RT-qPCR Data Analysis From Cq to Fold Change

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is a laboratory technique used to measure the amount of a specific RNA molecule, which reveals the level of gene expression. By quantifying how active a particular gene is in a cell or tissue, scientists can understand how cellular activity changes in response to various conditions, such as disease or treatment. This guide covers the analysis of RT-qPCR data, from the initial raw output to the final interpretation of gene expression changes.

Understanding Raw RT-qPCR Output

The result from an RT-qPCR instrument is an amplification plot, which graphically displays the accumulation of PCR product over time against the PCR cycle number. This plot has a characteristic sigmoidal shape, beginning with a flat baseline phase where fluorescence is low and indistinguishable from background noise. Following the baseline, the reaction enters the exponential phase, where the amount of PCR product approximately doubles with every cycle, leading to a sharp increase in the fluorescence signal. The curve then levels off into a plateau phase as reaction components are depleted.

To extract a quantitative value, a fluorescence threshold is set above the baseline but within the exponential phase of the curve. The point at which a sample’s fluorescence crosses this threshold is the Quantification Cycle (Cq). A lower Cq value means a larger amount of the target nucleic acid was present at the start, as fewer cycles were needed to generate a detectable signal. Conversely, a higher Cq value signifies a smaller starting quantity, requiring more amplification cycles to reach the threshold.

Essential Quality Control Checks

Before proceeding with calculations, it is necessary to verify the quality of the data. A primary tool for this is the melt curve analysis, which assesses the specificity of the amplified product. This analysis is performed after amplification by slowly increasing the temperature and monitoring the change in fluorescence as the double-stranded DNA dissociates. A single, sharply defined peak indicates that a single, specific DNA product was amplified. The presence of multiple peaks suggests the amplification of non-specific products or primer-dimers, which can interfere with accurate quantification.

Another quality control measure is the determination of PCR efficiency. For analysis methods to be accurate, it is assumed that the product doubles each cycle, an efficiency of 100%; efficiencies between 90% and 110% are considered acceptable for reliable results. Efficiency is determined by creating a standard curve from a series of known sample dilutions and plotting their Cq values against the logarithm of their concentrations. The slope of the resulting line is used to calculate efficiency, and poor efficiency may be caused by suboptimal primer design or inhibitors in the sample.

The Relative Quantification Method

Relative quantification is the most common analysis method, determining the change in a target gene’s expression relative to a reference gene and a control sample. This is achieved using the delta-delta Cq (2^-ΔΔCq) method, which involves several calculation steps.

The first step is normalization, which corrects for variations in starting material between samples using a stable reference gene. The Cq value of the target gene is normalized by subtracting the Cq value of the reference gene for that sample. This calculation yields the delta Cq (ΔCq) value: ΔCq = Cq(target gene) – Cq(reference gene).

Next, the expression in test samples is compared to a control or calibrator sample, which represents the baseline or untreated state. The ΔCq of the test sample is compared to the ΔCq of the control sample by subtraction. This provides the delta-delta Cq (ΔΔCq) value: ΔΔCq = ΔCq(test sample) – ΔCq(control sample).

For example, consider an experiment with a target gene (Gene X) and a reference gene (RefGene) in treated and control samples. If the treated sample has a Gene X Cq of 22 and RefGene Cq of 20, its ΔCq is 2. If the control sample has a Gene X Cq of 25 and RefGene Cq of 20, its ΔCq is 5. The ΔΔCq is then 2 (treated) – 5 (control) = -3.

The final step is calculating the fold change in gene expression using the formula: Fold Change = 2^-ΔΔCq. Using the example above, the fold change would be 2^-(-3), which equals 8. This result indicates that Gene X expression is eight times higher in the treated sample compared to the control, transforming the logarithmic Cq values into an intuitive linear fold change.

Interpreting and Presenting the Final Results

The calculated fold change represents the target gene’s relative expression in a test sample compared to a control. A value greater than 1 signifies upregulation, meaning the gene is more active in the test condition. For instance, a fold change of 2 indicates a twofold increase in expression. Conversely, a value less than 1 indicates downregulation, and a fold change of 0.5 represents a twofold decrease. A fold change of 1 means there is no change in expression.

Experiments include multiple samples for each condition, known as biological replicates, to account for biological variability. Each sample is also often run multiple times in the assay, known as technical replicates, to control for procedural variation. Cq values from technical replicates are averaged before calculations. The resulting fold change values from the biological replicates are then averaged to provide a mean expression change for each group.

The most common way to present these results is through a bar graph. Each bar represents the mean fold change for an experimental group, with the control group set to a baseline of 1. To represent the variability within the data, error bars depicting the standard deviation or standard error of the mean are added to each bar. These error bars provide a visual indication of the precision of the estimated fold change.