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Direct Disease Diagnosis by DNA computing 2004.2.10 임희웅 Profiling DNA Diagnosis Yes or No RNA Protein DNA Computing Micro-array vs. DNAC Sample tissue Reference mRNAs cDNA/Tagging Hyb with probes Probe design with NACST Hyb in array Digestion with S1 or bead separation Preparation of input molecules Scanning Molecular algorithm Statistical processing Readout 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 디지탈지노믹스 위탁 연구 기관 폐암과 정상 폐조직 샘플의 microarray 분석 Model case에 대한 DNA computing 방법 개발 폐암 진단을 위한 표지 유전자 선별 진단용 DNA computing을 위한 알고리즘 구축 DNA computing에 의한 폐암 진단 방법 구현 1차년도 2차년도 3차년도 폐암 진단 DNA computing chip 시제품 개발 Formulation Model x3 Expression level (concentration) x1 x1t1 x2t2 x3t3 0 x2 (+) (-) Gene1: x1 Gene2: x2 Weighted sum Gene3: x3 sum x1t1 x2t2 x3t3 Classification with threshold 0 yes no t1, t2, t3 are predetermined constants from training samples sum 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 and Input data generation Analysis and Classification Preprocessing and Input Data Generation RNA1 RNA1 RNA1 RNA2 RNA2 RNA3 RNA4 Expressed RNA Probe1 Probe1 Probe1 Probe1 + hybridization Probe2 Probe2 Probe2 Probe2 Probe3 Probe3 Probe3 Probe3 Probes RNA1 RNA1 RNA1 Probe1 Probe1 Probe1 RNA1 RNA1 RNA1 Probe1 Probe1 Probe1 RNA2 RNA2 Probe2 Probe2 RNA2 RNA2 Probe2 Probe2 S1 exonuclease RNA3 Probe3 RNA3 Probe3 RNA4 Probe1 Probe2 Probe2 Probe3 Probe3 Probe3 Hybridization Product •Input generation for Computing •Expression level concentration 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. PNA Hybridization between total RNA and DNA or PNA t-value의 부호에 따라서 probe를 DNA 혹은 PNA로 만들어서 hybridization Digestion of ssRNA region Exonuclease treatment Electrophoresis and staining Readout by scanning Staining with intercalating dye Decision by relative amount 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 Readout by fluorometer Modified probes: amine기를 이용한 Column separation PCR clean-up kit Hybridization by modified complementary strands Fluorescence intensity로부터 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. Control Tumor Wavelength Exonuclease Normal Mix Wavelength Normal Molecular beacon Tumor Mixture Wavelength 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 Have to hide the gene names! Etc Verification of our method by wet-lab experiment in test tube Notice! Verification of weighted sum model by plotting real profile data 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 Without separation Separation: separation based on fluorescence, DNA/PNA probe Comparison: Measurement of the signal that is proportional to the number of nucleotides (like absorbance) 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 Separation Method I Tag for separation (fluorophore) RNA1 RNA1 RNA1 RNA2 RNA2 RNA3 RNA4 + hybridization Probe1 Probe1 Probe1 Probe1 Probe2 Probe2 Probe2 Probe2 Probe3 Probe3 Probe3 Probe3 RNA1 RNA1 RNA1 Probe1 Probe1 Probe1 RNA1 RNA1 RNA1 Probe1 Probe1 Probe1 RNA2 RNA2 Probe2 Probe2 S1 RNA3 exonuclease Probe3 RNA2 RNA2 Probe2 Probe2 RNA3 RNA4 Probe1 Probe2 Probe2 Probe3 Probe3 Probe3 Probe3 RNA1 RNA1 RNA1 Probe1 Probe1 Probe1 RNA1 RNA1 RNA1 Probe1 Probe1 Probe1 RNA3 RNA2 RNA2 Probe2 Probe2 separation Probe3 RNA3 Probe3 RNA2 RNA2 Probe2 Probe2 RNA1 RNA1 RNA1 Probe1 Probe1 Probe1 RNA3 Probe3 RNA2 RNA2 Probe2 Probe2 Linear signal amplification w/o bias Comparison of nucleotides amounts Separation Method II RNA1 RNA1 RNA1 RNA2 RNA2 RNA3 RNA4 Probe1 Probe1 Probe1 Probe1 + hybridization PNA Probe2 Probe2 Probe2 Probe2 Probe3 Probe3 Probe3 Probe3 Blue block: DNA probe Green block: PNA probe RNA1 RNA1 RNA1 Probe1 Probe1 Probe1 RNA1 RNA1 RNA1 Probe1 Probe1 Probe1 RNA2 RNA2 Probe2 Probe2 Exonuclease RNA3 Probe3 RNA2 RNA2 Probe2 Probe2 RNA3 RNA4 Probe1 Probe2 Probe2 Probe3 Probe3 Probe3 Probe3 Group I RNA1 RNA1 RNA1 Probe1 Probe1 Probe1 RNA1 RNA1 RNA1 Probe1 Probe1 Probe1 RNA2 RNA2 Probe2 Probe2 RNA3 Separation by charge RNA3 Probe3 Group II Probe3 ∵ PNA has no charge, and therefore, nucleic acids of group II will show less mobility than those of group I RNA2 RNA2 Probe2 Probe2 RNA1 RNA1 RNA1 Probe1 Probe1 Probe1 RNA3 Probe3 RNA2 RNA2 Probe2 Probe2 Linear signal amplification w/o bias Comparison of nucleotides amounts Without Separation RNA1 RNA1 RNA1 Probe1 Probe1 Probe1 RNA2 RNA2 Probe2 Probe2 RNA3 Probe3 (+) (-) Positive! •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. 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
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