Quantitative Polymerase Chain Reaction (qPCR) measures the amount of RNA produced from a specific gene, reflecting its expression level. To accurately determine whether a target gene’s expression changes between different biological samples, a stable internal control is required. Choosing the correct internal control, or housekeeping gene, is fundamental to obtaining reliable and meaningful results in any qPCR experiment.
Defining Housekeeping Genes
Housekeeping genes (HKGs) are required for the basic maintenance and survival of a cell, regardless of its specific function or experimental conditions. These genes encode proteins involved in essential cellular processes, such as metabolism, cytoskeletal structure, and RNA transcription or translation. Their expression is expected to be relatively consistent, or constitutively expressed, across most cell types and tissues because they are needed for day-to-day cellular operations.
Scientists rely on this stable expression, which is why HKGs are often referred to as reference genes or endogenous controls. Common examples used in human and mouse studies include GAPDH (Glyceraldehyde-3-phosphate dehydrogenase), involved in glycolysis, and ACTB (beta-actin), which helps form the cellular skeleton. Other candidates include ribosomal RNA genes, like 18S rRNA, and genes related to protein synthesis, such as UBC (Ubiquitin C).
Why Normalization is Essential in Gene Expression Studies
Gene expression measurements using qPCR are highly susceptible to various sources of technical and biological variation, which can skew results if not corrected. One major source of error is the difference in the quantity and quality of the starting material, the RNA extracted from the samples. For instance, one sample might yield more total RNA or have lower RNA integrity than another, simply due to slight variations in the extraction process or the number of cells harvested.
This variability carries through the entire process, including the reverse transcription step, where RNA is converted into complementary DNA (cDNA), and the final qPCR amplification. Without a built-in control, it is impossible to know if a measured difference in a target gene’s expression is a true biological effect or merely a reflection of a technical error, such as inaccurate pipetting or differences in reaction efficiency.
Normalization using a housekeeping gene solves this problem by providing an internal benchmark. The HKG expression level is measured in parallel with the target gene in every sample. By expressing the target gene’s signal as a ratio relative to the HKG’s signal, sample-to-sample variation introduced during preparatory steps is accounted for. This ensures that any observed changes in the target gene reflect true biological differences between the samples being compared.
Criteria for Selecting and Validating Reference Genes
The most important consideration when selecting a housekeeping gene is its expression stability under the specific experimental conditions being tested. While genes like GAPDH and ACTB are commonly assumed to be stable, their expression can actually fluctuate significantly in response to different treatments, tissues, or disease states. For example, a gene that is stable in a healthy liver tissue might become highly variable in a cancerous tumor or after drug treatment.
Therefore, simply choosing a common HKG without validation is a recognized methodological error. Scientists must first screen a panel of several candidate reference genes to determine which ones exhibit the lowest expression variability across all the experimental samples. The gene’s expression level should also ideally be similar to that of the target gene to ensure accurate comparison.
Dedicated software programs, such as geNorm, NormFinder, and BestKeeper, analyze the qPCR data for candidate genes. These algorithms calculate an expression stability value (M-value) and rank the candidates from most to least stable. A lower M-value indicates a more stable reference gene suitable for the experiment. Using the geometric mean of two or more validated reference genes is often recommended to achieve the most reliable normalization, minimizing the risk associated with relying on a single, potentially unstable, control.
Applying Housekeeping Genes for Relative Quantification
The raw data generated by a qPCR machine is the Cycle threshold (\(\text{C}_{\text{t}}\)) value, which represents the number of amplification cycles required for the fluorescent signal to cross a set threshold. A lower \(\text{C}_{\text{t}}\) value means the gene was highly expressed in the sample, as less cycling was needed to detect it. The \(\text{C}_{\text{t}}\) value of the housekeeping gene provides the basis for the calculation of relative gene expression.
The first step in relative quantification is the \(\Delta \text{C}_{\text{t}}\) calculation, which is the difference between the target gene’s \(\text{C}_{\text{t}}\) and the reference gene’s \(\text{C}_{\text{t}}\) for each sample. This step normalizes the target gene expression to account for variations in the sample input and technical efficiency. The resulting \(\Delta \text{C}_{\text{t}}\) value is essentially the target gene’s expression relative to the internal control.
To compare gene expression between an experimental group and a control group, the \(\Delta \Delta \text{C}_{\text{t}}\) method is typically used. This involves subtracting the average \(\Delta \text{C}_{\text{t}}\) of the control samples from the \(\Delta \text{C}_{\text{t}}\) of every other sample. The final result, the fold change in gene expression, is calculated using the formula \(2^{-\Delta \Delta \text{C}_{\text{t}}}\). This calculation shows the relative change in target gene expression in the experimental sample compared to the control, standardized against the housekeeping gene.