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- 1. Real time andQuantitative (RTAQ) PCR or….. for this audience… “ so I have an outlier and I want to see if it really is changed” Nigel Walker, Ph.D. Laboratory of Computational Biology and Risk Analysis, Environmental Toxicology Program, NIEHS
- 2. I’ve got 30minutes- what am I going to say? z What I am going to tell you Ñ Overview of the technology Ñ Overview of Software based quantitation Ñ Assay Designs Ñ Types of Quantitative analysis z What I am not going to tell you Ñ How to design primers Ñ How to use different types of PCR machines Ñ All there is to know- this is just a brief intro
- 3. Conventional RT-PCR z Reversetranscription (RT) of cDNA from RNA Ñ Oligo d(T) Ñ Random Hexamer Ñ Gene specific primer z PCR amplification of a defined DNA sequence from cDNA Ñ Traditionally a 3-phase multi-cycle reaction Ñ Denaturation, annealing, primer extension z Electrophoretic separation of PCR products (amplicons) Ñ PCR for specific number of cycles Ñ Run products on an agarose gel
- 4. Real-time PCR iskinetic z Detection of “amplification-associated fluorescence” at each cycle during PCR z No gel-based analysis at the end of the PCR reaction z Computer based analysis of the cycle-fluorescence time course Increasing fluorescence Linear plot PCR cycle
- 5. Specialized Instrumentation isneeded z 96-well format Ñ ABI SDS 7700 Ñ ABI 7000 Ñ ABI 5700 Ñ BioRad Icycler Ñ Stratagene Mx4000 z Capillary tube format- Ñ Roche Light cycler z 384-well format HT systems Ñ ABI SDS 7900
- 6. Software-based analysis z Dataacquisition Ñ Fluorescence in each well at all cycles. Ñ Software-based curve fit of fluorescence vs cycle # z Threshold Ñ Fluorescence level that is significantly greater than the baseline. Ñ Automatically determined/User controlled z CT (Cycle threshold) Ñ Cycle at which fluorescence for a given sample reaches the threshold. Ñ CT correlates, inversely, with the starting concentration of the target. Ñ Varies with threshold- not transferable across different plates
- 7. Software-based analysis Baseline CT (cyclethreshold) Increasing fluorescence Threshold (10sd) Log plot
- 8. Example analysis ofCYP1A1 •SYBR Green detection •10-fold dilution series No RNA controls CT values Threshold
- 9. Linear range forCYP1A1 by RTAQ-PCR 1pg-100ng Total RNA 95% E z CT correlates, inversely, with the starting concentration of the target.
- 10. Amplification-associated fluorescence z Fluorescentdye Ñ Detects accumulation of DNA (SYBR green) z FRET (Fluorescent Resonance Energy Transfer) based. Ñ Detect accumulation of a fluorescent molecule (Taqman) Ñ Detect accumulation of specific DNA product-(Molecular Beacons, LC)
- 11. Methods of fluorescencedetection Light Cycler Molecular Beacons Taqman SYBR Green R Taq Q R Q “Direct-NS” “Indirect” “Direct-S” D A “Direct-S”
- 12. Dye-based FRET-based z Upside ÑQuick Ñ No primer/probe optimization z Downside Ñ Non-specific z Application Ñ Many genes few samples Ñ “That sounds like just the ticket for checking my microarray data” z Upside Ñ Specific z Downside Ñ Primer/probe optimization Ñ More costly z Application Ñ Many samples few genes
- 13. Primer sets z Thereare no large databases of primer sets Ñ We attempted to get a database going early on with little success. z Commercial pre-developed assays are available z Dye-based Ñ Existing primers could be adapted but may not be the best z Probe-based Ñ Unlikely that existing PCR primer pairs will be suitable Ñ Primers and probes designed to match specific reaction conditions. z Primer design Ñ Primer Express™ software facilitates design (for ABI system) (SCL copies) Ñ No optimization of primer annealing temperature Ñ Multiple primer sets can use same default cycling conditions on SDS7700
- 14. Quantitation z Absolute quantitation(eg. copies/ug RNA) Ñ Interpolate CT vs standard curve of known copies of nucleic acid Ñ Total RNA, in vitro transcribed RNA, DNA z Unit-less quantitation (arbitrary values/ug RNA) Ñ Interpolate CT vs dilution curve of a “quantitator standard RNA”
- 15. Relative quantitation z ∆CTbetween “control” and “treated” RNAs on a single plate Ñ Fold-difference Ñ Cannot compare Ct between samples on different plates z ∆CT between “calibrator” RNA sample and unknown RNA Ñ Same calibrator RNA can be on multiple plates z ∆∆CT between “control” and “treated” Ñ Fold change-normalized to a separate reference gene/sample
- 16. Relative fold change zCT inversely correlated with starting copies z Each cycle there is a “doubling” of amplicons (assuming 100% efficiency) z Difference in 1 cycle therefore a 2=fold difference in copies Fold change = 2∆ CT ∆Ct = 3.31 Fold difference in starting copy number = 2 3.31 = 9.9
- 17. Correlation of real-timeand microarray R2 = 0.6888 0.1 1 10 100 0.1 1 10 100 Microarray fold Data from Martinez et al (submitted), SYBR, 2∆Ct
- 18. Efficiency adjustment For a∆Ct=1, Fold change = efficiency z 2∆ CT assumes a 100% efficient amplification z For single gene efficiency adjustment use Ñ Fold change = e ∆Ct Ñ Where efficiency(e%) = 10 (-1/slope) z For a ∆Ct=1, Fold change = efficiency
- 19. Calculation of Efficiency zBased on a linear plot of CT vs. log copies: z Efficiency(e%) = 10 (-1/slope) z 100% efficiency (2 copies each cycle) slope of –3.3219. Slope = -3.462 e = 10 (-1/3.462) = 1.95 1.95 copies per cycle ∆Ct = 3.3 Fold= (1.95) 3.31 = 9.1 fold
- 20. Efficiency adjusted Normalization zFold-change can be “normalized” relative to a “reference gene” z Reference can be a separate sample on the plate z Beware of the interpretation of a normalized fold change Ñ Assumption that the reference gene is “unaffected” by treatment
- 21. Can I reallydetect <2X changes? z How can you be sure a difference is real? Ñ T-test on triplicates CTs z Sensitivity of discrimination is dependent upon.. Ñ Efficiency Ñ Assay variability Ñ Number of Replicates
- 22. Variability impact on∆Ct z Taqman or SYBR z Standard deviations (n = 9) Ñ 0.2-0.5 cycles Ñ CV, 1-2% on CT values z Power calculation for ∆Ct =1, 90%, p<0.05 (T-test) Ñ sd=0.25, n = 3 Ñ sd =0.33, n = 4 Ñ sd= 0.5. n = 7
- 23. Issues of assaydesign z RNA specific sets -ie Primers spanning intron location Ñ If you know the gene and have the time go for it. Ñ Not all genes in database and annotated esp. rat z Do you need RNA specific sets? Ñ RNA expression 103-108 copies/100ng total RNA Ñ 100 ng RNA approx = 100 single gene copies (assuming 1% DNA contam) z Reverse transcription Ñ Gene specific primer is best especially if using a synthetic RNA standard Ñ Oligo d(T)-may not be good for 5’ end targets Ñ Random hexamers - poor for synthetic RNA standard
- 24. Some Take-home advice zYou’re not in Kansas anymore, so do your homework first Ñ Learn the concepts before you do the assay z Specialized machines Ñ Make contact with someone who will let you use their machine z The devil is in the details. Ñ You can get the same CT from very different curves of different quality z You still need gels Ñ While quantitation is gel-free, a picture tells a thousand words z Replicate Ñ You can detect a 2X change with duplicates but is it for real?
- 25. Acknowledgements/Information sources z ChrisMiller- now the ABI Field Application Specialist! z NIEHS Real-time PCR webpage Ñ http://dir.niehs.nih.gov/pcr z Applied Biosystems Ñ http://www.appliedbiosystems.com/apps z BioRad ICycler Ñ http://www.bio-rad.com/iCycler/ z Stratagene Ñ http://www.stratagene.com/q_pcr/index.htm z Light Cycler Ñ http://biochem.roche.com/lightcycler/lc_sys/lc_sys.htm
- 26. Additional Reference Michael W.Pfaffl. A new mathematical model for relative quantification in real- time RT-PCR. Nucleic Acids Research 29(9): 2005-2007, 2001.