Direct Disease Diagnosis by DNA computing

2004.2.10임희웅

Profiling

DNA

RNA

Protein DNA Computing

Diagnosis

YesorNo

Micro-array vs. DNACSample tissue

mRNAs

cDNA/Tagging

Hyb in array

Scanning

Statistical processing

Hyb with probes

Digestion with S1 or bead separation

Molecular algorithm

Readout

Probe design with NACST

Reference

Preparation of input molecules

Micro-array 진단칩 DNAC 진단칩

Sample 환자 RNA 환자 RNA

Instruments

분자생물학 실험장비 ( 항온기 , 원심분리기

등 )Scanner

Computer

?

Time 1~2 day <1 day, ~hours

Human intervention

Yes No

Objective Diagnosis of disease

Target disease: Lung cancer Transcribed mRNAs in the region of interest Target gene: As less as possible, 2~3 genes or more Simplify the diagnosis process: Yes/No problem

추진전략

폐암과 정상 폐조직 샘플의 microarray 분석

폐암 진단을 위한 표지 유전자 선별

진단용 DNA computing 을위한 알고리즘 구축

DNA computing 에 의한 폐암 진단 방법 구현

폐암 진단 DNA computing chip 시제품 개발

Model case 에 대한DNA computing 방법 개발

디지탈지노믹스 위탁 연구 기관

1 차년도2 차년도3 차년도

Formulation Model

Gene1: x1

Gene2: x2

Gene3: x3

Expression level(concentration)

Weighted sum

Classificationwith threshold 0

332211 txtxtxsum

t1, t2, t3 are predetermined constants from training samples

sum

no

yes

x1x2

x3 0332211 txtxtx

(+) (-)

Implementation: Profiling and Classification with DNAC

How to implement… Implementation of weighted sum by t-value

Positive/Negative weight Multiplication and summation

Classification by threshold value Method

Preprocessing andInput data generation

Analysis andClassification

Preprocessing and Input Data Generation

RNA1

RNA2

RNA3

RNA4

RNA1RNA1

RNA2+

hybridization

Probe1

Probe2

Probe3

Probe1Probe1

Probe1

Probe2Probe2

Probe2

Probe3Probe3

Probe3

Expressed RNA Probes

RNA1

Probe1RNA1

Probe1RNA1

Probe1

RNA2

Probe2RNA2

Probe2

RNA3

Probe3

RNA4

Probe1 Probe2 Probe3Probe2 Probe3

Probe3

RNA1

Probe1RNA1

Probe1RNA1

Probe1

RNA2

Probe2RNA2

Probe2

RNA3

Probe3

•Input generation for Computing

•Expression level concentration

S1exonuclease

Hybridization Product

DNAC Algorithm Basic Framework

Preprocessing by hybridization of probes and expressed RNAs. Detailed algorithm is determined by probe (DNA, PNA,

molecular beacon) and modification. Weight probe, modification

Weight Encoding SYBR CyX-nucleotide Molecular beacon

SYBR SYBR

Intercalating dye (cf. ETBR) Method

Hybridization digestion separation signal comparison

Separation: charge difference of DNA vs. PNAHybridization between total

RNA and DNA or PNA

Electrophoresis and staining

Readout by scanning

Digestion of ssRNA region

Staining with intercalating dye

t-value 의 부호에 따라서 probe 를 DNA 혹은 PNA 로 만들어서 hybridization

Decision by relative amount

Exonuclease treatment

CyX-nucleotide Weight encoding

Dye modification ratio in probe proportional to weight value Sign of weight: Red vs. Green

Method Hybridization elimination of unbound probe Read out

Hybridization between total RNA and modified probes

Elimination of unbound probes

Column separationPCR clean-up kitHybridization by modified complementary strands

Readout by fluorometer

Modified probes: amine 기를 이용한

Fluorescence intensity 로부터 decision

Molecular Beacon Weight encoding

Sign red/green dye in Molecular Beacon Weight value # of Molecular Beacon per mRNA

Pros and Cons Need no separation Need no digestion But, high cost.

Tumor

Normal

Mix

Mixture

Exonuclease

Control

Wavelength

Wavelength

Wavelength

Normal

Tumor

Molecular beacon

To do… Preliminary experiment before Lung cancer

Real data from Digital Genomics Inc. Real genes from Digital Genomics Inc. (Cell line) Verification of classification model

Verification of weighted sum model by plotting real profile data Verification of our method by wet-lab experiment in test tube

Notice! Have to hide the gene names!

Etc Consideration of the implementation on Lab-on-a-Chip Other statistical method for diagnosis

Paper Title Direct Disease Diagnosis by DNA Computing Novel Molecular Algorithm for Disease Diagnosis

Old Slide

Detailed Method Implementation of weighted sum and detection With or without separation

With separation Separation: separation based on fluorescence, DNA/PNA probe Comparison: Measurement of the signal that is proportional to the number

of nucleotides (like absorbance) Without separation

Detection by modification of every nucleotide? Weight representation

Probe length Execution of weighted sum by only the combination of hybridization and

S1 nuclease digestion (or bead separation) Multiplication counting the total nucleotides number

# of dye in probe Molecular beacon # of dye modification proportional to weight Representation of (+)/(-): fluorescence

RNA1

RNA2

RNA3

RNA4

RNA1RNA1

RNA2+

hybridization

Probe1

Probe2

Probe3

Probe1Probe1

Probe1

Probe2Probe2

Probe2

Probe3Probe3

Probe3

Tag for separation

(fluorophore)

Separation Method I

Probe1

RNA3

Probe3

S1exonuclease

RNA4

Probe1 Probe2 Probe3Probe2 Probe3

Probe3

RNA3

Probe3

RNA1

RNA2

RNA1

Probe1Probe1

RNA1

Probe1

RNA1RNA1

Probe1Probe1

RNA1

Probe2RNA2

Probe2RNA2

Probe2RNA2

Probe2

Probe1

RNA1RNA1

Probe1Probe1

RNA1

separation

Probe1

RNA1RNA1

Probe1Probe1

RNA1

RNA3

Probe3

RNA2Probe2

RNA2

Probe2

RNA2Probe2

RNA2

Probe2

RNA3

Probe3

Probe1

RNA1RNA1

Probe1Probe1

RNA1

RNA2Probe2

RNA2

Probe2

RNA3

Probe3

Comparison of nucleotides

amounts

Linear signal

amplification w/o bias

RNA1

RNA2

RNA3

RNA4

RNA1RNA1

RNA2+

hybridization

Probe1

Probe2

Probe1Probe1

Probe1

Probe2Probe2

Probe2

Probe3Probe3

Probe3Probe3

Blue block: DNA probeGreen block: PNA probe

Separation Method II

PNA

Probe1

RNA3

Probe3

Exonuclease

RNA4

Probe1 Probe2 Probe3Probe2 Probe3

Probe3

RNA3

Probe3

RNA1

RNA2

RNA1

Probe1Probe1

RNA1

Probe1

RNA1RNA1

Probe1Probe1

RNA1

Probe2RNA2

Probe2RNA2

Probe2RNA2

Probe2

Probe1

RNA1RNA1

Probe1Probe1

RNA1

Separation by charge

Probe1

RNA1RNA1

Probe1Probe1

RNA1

RNA3

Probe3

RNA2Probe2

RNA2

Probe2

RNA2Probe2

RNA2

Probe2

RNA3

Probe3

∵ PNA has no charge, and therefore, nucleic acids of group II will show less mobility than those of group I

Group I

Group II

Probe1

RNA1RNA1

Probe1Probe1

RNA1

RNA2Probe2

RNA2

Probe2

RNA3

Probe3

Comparison of nucleotides

amounts

Linear signal

amplification w/o bias

Without Separation

RNA3

Probe3

Probe1

RNA1RNA1

Probe1Probe1

RNA1

RNA2

Probe2RNA2

Probe2

•If it is possible to modify every nucleotide in probes…

•Modify every nucleotide in probe differently along the sign of the weight.

•Diagnosis by observing the final signal of preprocessed input data.

(+) (-)

Positive!

To do… Verification of classification model

Verification of weighted sum model by plotting real profile data

Other statistical method for diagnosis Available experimental technique or new

Other amplification/detection methods Signal amplification (up to detection limit) Consideration of the implementation on Lab-on-a-

Chip