Real-Time PCR Validations

Relative gene expression Example experiment: I have 2 samples: untreated and treated. Question: What happens to the expression of gene X when I treat the cells? Answer: Expressed as a fold change. 2

Sample Preparation Untreated Cells Treated Cells Nucleic acid isolation RNA DNase-treatment RNA Reverse transcription 3 cdna cdna

Ambion RNA Isolation Products Tissue Disruption MELT ** Small RNA Isolation mirvana mirna Isolation Kit PARIS mirvana PARIS Kit Bead-Based Isolation MagMAX -96 Kits MagMAX Viral Kits ** MagMAX Total Nucleic Acid Isolation Kit ** 4 Total RNA Isolation RiboPure Kits RNAqueous Family Tri Reagent ** LeukoLOCK RecoverAll Total Nucleic Acid Isolation from FFPE Tissues ** Complete Solution TaqMan Gene Expression Cells-to-CT TaqMan mirna Cells-to-CT TaqMan PreAmp Cells-to-CT TaqMan Fast Cells-to-CT ** RNA/DNA Kits

Sample Preparation Untreated Cells Treated Cells Nucleic acid isolation RNA DNase-treatment RNA Reverse transcription 5 cdna cdna

TURBO DNA-free free Removal of DNA Further Enhanced by >600-fold 6 Reagent included to completely remove DNase without phenol treatment or heating

Sample Preparation Untreated Cells Treated Cells Nucleic acid isolation RNA DNase-treatment RNA Reverse transcription 7 cdna cdna

High Capacity cdna Reverse Transcription Kit Allows up to 10ug of total RNA to be reverse transcribed in a 100ul reaction Uses random primers for quantitative conversion of 0.1 ug to 10 µg of total RNA to cdna Converts large amounts of RNA to cdna for screening multiple targets by PCR for more stable long term storage 8

What s s New at Applied Biosystems? High Capacity RNA-to-cDNA Kit Two tubes High Capacity RNA-to-cDNA Kit Enzyme and Buffer Validated with Cell-to-CT Kits Integrated RNase inhibitor Shorter incubation times (30-60min) Random priming / Oligo(dT) Up to 10 logs of dynamic range Enhanced sensitivity 9

What s s New at Applied Biosystems? High Capacity RNA-to-cDNA Master Mix Single tube, complete first-strand cdna synthesis master mix reduces experimental variability and streamlines workflow 5X concentration provides robust results even with dilute RNA samples and rare or abundant transcripts Liquid format between 10 and 20 C eliminates freeze/thaw cycles and maximizes chemistry stability Optional No-RT Control formulation for assay validation Convenient 96-well plate format for automated, high-throughput applications (20uL rxn, 4uL MM) 10 40 minute incubation

Oligo(dT) ) discouraged 1. Oligo(dT) ) priming more sensitive to nucleases 2. 18S does not have a poly(a) tail 3. Oligo(dT) ) can bind internally to varying degrees 4. Oligo(dT) ) may not prime to 5 5 end 5 assay exon 1 exon 2 exon 3 TTTTTTTTTT AAAAAAAAAA 11

Two-Step RT PCR or One-Step RT PCR Two-Step Step 1: cdna is prepared using a separate RT procedure Step 2: cdna is then added to the Real-Time PCR reaction One-Step Add RT enzyme + TaqMan Gene Expression MM 10-30min RT step followed by Real-Time PCR reaction AgPath-ID RT PCR Kit, EZ RT PCR Kit, One-Step RT PCR Kit 12

Starting Sample Quantities DNA 100 pg 1 µg SNP Genotyping 2-20 ng cdna Gene Expression 1-50 ng TLDA Total RNA One-Step 1-2 ng/well RNA 100 pg 1 µg mrna 10 pg 100 ng 13

Sample Preparation Untreated Cells Treated Cells Nucleic acid isolation RNA DNase-treatment RNA Reverse transcription 14 cdna cdna

Perform Real-Time PCR 15

Gene Expression Untreated Cells Treated Cells My gene expresses at level X RNA RNA My gene expresses at level Y 16 cdna cdna

What you want to know... Y (treated) X (untreated) 17

Real-Time Quantitative PCR: Relative Gene Expression 18

Relative gene expression Three separate validation experiments 1. Choice of normalizer (endogenous control) gene 2. Linear dynamic range of reverse transcription 3. PCR efficiencies of assays 19

Validation #1 - Choice of a normalizer (endogenous control) gene 20

In a relative gene expression experiment, we always look at two genes... Gene of interest (target) Normalizing gene (control) 21

What is a normalizer? (a.k.a. control gene, housekeeping gene, endogenous control ) A gene that we show to be expressed consistently in all sample types, regardless of treatment, tissue origin, time point, etc. 22

Functions of a normalizer Corrects for... Variable input sample mass Nucleic acid extraction efficiency Reverse transcription efficiency Pipette calibration errors 23

What should you use for a normalizer? 24

Where to begin your search Seek advice from the literature or from colleagues working in your system about what genes seem to remain stable. 25

Always validate your candidate normalizer genes Don t simply trust what others say is a good choice as a normalizer. Prove that this choice is stable in your system. 26

How to validate candidate normalizers - Isolate RNA from a representative group of samples and DNase-treat 27

Quantitating RNA Fluorometer with a fluorophore for detecting single-stranded nucleic acid; small cuvette or capillary/microplate spectrophotomer NanoDrop (1-2µl of undiluted sample) Bioanalyzer (microfluidics based system) 28 Always check linear range of instrument!

How to validate candidate normalizers - Isolate RNA from a representative group of samples and DNase-treat - Perform reverse transcription on each sample 29

How to validate candidate normalizers - Isolate RNA from a representative group of samples and DNase-treat - Perform reverse transcription on each sample - Do Real-Time with candidate normalizers using equal volumes of cdna 30

Human Endogenous Control Plate <0.5 0.2 STDEV ~ 0.5 Ct Difference 32

33 Questions?

Validation #2 - Dynamic range of experiment 34

What is the dynamic range? The range of RNA concentrations over which we observe linear reverse transcription efficiencies (target-specific). 35

Why do we care? We want to make certain that we start with an appropriate amount of RNA for each gene. Otherwise, our data could wind up being inaccurate. 36

How do we check the dynamic ranges of our genes? Run RNA-based standard curves using all genes (normalizer and targets) Use RNA that you have plenty of don t use precious samples 37

How to set up an RNA-based standard curve 38 RNA (undil.) 1:10 dilution 1:100 1:1000 1:10,000 RT RT RT RT RT cdna cdna cdna cdna cdna Run real-time standard curves using equal volumes of each cdna.

How to set up an RNA-based standard curve 39 RNA (undil.) 1:10 dilution 1:100 1:1,000 1:10,000 Run real-time standard curves using equal volumes of each cdna.

How to set up an RNA-based standard curve RNA (undil.) 1:10 dilution 1:100 1:1000 1:10,000 RT RT RT RT RT cdna cdna cdna cdna cdna 40 Run real-time standard curves using equal volumes of each cdna.

Linear RNA standard curves Ct 1 ng Normalizer 0.01 ug Target gene 0.1 ug 1 ug (Note: each point run in triplicate) 10 ug 41 Initial [RNA]

Not-so so-linear standard curves Target gene Ct Q: What s causing this effect? A: Partial inhibition of RT step or Saturation Normalizer 42 Initial [RNA]

Effect of inhibitors on RT Dirty sample RNA (undil.) 1:10 dilution 1:100 1:1000 1:10,000 Partial inhibition RT RT RT RT RT less cdna cdna cdna cdna cdna 43

Some RT inhibitors Melanin Polysaccharides Hemoglobin Chlorophyll Heparin EtOH >1% v/v Proteinase K Guanidinium Phenol >0.2% v/v DTT >1mM DMSO >5% EDTA >50mM SDS >0.01% w/v Sodium Acetate >5mM Mercaptoethanol Use MEGAclear to clean your RNA 44

Different genes, different effects Target gene Use less RNA in RT reactions Ct Normalizer 45 Initial [RNA]

One other possible observation Effect of the Poisson Distribution Ct Simeon Poisson 46 Initial [RNA]

Limited range to work with (2 logs) Ct 47 Initial [RNA]

What do RNA standard curves tell us? The minimum and maximum target concentrations (RNA) at which our measurements will be accurate Ct Ct 48 log quantity log quantity

49 Questions?

Validation #3 - PCR efficiencies of assays (targets and normalizer) 50

Two ways to do relative quantitative PCR Comparative Ct (ΔΔCt) Method Relative Standard Curve Method Sample-to to-sample fold changes 51

How do they differ? Comparative Ct (ΔΔCt) Method No standard curves for each experiment Easier, cheaper, higher throughput Relative Standard Curve Method Run standard curves with each gene in each experiment More work, costlier, lower throughput 52

ΔΔCt generally the method of choice Comparative Ct (ΔΔCt) Method No standard curves for each experiment Easier, cheaper, higher throughput Relative Standard Curve Method Run standard curves with each gene in each experiment More work, costlier, lower throughput 53

One big requirement of ΔΔCt Your two genes (target and normalizer) must have approximately the same amplification efficiencies! 54

Steps for doing ΔΔC T Efficiencies are sometimes confirmed by running the assays side-by-side and confirming that they have parallel amplification curves. 55

Target and control have equal efficiencies in geometric phase Normalizer Target 56

57 More mathematical approach...

Run standard curves with both genes and compare slopes Slope = -3.38 Ct Slope = -3.32 [cdna] Target Normalizer 58

Standard curves give us an idea of PCR efficiencies For example, a slope value = -3.3 suggests the primers are amplifying with approximately 100% efficiency A more negative number (say, -3.5) suggests a less than 100% efficient reaction 59

Suggestions for standard curves Cover as many logs as possible Don t make a three-point standard curve of two-fold dilutions Do make a six-point standard curve of ten-fold dilutions Do eliminate outlying replicates or entire dilution points 60

Standard curve Slope = -3.199 R 2 = 0.949 61

Omit outliers X X X 62

Removal of outliers Slope = -3.277 R 2 = 0.999 63

Final suggestion for standard curves Cover as many logs as possible Don t make a three-point standard curve of two-fold dilutions Do make a six-point standard curve of ten fold dilutions Do eliminate outlying replicates or entire dilution points For low concentration targets, you may need to use plasmid 64

Getting Started Guides 65

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Real-Time Technical Support: 800-762 762-4001 Ambion Technical Support: 800-888 888-88048804 Additional Learning: learn.appliedbiosystems.com 67

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