How do you analyse real-time PCR data including Ct values, amplification efficiency, and melt curves?

Real-time PCR analysis requires: (1) setting the baseline and threshold for Cq determination, (2) calculating amplification efficiency from standard curve slope (E = 10^(−1/slope) − 1), (3) normalising target genes to reference genes (delta-Cq or delta-delta-Cq), and (4) verifying specific amplification via melt curve analysis.

Introduction to Real-Time PCR Data

Real-time PCR monitors the accumulation of PCR product in real time by measuring fluorescence after each cycle. The instrument generates amplification curves from which two primary data types are extracted: the threshold cycle (Ct or Cq) and the melt curve (for dye-based qPCR). Proper data analysis is critical for reliable, reproducible results. The MIQE guidelines provide a framework for best practices in qPCR experimental design and data analysis.

Understanding Ct Values

The threshold cycle (Ct) is the cycle at which fluorescence crosses a defined threshold above background. Ct is inversely proportional to starting target quantity: more template = lower Ct. A well-designed assay should have a dynamic range of 5–7 log10 orders with R2 > 0.98. Typical Ct values: 15–20 (abundant), 20–25 (moderate), 25–30 (low), 30–35 (very low). No-template controls should have Ct > 35 or undetermined.

Amplification Efficiency

Amplification efficiency (E) is calculated from a standard curve: E = 10(-1/slope). Acceptable range: 90–110% (slope −3.58 to −3.10). Low efficiency (<90%) suggests poor primer design or inhibitors. High efficiency (>110%) suggests non-specific amplification or primer-dimer. For ΔΔCt analysis, target and reference efficiencies must be within 10% of each other, or use the efficiency-corrected Pfaffl method.

Melt Curve Analysis

Melt curve analysis monitors fluorescence while slowly increasing temperature from 60°C to 95°C. A single peak indicates a specific product. Multiple peaks indicate non-specific products or primer-dimer (typically melting at 75–80°C). Broad peaks may indicate heterogenous products. Peak Tm shifts between samples may indicate sequence variation (useful for HRM genotyping).

Standard Curve and Absolute Quantification

For absolute quantification, serially dilute a known DNA standard across 5–7 orders of magnitude, run in triplicate, plot Ct vs. log concentration, and fit a linear regression. Check R2 > 0.99 and slope −3.3 ± 0.2. Interpolate unknown concentrations from the standard curve. The quality of the standard is paramount — use authenticated, quantified standards.

Relative Quantification (ΔΔCt)

Normalise to a reference gene: ΔCt = Ct(target) − Ct(reference). ΔΔCt = ΔCt(treated) − ΔCt(control). Fold change = 2(-ΔΔCt). If efficiencies differ, use the Pfaffl method. Validate reference gene stability using geNorm or NormFinder. Using 2–3 reference genes improves accuracy.

Troubleshooting qPCR Data

  • High Ct variation (SD > 0.5): Pipetting inconsistency. Use master mix and calibrate pipettes.
  • Poor standard curve (R2 < 0.95): Serial dilution errors. Prepare fresh standards.
  • NTC amplification: Contamination or primer-dimer. See the primer dimer guide.
  • Low fluorescence: Insufficient probe concentration or poor dye binding.
  • Melt curve shoulder: Secondary product. Increase annealing temperature.

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