qPCR is one of the most powerful and most misused techniques in molecular biology. Done right, it gives precise quantitation of gene expression. Done sloppily, it produces irreproducible numbers that look authoritative on a graph.
Step 1: Design and validate primers
For qPCR, primers should:
- Produce amplicons of 70–150 bp
- Span an exon-exon junction to avoid amplifying genomic DNA
- Have a Tm of 58–62 °C with paired primers within 2 °C of each other
- Show no secondary structure or primer-dimer formation
Validate by running a standard curve: serial 10-fold dilutions of cDNA across at least 5 points, run in triplicate. Calculate efficiency from the slope: efficiency = 10(-1/slope) – 1. Aim for 90–110% efficiency and R² > 0.98.
Step 2: Choose your detection chemistry
SYBR Green: Intercalating dye that fluoresces when bound to dsDNA. Cheap, easy, but non-specific — primer-dimers and off-target products also produce signal. Always include a melt curve at the end of the run.
TaqMan probes: Sequence-specific fluorescent probe. More expensive, but more specific and allows multiplexing. Standard for clinical applications and validated assays.
Step 3: Choose reference genes
Normalization is the most overlooked part of qPCR. Common reference genes (GAPDH, β-actin, 18S, HPRT) are not always invariant — they can change with treatment, cell type, or stress.
- Use 2–3 reference genes and average their geometric mean (geNorm method)
- Validate stability across your specific conditions before relying on a reference gene
- Tools like NormFinder or geNorm rank candidate references by stability
Step 4: Plan your plate layout
For a 96-well plate, consider:
- Triplicate technical replicates for each sample-primer combination
- No-template controls (NTC) for every primer pair
- No-RT controls for at least one sample per group
- Inter-plate calibrators if running multiple plates
- Random distribution of samples to avoid edge effects
Step 5: Run the experiment
Standard cycling for SYBR Green:
- Initial denaturation: 95 °C, 2–10 min
- 40 cycles: 95 °C 15 s, 60 °C 30 s, 72 °C 30 s
- Melt curve: 65–95 °C with 0.5 °C increments
Step 6: Analyze the data
ΔΔCt method
For relative quantification with assumed 100% efficiency:
- ΔCt = Ct(target) − Ct(reference)
- ΔΔCt = ΔCt(treatment) − ΔCt(control)
- Fold change = 2−ΔΔCt
Pfaffl method
Accounts for differences in primer efficiency between target and reference. Preferred when efficiencies differ from 100%.
Common pitfalls
- Skipping the melt curve — without it, you don’t know if SYBR signal came from your target or primer-dimers
- Using one reference gene blindly
- Reporting Ct values directly — always normalize and report fold-change
- Treating technical replicates as biological replicates
- Pipetting errors — the single biggest source of variability
Reporting standards
Follow MIQE guidelines. At minimum report: primer sequences and amplicon size, RT enzyme and conditions, reference genes and validation, standard curve efficiency and R², number of biological and technical replicates, statistical methods.
qPCR is precise when planned carefully and noisy when run on autopilot. Validate primers, validate reference genes, and treat replicates as the meaningful unit.
