지듯이 열고, microarray를 넣는다.

5. Cassette 좌측의 lever를 움직여, microarray를 고정시킨다.

6. Scanner 덮개를 닫으면, scan할 준비가 된다.

9. “Prescan ” icon을 click한다.

- 전체 microarray의 대략적인 실험 결과, scan하기 위한 spot 위치등의 파악.

10. Spot의 위치가 파악되면, “stop ” icon을 click하여, prescan을 멈춘다.

② Prescan으로 spot의위치가 파악된다.

③ “Stop” icon을 clickPrescan

진행 방향

① “Prescan” 시작

18. Gridding file을 선택하면 각 spot(feature)에 맞게 제작된 grid box가 나온다. Mouse와 keyboard를 사용하여, 모든 grid box를 각 spot(feature)에 알맞게 위치시킨다. (자동으로 alignment하므로, 정확하게 맞출 필요는 없다.)

크게 확대하여 작업하면 더욱 정확하게위치 시킬 수 있다.

20. Alignment가 완료된 후, “Analyze ” icon을 click.

→ 모든 data가 excel의 형식으로 나타난다.

① “Analyze” icon을click.

② Data가 analysis 되는진행상황이 보인다.

Alignment가 된 feature Data로 쓸 수 없어 flag-out된 feature

21. Data analysis가 끝난 후, excel과 scatterplot 형식으로 output되는 data.

Result panel Scatter-plot panel

2X up-regulated genes

2X down-regulated genes

22. “Results” panel에서, Sum of Median 값이 <1000인 feature를 제거(flag-

out)하여, false-positive를 최대한 감소시킨다.

① “sum of median”을click하면, 자동으로sorting이 된다.

② mouse와 shift+mouse를 사용하여 flag-out할 범위를 지정한다.

③ 범위가 지정된 후,mouse 오른쪽 click하여, “Flag-bad”를선택하면, 지정된범위는 삭제된다.

Flag-out된 feature는Scatter-plot에서 사라진다.

Profiling Patterns of Gene expression to HCV core protein in Hepatocytes

Unsuitable spots flaggedand excluded

Gridding

GeneClustering

net signal

Average pixel intensity of each spotminus local background intensity equals net signal of each spot

Up-regulated genes

Down-regulated genes

Test : core expression ( Tet-minus ) Cy5

Reference : no core expression (Tet plus) Cy3vs

3h6h

48hrs

465 gene > 2 fold on at least one array > 80% good spot > 50 Ch1 and Ch2 net norm

Clustering 465 genes selected

Down- Up-

Insufficient

TreeView

200 10000 50.00 5.644800 4800 1.00 0.009000 300 0.03 -4.91

Cy3 Cy5Cy5Cy3

Cy5Cy3log2

Gen

es

Experiments

DNA microarray 분석 system

Up

Down

Clustering

Data Submit

Data Extract

Graphical displayGT DB

Experimentsn-1

Tree View

Down-regulated

Up-regulated

normal cancer

oncogene

Tumor surppressor

Target discovery and development via expression profiling

Low grade lesions

high grade lesions

Normaltissue

CancerHSIL CIS SCCASCUS LSIL

SIL : Squamous intraepithelial lesionCIS : Carcinoma in situSCC : Squamous cell carcinoma

Low SIL High SIL

Development of Cervical Cancer

Phenotype--- host molecular classification

HPV genotype

Expression Kinetics during disease progression

Low grade: Group A

High grade: Group B

Diff

eren

t ial ly

exp

res s

ed g

e nes

Normal LSIL HSIL CIS Cancer

Selection for genes gradually down-regulated in cancer formation

Expression profile of cervical biopsy sample

(ANOVA, p < 0.01)

Gene expression signature as PredictorOf Survival in Breast Cancer

-Inkjet-synthesized oligonucleotide microarrays, 25000 oligos-70-gene prognosis profile,98 patients-295 consecutive breast cancer patients-Gene-expression profile is a more powerful predictor of-Disease outcome in young breast cancer patients thanStandard systems

Van de Vijver MJ, et al. NEJM2002. Van’t Veer LJ, et al. Nature 2002

MammaPrint R Test Technologyprovides the means for selecting patients who would benefit from adjuvant therapy and those who can be spared the serious impact of these treatments

-Regulating cell cycle-Invasion-Metastasis-angiogenesis

Primary breast tumorSupervised classification70gene selectedStrongly predict poor prognosis

Agendia,Inc

Personalized approach and classification to Cancer through Genomics and Epigenomics

Phenotype

Genotype

Early detection and right classification

New generation of in-vitroDiagnosis and prognosis-> better treatment

Diagnostics move towards earlier detection and differential diagnostics for unmet medical needs

utilizing Nucleic Acid-based Molecular markerstumormass(cell)

108

104

GeneticPredisposition

tests

dysplasia Early cancer

Cancer screening test

Tumor diagnostics

Timeline of carcinogenesisCancer in-situ Clinically overt cancer Relapse/metastasis

Early detection( non or less invasive))

Theranostics(differential)

MonitoringTherapyrecurrences

DNA-based DNA or RNA- based

Tumor

Control vs

Tumor

SNPs

CGH, cDNA

Oligonucleotide

Antibody chip

Analysis

TumorclassificationIdentification ofclusters orIndividual markergenes

Clinicalvalidation with

tissuemicroarrays

General scheme of the procedure used in tumor expression profiling for target identification and validation

Microarrayhybridization

Methylation

Genetic

Epigenetic

We need clinical research collaboration

Novel markersClinical assessment of

predictive valuediscovery

Standard of care

Pilot studies &Assay development

Phase I

Retrospective clinicalanalysis

Phase II

Prospective confirmatoryanalysis

Phase III

Phase IVMulti-institutionalValidation trials

SNPs

CGH, cDNA

Oligonucleotide

Antibody

Methylation

Eearly detectionprognosis

Thank you