Technique hub

qPCR protocols — versioned, with troubleshooting and fixes.

Quantitative PCR (qPCR) measures DNA amplification in real time and reports a Cq — the cycle where signal crosses threshold. Most failures cluster in a few places: no amplification or a high Cq from too little template or inhibitors, primer dimers that fire in the no-template control, and amplification efficiency outside the acceptable range that quietly biases every fold-change. The qPCR protocols below keep those fixes visible — version by version.

Versioned & attributed protocols

Negative results recorded inline

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Running conventional endpoint PCR instead? See the PCR protocols hub for no-bands, nonspecific-bands and primer-dimer troubleshooting.

SYBR Green v5

Step by step

A qPCR, step by step — and where each one fails.

The real-time PCR workflow, from primer validation to ΔΔCt. Vendor pages list the steps; what they leave out is the failure that lives in each one. We keep both together.

Design & validate primers

Design primers (and probe) for a short amplicon, then check specificity in silico and by melt curve.

Most common failure Dimer-prone primers amplify in the no-template control and inflate signal.

Build a standard / dilution series

Make an accurate serial dilution to measure amplification efficiency and set the dynamic range.

Most common failure Inaccurate dilutions distort the slope, so the calculated efficiency is wrong.

Assemble the master mix

Combine SYBR or probe master mix with primers; aliquot, keeping pipetting consistent across replicates.

Most common failure Hand-pipetting each well drives high replicate CV and unreliable Cq.

Add template / cDNA

Add a defined amount of cDNA or DNA; include NTC and, for RT-qPCR, a no-RT control.

Most common failure RT inhibitors or too little cDNA give a high Cq or no amplification at all.

Set up the plate & run

Seal the plate, set the cycling and the passive reference, and run on the instrument.

Most common failure Wrong passive reference or a poor seal causes baseline drift and well-to-well artefacts.

Inspect the melt curve (SYBR)

After cycling, read the dissociation curve — a single sharp peak means a single product.

Most common failure Multiple or low-Tm peaks reveal nonspecific product or primer dimers masquerading as signal.

Set baseline & threshold

Place the threshold in the exponential phase, consistently across the plate, to read Cq.

Most common failure A threshold set in the wrong place shifts every Cq and corrupts the comparison.

Analyze (ΔΔCt or standard curve)

Quantify relative to reference genes (ΔΔCt) or absolute against the standard curve.

Most common failure Assuming 100% efficiency when it isn't biases every fold-change in the ΔΔCt result.

Troubleshooting

qPCR troubleshooting, grouped by what went wrong.

Five failure families cover almost every qPCR that doesn't work the first time. Match the symptom, find the likely cause, apply the fix.

No amplification / high Cq

Symptom	Likely cause	Fix
Flat curve or Cq far later than expected	Too little template; RT failed; inhibitors carried over	Increase cDNA input; check the RT step and no-RT control; clean up the template
Whole plate shifted late	Degraded master mix or wrong cycling/annealing	Use fresh master mix; set annealing/extension to the validated temperature
Probe assay flat, SYBR works	Probe degraded or mismatched to the amplicon	Verify probe sequence/storage; confirm reporter–quencher chemistry

Primer dimers

Symptom	Likely cause	Fix
Amplification in the no-template control	Primers self-prime; primer concentration too high	Lower primer conc to 100–200 nM; redesign to remove 3′ complementarity; hot-start
Low-Tm shoulder on the melt curve	Dimer product melting below the amplicon	Raise annealing stringency; reduce primers; confirm a single peak before trusting Cq

Poor efficiency / standard curve

Symptom	Likely cause	Fix
Slope outside −3.1 to −3.6	Inhibitors, pipetting error, or sub-optimal primers	Re-make the dilution series; dilute out inhibitors; re-optimize primer conc/annealing
R² below 0.99	Imprecise dilutions or replicate scatter	Use reverse pipetting; add replicates; remake standards with fresh tips
Narrow dynamic range	Standards don't span the sample Cq range	Extend the dilution series so samples fall inside the validated range

High replicate CV / variability

Symptom	Likely cause	Fix
Replicate Cq spread >0.5 cycles	Pipetting small volumes; bubbles; edge effects	Increase reaction volume; spin the plate; avoid or buffer-fill edge wells
Random well-to-well noise	Poor plate seal or passive-reference issue	Re-seal; verify the passive reference setting for your instrument

Nonspecific amplification

Symptom	Likely cause	Fix
Multiple melt peaks	Primers bind off-target; annealing too permissive	Raise annealing; redesign primers; confirm specificity on a gel

The moat

Every fix stays attached to the protocol.

The primer concentration that cleared the dimer, the redesign that pulled efficiency back to 99%, the melt-curve QC step that caught a nonspecific product — on jnar that knowledge lives in the protocol, version by version. Not in someone's notebook, not in a folder of qpcr_v3_FINAL.docx copies.

✓What changed and why, kept side by side
✓Negative results recorded as first-class data
✓Every change attributed via ORCID

How protocol version control works →

SYBR qPCR · target gene assay v5

FIX HISTORY

Primer conc 300 → 150 nM

Removed primer-dimer peak in the NTC · L. Okafor

Primers redesigned; anneal/extend at 60 °C

Efficiency 87% → 99% · M. Rossi

Added a melt-curve QC step

Caught nonspecific product · S. Devi

Switched to validated reference genes

Stabilized ΔΔCt normalization · J. Park

Initial published method

L. Okafor

− Primers 300 nM · efficiency 87%

+ Primers 150 nM, redesigned · efficiency 99%

In depth

qPCR efficiency & standard curves, fixed properly.

Amplification efficiency is read straight off the standard-curve slope: E = 10^(−1/slope) − 1. A slope of −3.32 corresponds to 100% efficiency — the target doubling every cycle. Acceptable assays fall between roughly 90% and 110% (slope −3.6 to −3.1), with R² above 0.99. Efficiency matters because the ΔΔCt method assumes your target and reference amplify at the same, near-perfect rate; when they don't, every fold-change is biased.

A slope outside that window usually points to inhibitors, imprecise dilutions, or sub-optimal primers. Remake the dilution series with fresh tips and reverse pipetting, dilute the template to push inhibitors below their effect threshold, and re-optimize primer concentration and annealing temperature. Low R² is almost always replicate scatter — add replicates and tighten pipetting.

Keep the standards spanning the dynamic range your samples actually occupy; a curve that doesn't reach the sample Cq forces extrapolation into unvalidated territory. And validate efficiency before trusting any quantification — it's the single check that most often separates a real result from an artefact.

On jnar, the primer re-optimization that pulled your efficiency back into range is recorded against the protocol version, so the next person inherits a validated assay instead of re-deriving it.

Looking for a qPCR protocol PDF?

A PDF goes stale the moment someone re-optimizes the primer concentration. Import your methods PDF into jnar instead and get a structured, versioned qPCR protocol with a living fix history — the efficiency fixes and their reasons stay together. Your first AI conversion is free.

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Explore variants

SYBR Green qPCR Probe-based (TaqMan) qPCR One-step RT-qPCR Efficiency & standard curve Reference-gene selection qPCR troubleshooting

qPCR troubleshooting, answered.

Why is there no amplification or a very high Cq in my qPCR?

A flat curve or a Cq much later than expected usually means too little template, a failed reverse-transcription step, or PCR inhibitors carried over from extraction. Increase the cDNA input, check the RT reaction against a no-RT control, and clean up the template. If the whole plate is shifted late, suspect a degraded master mix or the wrong annealing temperature — run fresh mix at the validated cycling conditions.

How do I get rid of primer dimers in qPCR?

Primer dimers show up as amplification in the no-template control and as a low-Tm shoulder on the melt curve. Lower the primer concentration to roughly 100–200 nM, redesign the primers to remove 3′-end complementarity, raise the annealing stringency, and use a hot-start enzyme. Always confirm a single sharp melt peak before you trust a Cq — a dimer can otherwise be read as real signal.

What is good qPCR efficiency, and how do I fix a bad standard curve?

Amplification efficiency comes from the standard-curve slope: E = 10^(−1/slope) − 1. A slope of −3.32 is 100% efficiency, and acceptable assays fall between about 90% and 110% (slope −3.6 to −3.1) with R² above 0.99. If the slope is outside that range, suspect inhibitors, imprecise dilutions, or sub-optimal primers — remake the dilution series, dilute out inhibitors, and re-optimize primer concentration and annealing.

What are the steps of a qPCR protocol?

Design and validate primers (and probe), build an accurate dilution series, assemble the master mix, add template plus NTC and no-RT controls, set up and run the plate, inspect the melt curve for a single product, set the baseline and threshold consistently, then analyze by ΔΔCt or against the standard curve. Each step has a characteristic failure — the melt curve catches dimers and the standard curve tells you the efficiency.

How is qPCR different from conventional PCR?

Conventional (endpoint) PCR tells you whether a product is present after cycling, read on a gel; quantitative PCR measures product accumulation in real time and reports a Cq — the cycle where signal crosses threshold — so you can quantify how much was there to start. The failure modes overlap (primer dimers, nonspecific bands) but qPCR adds Cq, amplification efficiency and standard-curve concerns. See our conventional PCR hub at /protocols/pcr.

Do you have a qPCR protocol PDF I can download?

Rather than a static PDF that's out of date the moment someone re-optimizes the primer concentration, jnar lets you import your own qPCR protocol — drop in a methods PDF and turn it into a structured, versioned protocol with a visible fix history, so the efficiency fix or melt-curve QC stays attached to the method. Your first AI conversion is free at /import.

Have a qPCR assay of your own?

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