1 / 28

Direct Disease Diagnosis by DNA computing

Direct Disease Diagnosis by DNA computing. 2004.2.10 임희웅. Profiling. Diagnosis Yes or No. DNA. RNA. DNA Computing. Protein. Micro-array vs. DNAC. Sample tissue. Reference. mRNAs. cDNA/Tagging. Hyb with probes. Probe design with NACST. Preparation of input molecules.

carol
Download Presentation

Direct Disease Diagnosis by DNA computing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Direct Disease Diagnosis by DNA computing 2004.2.10 임희웅

  2. Profiling Diagnosis Yes or No DNA RNA DNA Computing Protein

  3. Micro-array vs. DNAC Sample tissue Reference mRNAs cDNA/Tagging Hyb with probes Probe design with NACST Preparation of input molecules Hyb in array Digestion with S1 or bead separation Scanning Molecular algorithm Statistical processing Readout

  4. 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

  5. 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

  6. x3 x2 x1 (-) (+) yes no sum Formulation Model Expression level (concentration) Classification with threshold 0 Gene1: x1 Weighted sum Gene2: x2 Gene3: x3 t1, t2,t3 are predetermined constants from training samples

  7. 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 datageneration Analysis and Classification

  8. RNA1 RNA2 RNA3 RNA4 RNA1 RNA1 RNA2 Probe1 Probe1 Probe1 Probe1 Probe2 Probe2 Probe2 Probe2 Probe3 Probe3 Probe3 Probe3 Preprocessing and Input Data Generation + hybridization Expressed RNA Probes

  9. RNA1 RNA4 RNA1 RNA3 RNA1 RNA2 RNA2 Probe2 Probe3 Probe1 Probe1 Probe1 Probe2 RNA1 RNA1 RNA1 Probe1 Probe1 Probe1 RNA2 RNA2 S1 exonuclease Probe2 Probe2 RNA3 Probe3 • Input generation for Computing • Expression level concentration Probe1 Probe2 Probe3 Probe2 Probe3 Probe3 Hybridization Product

  10. 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

  11. t-value의 부호에 따라서 probe를 DNA 혹은 PNA로 만들어서 hybridization Exonuclease treatment Staining with intercalating dye Decision by relative amount 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 Digestion of ssRNA region Electrophoresis and staining Readout by scanning

  12. 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 Modified probes: amine기를 이용한 Column separation PCR clean-up kit Hybridization by modified complementary strands Elimination of unbound probes Readout by fluorometer Fluorescence intensity로부터 decision

  13. 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.

  14. Control Tumor Wavelength Normal Exonuclease Mix Wavelength Normal Tumor Molecular beacon Mixture Wavelength

  15. 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

  16. Old Slide

  17. 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

  18. Separation Method I Tag for separation (fluorophore) RNA1 RNA1 RNA1 Probe1 Probe1 Probe1 RNA2 Probe1 RNA2 + hybridization Probe2 Probe2 Probe2 RNA3 Probe2 Probe3 RNA4 Probe3 Probe3 Probe3

  19. RNA3 RNA4 Probe3 RNA1 RNA1 RNA1 Probe1 RNA1 Probe1 RNA1 Probe1 RNA1 Probe1 Probe1 RNA2 Probe1 RNA2 S1 exonuclease Probe2 Probe2 RNA2 RNA2 Probe2 Probe2 RNA3 Probe3 Probe1 Probe2 Probe3 Probe2 Probe3 Probe3

  20. RNA1 RNA1 RNA1 RNA1 Probe1 RNA1 Probe1 RNA1 Probe1 Probe1 Probe1 Probe1 RNA3 separation RNA2 Probe3 RNA2 Probe2 Probe2 RNA3 RNA2 Probe3 RNA2 Probe2 Probe2

  21. RNA1 RNA1 RNA1 Probe1 Comparison of nucleotides amounts Probe1 Probe1 Linear signal amplification w/o bias RNA3 Probe3 RNA2 RNA2 Probe2 Probe2

  22. Separation Method II RNA1 RNA1 Probe1 RNA1 Probe1 Probe1 Probe1 RNA2 Probe2 PNA RNA2 Probe2 + hybridization Probe2 Probe2 RNA3 Probe3 Probe3 Probe3 Probe3 RNA4 Blue block: DNA probe Green block: PNA probe

  23. RNA3 RNA4 Probe3 RNA1 RNA1 RNA1 Probe1 RNA1 Probe1 RNA1 Probe1 RNA1 Probe1 Probe1 Exonuclease RNA2 Probe1 RNA2 Probe2 Probe2 RNA2 RNA2 Probe2 Probe2 RNA3 Probe3 Probe1 Probe2 Probe3 Probe2 Probe3 Probe3

  24. Group I RNA1 RNA1 RNA1 RNA1 Probe1 RNA1 Probe1 RNA1 Probe1 Probe1 Probe1 Probe1 Separation by charge RNA3 RNA2 Probe3 RNA2 Probe2 Probe2 Group II RNA3 RNA2 Probe3 RNA2 Probe2 Probe2 ∵ PNA has no charge, and therefore, nucleic acids of group II will show less mobility than those of group I

  25. RNA1 RNA1 RNA1 Probe1 Comparison of nucleotides amounts Probe1 Probe1 Linear signal amplification w/o bias RNA3 Probe3 RNA2 RNA2 Probe2 Probe2

  26. Without Separation RNA1 RNA1 Positive! RNA1 Probe1 Probe1 Probe1 • 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. RNA2 RNA2 Probe2 Probe2 RNA3 Probe3 (+) (-)

  27. 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

More Related