BE.109:Systems engineering/RT-PCR data analysis
Introduction
There’s a saying that goes something like: “it’s not what you don’t know; it’s what you know that isn’t so.” In other words, it’s easy to head down the wrong path based on an incorrect presumption or misunderstanding. The results from quantitative PCR can easily mislead an investigator. Errors are introduced because the number of sample manipulations is large, and the readout is indirect. A pessimist might argue that the number of assumptions makes the data uninformative. A more optimistic observer might believe that good experimental controls and a healthy dose of skepticism can make the results from this technique informative. To decide for yourself, it will be important to understand the theory underlying quantitative PCR and the experimental design that you are using.
Quantitative PCR is possible since each round of amplification should theoretically double the number of DNA products in the reaction tube. This theoretical efficiency can only be achieved with an abundant pool of dNTPs and primers and it will futher depend on experimental conditions that might vary, like the length of the sequence to be amplified and its sequence. Invariably, however, as the pool of dNTPs and primers deplete, fewer transcripts are made in each round. A graph of transcript number vs. PCR cycle number would reveal the exponential increase in transcript number and its plateau as the reagents are depleted.
Data from: Takara products catalog
In the sample data above, the product of seven independent reactions is being monitored and shown with curves of different colors. It’s clear from the data that each reaction is initiating the exponential phase of PCR and then reaching a plateau, except for the last two reactions that didn’t start their exponential phase until later in the PCR cycling program. The data illustrates how reactions may begin their exponential phase at a different cycle numbers. What is fundamentally important to understanding this method is the fact that the cycle number at which the exponential phase begins depends on the number of starting templates in the reaction. Fortunately, the number of transcripts (shown on the y-axis) can be sensitively and accurately determined by measuring fluorescence units. Recall the reactions you set up last time included SYBR Green, a dye that fluoresces nearly 300X more strongly when it is bound to double-stranded DNA. Thus fluorescence is used as a proxy measurement for amplified product.
“Ct” is the term used to compare quantitative PCR profiles like the seven shown above. Ct is a user-defined fluorescence threshold that the reactions all cross. It is possible to set the threshold anywhere in the exponential phase. A second user-defined parameter is the reaction’s baseline, which must be subtracted to remove machine noise. As you work with your data today, you should consider how your choices for these two parameters affect your data interpretation.
With the baseline subtracted and the Ct defined, it is possible to compare the amount of starting template because in the exponential phase of amplification, the amount of double stranded DNA (y) is equal to the starting number of templates (N0) times 2n, where n is the number of PCR cycles. For example, by starting with 1000 copies of template DNA, you find that reactions crosses Ct at 28th cycle, then a reaction that crosses Ct at the 29th cycle had 500 copies of the target initially, a reaction that crosses Ct at the 30th cycle started with 250 copies of the amplified target, etc. In other words: reactions that differ by 1 Ct began with a 2X difference in starting target.
What Ct can reveal is dependent on the design of your experiment. Commonly, Ct is expressed relative to some “housekeeping” gene whose expression is expected to be unchanged by the experimental manipulations that affect the genes of interest. In your experiment, you are using cell numbers as standards, assuming that each cell will have one copy of the LacZ gene to amplify. Today you will use the data from these standards to generate a standard curve that relates amount of product to cell number. Using this standard curve, it may be possible to know the absolute number of LacZ transcripts/cell in each of the experimental conditions you tried.
Before any grand conclusions can be drawn from your data, there is one important question to ask. How do you know that changes in fluorescence arise from lacZ amplification? It is easy to imagine that the PCR primers can recognize other templates in your reactions and so are producing many species of double stranded DNA molecules. Since you’re not running the products out on a gel, how can you detect other products? The most common method is to run a “melting curve” at the very end of the amplification cycles. By raising the temperature of the final reactions, you can denature the double stranded DNA, changing its fluorescence. Sequence and length of the DNA will determine the temperature at which the melting occurs. For example, in the data shown below, there is one species of DNA that is denaturing at the same temperature in all the samples. Before you begin to look at your own data you should imagine what the melting curve might look like for a reaction with multiple products.
Data from: Takara product catalog
