qPCR Outsourcing 101: What to Vet Before You Send Samples

qPCR is often the first assay teams outsource, because it feels low-risk: it's a standard, well-understood method with decades of literature behind it. That familiarity is exactly why bad data slips through unnoticed — a fold-change number looks just as confident whether the underlying Cq values are clean or the reference gene was wrong for your model.

Here's what actually determines whether qPCR results are trustworthy, and what to ask before you ship RNA.

"A fold-change number looks just as confident whether the underlying Cq values are clean or the reference gene was wrong for your model."

1Get RNA quality metrics back, not just "the assay ran"

Degraded or low-purity RNA doesn't always fail outright — it often just produces noisier, less reproducible Cq values that look plausible in isolation. A CRO that skips quality control on incoming samples is passing that risk straight to your data.

Ask directly: "Will you report RIN (RNA Integrity Number) and concentration for every sample before running the assay, and will you flag samples that fall below spec?" A RIN below 7 (or a 260/280 ratio outside 1.8–2.1) should trigger a conversation before the plate is run — not get buried in an appendix afterward.

2Ask how reference genes are chosen — and validated

The ΔΔCt method assumes your reference gene (GAPDH, ACTB, or similar) is stable across every condition you're testing. That assumption breaks constantly — many "housekeeping" genes shift under stress, treatment, or disease conditions, which silently distorts every fold-change calculated against them.

A rigorous CRO validates reference gene stability computationally (geNorm or NormFinder are the standard tools) using two or more candidate reference genes, rather than assuming one gene is stable because it usually is. If a vendor can't describe how they picked — or validated — your reference gene, that's worth pausing on before you commit a full experiment to it.

3Confirm primer efficiency is validated per pair

The ΔΔCt calculation assumes ~100% amplification efficiency for every primer pair. Real efficiency typically falls in a 90–110% range, and outside that band the fold-change math becomes measurably wrong — more so the larger the fold-change. This is especially easy to miss with custom or newly designed primers that haven't been validated against a standard curve.

Ask whether primer efficiency is validated with a dilution series before your samples are run, and ask to see the efficiency value for each target in your final data — not just a pass/fail note.

4Insist on raw Cq values, not summaries

A results table showing "3.4x upregulated" is not something you can audit, reanalyze, or combine with future runs. You need the raw Cq value for every well, per target, per sample — plus the ΔCt and ΔΔCt calculations that got you to the final number. Without raw Cq data, you can't catch outlier wells, can't re-normalize against a different reference gene later, and can't merge this run with a future one for a combined analysis.

5For large plates, ask about normalization

384-well plates increase throughput, but they also increase exposure to edge effects and thermal gradients across the plate — real sources of well-to-well variation that have nothing to do with your biology. If your study spans more than one plate, plate-to-plate normalization matters for the same reason: without it, "difference between conditions" can just mean "difference between plates run on different days."

The short version

A good qPCR CRO will, without prompting: report RNA quality metrics before running the assay, validate reference gene stability with two or more candidates, confirm primer efficiency per target, hand back raw Cq data alongside fold-change, and normalize across plates and wells as standard practice. If any of these require a follow-up email to get an answer, expect the same friction on every future run.