qPCR

Why it matters

Quantitative PCR (qPCR, also called real-time PCR) measures how much of a target sequence was in a sample, not just whether it was there. Research labs use it to compare gene expression between treated and untreated cells. Clinical labs use it to detect and quantify pathogens (viral load, for example). If you work in a molecular lab, you will read Ct values and fold changes every week.

The core idea: watch the reaction as it happens

A qPCR instrument is a thermal cycler that measures fluorescence in each well after every cycle. The fluorescence comes from either:

• a DNA-binding dye such as SYBR Green, which glows when bound to double-stranded DNA, or
• a probe (for example a hydrolysis or "TaqMan" probe), which glows only when the specific target is copied.

Plotting fluorescence against cycle number gives an S-shaped amplification curve. The software sets a threshold in the exponential part of the curve. The Ct (cycle threshold, also called Cq) is the cycle at which a well's curve crosses it.

The key fact: more starting template means a lower Ct. If efficiency is near 100 percent, the target doubles each cycle, so a sample with twice as much template crosses one cycle earlier. A difference of 3.3 cycles is about a 10-fold difference.

Relative expression: the delta-delta-Ct method

To compare expression of a target gene between a treated and a control sample, you also measure a reference gene (for example GAPDH or ACTB) whose expression should not change. It corrects for differences in how much RNA went into each reaction. The Livak method:

1. Delta Ct for each sample: Ct(target) - Ct(reference).
2. Delta-delta Ct: delta Ct(treated) - delta Ct(control).
3. Fold change = 2^(-delta-delta Ct).

Worked example. Means of triplicate wells:

Delta-delta Ct = 4.8 - 7.0 = -2.2. Fold change = 2^2.2 = 4.6. The target is about 4.6 times higher in treated cells. A positive delta-delta Ct gives a fold change below 1, meaning lower expression.

When the method is valid

The 2^-delta-delta-Ct formula assumes:

• Both assays have efficiency close to 100 percent (about 90 to 110 percent), checked with a dilution series standard curve. If they differ, use an efficiency-corrected method (such as Pfaffl).
• The reference gene really is stable under your conditions. Many labs validate two or three reference genes.
• Ct values are in a reliable range. Very late Ct values (often above about 35) are noisy and may be non-specific.

Good practice is summarised in the MIQE guidelines (Bustin and colleagues, Clinical Chemistry, 2009, https://doi.org/10.1373/clinchem.2008.112797, checked on 2026-10-23), which list what a qPCR experiment must report.

Controls and quality checks

• No-template control (NTC): should show no amplification, or a Ct far later than any sample.
• No-reverse-transcriptase control (no-RT): for RNA work, shows whether genomic DNA is giving signal.
• Technical replicates: usually triplicates. Replicate Ct values more than about 0.3 to 0.5 cycles apart suggest pipetting problems.
• Melt curve (dye-based assays): one clean peak means one product; extra peaks mean primer dimers or off-target products.

Absolute quantification

Clinical and environmental labs often need actual copy numbers. They run a standard curve of known copy numbers and read unknowns off it. The slope of Ct against log10(copies) gives efficiency: E = 10^(-1/slope) - 1. A slope of -3.32 means 100 percent efficiency.

Common mistakes

• Subtracting in the wrong order, so the fold change is inverted.
• Averaging fold changes instead of averaging Ct values first.
• Using a reference gene that the treatment actually changes.
• Ignoring a positive NTC or no-RT control.
• Reporting fold changes from Ct values near 38.

Quick recap

• Lower Ct = more starting target; about 1 cycle = 2-fold at 100 percent efficiency.
• Delta Ct = target - reference; delta-delta Ct = treated - control; fold = 2^(-delta-delta Ct).
• Check efficiency, reference stability, replicates, NTC, no-RT and melt curves.
• Follow MIQE when you report results.

Try it

Open the Do tab. A Python function computes delta Ct for you; you write the delta-delta-Ct and fold-change line.