Measuring Gene Expression Using PCR

qPCR is considered the gold standard for measuring changes in gene expression and can detect as little as a single target molecule per reaction. Characterization lends itself well to scenarios where there are either a small number of targets to measure but large numbers of samples or the reverse scenario.

Before analyzing clinical samples, the first step is to identify a suitable sample handling protocol that enables samples to arrive at the clinical laboratory from the clinical site intact and with an appropriate quality level. A representative selection of samples should be subjected to the same storage and shipping conditions that will be used in the clinical trial to validate the pre-analytical sample handling process.


Diagram of the stages of sample RNA extraction prior to analysis using qPCR.



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qPCR Assay Validation

While qPCR is a well-established technique to measure gene expression, there are factors to carefully consider when validating the method in order to ensure the generation of reliable data.

  • Selection of Reference Genes

    The most common approach for gene expression studies is a relative quantitation approach. When implemented, it is crucial that the expression of the reference genes used for normalization are not affected by the therapeutic in order to prevent skewed data. Selection of stable reference genes can be achieved by screening a panel of candidate reference genes in a representative selection of treated versus untreated samples using a reference gene selection algorithm.

  • PCR Efficiency

    It is important to account for differences in amplification efficiencies of the genes of interest. In a perfectly efficient qPCR assay, the amount of PCR product exactly doubles with each subsequent amplification cycle; however, most assays/primer sets are not exactly 100% efficient. These deviations from 100% efficiency can skew the data if not accounted for in calculations of relative quantities. Once the efficiency of each assay is known, the value is incorporated into the analysis for a more accurate measurement of gene expression.

    qPCR Efficiency

    Graph displaying qPCR efficiency using a dilution series of synthetically generated DNA bearing the amplicon sequence (Ct = threshold cycle).

  • Limits of Detection and Quantitation

    It is important to understand the limits of the assay in terms of lower limit of detection (LLOD) and lower limit of quantification (LLOQ). LLOQ is the lowest concentration of a target that can be accurately quantified, and the LLOD is the lowest concentration of a target that can be reliably detected, but not accurately quantified. Defining these limits is especially important if the targets are expected to be low in abundance. This must be determined experimentally in each laboratory using the same reagents, equipment, and operators that will be used in the analysis of the clinical samples.

  • PCR Inhibitors

    Certain sources of RNA contain significant levels of PCR inhibitors, which can affect qPCR assay performance if they are not efficiently removed during the extraction process. One way of determining whether PCR inhibitors are present in a sample is to take a synthetic DNA template and compare the CT values when measured alone and spiked into a comparable biological matrix. If a significant level of inhibition is observed, the RNA extraction protocol may have to be further optimized to remove contaminating PCR inhibitors.

    Testing for the presence of qPCR inhibitors

    Chart displaying synthetic DNA amplified alone and spiked into extracted cDNA to test for the presence of qPCR inhibitors.

  • Normal Intra-Donor Variation

    Other confounding factors that could be assessed during the validation phase include the normal level of variation within each donor over time. To assess this, samples are taken from donors at several timepoints that are similar to the sampling timepoints that will be used in the trial. A variation of twofold up or down can be considered within normal levels of variation, a threshold that a candidate therapeutic would have to exceed in either direction for the change to be considered potentially significant.

    RNA Analysis by qPCR

    Four individual charts displaying data from four biomarkers in whole blood stimulated ex vivo with LPS, with or without the control drug dexamethasone. RNA was extracted, reverse transcribed, and analyzed by qPCR to detect changes in the expression of IL-1β, IL-6, IFN-γ, and TNF-α.

There are several assay parameters which must first be established in order to truly benefit from the highly sensitive nature of qPCR for gene expression profiling. This technique can then be used in tandem with data at the protein level gathered using additional techniques, such as Luminex multiplex cytokine analysis or flow cytometry, to gain a wider understanding of the effect of a candidate therapeutic in early phase clinical trials.

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Frequently Asked Questions (FAQs) about qPCR Gene Expression Profiling

  • What sample types can be analyzed using qPCR?

    qPCR can be used to analyze many different biological sample types, including cultured cells, whole blood, PBMCs, tissues, fixed-formalin paraffin embedded (FFPE) samples, and cell-free samples such as cell culture supernatants, plasma and cerebrospinal fluid (CSF) to analyze cell-free, or circulating free DNA (cfDNA).

  • How sensitive is qPCR?

    For a well-optimized assay, qPCR can detect as little as one copy of target sequence per reaction. For accurate quantitative analysis, the limit is usually around 10 copies per reaction. In terms of fold-changes in gene expression, differences as small as half-fold can be detected.

  • Is multiplexing possible with qPCR?

    Between three and five targets can be analyzed in parallel, depending on the qPCR machine. Additional validation is usually recommended to ensure that multiplexing does not affect the performance of each individual assay.