Gene expression analysis
Gene expression profiling simultaneously compares the expression levels of multiple genes between two or more samples. With this type of analysis, scientists can establish the molecular basis of phenotypic differences and select gene targets for in-depth study. Gene expression analysis provides valuable insight into the role of differential gene expression in normal biological states and diseases.
Why use nanoplate dPCR for gene expression analysis
- Detect small-fold changes with high precision
- Obtain results with high accuracy even for low-abundance targets
- Achieve high precision and high dynamic range of log5 with Nanoplate 26K or perform economical runs with 12 µL reactions and high-throughput options for “similar” expressed targets on a Nanoplate 8.5K
Common methods for gene expression analysis
Gene expression analysis with nanoplate dPCR
Customer experience: QIAcuity for gene expression analysis
“I had worked with digital for many years…It was wonderfully accurate but always a lot of work to get it right [with other systems]. But now the QIAcuity just makes it so easy and reliable.”
Related dPCR products for gene expression analysis
White papers and literature on dPCR for gene expression analysis
Digital PCR webinars on gene expression analysis
Scientific publications with dPCR for gene expression analysis
Graves LE, et al. Targeted editing of the 21-hydroxylase locus confers durable therapeutic effect in a murine model of congenital adrenal hyperplasia. Mol Ther. 2026;34(2):1138–1151.
Chen Y, et al. A systematic benchmark of Nanopore long-read RNA sequencing for transcript-level analysis in human cell lines. Nat Methods. 2025;22(4):801–812.
Ham DJ, et al. Muscle fiber Myc is dispensable for muscle growth and its forced expression severely perturbs homeostasis. Nat Commun. 2025;16:3190.
FAQs on dPCR for gene expression analysis
Q1: Is reference gene normalization required for dPCR gene expression analysis?
A1: Even though dPCR provides absolute copy numbers, gene expression experiments could still be affected by variable RNA quality and yield between samples, variability in reverse transcription efficiency and input cDNA amount and differences in cell number or tissue composition per sample. Therefore, it’s recommended to compare biological samples with some type of normalization. For typical dPCR gene expression experiments, plan to use validated reference genes and normalize target copies to those (e.g., copies of target per copy of reference, or per geometric mean of multiple references).
Q2: Can digital PCR detect small fold changes in gene expression?
A2: In dPCR on the QIAcuity, fold changes of >5% can be resolved.
Q3: How do dMIQE guidelines apply to digital PCR gene expression experiments?
A3: dMIQE (especially dMIQE2020) applies directly to RT‑dPCR gene expression experiments and essentially extends MIQE principles to ensure that your RT step, assay design and reporting are sufficiently detailed to allow reproducibility and correct interpretation. The dMIQE2020 expects you to report details on specimen and extraction, reverse transcription, oligonucleotides and targets, the dPCR protocol, assay validation and data analysis. Find out more here: MIQE 2.0: Revision of the Minimum Information for Publication of Quantitative Real-Time PCR Experiments Guidelines and Digital MIQE Guidelines Update: Minimum Information for Publication of Quantitative Digital PCR Experiments for 2020 | Clinical Chemistry | Oxford Academic