Top 5 rookie qPCR mistakes and how to avoid them
By: Roche Life Science
Posted: January 12, 2016 | Lab Life - Real-Time PCR
Whether you perform qPCR for the first time or just want to be sure you maximize your chances for success, there are a few basic principles to be aware of. Here, we will describe the top 5 rookie qPCR mistakes and how to best avoid them:
1. Poor quality RNA. We can't emphasize enough the importance isolating high-quality RNA. This is arguably the most critical step for the successful preparation of your cDNA and being able to perform efficient and reproducible RT-qPCR. Therefore, it is essential that you get the most from your RNA isolation procedure and maximize the yield of non-degraded RNA during extraction. RNA is extremely sensitive to degradation by RNases and must be handled with care during nucleic acid isolation steps. Degraded or contaminated RNA can negatively affect the efficiency and yield of your RT-qPCR experiment. Your lab bench, pipettes and tips must be RNase-free, and extracted RNA must be stored in an RNase-free solution. When you assess RNA purity using the spectrophotometer, the ratio of the absorbance at 260 and 280 nm (A260/280) should be in the range of 1.8-2.0. If lower, this may suggest contamination of your sample with phenol or proteins. This can sometimes be ameliorated by ethanol precipitation, but if not, the sample is unlikely to be of optimal quality for further experimentation.
2. Poor primer or probe design. qPCR primer design software is readily available online and can save you a lot of precious time and energy. However, it is essential to understand some of the fundamental basics of primer design for successful PCR, including optimal primer length (~18-30 nucleotides) and melting temperatures (Tm), appropriate GC content, complementarity, amplicon length, secondary structure and avoidance of intra- or inter-primer homology that may result in self-dimers or primer dimers. Most primer design programs take into account these parameters to optimize primer design; however, you should be aware of the variables that are being taken into account for potential troubleshooting later on. If you would rather spend your time on something else than primer or probe design, you can also trust expert companies to design and validate the assays for you. Check out your options from Roche here.
3. Not using a master mix and organizing your experiments. This is essential, because any error introduced early on is amplified during PCR cycling. Using a master mix for your reagents minimizes experimental variability and thus improves reproducibility by reducing well-to-well and sample-to-sample variations. Similarly, it is critical to stay organized in the design and execution of your qPCR experiments. This means making a table of your primers and cDNA to know what is going where. Nothing's worse than getting distracted and forgetting where you have and haven't pipetted, so it is essential to have an organized system in place for your pipetting. Some researchers choose their design in a pattern, such as setting up primers in alphabetical order. Some choose to use a new box of 96 pipette tips and line it up with a 96-well plate. This way, as you load your cDNA, you can match the tips to the wells a la Battleship-style (i.e., tip C7 to well C7 and so forth). This can help you keep track of where you are on the plate at all times. When using a multichannel pipette, remember that practice is key. Be sure your multichannel pipette is correctly calibrated, your tips are fixed and correctly set to the channels, and the pipette is held at an even angle when dispensing and parallel to the wells so the tips never touch the walls of the well. No matter the pipetting strategy, don't forget to spin your plate down at the end to get any liquid off the well walls.
4. Skimping on controls. It is important to include the appropriate controls in setting up your RT-qPCR assays. You should include a negative control that lacks your template RNA or cDNA and replace the volume with nuclease-free water, termed the "no template control" or NTC. This can detect cross contamination of surfaces or reagents (i.e. master mix, primers) as well as primer dimer formation. Thus, if you see amplification in this sample, you should run a gel to check for primer dimer or your target. In addition, you should run a RT negative control, where there is no reverse transcriptase, termed a "No amplification control" or NAC. If product is observed, this suggests DNA contamination of your sample.
5. Not validating your reference gene. The amplification of an endogenous control (or reference) gene allows for normalization of target gene expression by comparison of CT values. This can account for differences in the amount or quality of starting material (RNA or cDNA) as well as differences in RNA preparation methods or cDNA synthesis. A reliable reference gene is one whose expression level is unaffected by your experimental variables or interventions and does not differ between the relevant physiological states of your sample conditions.
Historically, genes involved in basic metabolism or cellular structure have been used as housekeeping genes, as their involvement in essential cellular processes ensures their continuous expression. Examples include beta actin (ACTB), 18S ribosomal RNA (RRN18S), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), beta-2-microglobulin (B2M), beta tubulin (TUBB), ribosomal proteins L13a and L19 (RPL13A, RPL19), and various others.
However, the assumption that the expression of housekeeping genes is unchanged across a range of experimental conditions is innately flawed. Thus, reference genes must be validated for each set of experimental conditions and proposed interventions. Indeed, you may even choose to utilize multiple reference genes to optimize the accuracy of your normalization. To help with this, there are now widely available algorithms to identify reliable reference genes, including geNorm, NormFinder, BestKeeper, comparative CT, and Refinder, which encompass the four prior computational algorithms. These stability algorithms can help to identify candidate reference genesyou're your experimental comparison.
So the next time you're faced with a pesky qPCR obstacle, don't be afraid to seek out some assistance from those who've stumbled along this path before you. This could mean exchanging ideas with your colleagues, finding peer groups on social media or asking your suppliers for technical support.