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Real time PCR

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					Principle of Real-Time qPCR
             AND
        Applications


                 Han-Oh Park, Ph.D.
                   President & CEO

                BIONEER CORPORATION
   -목 차-
      회사소개

Principle of Real-Time
qPCR AND Applications

      Exicycler

  Understanding
 Real Time PCR Data
    회사 개요
회 사 명                   ㈜바이오니아 (BIONEER CORPORATION)
대표이사         박한오                  주   소         대전광역시 대덕구 문평동 49-3

종업원수         175명                 설 립일               1992년 8월 28일

자 본금       5,675백만원              발행주식수         11,350,000주 (1주 액면가액 500원)

                                 최근 매출
홈페이지    www.bioneer.co.kr                  116억원(’06), 100억원(’05), 92억원(’04)
                                   액

    인원 현황                                                     (2007년 6월말 현재)

[직능별]      연구직              생산직           영업직          관리직            계

  인원수        57             70            25            23           175

[학력별]       박사              석사            학사            기타            계

  인원수        16             48            55            56           175
          유전자신약       유전자진단시약
            사업         /시스템 사업




  BT사업            BIT사업          NBT사업




합성유전자 유전자시약   유전자장비     Bio      나노바이오
  사업    사업      사업    Defense      사업
  기반 사업군                 유전자      미생물
                         신약       신약

                          Bio     나노
유전자신약 유전자   합성          Defense
       시약       유전자               바이오
 Tool
            유전자 진단시약

 합성 유전자       유전자
             진단시스템            Star
유전자 장비

             Cash Cow
  1차 세계화 전략         2차 세계화 전략          3차 세계화 전략

   2001~2005          2006~2010         2011~

 생명공학 핵심소재인       세계 중요 바이오 클러스터에      글로벌 바이오텍
                   유전자 사업기반 구축         기업으로 성장
합성 유전자를 선도사업
으로 세계 Marketing   유전자 진단, 나노 바이오,      유전자 신약 회사로
  Network 구축      Biodefense분야의 글로벌화     위상 확립



          미국 현지공장 설립  고품질의 합성유전자 현지공급
               ABADIS(Biodefense분야) 해외수출추진
          미국, 유럽 현지법인 설립으로 2차 세계화 전략 진행 중
      중국 판사처 설립  잠재적 거대시장 진입을 위한 사전 준비작업 진행
이 름      직 책                                        약    력
                     서울대 화학과 졸업 / KAIST 생화학 석사,박사
                     KIST 생명공학연구원 연구원/ KAIST 바이오시스템학과 겸임교수/ 서강대 바이오
                    융합과정
박한오      대표 이사        겸임교수
                     BIT산업협의회 회장/ 한국산업미생물학회 부회장/ 한중생명공학 협력센타 운영
                    위원
                     서울대 화학과 석사 / Univ. of Michigan 박사수료
이재돈   유전자신약 사업부장     KAIST, Univ. of Michigan, Organic Synthesis Lab 연구원

                     고려대학교 전기공학과
박한이   유전자시스템사업부장     삼성전기 회로사업부 연구실 / LG전자 중앙 연구소 선임연구원

                     서울대학교 농화학과 / KAIST 생명과학과 박사
박해준   유전자진단사업부장      목암생명공학연구소 진단연구실 수석연구원

                     연세대학교 화학공학과 / KAIST 화학공학과 박사
김재하   나노바이오 사업부장     대한유화㈜ 기술연구소 / KAIST 화학공학과 반응표면 연구실 연구원

                     서울대학교 미생물학과 / Univ. of North Carolina at Chapel Hill 박사
최영철   유전자신약연구소장      KAIST 유전공학센터, St. Jude Children's Research Hospital 연구원

      바이오인포메틱스사업부    고려대학교 생물학과 / KAIST 생명과학과 박사
원세연                  과학기술정보연구원 선임연구원 / 생물정보연구소 대표
           장
                     중국 북경의과대학 / 서울대학교 보건학 박사
문 용   중국 판사처 수석대표    중국 북경의과대 교수 / 북경의대 한국주재 대표

                     영남대학교 미생물학과 / 충북대학교 미생물학과 박사
유재형     영업본부장        대전보건대학 겸임교수 / ㈜씨젠 기술이사(CTO)

                     충북대학교 경영학과 / 원가관리사
정진평    경영지원본부장       오성사 기획실 / 스카이전자 경리부
                                                                            도약기
                                                                            2004~
                                                                  04.07 합성유전자 차세대 세계일류상품
                                                                          선정 (산자부)
                                        성장기                       04.09 합성유전자 NT(신기술)인증
                                      2000~2003                         (산자부)
                                                                  04.11 Exicycler EM마크 인증(산자부)
                              00.03 제품4종 조달청 우수제품 선정
                                    제1회 바이오산업협회상 수상               05.07 유럽현지법인(BEL) 설립
                              00.12
                              01.04 미국 현지법인 설립                    05.10 한국응용생물화학회 기술상 수상
       설립기                    01.05 염기서열자동분석장치 출시                 05.10   100만불 수출의 탑 수상
   1992~1999                  01.07 2001 대한민국기술대전
                                    산업자원부 장관상 수상
                                                                  05.12   코스닥 상장
                                                                  06.03   나노바이오사업부 신설
92.08 ㈜한국생공 설립                02.05 월드컵 테러방지 생물학탐지기,                      미국 현지법인 유전자합성공장 준공
    (한국생명연 연구원창업1호)                                               06.10
                                    식별기 개발 및 상용화                  06.11   300만불 수출의 탑 수상
95.06 제1호 기술개발시범기업 선정         02.07 Korea Technology Fast50선정
95.07 ㈜바이오니아로 상호변경            02.07 벤처기업대상 대통령 표창 수상
97.05 97유망선진 기술기업 지정
                              03.03 DTT Asia Pacific Technology
98.04 동경 국제게놈학회                       FAST50 선정 (바이오텍 분야 2위)
     Excellent Poster Award
98.12 ISO 9001품질시스템 인증
99.01 유전자증폭시약제조기술 미국 특허
    기술경쟁력 우수기업 지정(중소기업청)
Principle of Real-Time qPCR
Principle of PCR

         Basics of PCR
                                                               1 Cycle

                Heating   Cooling                  Extension

                 95℃       55℃                      72℃



                                                                    Cycling


                                      Polymerase
                             Primer



Target
 DNA                                                      Cycling
Principle of PCR




                   Cockerill FR III. Arch Pathol Lab Med. 2003;127:1112
Disadvantages of PCR

  Basics of PCR
                                                    ?
                                                        Ideal graph



            1 Cycle
                      End-Point PCR analysis is not quantitative


            2 Cycle




                                                         Real graph




           3 Cycle




           N Cycle
Disadvantages of PCR


What is Wrong with Agarose Gels?

   Poor precision
   Low specificity
   Size-based discrimination only
   Low sensitivity/Resolution
   Short dynamic range (< 2 logs)
   Possibility of human errors
   Cross-contamination
   Results are not expressed as numbers
   Ethidium bromide staining is not very quantitative
Real-Time PCR




                   Real-Time PCR
 Real-time PCR monitors the fluorescence emitted during
  the reaction as an indicator of amplicon production at
 each PCR cycle (in real time) as opposed to the endpoint
                         detection
Principle of Real-Time PCR


 Key components of Real-Time qPCR


            1. Fluorescence
               Dyes
    Real-
    Time    2. Optical
               Components
   qPCR
            3. Thermal
               Cycler




                            Fast, Accurate and Quantitative Results
Principle of Real-Time PCR




                             Nigel Walker, NIEHS
Advantage of Real-Time PCR

Real-time PCR vs. Conventional PCR
                  Real-Time PCR                      PCR
  Sensitivity             High                        Low
  Specificity            High                          Low
                  -use specific probes      -only size discrimination
  Quantitative              Yes                        No
    results       -Specific fluorescence         -EtBr staining
   Detection         Probe-specific               Agarose gel
    method           Fluorescence               Electrophoresis
Detection range        Wide range             Short range (<2 log)

 Reaction time             1 hr                       3-5 hr
Post-PCR steps             No              Agarose gel electrophoresis

    Cross-                  No                         Yes
 contamination       -Closed system               -Open system
                       - Single step             - Multiple steps
Glossary of Real-Time PCR

  Amplification plot: The plot of cycle number vs. fluorescence signal which
                  correlates with the initial amount of target nucleic acid during the
                  exponential phase of PCR
  Baseline: The initial cycles of PCR during which there is little change in
                  fluorescence signal (usually cycles 3 to 15)



                                         Amplification plot
   Fluorescence




                                                                       Threshold
                                        Ct



 Baseline                                    cycle
Glossary of Real-Time PCR

 Threshold: the fluorescence measurement at which product can be
                 distinguished from background. Threshold should be set in the region
                 associated with an exponential growth of PCR product

 Ct (Threshold cycle): The cycle number at which the fluorescence generated
                 within a reaction crosses the threshold. It is inversely correlated to the log
                 of he initial copy number



                                         Amplification plot
  Fluorescence




                                         Ct                            Threshold



 Baseline
                                                   cycle
Detection Chemistry
Primer & Probe

Primer
Short (Often < 50 nt) oligonucleotide sequence of DNA
Complementary to the beginning and the end of the target
   DNA sequence                                                     Probe

Needed to initiate the synthesis of new DNA in a PCR       Primer
   reaction
Involved in AMPLIFICATION

Probe
A single-stranded DNA with a specific base sequence
Labeled with fluorescence dyes (TaqMan probe)
Used to detect the complementary base sequence of
    target DNA/RNA by hybridization
Involved in DETECTION
Reporter dye / Quencher dye
Detection Chemistry


    DNA binding agents
    - Intercalating method: SYBR®Green I

    Fluorescent dyes
      Hydrolysis Probe
      - TaqMan® probe, Molecular Beacon
      Hybridization Probe
      - Dual oligo FRET probes
      Primer based Probe
      - Scorpion
SYBR Green dye

 Intercalating method
 1) Denaturation
                                        1)   Intercalating dye fluoresces
                            F
                F                   F        more brightly when bound to
                        F
                                             dsDNA.


 2) Annealing                           2)   DNA binding dyes are
                                F            inexpensive compared to the
                    F       F       F
        F                                    other probes.

 3) Extension
                                        3)   SYBR Green I, EtBr
    F       F   F
SYBR Green dye

 Intercalating method

1. Advantages:

 - Cheap, easy to use
 - Does not inhibit the reaction of amplification
 - Does not require any fluorescent probe
 - Does not require any particular expertise for the design of the probes
 - Is not affected by mutations in the target DNA

2. Disadvantages:

 - Impossible to make sure of specificity of amplicons
 - Bad pairing can lead to positive forgeries or an over-estimate of the
     quantification
 - Still unspecified mutagen capacity
TaqMan Probe

TaqMan® probe
 1) Denaturation
                                1)   Fluorescent reporter dye at the 5’
                    F       Q        end is quenched by fluorescent
                                     quencher dye at the 3’ end.

 2) Annealing
      Taq                       2)   When amplification occurs the
            F           Q
                                     TaqMan probe is degraded due
                                     to the 5'-->3' exonuclease activity
                                     of Taq DNA polymerase, thereby
 3) Extension                        separating the quencher from the
                F                    reporter during extension.
                        Q



                F   Q           3)   The TaqMan assay accumulates
                                     a fluorescence signal.
TaqMan Probe

TaqMan® probe

 1. Advantages:

 - Increased specificity
 - Better capacity of multiplexing

 2. Disadvantages:

 - Little expensive (dual-labeled probe)
 - Less effective and less flexible compared to other techniques in the
     real-time detection of specific mutation
 - Require the design of probes
Molecular Beacon Probe

 Molecular Beacon
 1) Denaturation         1)   A molecular beacon begins as a
                              stem-and-loop structure. The
                   F Q        sequences at the ends of the
                              probe match and bind, creating
 2) Annealing                 the stem
      Taq   F        Q


                         2)   When the probe binds to a single-
                              stranded DNA template, the
 3) Extension                 structure unfolds, separating the
                              quencher from the dye and
                   F Q
                              allowing fluorescence.
Molecular Beacon Probe


Molecular Beacon

1. Advantages:

 - Increased specificity
 - High flexibility for probe design
 - As the probes are not hydrolyzed, they are used at each cycle

2. Disadvantages:

 - Little expensive (dual-labeled probe)
 - Less effective and less flexible compared to other techniques in the
     real-time detection of specific mutation
 - Require the design of probes
FRET Probe

Hybridization Probe (FRET)
 1) Denaturation                     1)   FRET method designed two
                                          specifically probe. It labeled with
            F       F                     different dyes, such as at the 5’
                                          end of donor probe and at the 3’
                                          end of acceptor probe.
 2) Annealing
                          Energy
      Taq                 transfer
                    F F
                                     2)   At close proximity, the donor dye
                                          is excited by the light source and
 3) Extension                             the energy is transferred the
                                          acceptor dye. Subsequently,
                F   F                     fluorescent light is emitted at a
                                          different wavelength.
Applications of Real-Time PCR
Real-Time PCR Applications


         Clinical Diagnostics
         - Bacterial/ Viral pathogen detection
         - Absolute pathogen quantification
         Drug therapy efficacy / drug monitoring
         Differential gene expression
         RNAi
         - siRNA validation
         SNP Genotyping
         Others
         - Food pathogen testing
         - Forensic studies
Real-Time PCR Applications


• Structural Assay
   – Uses DNA, typically genomic extractions
       • Single Nucleotide Polymorphism

• Functional Assay
   – Uses RNA extractions
   – Uses reverse transcriptase to generate cDNA templates
      • Differential expression
      • Diagnostics involving gene expression
      • RNA interference

• Clinical Diagnostic Assay
   – Uses DNA or RNA extracted from patient’s samples
        • Viral/bacterial pathogens
Quantification Strategies

  Absolute quantification
  - Used to quantitate unknown samples by interpolating their
     quantity from a standard curve.
  - The standard is a known DNA sample whose concentration is
     known absolutely.
  - The accuracy of the absolute quantification assay is entirely
    dependent on the accuracy of the standards.

  Relative quantification
  - Used to analyze changes in gene expression in a given sample
    relative to another reference sample.
  - A comparison within a sample (DNA or cDNA) is made with the
     gene(s) of interest to that of an endogenous control gene.
  - Quantification is done relative to the control gene.
Absolute Quantification

Pathogen Detection/Clinical Diagnostics
  Detection of specific genes in pathogens (virus, bacteria, fungi, etc)
  isolated from patient’s samples
  Quantification: copy numbers of infected pathogens
  Therapeutic Drug Monitoring/Screening

           Varicella-Zoaster Virus – Real-Time PCR




                             106 copies
Absolute Quantification

Absolute Quantification




      Ct value



                                                         Sample 1
                    Threshold                             Ct:14
                                                      Conc: 2,500copy




The Ct value correlates strongly with the starting copy number.
It is linear with the log of starting DNA concentration.
Relative Quantification

Differential Gene Expression

  Compares transcriptional levels of genes between control and
  experimental samples
  - Tissue distribution of target gene
  - Drug screening/Drug efficacy
  - Gene expression profiling after drug treatment

  Endogenous controls are used to normalize the data

  Endogenous controls are genes common to both control and
  experimental samples that do not change their expression levels
  under the experimental conditions
          - GAPDH
          - β-Actin
          - 18s ribosomal subunit
Relative Quantification (PCR)

 Experiment Design (Traditional / Relative quantification)
      Cell line A                                            Cell line A + Drug




           mRNA                                                        mRNA

        PCR using same amount of mRNA after spectrophotometer quantification

                        E       R       R       O        R
Relative Quantification (Real-Time PCR)

 Experiment Design (Real-Time PCR /Relative quantification)
     Cell line A                             Cell line A + Drug




          mRNA                                         mRNA




        Actin      Target                          Actin      Target
Relative Quantification

    Relative Quantification (Drug-induced gene expression)

Control cell lines
                             Actin   Target
                                                           A > B (21 : 2 times much)

                                                            Sample A : 16 Ct
                                                            Sample B : 17 Ct
                                                                 => Delta Ct = 1 Ct



Control cell lines
   + Drug A          Actin       Target                    A > B (24 : 16 times much)

                                                            Sample A : 17 Ct
                                                            Sample B : 21 Ct
                                                                 => Delta Ct = 4 Ct

     Drug A treatment induced decreased target X gene expression
Relative Quantification

 Relative Quantification (Tissue distribution / Delta Ct Methods)
  Brain tissue
           Actin       Target X
                                                        Actin : 17 Ct
                                                        Target : 21 Ct
                                                               => Delta Ct = 4 Ct



   Liver tissue
               Actin       Target X
                                                         Actin : 22 Ct
                                                         Target : 26 Ct
                                                               => Delta Ct = 4 Ct



          Target gene expression level between brain and liver tissue is same
SNP Genotyping


    Single Nucleotide Polymorphism (SNP)

    DNA sequence variations that occur when a single nucleotide (A, T,
      C, or G) in the genome sequence is altered

    How many SNPs are there in humans today?

     - Human mutation rate is ~ 2.5 x 108 mutation/site/generation
     - ~150 mutations/diploid genome/generation
     - 6.3 billion people in the world = 945,000,000,000 mutations in the
        world today

    The most common type of sequence difference between alleles

    Provide a way to detect direct associations between
     allelic forms of genes and phenotypes
SNP Genotyping (Real-Time PCR)

   Allelic Discrimination Assays (Single Nucleotide Polymorphisms)

                                         G
                   T
                   A                     A



                                         T
                   G
                   C                     C
SNP Genotyping (Real-Time PCR)



        G             T          T
        C             A          A

        G             T          G
        C             A          C
RNAi: siRNA validation


RNA interference:
double stranded RNA
(siRNA) forms a complex
(RISC) that binds to target
mRNA and leads to its
degradation

Target gene expression can
be quantified by Real-Time
PCR
Multiplex Analysis

   Different dyes for each target (Example: FAM, TET, VIC and JOE)
                                   Real-time detection of four different
                                   retroviral DNAs in a multiplex format.
                                   Each reaction contained four sets of PCR
                                   primers specific for unique HIV-1, HIV-2,
                                   HTLV-I, and HTLV-II nucleotide
                                   sequences and four molecular beacons,
                                   each specific for one of the four
                                   amplicons and labelled with a differently
                                   coloured fluorophore.
                                   HIV-1: Fluorescein
                                   HIV-2: Tetrachlorofluorescein
                                   HTLV-1: Tetramethylrhodamine
                                   HTLV-II: Rhodamine


                                   Vet JA et al. PNAS (1999)
Improving Reproducibility

    • Use clean bench (hood)
    •   Use aerosol resistant tips
    •   Use calibrated micropipettors
    •   Use large volumes (5µL and up)
    •   Pipette into each reaction vessel once


                        Same Reagents, Different Results




                Cycle                                      Cycle

         Good Technique                             Poor Technique
Special Features of ExicyclerTM
 Real Time Quantitative Thermal Block
 Exicycler™




The ExicyclerTM is a real time PCR system developed by Bioneer.
The ExicyclerTM is equipped with an optical system that fits above the thermal cycler.
It readily utilizes most fluorescent dyes, providing the widest choice of excitation /
emission wavelengths.
It can also be used as a standard thermal cycler for general PCR reactions.
Characteristics of ExicyclerTM

 1)     Superior optic module
      -    Light pipe technology (patented)

 2)     Light source
      -     Short Arc Lamp
      -     Life: 3,000 hrs
      -     High Power, high quality, low maintenance

 3)     Excitation filter
      -    Six position (blank 1, filter 5)
      -    Wavelength range: 490-640nm
      -    5 multiplexing

 4)     Emission filter
      -    Six position (blank 1, filter 5)
      -    Wavelength range: 520-670 nm
      -    5 multiplexing

 5)     Powerful 2D CCD detector
      -    High resolution and great sensitivity
      -    Resolution : 325K pixel

 6)    Thermal gradient
Superiors of ExicyclerTM

 Analysis can be viewed in real time.
 Melting curve can also be viewed in real time.
 Thermal gradient is compatible.
 Range of fluorophore excitation and emission is 490~670 nm.
 It can be used for multiplexing with 5 different fluorophores.
 Reliable results without using reference dye.
 Not necessary to decontaminate the sample block.
 96 samples can be tracked simultaneously.
 All data is accessible as Excel file and jpg image.
 Result reports, everyone can open on internet explorer.
 Software is friendly to beginner at qPCR.
 USB communication interface.
 Auto loading.
  Patents of ExicyclerTM


1) Real time monitoring apparatus for amplified nucleic acid product
   -Appl. Con. : ROK
   -Appl. Date: Jun. 18, 2002
   -Appl. No. : 10-2002-0033965        Intensity Profile at Sample Reactor(Patents)

                                         A company                    BIONEER




2) Real time monitoring apparatus
   -Appl. Con. : ROK
   -Appl. Date : Apr. 3, 2003
   -Appl. No. : 10-2003-0021145
Optic module of ExicyclerTM

                        Lamp




                                 Light pipe
                   Camera lens
Comparison of Optics

  Exicycler                               Other Company




More homogeneous light intensities among wells
           => Reliable results without using reference dye
Comparison of Optics
  Exicycler




                   Without reference dye
  Other Company




  Without reference dye                    With reference dye
Diversity of fluorescence dyes



                  Excitation/Emission wavelength

   Position   Excitation   Emission               Dye

      1          490         520      Fluorescein, FAM, SYBR®Green

      2          520         550               JOE, TET

      3          550         580              TAMRA, CY3

      4          580         610        Texas Red, ROX, RED610

      5          640         670             CY5, RED 670
Classified by light sources

  Laser
    High cost and high power
    Small interference
    High maintenance cost
    Limited number of colors
    (Laser based excitation limits fluorophore range)
    Can only be used for well scan type (ABI 7900)

  LED
    Low cost and low power
    More number of colors
    Inadequate for high throughput system
    Normally used for well scan type (sequential data acquisition)
    Chromo4, LightCycler, Opticon series, Rotor-Gene
Classified by light source


  Lamp
    Suitable for multi-color
    Unique solution for 2 dimension sensor
    (Simultaneous data acquisition)
    Suitable for high throughput system
    High cost optic component
    ABI 7500, 7300, iCycler series, LC480, Exicycler
Classified by sensor and thermal cycling


   Point sensor (PMT, photodiode) and rotating mechanism

     Low price and high temperature uniformity

     Difficult to handle, can’t support storage function

     No gradient function to aid in optimization

     Air heating not as accurate as Peltier

     No system modularity

     Special consumables and loading block are required

     LightCycler (discontinued), Roter-Gene
Classified by sensor and thermal cycling

    Point sensor (PMT, photodiode) and scanning mechanism
     Cheap, simple, easy to calibrate

     Time lag due to scanning
     High maintenance cost
     Longer running time
     Chromo4, MX3005P


    2D sensor (CCD)
     Simultaneous detection (suitable for high throughput)

     Increased material costs, hard to calibrate

     ABI 7500, ABI 7300, iCycler, LC480, Exicycler
Market trend


 2D CCD is the current main stream for sensor
   ABI 7500, ABI 7300, iCycler, Roche LC480


 High throughput
   Must support plate format
   Bioneer is developing 1536 well format real time PCR currently


 Powerful and various analysis software
   SNP typing, Absolute & Relative Quantification,
   Infection Assay (using internal control)
   Comparison


                                                         Lightcycler
 Name         iCycler      SmartCycler     RotorGene                      ABI7500      Opticon 2      Exicycler
                                                              2
                                                                         Applied
Company        Bio-rad       Cepheid        Corbett        Roche                       MJ research     Bioneer
                                                                         biosystem

Detection   Simultaneous   Simultaneous    Sequential     Sequential    Simultaneous   Sequential    simultaneous


  Type         96 well        16 tube      32, 72 tube   32 capillary      96 well       96 well       96 well


Gradient         O              ×              ×              ×              x             O              O


 Light      Tungsten                                                    Tungsten                      Short Arc
                               LED            LED         Blue LED                        LED
 source     Halogen Lamp                                                Halogen Lamp                  lamp


                           Silicon                         Photo-                                       16 bit
Detector     10 bit CCD                       PMT                        10 bit CCD       PMT
                           Photodetector                   hybrid                                       CCD

 Ex /Em
             400~700 ㎚      450~650 ㎚      470~660 ㎚     470~710 ㎚       488~650 ㎚     470~700 ㎚      490~670 ㎚
 range

Multiplex      4 color        4 color        4 color       6 color         5 color       2 color       5 color
Experimental Results in ExicyclerTM

  SYBR Green I data
22000


20000


18000


16000
                                                                                                                                                        Without reference dye
14000


12000


10000                                                                        100000


8000


6000
                                                                              10000
4000


2000


   0                                                                           1000
        1   2   3   4   5   6   7   8   9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40




                                                                                100




                                                                                 10




                                                                                 1
                                                                                      1   2   3   4   5   6   7   8   9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
Experimental Results in ExicyclerTM

  TaqMan data (FAM)
20000


18000


16000


14000
                                                                                                                                                              Without reference dye
12000


10000


8000

                                                                                        100000
6000


4000
                                                                                        10000

2000


   0
                                                                                          1000
        1   2   3   4   5   6   7   8   9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40




                                                                                          100




                                                                                           10




                                                                                            1
                                                                                                 1   2   3   4   5   6   7   8   9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40




                                                                                           0.1
  Understanding
Real Time PCR Data
Refreshing Logarithms and
Exponentials




                       32   64


              8   16             …
      2   4
  1


t=0   1   2   3   4    5    6
Refreshing Logarithms and
Exponentials

                                        Population Size vs. time

                           70

                           60
     Population Size (N)




                           50

                           40

                           30

                           20


                           10

                           0
                                0   1       2     3       4    5   6   7
                                                   Time (t)
Refreshing Logarithms and
Exponentials
                  8

                               f(x) = bx
                  6



                  4

                                      f(x) = logbx
                  2



                  0
   -6   -4   -2        0   2          4       6      8

                  -2



                  -4
Refreshing Logarithms and
Exponentials
  a>1


• For f(x)=bxa

a>1              a=1




a<1              a<0
                                                                              Regular plot
                                                               30 0


                                                               25 0


                                                               20 0




                                              weight W (lbs)
                                                               15 0


                                                               10 0


                                                                50


                                                                  0
                                                                      50    75    10 0    12 5       15 0            17 5   20 0
                                                                                     length L (cm)




                    Semi-log plot                                                                                            log-log plot
                                                                                                                 6
          6

        5.5                                                                                                    5.5


          5                                                                                                      5


        4.5                                                                                                    4.5




                                                                                                       ln(W)
ln(W)




          4                                                                                                      4


        3.5                                                                                                    3.5

          3                                                                                                      3


        2.5                                                                                                    2.5
              50   75   10 0    12 5       15 0                   17 5     20 0                                      4      4.25   4.5           4.75   5   5.25
                           length L (cm)                                                                                                 ln(L)
CYCLE NUMBER    DNA copy number
                                                          Copies of DNA=2N
            0                     1
                                                          1.8E+09
            1                     2
            2                     4                       1.6E+09

            3                     8                       1.4E+09
            4                    16
                                                          1.2E+09
            5                    32




                                        DNA copy number
            6                    64                       1.0E+09
            7                   128
                                                          8.0E+08
            8                   256
            9                   512                       6.0E+08

           10                 1,024                       4.0E+08
           11                 2,048
                                                          2.0E+08
           12                 4,096
           13                 8,192                       0.0E+00
                                                                                          0   5   10   15     20    25    30    35
           14                16,384
                                                                                                       PCR cycle
           15                32,768
                                                                                     10
           16                65,536
           17               131,072                                                  9

           18               262,144                                                  8

           19               524,288                                                  7




                                                             DNA copy number (log)
           20             1,048,576                                                  6
           21             2,097,152
                                                                                     5
           22             4,194,304
                                                                                     4
           23             8,388,608
                                                                                     3
           24            16,777,216
           25            33,554,432                                                  2

           26            67,108,864                                                  1
           27           134,217,728                                                  0
           28           268,435,456                                                       0   5   10    15     20    25    30    35

           29           536,870,912                                                                     PCR cycle

           30         1,073,741,824
           31         1,400,000,000   PCR reagent is the limiting factor!
           32         1,500,000,000
           33         1,550,000,000
           34         1,580,000,000
PCR Phases



                                     Plateau

                   Linear
                                Ethidium-Gel
                                  detection
Log [DNA]
             Exponential



                      Cycle #
Variable Linear Phase
Plateau Effect
SERIES OF 10-FOLD DILUTIONS
CYCLE AMOUNT OF DNA AMOUNT OF DNA AMOUNT OF DNA AMOUNT OF DNA

  0
  1
      100% EFFICIENCY 90% EFFICIENCY 80% EFFICIENCY 70% EFFICIENCY
                      1
                      2
                                     1
                                     2
                                                    1
                                                    2
                                                                   1
                                                                   2
                                                                          Effects
                                                                         1,200,000,000

                                                                         1,000,000,000
  2
  3
  4
                      4
                      8
                     16
                                     4
                                     7
                                    13
                                                    3
                                                    6
                                                   10
                                                                   3
                                                                   5
                                                                   8
                                                                              of
                                                                          800,000,000

  5
  6
  7
                     32
                     64
                    128
                                    25
                                    47
                                    89
                                                   19
                                                   34
                                                   61
                                                                  14
                                                                  24
                                                                  41
                                                                        Efficienc
                                                                          600,000,000
                                                                       AFTER 1 CYCLE:
                                                                               y
                                                                          400,000,000
  8                 256            170            110             70    100% => 2.00x
  9                 512            323            198            119     90% => 1.90x
                                                                          200,000,000
  10
  11
                  1,024
                  2,048
                                   613
                                 1,165
                                                  357
                                                  643
                                                                 202
                                                                 343
                                                                        80% => 1.80x
  12              4,096          2,213          1,157            583    70% => 1.70x
                                                                               0
  13              8,192          4,205          2,082            990                     0   10
  14             16,384          7,990          3,748          1,684
  15             32,768         15,181          6,747          2,862
  16             65,536         28,844         12,144          4,866
  17            131,072         54,804         21,859          8,272
  18            262,144        104,127         39,346         14,063
  19            524,288        197,842         70,824         23,907
  20          1,048,576        375,900        127,482         40,642    AFTER N CYCLES:
  21          2,097,152        714,209        229,468         69,092
                                                                         fold increase =
  22
  23
              4,194,304
              8,388,608
                             1,356,998
                             2,578,296
                                              413,043
                                              743,477
                                                             117,456
                                                             199,676
                                                                           (efficiency)n
  24         16,777,216      4,898,763      1,338,259        339,449
  25         33,554,432      9,307,650      2,408,866        577,063
  26         67,108,864     17,684,534      4,335,959        981,007
  27        134,217,728     33,600,615      7,804,726      1,667,711
  28        268,435,456     63,841,168     14,048,506      2,835,109
  29        536,870,912    121,298,220     25,287,311      4,819,686
  30      1,073,741,824    230,466,618     45,517,160      8,193,466
                1,200,000,000
                                            100% EFF
                1,000,000,000               90% EFF




AMOUNT OF DNA
                                            80% EFF
                 800,000,000                70% EFF

                 600,000,000

                 400,000,000

                 200,000,000

                           0
                                    0            10       20      30
                                              PCR CYCLE NUMBER

                10,000,000,000
                 1,000,000,000              100% EFF
                  100,000,000               90% EFF
AMOUNT OF DNA




                                            80% EFF
                   10,000,000
                                            70% EFF
                     1,000,000
                      100,000
                       10,000
                         1,000
                          100
                           10
                                1
                                        0         10      20      30
                                               PCR CYCLE NUMBER
SERIES OF 10-FOLD DILUTIONS
Anatomy of an Amplification Plot



                                Sample


             Threshold                                    ∆Rn
    Rn




               Baseline              No Template
                                     Control (NTC)
                           CT

         0        10            20        30         40
                         cycle number
Basic Knowledge on Statistics
          •   Normal distribution

                     1    1 ( x  )
                                     2
          f ( x)       e 2
                    2
                                      is the mean

                               is the standard deviation
                                      Variance is
                                         2


                        


                                           79
Example: The normal distribution is the most important
distribution in Statistics. Typical normal curves with
different sigma (standard deviation) values are shown
below.
• The standardized value of x is defined
  as
                x
          z
                  
• It is also called a z-score.
Central Limit Theorem


• A very important result in statistics that
  permits use of the normal distribution for
  making inferences (hypothesis testing and
  estimation) concerning the population mean.
• If a variable x (with any distribution) has a
  population mean x and standard deviation ,
  then: the distribution of sample means (from
  samples of size n taken from the population),
  has the following distribution as n tends to
  infinity:                 
                X ~ N ( ,
                n             )
                          n
Confidence Interval for the Mean



 • A way of expressing the uncertainty in x
   as an estimate of .
 • 95% confidence interval says that on
   average 95 % of the time, if you
   estimate an interval for  this way, the
   true value of  will be inside the interval.
Common “Z” levels of confidence


• Commonly used confidence levels are
  90%, 95%, and 99%
           Confidence    Z-value
             Level        (Z-score,
                        Critical value)

               80%          1.28
               90%         1.645
               95%          1.96
               98%          2.33
               99%          2.58
              99.8%         3.08
              99.9%         3.27
Confidence Interval



• So, we need to count the number of
  standard deviations from the mean
                       
             xz   *

                       n
• But we don’t know .
When  is unknown


• In most cases knowledge of the true
  variability of the measurement will not be
  available.
• In these cases, we proceed by substituting 
  with the sample standard deviation s:
                         n

                       xi  x 
                                    2


       s    s2     i 1

                             n 1

• And basing our inference on the t distribution
  with n-1 degrees of freedom (where n is the
  size of the sample).
t-statistic




        Sample mean – Population mean
 t=                                     s is the
                    s                    sample
                                        standard
                                        deviation
                     n
The t distribution




• The t distribution is symmetric, and centered around
  zero.
• It has “fatter” tails compared to the standard normal
  distribution.
• The t distribution is defined by n-1 degrees of
  freedom (n is the sample size).
Degrees of Freedom


• Essentially the number of independent
  pieces of information provided by the
  sample.
• Initially, every sample has n
  independent pieces of information (as
  many as the number of observations).
• If we know the first n-1 observations,
  we can compute the nth one, and thus
  there are n-1 independent pieces of
 information.
Baseline


• Background or noise signal is often
  normally distributed.
• Signal is meaningful only if it is higher
  enough than the background signal.
• Baseline value is a single value or a
  function to represent the background
  signal.
• Baseline is the cycles used to calculate
  the baseline value.
Baseline Set Too Low
Baseline Set Too High
Threshold


• At least 5 to 10 z-value higher than the
  mean of the distribution of background
  signal.
• Many competing factors: background
  noise, stabilized regions in exponential
  phase for all replicates, maximizing
  sensitivity
                                  Log phase   Level off/ plateau
DNA copy number (log)




                                                        2
                                                    4
                                                8

                                              16
                                              32




                        PCR cycle (Ct)
DRn

• Rn (normalized reporter) is the fluorescence emission
  intensity of the reporter dye multiplied by the
  calibration factor.
• Rn+ is the Rn value of a reaction containing all
  components (the sample of interest); Rn- is the Rn
  value detected in NTC.
 DRn is the difference between Rn+ and Rn-. It is an
  indicator of the magnitude of the signal generated by
  the PCR.
 DRn is plotted against cycle numbers to produce the
  amplification curves and gives the CT value.
Types of Quantification



• End-point quantification
  – SNP typing
  – Pathogen detection
• Absolute quantification
• Relative quantification
• Melting Curve (for Cyber Green I)
Absolute Quantification
                                                            Generating a standard
                                                                   curve
                                                           Rep 1       Rep 2       Rep 3
                 •   Absolute/Relative quantification       0 hrs       0 hrs       0 hrs
                                                          12 hrs pi   12 hrs pi   12 hrs pi
    •       Used serial diluted standards of known        24 hrs pi   24 hrs pi   24 hrs pi
             concentration to generate a standard
                             curve.
        •       Standard curve is a linear relationship
                between the ct and the initial amount           Pooled sample
                     amounts of RNA or cDNA.
       This allows the determination of the
            •
    concentration of unknowns based on their
                     ct values
                                                                Dilution 1 (10-1)
•       Assumes that all standards and samples
                                                                Dilution 2 (10-2)
         have equal amplification efficiencies.                 Dilution 3 (10-3)
        •     The concentrations of serial dilutions
            should encompass all samples and stay         Carry out runs in triplicates
             within the range that can be quantified
Comparative CT (∆∆ CT) Method


 • CT (target gene, control) – CT (endog.
   refer. gene, control) = ∆CT,cont (control
   tissue)
 • CT (target gene, exp.) – CT (endog.
   refer. gene, exp.) = ∆CT,exp
   (experimental tissue)
 • ∆CT,exp - ∆CT,cont = ∆∆CT
    Target gene exp = 2   (average ∆∆CT)
    Target gene cont
Comparative CT (∆∆ CT) Method with
Efficiency Correction


 • ∆∆ CT assumes similar efficiencies

 Target gene exp = target )∆CT target (cont - exp)
                  (E
 Target gene cont  (E      )∆CT ref (cont - exp)
                         cont
Comparative CT (∆∆ CT) Method with
Efficiency Correction
Exist/Non-Exist Assay


• Internal Positive Control (IPC)
• Four types of reactions
  1. No Amplification Control (NAC) – blocked
     IPC, to calculate IPC threshold
  2. No Template Control (NTC) – to calculate
     target threshold
  3. Target
  4. Unknown sample
Exist/Non-Exist Assay


• Statistical methods for choosing
  thresholds
• Three possible outcomes
  1. Target below threshold, IPC above
     threshold  Minus
  2. Target above threshold, IPC above
     threshold  Plus
  3. Target below threshold, IPC below
     threshold  Undetermined
SNP Typing



    • Using TaqMan
      – One reporter for each allele
      – k-means clustering
    • Using Cyber Green I
      – Primers with different lengths
      – Melting curve examination
Thank you !

				
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