PCR (Polymerase Chain Reaction) is a notoriously finicky operation. While current reagent technologies have made it a very robust procedure overall, practice has shown that tiny variations in temperature, primer concentrations, and buffer components can still create large differences in the dynamics of the chain reaction.
If you are performing “standard,” endpoint PCR, these differences may not be of great interest. A suitable amount of PCR product for downstream applications can usually be made in conditions that are far from optimal. However, if you are performing real-time PCR, also known as quantitative PCR (qPCR), such tiny differences can cause big problems with your data if you aren’t careful with how the reactions are prepared.
One of the major concerns that arises when performing large-scale qPCR experiments is the question of how to handle a set of samples that must be broken down across multiple plates and PCR runs. It’s so convenient – and conducive to the direct comparison that common ΔΔCT analysis of qPCR is built upon – to run all samples in a single plate. However, it really doesn’t take much stretching of your experimental conditions, adding target genes here and biological replicates there, to expand the scope of your study beyond the 96 or 384 wells that your thermocycler is set up to handle. And all of those tiny variations mentioned above…well, they’re quite meaningful when they shift the dynamics of the PCR reaction on a run-to-run basis, causing noticeable differences for specific samples within your overall set.
Is it okay to run a qPCR experiment across multiple plates and PCR runs?
The short answer is absolutely. We would be very limited if we couldn’t do this. The question becomes: What must you do to best carry out a qPCR experiment that requires multiple, separate plates to complete?
There is one, single most important rule to follow when conducting such an experiment: If possible, run all of the samples for a particular gene on the same plate. The error introduced by slight differences in PCR conditions from one run to the next has the largest impact when a specific gene of interest is split up. By keeping the reactions targeting a single gene in the same run, you can minimize this error. This includes your reference gene (or genes)!
Getting more than one gene onto a single plate is great. But, you should never give up the chance to group all of a single gene’s reactions together just to consolidate batches on your plates. In other words, even if you can fit three genes’ worth of reactions into two qPCR runs, if those two plates require you to split up one of the genes, you’re better off running three separate plates and keeping the individual genes together. The chance for variance usually isn’t worth the hassle.
Now, it is entirely possible that your requirements for a single gene extend beyond the number of samples you can fit onto a single plate. When this occurs, you will probably want to include inter-run calibrators in each run, to normalize results. This is a simple matter of including identical reactions on each plate that requires standardization so that your data aren’t marred by run-to-run variation.
Outside of inter-run calibration, there are several steps you can take to minimize the degree of correction that is required from one plate to the next. These largely come down to applying a commonsense approach to standardizing all of the reactions within a single gene assay. For example, whenever possible, you should make a single master mix for all reactions, on all plates. When this isn’t feasible due to the timing of the reactions, you should be sure to use as many identical components as possible; even different batches of primer dilutions or lots of commercial master mixes can lead to slight variation. It probably goes without saying that you should make sure your plates and covers are identical, and ideally from the same product lot themselves…and it certainly goes without saying that you should use the same PCR instrument for each run.
In many cases, you may even find that for less formal inquiries that aren’t destined for publication, fastidious attention to standardization, including selecting identical thresholds, is enough to adequately compare reactions from multiple qPCR plates. However, it is very important to not underestimate the impact that run-to-run variation can have on your gene expression data. Visikol has extensive experience planning and executing gene expression experiments of all sizes. Feel free to contact their scientists with your questions or to plan the qPCR assay that will best meet your research needs.