MIQE Guidelines Qualitätskontrolle in der real-time RT-qPCR

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MIQE Guidelines Qualitätskontrolle in der real-time RT-qPCR 2. Life Science Conference 3. - 4. Mai 2012 Analytik Jena, Jena, Germany Prof. Dr. Michael W.

1 MIQE Guidelines Qualitätskontrolle in der real-time RT-qPCR 2. Life Science Conference Mai 2012 Analytik Jena, Jena, Germany Prof. Dr. Michael W. Pfaffl Physiology Weihenstephan Technical University of Munich Weihenstephaner Berg Freising-Weihenstephan Germany Michael.Pfaffl@wzw.TUM.de

2 MIQE is a set of guidelines that describe the minimum information necessary for evaluating qpcr experiments The goal is: To provide guidelines for authors, reviewers and editors to measure the technical quality of submitted manuscripts To establish a clear framework to conduct quantitative RT-PCR experiments To support experimental transparency To increase reproducibility between laboratories worldwide To promote more consistent, more comparable, and more reliable results To standardize international qpcr nomenclature To increase reliability of results to help to insure the integrity of scientific work, with major focus on biological relevance

Sampling NA extraction RT qpcr target information qpcr

3 The MIQE checklist: 9 titles 85 sub-titles 57 essential information 28 desirable information Experimental design Sampling NA extraction RT qpcr target information qpcr primers qpcr protocol qpcr assay validation Data analysis & statistics

4 Insufficient information about: Central problem areas in qpcr 1. Sampling technique & Sample quality 2. Extraction quality & extraction efficiency Pre-PCR steps 3. RNA quality (quantity, integrity, and purity) 4. Experimental design & Type of replicates (=> statistics) 5. qpcr protocol (=> unspecific products) 6. Assay validation (specificity, sensitivity, reproducibility, quantification range, LOD, LOQ ) 7. Raw data handling (=> Background correction) 8. Cq data handling - Handling of outlier 9. Normalization strategy (one RG, multiple RGs, test for optimal RGs) Post-PCR steps 10. Result interpretation => significance of fold change 11. Statistical test => biological relevance

6 mrna microrna Q 1: Which is an appropriate evaluation system? Q 2: Impact on mrna and microrna integrity on real-time RT-PCR performance? (Becker et al., Methods 2010) Q 3: Impact on quantitative result?

7 QC Steps and variables of a successful mrna quantification using real-time RT-PCR (1) Pre-PCR RNA processing Steps and variables of a successful mrna quantification using real-time RT-PCR (2) tissue sample sampling Sampling method: Biopsy Fixed material RNA Later 1 st extraction buffer RNA storage 80 C => native RNA RNA cdna PCR nucleic acid isolation RT Extraction method: Efficiency of RT: total RNA vs. mrna liquid-liquid Fresh blood columns Tissue storage Automatic via robot Tissue sampling Liquid Nitrogen RNA integrity: Tissue Bioanalyzer 2100 Experion Solitaire cells Biopsy Nano-Drop M-fold LCMD Tissue storage RNA Extraction total RNA RT enzyme type RT temperature Primers: poly-t Primer Random-hexamers Specific primer Primer mixtures one-step RT-PCR two-step RT-PCR mrna microrna small RNA Both fractions in parallel real-time PCR amplification Efficiency & Specificity of real-time PCR: Primer design Primer specificity multi-species Primer mrna abundance cdna input Polymerase types & Mixtures Robot vs. hand made Liquid-liquid, Spin-columns, Magnetic beads RNA stabilization RNA storage RT-PCR product 2-step, 3-step, or 4-step qpcr quantification strategy qbase, Normfinder, etc. statistics success detection = Ct processing test & software biological meaningful results Detection method: SYBR Green I Probes: Beacons, Scorpions, etc. Quantification strategy: absolute quantification type of calibration curve? BioInformatics: CP vs. quantified molecules raw data vs. background correction normalization with RG Normality of data (???) Fit point method relative quantification t-test (?) TaqMan fitting (10x SD) total RNA, cells, tissue mass ANOVA (on the ranks?) RNA Quality Control 2 nd derivative maximum normalization with RG SAS, SPSS, Excel, Sigma Stat other models (Log. or Sig.) normalization via an index of Permutation test Mixed models more (> 3) RGs Randomization test (REST) RNA Quantity (OD other curve manipulations genorm, 260 ; NanoDrop; REST, BestKeeper, Pico.??? Green) RNA Quality = Integrity & Purity Integrity Bioanalyzer 2100 Experion QIAxel High-resolution agarose-gel (>4%) 5 3 assay Purity & Inhibitors Photometer (OD scan; OD 260 / OD 280 ) Nano-Drop (OD scan; OD 260 / OD 280 ) Test for inhibitors, e.g. SPUD assay

RIN / RQI < 5 RIN / RQI > 5 Downstream applications:

8 Validation of total RNA integrity tissue total RNA extraction Quality Control via Experion or BA 2100 RIN / RQI < 5 RIN / RQI > 5 Downstream applications: qrt-pcr, expression profiling, hybridisation array, NGS, etc.

9 Various total-rna qualities analysed ladder marker Intact RNA RIN / RQI: 9.5 marker 5S 18S 28S rrna marker RIN / RQI: 5.6 marker degraded RNA RIN / RQI: 2.8 Fleige & Pfaffl, et al., Mol Aspects Med 2006; Fleige, et al., Biotechnolgy Letters 2006

8 degradation gradient of mixtures between intact

10 Degradation gradient Marker WBC RIN: 9.5 total RNA extracted from bovine tissues 18 S 28 S analyzed in Bioanalyzer artificial degradation Marker WBC RIN: 2.8 degradation gradient of mixtures between intact and degraded RNA 18 S 28 S analyzed in Bioanalyzer Fleige, et al., Biotechnolgy Letters 2006

$Effect of RNA degradation in muscle RNA in long RNA (> 200 nt) and small RNA (< 200 nt) fractions linear$ 11 degradation steps on NANO eukaryote RNA chip analysis of muscle small RNA 11 degradation steps on Small RNA

11 degradation steps on NANO eukaryote RNA chip analysis of muscle small RNA 11 degradation steps on Small RNA

11 Effect of RNA degradation in muscle RNA in long RNA (> 200 nt) and small RNA (< 200 nt) fractions linear gradient 11 artificial UV degradation steps one-tube extraction procedure (Qiagen) analysis of muscle long RNA 11 degradation steps on NANO eukaryote RNA chip analysis of muscle small RNA 11 degradation steps on Small RNA chip intact RNA => degraded RNA intact RNA => degraded RNA Becker et al., METHODS 2010 The ongoing evolution of qpcr

mature microrna NANO eukaryote RNA chip Integrity analysis of mrnas Nano eukaryote RNA chip Agilent Technologies Bioanalyzer 2100 small RNA chip Integrity analysis of small RNAs Small RNA chip

12 mature microrna NANO eukaryote RNA chip Integrity analysis of mrnas Nano eukaryote RNA chip Agilent Technologies Bioanalyzer 2100 small RNA chip Integrity analysis of small RNAs Small RNA chip Agilent Technologies Bioanalyzer 2100 trna e.g. small rrna pri-microrna pre-microrna Small RNA chip data output: 1. concentration small RNA [pg] 2. concentration microrna [pg] 3. relative amount of microrna as part from small RNA [%]

13 crossing point (Ct) quantification cycle [Cq] Influence on RIN value on mrna expression in WBC average slope = tissues n = 21 r 2 = p < UBQ LDH Caspase 3 IL-1 beta ACTB p53 H RIN Becker et al., METHODS 2010 The ongoing evolution of qpcr

14 crossing point (Ct) quantification cycle [Cq] Influence on RIN value on microrna expression in WBC average slope = tissues n = 24 r 2 = p < mir-195 mir-122 mir-1 mir-145 mir-25 mir-191 mir-101 let-7a RIN Becker et al., METHODS 2010 The ongoing evolution of qpcr

15 Steps and variables of a successful mrna quantification using real-time RT-PCR Steps and variables of a successful Steps and variables of a successful mrna quantification using real-time RT-PCR (1) mrna quantification using real-time RT-PCR (2) tissue sample RNA cdna PCR RT-PCR product quantification strategy statistics success nucleic acid real-time PCR detection = sampling RT Ct processing test isolation amplification & software biological meaningful results Detection method: Quantification strategy: Sampling method: Extraction method: Efficiency of RT: Efficiency & Specificity SYBR Green I absolute quantification BioInformatics: Biopsy total RNA vs. mrna RT enzyme type of real-time PCR: Probes: Beacons, Scorpions, etc. type of calibration curve? CP vs. quantified molecules Fixed material liquid-liquid RT temperature Primer design raw data vs. background correction normalization with RG Normality of data (???) Fresh blood columns Primers: Primer specificity Fit point method relative quantification t-test (?) Tissue storage Automatic via robot poly-t Primer multi-species Primer TaqMan fitting (10x SD) total RNA, cells, tissue mass ANOVA (on the ranks?) Liquid Nitrogen RNA integrity: Random-hexamers mrna abundance 2 RNA Later Bioanalyzer 2100 Specific primer cdna input nd derivative maximum normalization with RG SAS, SPSS, Excel, Sigma Stat other models (Log. or Sig.) normalization via an index of Permutation test 1 st extraction buffer Experion Primer mixtures Polymerase types & Mixtures Mixed models more (> 3) RGs Randomization test (REST) Detection method: Quantification RNA storage 80 C Nano-Drop Robot vs. hand made strategy: BioStatistics & BioInformatics: one-step RT-PCR other curve manipulations genorm, REST, BestKeeper,.??? => native RNA M-fold Dye: SYBR, HRM dyes two-step RT-PCR absolute quantification 2-step, 3-step, or 4-step qpcr Cq qbase, vs. Normfinder, quantified etc. molecules Probe: Taqman, Beacons, Scorpions Raw data vs. background correction Fit point method Background fitting (10x SD) 1 st 2 nd 3 rd derivative maximum other models: logistic / sigmoidal / NLR / CalQPlex / E-method Multiple and/or mixed models Other curve manipulations 2-step, 3-step, or 4-step qpcr type of calibration curve? normalization with RG relative quantification total RNA, cells, tissue mass normalization with one RG normalization via an RG Index (> 3 RGs) genorm, Normfinder, BestKeeper qbase, REST, GenEx, etc. Normality of data (?) t-test (?) ANOVA (on the ranks?) SAS, SPSS, Excel, Sigma Stat Permutation test Randomization test (REST) Bootstrapping (REST-2009) Cluster analysis Multiple regression analysis Multi-dimensional modeling

17 Quantification Strategies in real time qrt-pcr M.W. Pfaffl, BioSpektrum 2004 (Sonderausgabe PCR) absolute quantification relative quantification external calibration curve one color detection system SYBR Green I external calibration curve two color detection system e.g. Probes normalization via one reference gene via reference gene index >3 RG external calibration curve without any reference gene ROX / FL / IS ROX / FL / IS external calibration curve RT-PCR product plasmid DNA in vitro transcribed RNA synthetic DNA oligos synthetic RNA oligos without real-time PCR efficiency correction 2 (- Cq) with real-time PCR efficiency correction REST, qbase LC software, etc.

18 Absolute quantification of IGF-1 receptor 50 two step qrt-pcr efficiency (recombinat RNA) = 1.81 (n = 4; r = 0.998; 2 * * 10 9 recrna standard molecules) two step qrt-pcr efficiency (native mrna molecules) = 1.78 (n = 4; r = 0.939; ng total muscle RNA) cycle number of crossing point e+5 1e+6 1e+7 1e+8 1e+9 IGF-1 receptor recombinant RNA and native mrna start molecules Pfaffl et al., 1998

19 Quantification strategies in real-time RT-PCR Absolute Quantification using calibration curves DNA based calibration curve using a purified RT-PCR product (Einspanier et al. 1999, etc ) recombinant DNA (plasmid DNA) calibration curve (Bustin, 2000; Pfaffl & Hageleit, 2001) calibration curve using a synthetic DNA oligo-nucleotide (Bustin, 2000; Bustin 2005) calibration curve using a synthetic gene (DNA) RNA based recombinant RNA (in vitro transcript) calibration curve (Pfaffl & Hageleit, 2001) calibration curve using a synthetic RNA oligo-nucleotide (Bustin et al. 2000, 2004, etc ) calibration curve using a synthetic mrna Data based Copy & Paste of previously performed calibration curves (LC software; Roche Diagn.)

20 ER intra-assay & inter-assay variation intra-assay variation: within one LightCycler 1.0 run inter-assay variation: between different LightCycler 1.0 runs ER-alpha intra-assay variation CV = 18.7% (n = 3) ER-alpha inter-assay variation CV = 28.6% (n = 7) 1e % 14.4% 7.3% 7.9% 33.6% 13.7% 22.8% 1e % 31.9% 46.2% 20.6% 15.1% 21.4% 23.8% 1e+9 1e+9 detected molecules 1e+8 1e+7 1e+6 1e+5 1e+4 detected molecules 1e+8 1e+7 1e+6 1e+5 1e+4 1e e e e e e e e+9 1e e e e e e e e+9 input ss cdna molecules input ss cdna molecules using a recombinant plasmid DNA calibration curve (mean std.dev.; on molecule basis) Pfaffl, unpublished 1998

21 ER standard curve plasmid DNA molecules Rotor-Gene 6000

22 ER intra-assay & inter-assay variation (2007) intra-assay variation: within one RG-6000 run (n = 4) inter-assay variation: between different RG-6000 runs (n = 10) Invitrogen two-step SYBR GreenER Kit ER intra-assay variability (n = 4) over all CV = 1.35% ER inter-assay variability (n = 10) over all CV = 2.58% 1e+10 1e+9 1e+8 [ 4.99? ] e+10 1e+9 1e+8 [ 10.57? ] detected molecules 1e+7 1e+6 1e+5 1e+4 1e+3 1e+2 detected molecules 1e+7 1e+6 1e+5 1e+4 1e+3 1e+2 1e+1 1e+1 1e+0 1e+0 1e-1 NTC x3 10x4 10x5 10x6 10x7 10x8 10x9 1e-1 NTC x3 10x4 10x5 10x6 10x7 10x8 10x9 input ss cdna molecules input ss cdna molecules using a recombinant plasmid DNA calibration curve (mean std.dev.; on molecule basis) Pfaffl, 2009

24 Standard curve - Linear regression The goal of linear regression is to adjust the slope and intercept to find a line to best predict the values of unknown concentration from the Cq value. Minimizes the sum of the squares of the vertical distances of the points from the line. Efficiency = [10 (-1/slope) ] -1 Optimum is E = 100% y l kx High correlation coefficient

25 Determination principles of real-time PCR efficiency: by calculating the slope of the linear regression cycle number of crossing point (CP) ng cdna vs. CP(TyrA) slope = , E = 2.09 ng cdna vs. CP(PyrB) slope = ; E = 2.16 ng cdna vs. CP(Gst) slope = ; E = 1.99 regressions slope E = 10-1/slope 10 0, ,025 0,05 0,25 0,5 2, cdna input (ng)

26 Precision in the estimates distance from center SE lgcˆ i ( test ) SE k y. x 1 m 1 n k 2 CT n lg c i lg c i 1 i CT 2 2 number of test replicates number of standards std. curve range Confidence interval for estimated concentrations log cˆ i t 95%,2tails, n 2 SE logˆ c i TATAA Biocenter, Sweden

27 Standard curve - 95% confidence interval 40 95% confidence interval mean linear regression slope = r 2 = (on basis of 10 independent runs) 30 Cq values e+0 1e+1 1e+2 1e+3 1e+4 1e+5 1e+6 1e+7 1e+8 1e+9 plasmid start molecules

28 Asymmetric standard curve variance accumulation of PCR errors (variability) in low copy standards weak background correction for high copy standards prediction interval is much bigger than confidence interval standard curve is most reliable in the center

29 Validation of an absolute quantification of steroid receptors AR ER ER PR product length 172 bp 234 bp 262 bp 227 bp detection limit 12 molecules 2 molecules 10 molecules 14 molecules quantification limit 120 molecules 165 molecules 106 molecules 760 molecules quantification range test linearity Pearson correlation coefficient *10 10 molecules (r = 0.998) *10 9 molecules (r = 0.995) *10 10 molecules (r = 0.996) *10 9 molecules (r = 0.998) PCR efficiency 90.7% 81.2% 81.3% 93.9% intra-assay variation [CV] molecule basis inter-assay variation [CV] molecule basis 31.2% (n = 3) 18.7% (n = 4) 17.6% (n = 4) 5.7% (n = 4) 24.3% (n = 7) 28.6% (n = 4) 29.7% (n = 4) 25.7% (n = 4) Species specific T melt ( C) Homo sapiens [ 87.9 ] 83.5 Rattus norvegicus Callithrix jacchus (primate) Bos taurus [ 89.9 ] 90.1 [ 82.9 ] Ovis aries Sus scrofa Pfaffl et al., APMIS 2001

30 Relative Quantification in real time qrt-pcr relative quantification normalization via one reference gene via reference gene index >3 HKG external calibration curve without any reference gene ROX / FL / IS without real-time PCR efficiency correction with real-time PCR efficiency correction 2 (- Cq) REST, qbase LC software, etc.

31 Commonly used normalisation strategies in relative mrna quantification 1st - RNA input is adjusted according to known quantity and quality of extracted RNA (molecules/ng RNA; ag transcript/ng RNA; RNA quality markers, e.g RIN or RQI) according to mass / volume / amounts of cells of a tissue (molecule/tissue; mass of transcript/tissue; copies per counted/selected cells, transcripts per single-cell) according to an external marker ROX, Fluorescein, alien RNA, etc 2nd - GOI expression is normalised according to one constant expressed reference-gene (=> Cq) GAPDH, actins, albumins, cyclophilin, micro-globulins, histone subunits, rrnas, ALU repeats, etc according to an index containing more reference-genes (> 3) (=> Cq) genorm, BestKeeper, Normfinder, qbase versions, REST versions 3rd - further relative parameters comparing the RG normalized expression level ( Cq) with a second physiological stage (=> Cq): a non treated control the time point zero a healthy individual.???

32 fluorescence GAPDH (control) GAPDH (treatment) TNF (control) TNF (treatment) analysis line expression ratio = 2 - Cq Cq treatment Cq control cycle number

33 Normalisation according to an internal reference gene delta-delta Ct method for comparing relative expression results between treatments in real-time PCR ABI Prism Sequence detection System User Bulletin #2 (2001) Relative quantification of gene expression Cq = Cq target gene Cq reference gene expression ratio = 2 - [ ΔCq treatment - ΔCq control] expression ratio = 2 - ΔΔCq Livak KJ, Schmittgen TD. (2001) Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2 [- delta deltac(t)] method. Methods, (4):

34 Tissue matrix interfere with real-time PCR efficiency and amplification fidelity IGF-1 mrna amplification in three cattle tissues liver m. splenius m. gastrocnemius

35 PCR inhibitors: Hemoglobin, Urea, Heparin Organic or phenolic compounds Glycogen, Fats, Ca 2+ Tissue matrix effects Laboratory items, powder, etc. PCR enhancers: DMSO, Glycerol, BSA Formamide, PEG, TMANO, TMAC etc. Special commercial enhancers: Gene 32 protein, Perfect-Match, Taq-Extender, Q-Bond, AccuPrime, E. Coli ss DNA binding real-time PCR efficiency and amplification performance RNA / DNA degradation tissue degradation unspecific PCR products lab management DNA dyes DNA concentration PCR reaction components hardware: PCR platform & cups cycle conditions

37 Relative quantification of a target gene versus one internal control = reference gene (mostly a housekeeping gene) relative expression = 2 - [ Cq sample - Cq control ] relative expression = E target Cq target (control - sample) E reference Cq ref (control - sample) Pfaffl, Nucleic Acids Research 2001

38 Determination principles of real-time PCR amplification efficiency Direct methods: Dilution series (Rasmussen 2001, Peirson et al. 2003, etc.) Determination of absolute increase in fluorescence (Rasmusen 2001; Peccoud & Jacob 1998; Pfaffl 2001) Indirect methods: ( fit of mathematical models ) Sigmoidal model (Lui & Saint 2002; Rutledge 2003; Tichopad et al. 2002) Logistic model (Wittwer et al. 2000; Tichopad et al. 2003) Exponential model (Tichopad et al. 2003, Bar et al. 2003) Multiple-model fit sigmoidal, linear, and exponential (Tichopad et al. 2003) Comparative Quantitation Analysis Rotor-Gene software (Corbett Life Science / Qiagen) CalQPlex algorithm realplex software (Eppendorf) E-Method algorithm Light-Cycler software (Roche Applied Science) Third derivative method CFX Cycler Series (Bio-Rad) cycle number of crossing point (CP) 50 fluorescence (log) ,5 5 2,5 1 0, exponential phase (cycle 23-28) f y ng cdna vs. CP(TyrA) slope = , E = 2.09 ng cdna vs. CP(PyrB) slope = ; E = 2.16 ng cdna vs. CP(Gst) slope = ; E = 1.99 regressions 10 0,025 0,05 0,1 0,25 0,5 1 2, cdna input (ng) inter phase (cycle 29-34) LightCycler cycle e a x x ( b )

39 Determination principles of real-time PCR efficiency: by calculating the slope of the linear regression cycle number of crossing point (CP) ng cdna vs. CP(TyrA) slope = , E = 2.09 ng cdna vs. CP(PyrB) slope = ; E = 2.16 ng cdna vs. CP(Gst) slope = ; E = 1.99 regressions slope E = 10-1/slope 10 0, ,025 0,05 0,25 0,5 2, cdna input (ng)

Determination of RT real-time PCR efficiency 273 bp GAPDH amplicon calculated from 729 slope of the standard curves six different total RNA concentrations E =

40 Determination of RT real-time PCR efficiency 273 bp GAPDH amplicon calculated from 729 slope of the standard curves six different total RNA concentrations E = 10-1/slope E = E = triplicate Assumption-free analysis of quantitative real-time PCR data Ramakers et al. (2003) Neurosci Lett 339(1): 62-66

41 Calculation of real-time PCR efficiency: LinRegPCR Interface Ramakers et al., Neurosci Lett (1): data points in exponential phase 2. Data input from LightCycler and Applied Biosystems platforms

42 Standardized determination of real time PCR efficiency from a single reaction setup multi-model fitting Tichopad et al., 2003 NAR 31(20): e122 exponential fit 30 logistic fit fluorescence (f) logistic fit n=40 linear ground phase early exponent. phase log-linear phase n=6 plateau phase n=14 linear fit n=11 exponential fit n=9 SDM FDM linear fit cycle

43 Future of relative Quantification in real time qrt-pcr relative quantification normalisation via one reference gene via reference gene index >3 HKG external calibration curve without any reference gene ROX, Fluorescein, alien RNA without real-time PCR efficiency correction with real-time PCR efficiency correction 2 (- Cq) mean efficiency REST, qbase, LC software single-run efficiency CAmpER, REST 2009

Relative Expression Software Tool (REST) http://rest.gene-quantification.

44 Relative Expression Software Tool (REST) REST for low throughput applications ( ) REST-384 for high throughput applications ( ) REST-MCS multiple condition solver ( ) REST-RG direct import for sample specific qpcr efficiency and TOP (Cq) from Rotor-Gene software ( ) REST-2005 Stand alone application (March 2005) REST-2008 Stand alone application (October 2008) standard mode + single run efficiency correction REST-2009 Stand alone application (December 2009) standard mode + single run efficiency correction REST-2012 New features in 2012! Pfaffl MW, Horgan GW, Dempfle L. (2002) Nucleic Acids Res (9): e36 Relative expression software tool (REST) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Software development: 2001 & 2004 M.W. Pfaffl & G.W. Horgan 2005 M.W. Pfaffl & G.W. Horgan & Y.Vainshtein & P.Avery 2005 & 2008 M.W. Pfaffl & Corbett Life Science 2009 M.W. Pfaffl & Qiagen

46 Relative quantification of a target gene versus multiple-reference-genes ( = RG INDEX ) RG verification: genorm (Vandesompele et al. 2002, Genome Biology) Normfinder (Andersen et al., 2004; Genome Biology) BestKeeper (Pfaffl et al. 2004; Biotechnology Letters 2004) RG Index normalisation: qbase and qbaseplus (Hellemans et al, 2006 and 2008) REST-384 (Pfaffl and lots of international co-programmer, ) REST 2005/2008/2009 (in cooperation with Corbett Life Science and Qiagen) relative expression = E target ΔCq target (MEAN control MEAN sample) E INDEX ΔCq INDEX (MEAN control MEAN sample) Pfaffl, Kubista, & Vandesompele in Real-time PCR: an essential guide (Horizon Bioscience, 2nd edition, 2009)

47 The use of reference genes Criteria for a good reference gene: => Constant expression in all biological samples in the study No universal reference gene exists! The stability of the reference gene(s) should be validated for each individual study Normalizing to a regulated reference gene will distort data and wrong conclusions can be drawn Normalization may be improved by using the geometric average of multiple validated reference genes genorm or Normfinder are highly recommended Vandesompele et al., Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology (2002) 3(7): Andersen et al., Normalization of real-time quantitative RT-PCR data: a model based variance estimation approach to identify genes suited for normalization - applied to bladder- and colon-cancer data-sets. Cancer Research 2004 (64):

48 total variance genorm validation purpose of normalization: reduction of non-specific variation only genorm best reference genes are able to reduce most of the variation 3 good references 3 average references 3 bad references Vandesompele et al., Genome Biology, 2002

49 genorm and Normfinder find the optimum pair of reference genes genorm & NormFinder performed in Genex (Multid ver 5.2.1) more info on => GenEx.Gene-Quantification.info

50 How many reference genes are useful? Accuracy increases as long as the extra reference gene decreases SE n n i 1 SE 2 RG i < 1 n n i 1 SE 2 RG i

51 more info on => MIQE.Gene-Quantification.info

52 Thank you team! Thank you for your attention! I am happy to take questions!

Introduction to Real-Time PCR: Basic Principles and Chemistries

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