If you are performing quantitative PCR (qPCR), it is important to understand what a Ct value 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 honor St
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:
Ct – cycle threshold
Cp – crossing point
TOP – airfield
Cq – 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 the Cq value. Therefore, in this article we will only use Cq. 
What is the value of Cq?
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.
Although real-time PCR fluorescent dyes and probes should 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 the Ct value.
1.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).
2.The value of Cqis the number of PCR cycles at which your sample reaction curve intersects the threshold line. This value tells how many cycles it took to detect the actual signal from your samples. Real-time PCR procedures will have one reaction curve for each sample and thus many Cq values. Your cycler software calculates and records the Cq value for each of your samples.
Figure 1. Threshold level and Cq value in real-time PCR amplification curve.
Cq 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. Lower Cq values (typically below 29 cycles) indicate large amounts of target sequence. Higher Cq values (above 38 cycles) mean lower amounts of your target nucleic acid. High Cq values can also indicate problems with targeting or PCR, as described later in the pitfalls section of this article.
Your PCR instrument collects fluorescence data during each run. 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, Cq values 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 your Cq values. Some differences in Cq values between your samples will be due to biological events, eg up/down regulation of your target gene in response to treatment. However, Cq 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 your Cq values. 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
The efficiency of the PCR reaction depends on the yield of the master mix, primer specificity, primer annealing temperature and sample quality. 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 PCR efficiency for each pair of primers, run serial dilutions of your standard in five tenfold dilution steps and calculate R2, a statistical measure of how well one value predicts the other. For a PCR efficiency close to 100%, the R2 value should 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 have ruled out the 3 factors listed above, the most common causes of Cq delay are:
The template is too small - try using more templates.
Suboptimal nucleic acid isolation – consider your nucleic acid isolation protocol, quantify your DNA, run an agarose gel, try a different protocol/kit.
Weak reverse transcriptase activity during cDNA synthesis – reverse transcriptase is sensitive to degradation. Order a new one.
RNA/cDNA degradation - keep your workspace clean, improve RNA handling behavior, avoid multiple cDNA freeze-thaw cycles.
PCR inhibition. 
Additional causes include contamination or contamination of your cell line/culture, but these problems are usually identified before nucleic acid isolation.
Delta-Delta call Cq
To ensure that variations in Cq 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 Livak's method. Here, compare the Cq values of your sample with the Cq values of several reference genes (households).
It is imperative that you choose reporter genes whose 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.
The Delta-Delta Cq method makes a key assumption—that the amplification yields (PCR) of the reference and target samples are close to 100% and within 5% of each other. Other normalization methods include the Dealt-Cq method and the Pfaffl method. You can read more about qPCR data analysis methods here.
We hope this article has helped you understand Cq values. If you want more information about qPCR, be sure to check out our 8 Essential Documents and Reference Guides for Quantitative PCR (qPCR).
1. 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.
2. 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