1. EE381V: Genomic Signal Processing Lecture #19

2. ABI Prism ® 310 Genetic AnalyzerAffymetrix GeneChip ® Roche LightCycler ® DNA Sequencing Gene Expression Profiling (Microarrays) DNA Amplification (PCR systems) DONE Bio-molecular Identification and Detection 2

3. DNA Microarrays vs. PCR Systems 3 High throughput (microarrays) and high precision (polymerase chain reaction -- PCR) biosensors –essential research tools, a rapidly growing field in industry and medicine DNA Microarrays: high throughput (test tens of thousands of genes) –M. Schena, D. Shalon, R.W. Davis, P.O. Brown: “Quantitative monitoring of gene expression patterns with a complementary DNA microarray” Science (1995). –massively parallel biosensor arrays –used for studies of genetic diseases, drug discovery, genotyping (the specific genome of an individual), genetic pathway discovery, etc. Real-Time PCR: high precision (quantifies small # of DNA molecules) –K. Mullis and F. Faloona, “Specific synthesis of DNA in vitro via a polymerase-catalyzed chain reaction,” Methods Enzymol (1987). –in vitro replication (amplification) of DNA molecules –applications to diagnostics (viral and bacterial detection), cancer markers identification, genetic fingerprinting (as in forensics), etc.

4. Fundamentals of Detection Biological Assay Transducer/ Interface Detection Circuitry Detection Biochemical Uncertainties Fabrication and Process Variation Electronic Noise Quantization Noise Molecular Biology Biochemistry Fabrication/ Synthesis Processes Circuit Design Signal Processing Biological Sample Data ADC 1.4203 01011 DNA Microarrays Photodiode Image SensorAnalog to Digital Conversion Bio-molecular Detection Systems Bio-molecular detection systems, in general, have the following sub-blocks: 4

5. Analogy with Wireless Communications Physical layer of communication in very similar to the assay of biosensors: 5

6. Amplifying Biological Signals Biological Assay Transducer/ Interface Detection Circuitry Detection Biochemical Uncertainties Fabrication and Process Variation Electronic Noise Quantization Noise Biological Sample Data Amplification In biosensors, biochemical processes by which we can increase the number of specific analytes in a controlled fashion are called target amplification Biological signals (essentially, analytes) in biosensors can be amplified: 6

7. 12345 Number of bacteria Time Log phase (exponential growth) Stationary phase Death phase 1 2 3 4 5 Amplifying Biological Signals: An Example 7 Various types of bacteria can be “amplified” and detected

8. 5’ 3’ Template (region to be replicated) AC GT Polymerase Nucleotides ACGTT TGCAA 5’ 3’ ACGTT TGCAA 5’ 3’ 5’ 3’ Primer DNA Replication and Amplification DNA polymerase can be used to replicate an ssDNA template: DNA polymerase replicates the complementary strand of the DNA template by incorporating appropriate nucleotides into the strand 8

9. Template ssDNA DNA Polymerase Primer 1 2 3 Hybridization Polymerization DNA template, primer, nucleotides, and DNA polymerase are added together Primer hybridizes to its complementary sequence Polymerase incorporates nucleotides and replicates DNA DNA Replication and Amplification DNA polymerase can be used to replicate an ssDNA template: 9

10. ACGTT TGCAA 5’ 3’ Template (region to be replicated) 5’ 3’ 5’ 3’ 5’ 3’ Polymerization Denaturing ACGTT TGCAA 5’ 3’ 5’ 3’ Linear DNA Amplification Multiple DNA polymerization steps with high temperature steps in between can be used to amplify DNA: 10

11. “Amplification” Gain (G) Number of Cycles (M) Ideal Amplification Actual Amplification Deviation from ideal amplification is due to: 1.Incomplete polymerization 2.Amplicon-template hybridization Saturation is due to: 1.Amplicon-template hybridization 2.Enzyme degradation 3.Nucleotide depletion Is the efficiency of amplification 11 Linear DNA Amplification Cont’d Multiple DNA polymerization steps with high temperature steps in between can be used to amplify DNA:

12. If is the initial number of DNA strands and the final number, we have Expectation Variance For large, we can assume a Gaussian distribution for amplicons Only applicable to the linear region of amplification 12 Linear DNA Amplification Cont’d Linear DNA amplification has an expected behavior plus noise

13. The amplicon can be labeled during the PCR amplification: 12 Primer LabelingIncorporating Labeled Nucleotides Primer Amplicon Template Label Labeled Nucleotides Purification after amplification is needed to detect DNA Labeling During Amplification Purification after amplification is needed to detect DNA 13

14. The labeling process is in fact a stochastic chemical process, and its result is therefore probabilistic There is little data in literature regarding its uncertainty, but it seems that 5-10% error is typical Yield Error % 1 Original sample quantity Labeling During Amplification Cont’d 14

15. Polymerase Chain Reaction (PCR) If we replicate both strands of a dsDNA using polymerization, the number of amplicons will grow exponentially Efficiency of PCR: a fraction of the dsDNA successfully copied during a cycle 15

16. 16 In PCR a specific region of DNA specified by the probes can be amplified Polymerase Chain Reaction (PCR) Cont’d Amplification gain of PCR:

17. The amplicon can be labeled during the PCR amplification: 123 Primer LabelingIncorporating Labeled Nucleotides dsDNA Binding Dyes Primer Amplicon Template Label Labeled Nucleotides dsDNA Binding Dyes These dyes get activated only when they bind to dsDNA Purification after amplification is needed to detect DNA Labeling During Amplification Purification after amplification is needed to detect DNA 17

18. The amplification in PCR has an exponential phase but eventually saturates: Saturation Exponential Amplification gain: PCR Amplification 18

19. Saturation Exponential Saturation is due to: 1.Amplicon-template hybridization 2.Enzyme degradation 3.Nucleotide depletion Deviation from ideal amplification is due to: 1.Incomplete polymerization 2.Amplicon-template hybridization PCR Amplification The amplification in PCR has an exponential phase but eventually saturates: 19

20. Hepatitis B virus : DNA Sequence Start: 1 ctccacaacc ttccaccaaa ctctgcaaga tcccagggtg agaggcctgt atttccctgc 61 tggtggctcc agttcaggaa cagtaaaccc tgttccgact actgcctctc ccatatcgtc 121 aatcttctcg aggattgggg accctgcgct gaacatggag aacatcacat caggattcct 181 aggacccctg ctcgtgttac aggcggggtt tttcttgttg acaagaatcc tcacaatacc 241 gcagagtcta gactcgtggt ggacttctct caattttcta ggggggacca ccgtgtgtct 301 tggccaaaat tcgcagtccc caacctccaa tcactcacca acctcctgtc ctccaacttg 361 tcctggttat cgctggatgt gtctgcggcg ttttatcatc ttcctcttca tcctgctgct 421 atgcctcatc ttcttgttgg ttcttctgga ctatcaaggt atgttgcccg tttgtcctct 481 aattccagga tcttcaacca ccagcgtggg accatgcaga acctgcacga ctactgttca 541 aggaacctct atgtatccct cctgttgctg taccaaacct tcggacggaa attgcacctg 601 tattcccatc ccatcatcct gggctttcgg aaaattccta tgggagtggg cctcagcccg 661 tttctcctgg ctcagtttac tagtgccatt tgttcagtgg ttcgtagggc tttcccccac 721 tgtttggctt tcagttatat ggatgatgtg gtattggggg ccaagtctgt acagcatctt 781 gagtcccttt ttaccgctgt taccaatttt cttttgtctt tgggtataca tttaaaccct 841 aacaaaacta aaagatgggg ttactcttta aatttcatgg gctatgtcat tggatgttat 901 gggtcattgc cacaagatca catcatacaa aaaatcaaag aatgttttag aaaacttcct 961 gttaacaggc ctattgattg gaaagtctgt caacgtattg tgggtctttt gggttttgct 1021 gctcctttta cacaatgtgg ttatcctgct ttaatgccct tgtatgcctg tattcaatct 1081 aagcaggctt tcactttctc gccaacttac aaggcctttc tgtgtaaaca atacctgaac 1141 ctttaccccg ttgcccggca acggcccggt ctgtgccaag tgtttgctga cgcaaccccc 1201 actggctggg gcttggtcat gggccatcag cgcatgcgtg gaacctttct ggctcctttg 1261 ccgatccata ctgcggaact cctagccgct tgttttgctc gcagcaggtc tggagcaaac 1321 attctcggga cggataactc tgttgttctc tcccgcaaat atacatcatt tccatggctg 1381 ctaggctgtg ctgccaactg gatcctgcgc gggacgtcct ttgtttacgt cccgtcggcg 1441 ctgaatcccg cggacgaccc ttctcggggc cgcttgggac tctatcgtcc ccttctccgt 1501 ctgccgttcc gtccgaccac ggggcgcacc tctctttacg cggactcccc gtctgtgcct 1561 tctcatctgc cggaccgtgt gcacttcgct tcacctctgc acgtcgcatg gagaccaccg 1621 tgaacgccca ccacttcttg cccaaggtct tacataagag gactcttgga ctctctgtaa 1681 tgtcaacgac cgaccttgag gcatacttca aagactgttt gtttaaagac tgggaggagt 1741 tgggggagga gattagatta aaggtctttg tactaggagg ctgtaggcat aaattggtct 1801 gcgcaccagc accatgcaac tttttcacct ctgcctaatc atctcttgtt catgtcctac 1861 tgttcaagcc tccaagctgt gccttgggtg gctttggggc atggacattg acccttataa 1921 agaatttgga gctactgtgg agttactctc gtttttgcct tctgacttct ttccttcgct 1981 acgagatctt cttgataccg cctcagctct gtatcgggaa gccttagagt ctcctgagca 2041 ttgttcacct catcatactg cactcaggca agctatcctt tgctgggggg agctaatgac 2101 tctagctacc tgggtgggtg ttaatttgga agatccagca tctagggacc tagtagtcag 2161 ttatgtcaac actaatatgg gcctaaagtt caggcaacta ttgtggtttc acatttcttg 2221 tctcactttt ggaagagaaa cggtcataga gtatttggtg tctttcggag tgtggattcg 2281 cactccacca gcttatagac cacctaatgc ccctatctta tcaacacttc cggagactac 2341 tgttgttaga ggacgaggca ggtcctctag aagaagaact ccctcgcctc gcagacgaag 2401 gtctcaatcg ccgcgtcgca gaagatctca atctcgggaa tctcaatgtt agtattcctt 2461 ggactcataa ggtgggaaac tttacggggc tttattcctc tactgtacct gtctttaacc 2521 ctcattggaa aacaccttct tttcctaata tacatttaca ccaagacatc atcaaaaaat 2581 gtgaacaatt tgtaggtcca ctcacagtca atgagaaacg aagactgcaa ttaattatgc 2641 ctgctaggtt ttatccaaat gttaccaaat atttgccatt agataagggt attaaacctt 2701 attatccaga acatctagtt aatcattact tccaaaccag acattattta cacactcttt 2761 ggaaggcggg tatattatat aagagagaaa caacacgtag cgcctcattt tgtgggtcac 2821 catattcttg ggaacaaaag ctacagcatg gggcagaatc tttccaccag caaccctctg 2881 ggattctttc ccgaccacca gttggatcca gccttcagag caaactccgc aaatccagat 2941 tgggacttca atcccaacaa ggacacctgg ccagccgcca acaaggtagg agctggagca 3001 ttcgggctgg gattcacccc accgcacgga ggccttttgg ggtggagccc tcaggctcag 3061 ggcataatac aaaccttgcc agcaaatccg cctcctgcat ctaccaatcg ccagtcagga 3121 aggcagccta ccccgctgtc tccacctttg agaaacactc atcctcaggc catgcagtgg 3181 aa // Unique region 20 Example: Detecting Viruses with PCR

21. Unique region Primers can be designed such that exponential amplification only occurs in the presence of virus Amplification region Amplicons of PCR Amplification 21 Example: Detecting Viruses with PCR

22. 22 Example: Detecting Viruses with PCR Non-specific amplification results in linear growth, but specific amplification results in exponential growth

23. 23 Quantitative PCR Real-time PCR: the number of the DNA measured at the end of each cycle Three phases of RT-PCR reaction: background phase –background noise dominates the useful signal exponential growth phase –signal from the PCR products rises sufficiently above the background noise saturation phase –the efficiency decreases rapidly as the reaction enters the plateau

24. 24 Statistical Model of the PCR Random nature of the underlying biochemical process leads to variations in the PCR yield. – –efficiency not 1 – –efficiency drops in saturation Creation of non-specific byproducts in the replication process and primer-dimers further diminishes purity of the PCR product. Essentially, a branching process: generated DNA copies

25. 25 Statistical Model Cont’d Notation and further assumptions: – –x 0 : the initial number of target molecules (which we want to estimate) – –x n : the number of molecules at the end of the n th cycle – –p: the efficiency during both the background and exponential phase Branching process progression: – – : a random variable with zero mean and variance p( 1 -p)x n-1. The mean of x n is given by The variance of x n can be found as

26. 26 Statistical Model Cont’d Imperfect instrumentation and other biochemistry independent sources create a noise Measured quantity is where w n is modeled as a Gaussian k: the number of cycles in the background phase l: the number of cycles in the exponential phase y comprised of measurements taken in the exponential phase Introduce a new variable:

27. 27 Joint ML Estimation of p and x 0 Obtaining the distribution of y appears difficult – –we invoke the central limit theorem, approximate it by a Gaussian where we find that Now we can find the joint ML estimator as the solution to For a comparison, note that the state of the art approach consists of 2 steps:

28. Real-Time Multiplex PCR However… multiplex assays are tedious and time-consuming to establish, requiring lengthy optimization procedures due to noise, resolution varies with the sample size –to distinguish between 50 and 100 copies is much more challenging than to distinguish between 5000 and 10000 copies quest for high-throughput may compromise a major advantage of real- time PCR: small sample size A variant of real-time PCR enables simultaneous amplification of multiple targets of interest a broad dynamic range: up to 7 orders of magnitude high sensitivity: may be able to detect as few as 10 copies of each target requires small sample size 28

29. Sample size vs. number of wells let X P denote the number of molecules of a (single) target in a sample the sample is distributed among wells (say, N P of them) must ensure that each well contains at least one copy of each target easy to show that this translates to X P > 10N P. –so, on a 96-well plate, the sample should contain at least 10 3 copies of each target –this, of course, is the bare minimum which may not be sufficient to provide desired precision Design of experimentsDesign of experiments as the number of wells increases, the complexity of the experiment growsas the number of wells increases, the complexity of the experiment grows –primers must be delivered to appropriate wells –optimal parameters of the experiment (temperature, time profile) conceivably differ among various targets 29 Real-Time Multiplex PCR