Background

Quantitative PCR (qPCR) is one of the most widely used techniques for assessing relative transcript abundance due to its sensitivity, broad dynamic range, and accessibility [1]. It is a central method for validation of high-throughput transcriptomic data, such as RNA-seq, by providing an independent confirmation of differentially abundant RNA species identified with omics global analyses. In most routine applications, qPCR data are summarized using cycle quantification (Cq) values, which are then converted into relative abundance estimates through mathematical models [2]. In accordance with the Minimum Information for Publication of Quantitative PCR Experiments (MIQE) guidelines, the term Cq is used in this protocol instead of the historical cycle threshold (Ct) value [3].

The 2^-ΔΔCq method is the most widely used approach for relative quantification and is based on the assumption that amplification efficiencies are identical and close to optimal across all assays [4]. Although this assumption is sometimes approximately valid, amplification efficiency can vary dramatically between primer sets, samples, and even individual reactions [5]. Such variability may introduce systematic bias when efficiency differences are not taken into account [6]. To address these limitations, efficiency-corrected quantification models have been proposed to incorporate amplification efficiency into relative abundance calculations [7]. To do so, amplification efficiency is often estimated using standard curves generated from serial dilutions [8]. However, this strategy requires additional experimental steps and is sensitive to both dilution errors and variable levels of PCR inhibitors between samples [9].

An alternative method consists of estimating amplification efficiency directly from the exponential phase of individual amplification curves. Different approaches have been described, allowing estimation of per-reaction efficiency without relying on dilution series and providing a more direct representation of reaction performance [10]. Among these, LinRegPCR is a widely used and validated tool that derives efficiencies from the log-linear phase of each amplification curve [11]. In this protocol, efficiencies derived from amplification curve analyses with the LinRegPCR tool are integrated into a spreadsheet-based workflow to compute efficiency-corrected target quantities, normalize abundance using multiple reference genes, and calculate relative fold changes. This computational approach facilitates transparent and reproducible qPCR data analysis while remaining compatible with commonly available laboratory software, being completely in phase with current best practices for qPCR experiments, as summarized in the new version of MIQE guidelines [12].