Welcome to part 2 of our real-time PCR series. In part 1, we went through the basics of real-time PCR, its advantages over end-point PCR, a typical workflow, data output, and the choice of fluorescent labeling systems available.
In part 2, we take a look at the different quantification methods available, setup tips, primer design and quality control.
Quantification Methods - Which One Is Right for You?
During the planning stages of your experiment, you will need to decide which of the available quantification methods is most suitable for your research question. Let's have a look at the methods available and the ins and outs of each.
1. Absolute Quantification (Abs. Quant.)
Absolute quantification allows you to find out the absolute copy number of your target sequence in a sample, by comparing the amplification curves generated from your samples against a standard curve generated with known concentrations of your target sequence.
Popular applications for absolute quantification are viral copy number determination in blood donations and public safety e.g., how many microorganisms in bathing/drinking water?
Generating Standard Curves for Absolute Quantification
- Prepare a 10-fold serial dilution of your standard, which contains your target sequence at a known concentration.
- A real-time PCR standard curve is generated as a semi-log regression line with Ct value (Y-axis) vs. log of template DNA (X-axis).
- The slope of the curve will give you an idea of the PCR efficiency. Under optimal conditions, the number of amplicons should theoretically double during each cycle, and the PCR efficiency would be 2. Perfect PCR efficiency is indicated with a slope of -3.22. Modern instruments can readily calculate the PCR efficiency based on the standard curve.
- Because every factor that might influence a PCR reaction must work optimally for 100 % PCR efficiency, most PCR reactions have a PCR efficiency of less than 2.
- If your standard curve is linear, a PCR efficiency of slightly less than 2 shouldn't be a major concern for abs. quant. as long as independently generated standard curves using the same conditions yield similar results.
Considerations for Standard Selection
- Standard curves can be very reproducible, but they will only ever be as good as your standard! Reliable abs. quant. assumes that you know the absolute quantities of your standard. Common sources of standards are plasmid DNA, oligos, purified PCR amplicons, and others. When using these sources, the DNA concentration is measured by A260 and converted to the number of copies using the molecular weight of the target DNA sequence.
- The measurement unit of your unknown sample is defined by your standard, for example, copies/ng of total DNA, copies/cell, copies/ml of blood.
- You will need to get an idea of how stable your standard is during storage. You should at the very least prepare aliquots to avoid multiple freeze-thaw cycles of the same stock preparation. Make sure that you choose a standard that is possible to prepare consistently as required.
- Abs. quant. assumes identical amplification efficiencies for the native target (in a gDNA preparation for example) and the standard used to generate the standard curve. Consider how well your standard actually represents your unknown sample. For instance, is your unknown sample likely to contain PCR inhibitors? If so, take appropriate action to reduce this risk. Also, when attempting to quantify an mRNA transcript by abs. quant., it might be most relevant to use in vitro transcribed RNA as the standard.
2. Relative Quantification (Rel. Quant.)
This method measures the relative differences in mRNA expression levels between 2 or more samples. It is primarily used for gene expression analysis, where expression levels of target genes are compared with housekeeping genes. The units used to express relative quantities are irrelevant, and the relative quantities can be compared across multiple experiments.
These are generally defined as constitutively expressed genes that are required for normal cellular function, and are expressed in all cells of an organism.
Typical housekeeping genes include: actin, tubulin, GAPDH (glyceraldehyde-3-phosphate dehydrogenase), translation elongation factors and ribosomal RNAs. Each research field has its own commonly used housekeeper genes, so consulting the literature is a good idea.
Although many researchers rely on a single housekeeping gene, it is highly recommended to include several housekeeping genes in any rel. quant. experiment, as you can never be certain that the expression of your favorite housekeeping gene isn't affected by your experimental conditions.
Models for Relative Quantification
Standard curves are typically used in rel. quant. to calculate PCR efficiency of housekeeper and target PCRs. However, it is also possible to perform rel. quant. without standard curves, and different mathematical models are available for comparative gene expression analysis depending on whether you know the exact PCR efficiency or not. The comparative Ct method is widely used to compare the Ct value of one target gene to another in a single sample, using the formula: 2ΔΔCT (1). Although it's not necessary to know how these models are derived in order to use them, you can read more about them in the resources mentioned at the end of the article.
Excellent primer design is a prerequisite for real-time PCR regardless of how you perform quantification. There are several good FREE online resources available to help you get started, for example, Primer 3 and NCBI Primer Design Tools (2,3).
Although opinions on criteria for PCR primer design vary, here are a few general pointers that most users agree on:
- Try to span an intronic region if possible - this will ensure specificity towards cDNA in the event that your sample contains contaminating gDNA
- Aim for a melting temperature of approximately 60 °C
- Design primers so that your amplicon length is between 80-200 bp
- Watch out for secondary structures/primer dimers as these will greatly affect PCR efficiency
- To get an idea about possible off-target amplifications, it is a good idea to BLAST your final primer sequence against your organism of interest using NCBI Primer-BLAST
- For more specific tips on primer design, we suggest you consult your real-time PCR reagent supplier's guidelines
Setting up Real-Time PCR
PCR requires more controls than
traditional PCR for accurate quantification
- Always include 3 negative control wells for each primer pair, including primers for housekeeping genes
- Always include a no reverse-transcriptase (RT) control (in 3 wells) when analyzing mRNA
- To account for potential pipetting errors, test all housekeeping and target gene reactions in triplicate
- Plan your plate setup carefully, and try to devise a plate layout you can use again
- Differentiate the wells of your plate based on:
- The presence or absence of template
- Housekeeping or target gene
- Control condition or experimental condition
- Carefully program your cycling conditions and double-check them!
Post-Run Melting Curves
In part 1, we mentioned that it is possible to assess off-target amplification and primer dimer formation with post-run quality control checks. We were referring to post-run melting curves. To an extent, you can perform these checks by running your real-time PCR products on agarose gels, but this is time and reagent-consuming and won't allow you to discriminate between target and off-target amplicons in the same size range.
Post-run melting (or dissociation) curves are considered to be essential when using SYBR Green because SYBR Green binds dsDNA in a non sequence-specific manner.
You can incorporate a post-run melting curve into your real-time PCR run as follows:
Note: include the instructions for the melting curve in your PCR program.
- Immediately after the last round of PCR amplification, the reaction is subjected to a temperature gradient from 50-95 °C and fluorescence is monitored continuously. dsDNA is denatured during this step
- The point/temperature at which the dsDNA 'melts' into ssDNA is observed as a drop in fluorescence as the dye dissociates
- Your instrument's software will convert the melt curves into distinct melting peaks, where products of different lengths/properties are shown as distinct peaks
- Melting curves are also used in genotyping, because alleles that differ by one or a few nucleotides will have slightly different melting temperatures that are visible as distinct melting peaks. In this way one can distinguish homozygotes, which appear as a single peak, from heterozygotes, which appear as two peaks