Printing area: 5.4 X 1.8 cm

DNA Microarray /Expression

안성환

(주)지노믹트리

Genomictree GeneTrack Human DNA microarray

DNA Microarray

Intron

Exon: 4만gene - PCR product

- Oligo DNA

. Methylation assay

AAAA

RNA Protein

CGCGAT GCGCTA

CG C GAT C G GCGCTAAC

CH

^SNP 증폭

결실 C

DNA Chromosome

LOH

자극 전달

반응 MSI

expression

Methylation

Genetic and Epigenetic alteration through second hit affects on gene expression

miRNA

환경

유전자1

유전자2 유전자3

Central Dogma ? Central Dogma ?

DNA

mRNA Protein

근육세포 피부세포 신경세포

유전자1

유전자2 유전자3

Normal Cell Cancer Cell

어떻게 차이를 구분할 것인가?

정상세포와 암세포 : 유전자 활동차이

RNA identification and differential detection

in molecular level

Northern blot Primer extension RNase Protection RT-PCR

DD-PCR

Real time –PCR Microarray

Profile of Differential Expression Single gene

To

Genomic level

P32*

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

2

3X

6 2

3X

Northern Blot Array-based Blot

2 6

N

N N

N

N

N

Individual Amplification RT-PCR

Individual detection

Gel-based assay Microarray-based Assay

Hybridization RNA

From low to High throughput Assay

Multiplex amplification RT or PCR

Gel

electrophoresis

Human 35K

Row 27 x Column 27 x 48 blocks = 34,992genes Printing Area : 54 mm x 18 mm

Hybridization image

7.5 cm

Printing area: 5.4 X 1.8 cm

Genomictree

Each gene in each well of plate

Robotic machine

Pin head

Open slot

Pin : capillary

DNA microarray

-Total RNA

Target labeled

Reference(N) Test(T)

Cy5-dUTP Cy3-dUTP

Scan

microarray experiment

=

Hybridization Level

⇒ Cy5 Cy3

Normal

Tissue cancer

Tissue

Competitive hybridization

Equal mix

Reverse transcription

DATA MINING

control Test

Probe

Up regulated gene No change Down regulated gene 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 microarray를

GenePix 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 spot의 위치가 파악된다.

③ “Stop” icon을 click

Prescan

① “Prescan” 시작

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

크게 확대하여 작업하면 더욱 정확하게 위치 시킬 수 있다.

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

→ 모든 data가 excel의 형식으로 나타난다.

① “Analyze” icon을 click.

② Data가 analysis 되는 진행상황이 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”을 click하면, 자동으로 sorting이 된다.

② mouse와 shift+mouse 를 사용하여 flag-out 할 범위를 지정한다.

③ 범위가 지정된 후, mouse 오른쪽 click 하여, “Flag-bad”를 선택하면, 지정된 범위는 삭제된다.

Flag-out된 feature는 Scatter-plot에서 사라진다.

Profiling Patterns of Gene expression to HCV core protein in Hepatocytes

Unsuitable spots flagged and excluded

Gridding

Clustering Gene

net signal

Average pixel intensity of each spot minus 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) Cy3

vs 3h

6h

48hrs

465 gene

> 2 fold on at least one array

> 80% good spot

> 50 Ch1 and Ch2 net norm

Clustering

Down- Up-

Insufficient

Tree

View

200 10000 50.00 5.64 4800 4800 1.00 0.00 9000 300 0.03 -4.91

Cy3 Cy5

Cy5 Cy3

Cy5

log₂ Cy3

Genes

Experiments

DNA microarray 분석 system

Up

Down

Clustering

Data Submit

Data Extract

Graphical

display

GT DB

Experiments n-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

Normal

tissue ASCUS LSIL HSIL CIS SCC Cancer

SIL : Squamous intraepithelial lesion CIS : Carcinoma in situ

SCC : Squamous cell carcinoma

Development of Cervical Cancer

Phenotype---Æ host molecular classification

HPV genotype

Expression Kinetics during disease progression

Low grade : Group A

High grade : Group B

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 Predictor Of 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 than

Standard systems

Van de Vijver MJ, et al. NEJM2002.

Van’t Veer LJ, et al. Nature 2002

MammaPrint R Test Technology

provides 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 tumor Supervised classification 70gene selected

Strongly 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-vitro

Diagnosis and prognosis

-> better treatment

Diagnostics move towards earlier detection

and differential diagnostics for unmet medical needs utilizing Nucleic Acid-based Molecular markers

tumor mass (cell)

10

10

Genetic Predisposition

tests

dysplasia Early cancer Cancer screening test

Tumor diagnostics

Timeline of carcinogenesis

Cancer in-situ Clinically overt cancer Relapse/metastasis

Early detection( non or less invasive))

Theranostics(differential)

Monitoring Therapy recurrences

DNA-based

DNA or RNA- based

Tumor

Control vs Tumor

SNPs

CGH, cDNA

Oligonucleotide

Antibody chip Analysis

Tumor classification Identification of clusters or

Individual marker genes

Clinical validation with

tissue microarrays

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

Microarray hybridization

Methylation

Genetic

Epigenetic

We need clinical research collaboration

Novel markers

Clinical assessment of

predictive value discovery

Standard of care

Pilot studies &

Assay development

Phase I

Retrospective clinical analysis

Phase II

Prospective confirmatory analysis

Phase III Phase IV

Multi-institutional Validation trials

SNPs

CGH, cDNA

Oligonucleotide

Antibody

Methylation

Eearly detection prognosis

Thank you