Download - Direct Disease Diagnosis by DNA computing
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