Real Time P C R

Editor's Notes

• #9: . . . . . . . three Different things 1. Chemistry Use fluorescent DNA binding dyes or probes to monitor the production of PCR products by fluorescence Establish a linear correlation between the amount of the PCR product and fluorescence intensity 2. Detection Using fluorescence detection, amplification can be monitored in real time 3. Analysis Fluorescence data is analyzed with software that eliminates background, normalizes values and estimates template concentration
• #16: 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.
• #22: The TaqMan Animation is a little bit tricky with the clicks. Better to try this a few times before the first presentation.
• #23: If you think the Pac Man is not serious enough, you can easily erase the face and change the pie circle to normal circle. But I have the feeling, that the Pac Man is a good link from the “normal” world to the “biotech” world, specially TAs appreciate such examples.
• #26: Meassurement in annealing phase, nothing what a ABI 7000 can do!
• #27: Scorpions is a beacon attached to one of the PCR-primers. The yellow box shows sequence idendity. The STOP sign is a PEG moity, which blocks the polymerase.
• #28: Meassurement in annealing phase, so ABI 7000 users can’t use Scorpions!
• #34: We find that the ‘PCR base line subtracted curve fit’ option (see area inside pink box on the slide) which is the default analysis mode in the current version of the Biorad Icycler program (3.0a) Does not give such good results as the ‘PCR base line subtracted’ option. So we always use the latter.
• #35: As we saw with the theoretical curves, you should get a straight line relationship in the linear part of the PCR reaction. In this case the reaction is linear from ~20 to ~1500 arbitrary fluorescence units.
• #36: There are several methods to quantitate alterations in mRNA levels using real time PCR, let’s look at the standard curve method first.
• #37: In the following discussion the results shown will be those obtained in our laboratory using a BioRad Icycler Real-time PCR instrument. However, analysis with other instruments is similar. Here is a real-time PCR trace for a single well on a 96-well plate, cycles are shown along the X-axis, and arbitrary fluorescence units (actually these are fold increase over background fluorescence) are shown on the Y-axis - the results are similar to our theoretical graph (see insert) - except that the transition to the plateau phase is more gradual. This expt - and everything we are going to discuss - was done with SYBR green, which has very low fluorescence in the absence of double stranded DNA and very high fluorescence in the presence of double stranded DNA.
• #38: Here is the data from our dilution curve. If you are looking at efficiencies, you want to be sure that every time you do the PCR for the same gene you have the same slope since this is a measure of efficency - in this case you can see that all the samples are reasonably close (the lines are all parallel). If there is a difference in slope of one of your samples, it implies a problem in that tube (PCR inhibitor, problems with enzyme, etc).
• #39: This shows the same data as on the previous slide but on a logarithmic scale. The even spacing of the reactions is now much more obvious. So what the software actually measures for each well is the cycle number at which the fluorescence crosses an arbitrary line called the threshold - shown in orange. This crossing point is known as the Ct value. More dilute samples will cross at later Ct values.
• #40: 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.
• #41: This method was one of the first methods to be used to calculate these type of results. However, as we shall see, it is an approximation method. It makes various assumptions, and to prove that they are valid is, in our opinion, more time consuming that doing a few extra efficiency runs for the Pfaffl method.
• #42: If one looks at the same data that we discussed before. But for the time being ignore the data from the standard ‘loading control’ gene. The difference between the control and treated samples for il1-beta is shown by the red line. If we know the efficiency for il1-beta and the cycle number, we could calculate the fold change in il-1 beta- but there would be no loading control.
• #43: An approximate correction can be made for the loading control by calculating the difference between the IL1-beta Ct values and the RPLP0 values for the control samples, and then for the vitreous treated samples (represented by the two green lines in the slide). This makes an allowance for the fact that in the above case, there is slightly more mRNA in the vitreous treated samples (since the RPLP0 comes up slightly earlier). This difference (or delta Ct value) is shown by the two green lines above. The difference between the two delta values represents the shift as will be seen in the next slide.
• #44: The difference between the two delta Ct values (delta delta Ct, represents the corrected shift of the IL1-b. Since in this example in the vitreous treated sample the IL1-b has moved to the left of the standard, it has a negative value, but in maths subtraction of a negative value is equivalent to adding that value - which makes obvious sense (I hope) if you look at the diagram - the total shift is = the two green arrows added together. If the vit il1-b had shifted but remained to the right of the reference curve, the value would then be subtracted from the large green arrow to determine the shift.
• #47: However, the perfect standard does not exist, therefore whatever you decide to use as standard or standards should be validated for your tissue - if possible you should be able to show that it does not change significantly when your cells or tissues are subjected to the experimental variables you plan to use.