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REAL TIME PCR

USING SYBR GREEN

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In this presentation, we will be using Sybr green to monitor DNA synthesis. Sybr green is a dye which binds to double stranded DNA but not to single-stranded DNA and is frequently used to monitor the synthesis of DNA during real-time PCR reactions. When it is bound to double stranded DNA it fluoresces very brightly (much more brightly than ethidium bromide does, which is why we use Sybr Green rather than ethidium bromide; we also use Sybr green because the ratio of fluorescence in the presence of double-stranded DNA to the fluorescence in the presence of single-stranded DNA is much higher that the ratio for ethidium bromide). Other methods can also be used to detect the product during real-time PCR, but will not be discussed here. However, many of the principles discussed below apply to any real-time PCR reaction.

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THE PROBLEM

NEED TO QUANTITATE DIFFERENCES IN mRNA EXPRESSION

SMALL AMOUNTS OF mRNA

LASER CAPTURE
SMALL AMOUNTS OF TISSUE
PRIMARY CELLS
PRECIOUS REAGENTS

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THE PROBLEM

QUANTITATION OF mRNA

northern blotting
ribonuclease protection assay
in situ hybridization
PCR

most sensitive
can discriminate closely related mRNAs
technically simple
but difficult to get truly quantitative results using conventional PCR

Northern blotting and RPAS are the gold standards, since no amplification is involved.

In situ hybridization is qualitative rather than quantitative.

Techniques such as Northern blotting and ribonuclease protection assays (RPAs) work very well, but require more RNA than is sometimes available. PCR methods are particularly valuable when amounts of RNA are low, since the fact that PCR involves an amplification step means that it is more sensitive. However, traditional PCR is only semi-quantitative at best, in part because of the insensitivity of ethidium bromide (however, there are more sensitive ways to detect the product) and, in part, as we shall discuss later, because of the difficulties of observing the reaction during the truly linear part of the amplification process. Various competitive PCR protocols have been designed to overcome this problem but they tend to be cumbersome.

Real-time PCR has been developed so that more accurate results can be obtained. An additional advantage of real-time PCR is the relative ease and convenience of use compared to some of these older methods (as long as one has access to a suitable real-time PCR machine).

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NORTHERN

target gene

internal control gene

actin, GAPDH, RPLP0 etc

Ratio target gene in experimental/control = fold change in target gene

fold change in reference gene

control

expt

Corrected fold increase = 10/2 = 5

One question is how one uses real-time PCR to quantitate the amounts of DNA or cDNA. The first two calculation methods for real-time PCR results that we are going to focus on are equivalent to the calculations that one usually does when one does a Northern. This slide shows a virtual Northern with two lanes, one with RNA from control cells, the other with RNA from the experimental sample (eg drug treated cells). For the sake of argument, let’s say that there is 10x the amount of signal in the experimental sample compared to the control sample for the target gene. This could mean expression of the gene has increased 10-fold, or it could mean that there is 10x as much RNA in the expt lane. To check for this one usally does a so-called ‘loading control’ in which the blot is probed for expression of a gene which does not change (e.g. actin, GAPDH, cyclophilin, RPLP0 mRNAs; ribosomaL RNA). In this case, let’s say that the loading control shows that there is twice as much RNA in the expt lane. Thus the real change in the target gene is 10/2 =5 fold. We can express this in a more general fashion:

ratio target gene (experimental/control) = fold change in target gene (expt/control)

fold change in reference gene (expt/control)

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Standards

same copy number in all cells
expressed in all cells
medium copy number advantageous

correction more accurate

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Standards

The perfect standard does not exist

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Standards

Commonly used standards

Glyceraldehyde-3-phosphate dehydrogenase mRNA
Beta-actin mRNA
MHC I (major histocompatability complex I) mRNA
Cyclophilin mRNA
mRNAs for certain ribosomal proteins

E.g. RPLP0 (ribosomal protein, large, P0; also known as 36B4, P0, L10E, RPPO, PRLP0, 60S acidic ribosomal protein P0, ribosomal protein L10, Arbp or acidic ribosomal phosphoprotein P0)

28S or 18S rRNA

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But of course, the reaction can’t go on forever, and it eventually tails off and reaches a plateau phase, as shown by the figures in red. If we plot these figures in the standard fashion (top), we cannot detect the amplification in the earlier cycles because the changes do not show up on this scale. Eventually you see the last few cycles of the linear phase (pink) as they rise above baseline and then the non-linear or plateau phase (red) - in real life this starts somewhat earlier than shown here. If we plot the amount of DNA on a logarithmic scale, we can see the small differences at earlier cycles - in real time PCR we use both types of graph to examine the data. Note that there is a straight line relationship between the amount of DNA and cycle number when you look on a logarithmic scale - because PCR amplification is a logarithmic reaction.

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Linear ~20 to ~1500

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Linear ~20 to ~1500

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REAL TIME PCR

kinetic approach
early stages
while still linear

www.biorad.com

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www.biorad.com

2a. excitation filters

2b. emission filters

1. halogen tungsten lamp

4. sample plate

3. intensifier

5. ccd detector 350,000 pixels

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SERIES OF 10-FOLD DILUTIONS

So how do we measure differences in concentration of DNA or cDNA? This graph shows a series of 10-fold dilutions of a sample. As one dilutes the sample, it takes more cycles before the amplification is detectable. The blue line here is the same sample we have been following all along. Note that although the reactions show an orderly series of curves in order of dilution as they cross the orange line, if one looks at the upper parts of the curve, they are rather variable. Thus, if we stopped all these reactions at eg 33 cycles and ran a gel, it would indicate that the blue, red and purple reactions had the same amount of amplification, even though the reactions shown by the purple and red lines differ by a factor of 100 in the amount of dna.

Reactions which differed by a factor of 2 would expect to be 1 cycle apart (samples that differ by 10 would be ~3.3 cycles apart).

Is there any way to check that the correct fragments were amplified? One way to do some checking on the products is to do a melting curve.

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The real-time machine not only monitors DNA synthesis during the PCR, it also determines the melting point of the product at the end of the PCR. The melting temperature of a DNA double helix depends on its base composition (and its length if it is very short). All PCR products for a particular primer pair should have the same melting temperature - unless there is contamination, mispriming¹, primer-dimer² artifacts, or some other problem. Since SYBR green does not distinguish between one DNA and another, an important means of quality control is to check all samples have a similar melting peak. After real time PCR amplification, the machine can be programmed to do a melt curve, in which the temperature is raised by a fraction of a degree and the change in fluorescence is measured. At the melting point, the two strands of DNA will separate and the fluorescence will rapidly decrease. The software plots the rate of change of the relative fluorescence units (RFU) with time (T) (-d(RFU)/dT) on the Y-axis versus the temperature on the X-axis, and this will peak at the melting temperature (Tm). These are the melting curves for samples on the previous slide, a primer-dimer artefact would give a peak with a lower melting temperature (because it is such a short DNA). If the peaks are not similar, this might suggest contamination, mispriming, primer-dimer artefact etc - you want to be sure that the only thing you detect with SYBR green is the thing you want to detect, that is: a specific DNA fragment corresponding to the size predicted from the position of the primers on the cDNA (if you are looking at mRNA) or the genomic DNA, plasmid DNA, etc (according to what your target DNA is).

¹Mispriming: cDNAs made due to annealing of the primers to complementary, or partially complementary sequences on non-target DNAs.

²Primer-dimer artefacts: the primers can sometimes anneal to themselves and create small templates for PCR amplification - these are the so-called primer-dimer artefacts.

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SERIES OF 10-FOLD DILUTIONS

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SERIES OF 10-FOLD DILUTIONS

threshold

Ct

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threshold = 300

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STANDARD CURVE METHOD

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dilutions target DNA

dilutions reference DNA

target

primers

reference

primers

triplicates cDNA

Standard curve method

Here is an example of how one could set up a plate if one were using the standard curve method. By clicking on one of the symbols in the top line and then clicking on one of the wells in the plate diagram, one can define whether samples contain a standard (circles) or an unknown (squares) or negative control samples containing water instead of DNA (-) and whether one has single wells containing the same sample or duplicate or triplicate wells (in which case the duplicate or triplicate well are assigned the same number). The software also allows you to define your dilution factors for the standard curves, give them names, etc. The negative controls are to check that your primers and Taq/Sybr green/PCR mixes are not contaminated and to determine if your primers can form primer-dimer artefacts (these are most readily seen when there is no appropriate DNA for amplification).

In this case we have extracted RNA from control cells (C) or cells treated in some special fashion (eg drug treated cells) - here designated (E) for experimental. These were then copied into cDNA using reverse transcriptase. We have set up the plate so that there is a standard curve for the loading control or reference gene and also one for the gene of interest whose expression we think may change under the experimental condition (the so-called target gene). We normally just do a single point for each dilution of the standard curve since this gives a series of points which fit very well to a straight line. We usually do the cDNA samples in triplicate - so each sample will be done in triplicate for each gene.

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Dilution curve target gene

‘copy number’ target gene experimental

‘copy number’ target gene control

fold change in target gene=

copy number experimental

copy number control

You tell the software which dilution curve you want to use, and which unknowns you want it to quantitate using that curve. You don’t actually give it a real copy number - just start at some at some arbitrary number.

The machine will report the copy number or amount of DNA in each of your unknown samples and even average this for you. If you take the average of the copy number of the target gene in the experimental sample, divide it by the average copy number in the control sample,this will give the fold change in the target gene. You have now calculated the upper value in the ‘Northern’ formula we derived earlier on (slide 4).

Note the excellent fit of the standard curve data to a straight line - a perfect fit would have a correlation coefficient of 1.000 and here the correlation coefficient is 0.999.

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Dilution curve reference gene

‘copy number’ reference gene experimental

‘copy number’ reference gene control

Similarly, you can select the wells in which you amplified the reference gene and determine the relative amounts in the experimental sample compared to the control. This will give you the bottom value in the “Northern formula” from slide 4.

Now that you have both values, you can divide the target gene value (purple) by the reference gene value (blue) and obtain the ratio of the target gene in the experimental sample relative to the control sample, corrected for the reference gene (loading control).

This method will give the fold changes in the target and reference genes - so one can calculate a fold change corrected for any variations in the reference gene. The disadvantage is that you need a good dilution curve for both standard and reference genes on every plate - which would be at least 16 extra wells (including negative controls) for us. If there is any problem with either dilution curve, the data cannot be analyzed, or a suboptimal curve has to be used. So -- we prefer to use determine efficiency accurately (on multiple days) and then take an average of multiple results and use these separately - this makes experiments simpler but we need to think a bit more about the maths of calculating the results because this time the machine doesn’t do it for you.

We find that the standard curves are highly reproducible if you use a supplier who provides a mix with stablizer(s) for SYBR green.

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Real time pcr - week 1

Two different series of diluted DNAs to do standard curve plus two unknowns

RPLPO (ribosomal protein, reference gene)
alpha-5 integrin

Get standard curve and efficiency RPLP0 and alpha-5 integrin
Determine ratio of RPLP0 and alpha-5 integrin in two unknowns (cDNA 1 and cDNA 2)
Determine melting temperature RPLP0 and alpha-5 integrin
Each person will do either RPLP0 or alpha-5 integrin

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A

B

C

D

E

F

G

H

1

2

3

4

5

6

7

8

9

10

11

12

Date:

protocol:

RPLP0

-4

5uL

H₂O

5uL

H₂O

RPLP0

-5

RPLP0

-6

RPLP0

-7

RPLP0

-8

RPLP0

-9

RPLP0

-10

a5-int

-4

a5-int

-5

a5-int

-6

a5-int

-7

a5-int

-8

a5-int

-9

a5-int

-10

add a5-integrin master mix to this row

add RPLP0 master mix to this row

cDNA

1

cDNA

2

cDNA

1

cDNA

2

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4

NORTHERN

alpha

-

5 integrin

(target gene)

RPLP0 (reference)

Ratio alpha-5 integrin cDNA2 to cDNA1

=

fold change in alpha-5 integrin

fold

change

in RPLP0

cDNA1

cDNA2

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Importance of controls

negative control

checks reagents for contamination

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Importance of cleanliness in PCR

Contamination is major problem
Huge amplification contributes to this
Bacterial vectors contribute to this
Amplification of ds DNA is more sensitive than that of cDNA

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PFAFFL METHOD

M.W. Pfaffl, Nucleic Acids Research 2001 29:2002-2007

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EFFECTS OF EFFICIENCY

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AFTER 1 CYCLE

100% = 2.00x

90% = 1.90x

80% = 1.80x

70% = 1.70x

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AFTER 1 CYCLE

100% = 2.00x

90% = 1.90x

80% = 1.80x

70% = 1.70x

AFTER N CYCLES:

fold increase = (efficiency)ⁿ

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SERIES OF 10-FOLD DILUTIONS

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QUALITY CONTROL -EFFICIENCY CURVES

use pcr baseline subtraction (not curve fitting default option) - see next slide
set the threshold manually to lab standard
check all melting curves are OK
check slopes are parallel in log view
delete samples if multiple dilutions cross line together (usually at dilute end of curve)
delete samples if can detect amplification at cycle 10 or earlier
make sure there are 5 or more points
check correlation coefficient is more than 1.990

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QUALITY CONTROL -EFFICIENCY CURVES

use pcr baseline subtraction (not curve fitting default option)
set the threshold manually to lab standard
check all melting curves are OK
check slopes are parallel in log view
delete samples if multiple dilutions cross line together (usually at dilute end of curve)
delete samples if can detect amplification at cycle 10 or earlier
make sure there are 5 or more points
check correlation coefficient is more than 1.990

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PFAFFL METHOD

M.W. Pfaffl, Nucleic Acids Research 2001 29:2002-2007

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target

primers

reference

primers

triplicates cDNA

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IL1-b con

IL1-b vit

RPLP0 vit

RPLP0 con

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IL1-b con

IL1-b vit

AFTER N CYCLES: change = (efficiency)ⁿ

AFTER N CYCLES: ratio vit/con = (1.93)^29.63-18.03 =1.93^11.60= 2053

av =18.03

av =29.63

IL1-beta

So here we are just looking at the results from the wells containing the IL1-b primers. If we look at the difference in Ct values between the control and vitreous samples we see there is an 11.60 cycle difference. Earlier on we derived the equation that the change in amount of DNA after n cycles is equal to the efficiency to the power of n. We independently do multiple serial dilution curves on multiple days to determine an average efficiency and we can then plug that value into the formula. Note that when determining the difference in the Ct values (sometimes known as the ‘delta Ct’), we subtract the vitreous from the control value - this is so that increases will have a positive result and decreases a negative result for the delta Ct value.

If the amount of RNA is less in the vitreous sample the delta Ct would have a negative value and the change would have a value of less than 1.00. So, if there was half as much target mRNA the value for the change would be 0.5.

These calculations are easily done if you have an excel spreadsheet set up (see later slide).

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RPLP0 vit

RPLP0 con

AFTER N CYCLES: change = (efficiency)ⁿ

AFTER N CYCLES: ratio vit/con = (1.87)^19.93-19.80 =1.87^0.13= 1.08

RPLP0

av =19.80

av =19.93

Here we are just looking at the RPLP0 data - note, that, as expected for a good reference gene, there is not much difference between the two RNAs with regard to their levels of RPLP0 mRNA. The same amount of total RNA was used for reverse transcription of both RNAs, and the same amount of each reverse transcription reaction was used for real-time PCR. Since the Ct values are so close and the ratio of the reference gene in the two samples is close to one, this suggests that this is a good reference gene for these experiments.

Of course this method requires that you have determined the efficiency for your primers. We do this on multiple occasions, using the criteria for a good curve discussed above. We find that the values tend to be very reproducible if we use stabilized sybr green mixes. If we change manufacturer or if the formulation is changed, the efficiency will need to be determined again.

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ratio = change in IL1-B = 2053/1.08 = 1901

change in RPLP0

ratio = (E_target )^Δ^{Ct target (control-treated)}

(E_ref )^Δ^{Ct ref (control-treated)}

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An example of a step-by-step way to set up the calculations for the Pfaffl method in EXCEL.

Row 3, columns B, C, D, and E are the average Ct values from real time. In separate experiments, the average efficiency for the target gene was determined to be 1.936 and for RPLP0 was 1.943

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ΔΔCt

EFFICIENCY

METHOD

APPROXIMATION METHOD

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IL1-b con

IL1-b vit

RPLP0 vit

RPLP0 con

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IL1-b vit

RPLP0 vit

IL1-b con

RPLP0 con

av =19.80

av =19.93

av =18.03

av =29.63

Δ Ct = 9.70

Δ Ct = -1.7

Δ Ct = target - ref

Difference = ΔCt-ΔCt

= ΔΔCt

= 9.70-(-1.7)

= 11.40

control

experiment

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ΔΔCt = 11.40 for IL1-beta

2 ^ΔΔ^Ctvariant: assumes efficiency is 100% Fold change = 2^11.40 = 2702

But our efficiency for IL1-beta is 93%

Fold change = 1.93^11.40 = 1800

Pfaffl equation corrected for RPLP0 efficiency

Fold change = 1901

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RED: 83% efficiency

PURPLE: 93% efficiency

SERIAL 10-FOLD DILUTIONS

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RED: 94% efficiency

PURPLE: 94% efficiency

SERIAL 10-FOLD DILUTIONS

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assumes

minimal correction for the standard gene, or
that standard and target have similar efficiencies

2 ^ΔΔ^Ct variant assumes efficiencies are both 100%

approximation method, but need to validate that assumptions are reasonably correct - do dilution curves to check ΔCts don’t change

The only extra information needed for the Pfaffl method is the reference gene efficiency, this is probably no more work than validating the approximation method

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ΔΔCt

EFFICIENCY

METHOD

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Real time pcr - week 2

Two different cDNAs derived from cells which have undergone control or vitreous treatment
Do levels of alpha-5 integrin change relative to RPLPO?

Calculate according to Pfaffl method

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RNA from control RPE cells

RNA from TGF-b treated RPE cells

cDNA from control RPE

cDNA from TGF-b treated RPE cells

? Is there any change in a5-integrin expression ?

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OVERVIEW

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tissue

extract RNA

copy into cDNA

(reverse transciptase)

do real-time PCR

analyze results

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OVERVIEW

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tissue

extract RNA

copy into cDNA

(reverse transciptase)

do real-time PCR

analyze results

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IMPORTANCE OF RNA QUALITY

Should be free of protein (absorbance 260nm/280nm)
Should be undegraded (28S/18S ~2:1)
Should be free of DNA (DNAse treat)
Should be free of PCR inhibitors

Purification methods
Clean-up methods

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OVERVIEW

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tissue

extract RNA

copy into cDNA

(reverse transciptase)

do real-time PCR

analyze results

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Importance of reverse transcriptase primers

Oligo (dt)

Random hexamer (NNNNNN)

Specific

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REVERSE TRANSCRIPTION

adds a bias to the results

efficiency usually not known

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OVERVIEW

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tissue

extract RNA

copy into cDNA

(reverse transciptase)

do real-time PCR

analyze results

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Importance of primers in PCR

specific
high efficiency
no primer-dimers
Ideally should not give a DNA signal

cross exon/exon boundary

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EXON 1

EXON 2

INTRON 2

DNA

EXON 1

EXON 2

RNA

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How are you going to measure the PCR product

Directly

Sybr green
Quality of primers critical

Indirectly

In addition to primers, add a fluorescently labeled hybridization probe
Many different approaches to this, see Bustin J.Mol.Endocrinol. (2000) 25:169

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Importance of controls

negative control (no DNA)

checks reagents for contamination

no reverse transcriptase control

detects if signal from contaminating DNA

positive control

checks that reagents and primers work
especially importance if trying to show absence of expression of a gene

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Standards

same copy number in all cells
expressed in all cells
medium copy number advantageous

correction more accurate

reasonably large intron
no pseudogene
no alternate splicing in region you want to PCR

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RNA from control RPE cells

RNA from TGF-b treated RPE cells

cDNA from control RPE

cDNA from TGF-b treated RPE cells

? Is there any change in a5-integrin expression ?

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RNA from control RPE cells

RNA from TGF-b treated RPE cells

cDNA from control RPE

cDNA from TGF-b treated RPE cells

? Is there any change in a5-integrin expression ?

No RT for control RPE

(to see if any genomic DNA signal )

No RT for TGF-b treated RPE

(to see if any genomic DNA signal )

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THE REVERSE TRANSCRIPTION REACTIONS HAVE BEEN DONE FOR YOU

reactions done as 20ul reactions with oligo (dT) as primer and 1ug total RNA
reactions done under oil
reactions were incubated 1 hr 37C, then diluted to 150ul with water, and incubated in a boiling water bath for 10 mins
You will use 5uL of this diluted cDNA in your reactions

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A

B

C

D

E

F

G

H

1

2

3

4

5

6

7

8

9

10

11

12

Date:

protocol:

Con

RT

5uL

H₂O

add RPLP0 master mix to this row

add a5-integrin master mix to this row

Con

RT

TGF

RT

Con

- RT

TGF

RT

TGF

- RT

Con

RT

5uL

H₂O

Con

RT

TGF

RT

Con

- RT

TGF

RT

TGF

- RT

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SPECIAL THANKS TO

Dr. Joyce Nair-Menon and Lei Li for the use of their real-time PCR results

Anyone who has ever discussed their real-time PCR results with me

NEI - EY12711 for the money

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