If you are performing quantitative PCR (qPCR), it is important to understand what Ctvalue is. We'll take you through its key role in qPCR, its different names, how it's calculated and what it means when things seem to be going a little wrong.
Many names of Ctvalue
Before we begin to explain what the Ct value is, we want to take a moment to point out that this value has been given many names over the years, including:
- doest– threshold cycle
- doesPi– place of crossing
- TOP – airfield
- doesq– quantification cycle
These values are all the same, just with different names. In order to standardize qPCR nomenclature, MIQE (Minteriorandinformation about its publicationqreal-time quantitative PCRmxperiments) instructions recommend using Cqvalue. Therefore, we will use C in this articleqonly. 
What is CqValue?
Real-time PCR(often called qPCR) is usually performed to quantify the absolute amount of a target sequence or to compare the relative amounts of a target sequence between samples. This technique monitors target amplification in real time via a target-specific fluorescent signal emitted during amplification.
Despite the fact thatfluorescent dyes and real-time PCR probesshould be sequence specific, a significant amount of background fluorescence occurs during most real-time PCR experiments. It is important to override or account for this background signal in order to gather important information about your target. This problem is solved with two values in real-time PCR: the threshold line and Cqvalue.
- Threshold lineis the detection level or the point at which the reaction reaches a fluorescence intensity above the background level. Before running a PCR, you (or the software in your recycler) set a threshold level. This is literally the line on your graph that represents the level above the background fluorescence, which also intersects your response curve somewhere at the beginning of its exponential phase (Figure 1).
- Gqvalueis the number of PCR cycles at which your sample reaction curve intersects the threshold line. This value indicates how many cycles were requiredfind the right signalfrom your samples. Real-time PCR procedures will have a reaction curve for each sample and thus many CqPrices. Your recycling software calculates and records the Cqvalue for each of your samples.
Figure 1. Threshold level and Cqvalue in the real-time PCR amplification curve.
doesqThe values are the inverse of the amount of target nucleic acid present in your sample and correlate with the number of target copies in your sample. Kato GqValues (typically below 29 cycles) indicate high amounts of target sequence. Higher Cq values (over 38 cycles) mean lower amounts of your targetnucleic acids. High CqThe values may also indicate problems with targeting or PCR, as described later in the pitfalls section of this article.
YourPCR instrumentwill collect fluorescence data during each cycle. After about 15 cycles, you will have a good idea of your background fluorescence level - this will appear as a straight line starting at the zero cycle point. The threshold level will be slightly above this, but at the point where your samples start to enter the exponential phase of PCR amplification. Today, computer software calculates the exact point, and all modern cyclists have an automatic limit line setting in real time.
Real-time PCR records the amount of fluorescence emitted during a reaction in which all PCR components are abundant. In this way, CqValues are usually consistent between replicates in real-time PCR. Until the end point of the PCR reaction is reached, accumulated inhibitors, inactivated polymerases and restriction reagents create a lot of variation in the end values, which is why conventional PCR cannot be used quantitatively.
Many factors can affect Cqvalues. Some differences in Cqvalues between your samples will be due to biological events.g. up/down regulation of your target gene in response to treatment. However, CqThe values are as easily affected by the preparation of the PCR reaction as by the PCR components themselves. The most common areas of traps are:
1. Glavni miksevi
Fluorescence emission can be affected by pH and salt concentration in the solution. Any change in fluorescence emission will naturally change Cqvalues. Therefore, be sure to use only high-quality PCR components, and if using homemade solutions, check the pH and monitor salt deposition before each experiment.
2. Passive reference colors
Reaction values are the ratio of fluorescence of FAM dye (reference) to ROX dye (passive reference). Smaller amounts of ROX produce higher reaction values, assuming no change in FAM fluorescence.
3. Reaction efficiency
Efficiency of the PCR reactionit depends on the yield of the master mix, the specificity of the primer, the annealing temperature of the primer and the quality of the sample. In general, a PCR efficiency above 90% is acceptable. A PCR efficiency of 100% indicates that the target sequence of interest is doubled during each cycle. Perfect PCR efficiency corresponds to a 3.3 cycle change between 10-fold dilutions of your template.
To determine thatPCR efficiency for each primerpairs, perform serial dilutions of your standard in five tenfold dilution steps and calculate R2, a statistical measure of how well one value can predict the other. For PCR efficiency close to 100%, R2the value must be greater than 0.99.
Perform at least three iterations for each point on your standard curves. A higher number of copies is especially important for inserting a small number of copies, where variation between copies is more likely.
4. Other questions
Assuming you've ruled out the 3 factors above, the most common causes of late Cqprices are:
- The template is too small - try using more templates.
- Suboptimal Nucleic Acid Isolation - Review Nucleic Acid Isolation Protocol, Quantify Your DNA,spin the agarose gel, try another protocol/kit.
- Weak reverse transcriptase activity during cDNA synthesis – reverse transcriptase is sensitive to degradation. Order a new one.
- RNA/cDNA degradation - keep your workplace clean, improve itRNA manipulation behavior, avoid multiple cycles of freezing and thawing cDNA.
- PCR inhibition. 
Additional causes include contamination or contamination of your cell line/culture, but these problems are usually identified before nucleic acid isolation.
Calling Delta-Delta Cq
To make sure that the variants in CqThe values are due to real biological changes and not technical problems, you should normalize your results. The most popular normalization method is known as "Delta-DeltaCt” or the Foundry method. Here, compare Cqvalues of your sample in Cqthe values of manyreference genes (household);.
It is imperative thatselect reference geneswhose expression levels are not expected to change during your experiment. Common housekeeping genes include actin, alpha-tubulin, GAPDH and ubiquitin. It is wise to use at least two reference genes and keep in mind that what may be a reference gene for one study may not be appropriate for another.
Delta-Delta CqThe method makes a key assumption—that the amplification yields (PCR) of your reference and target samples are close to 100% and within 5% of each other. Other normalization methods include the Delta-Cq method and the Pfaffl method. You can read more about itqPCR data analysis methods here.
I hope this article helped you catch Cqvalues. If you want more information about qPCR, be sure to check out our tip11 qPCR tasks every researcher should knoww.
For a more comprehensive guide to PCR and PCR parameters, download ourfree PCR basics e-bookand become an expert.
- Bustin SA, et al.MIQE guidelines: minimum information for publishing quantitative real-time PCR experiments. Clin Chem. 2009. 55(4):611-22. doi: 10.1373/clinchem.2008.112797.
- C. Schrader, et al.PCR inhibitors - appearance, properties and removal. J Applied Microbiology. 113(5):1014-26. doi: 10.1111/j.1365-2672.2012.05384.x
Originally published July 2015. Updated and republished November 2020.
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Wrotedr. Nick Oswald