A new mathematical model for relative quantification in real-time RT-PCR

TLDR

Real‑time PCR is rapidly becoming a routine, accurate, and rapid tool for low‑abundance gene expression analysis, yet reliable quantification demands a reproducible methodology and a suitable mathematical model. This study aims to develop a new mathematical model for relative quantification of a target gene transcript against a reference gene in real‑time RT‑PCR. The model calculates the relative expression ratio solely from PCR efficiencies and crossing‑point deviations, incorporating control levels to standardise each run for RNA integrity, sample loading, and inter‑PCR variation. The model eliminates the need for a calibration curve and achieves high accuracy and reproducibility (<2.5 % variation) in LightCycler PCR.

Abstract

Use of the real-time polymerase chain reaction (PCR) to amplify cDNA products reverse transcribed from mRNA is on the way to becoming a routine tool in molecular biology to study low abundance gene expression. Real-time PCR is easy to perform, provides the necessary accuracy and produces reliable as well as rapid quantification results. But accurate quantification of nucleic acids requires a reproducible methodology and an adequate mathematical model for data analysis. This study enters into the particular topics of the relative quantification in real-time RT–PCR of a target gene transcript in comparison to a reference gene transcript. Therefore, a new mathematical model is presented. The relative expression ratio is calculated only from the real-time PCR efficiencies and the crossing point deviation of an unknown sample versus a control. This model needs no calibration curve. Control levels were included in the model to standardise each reaction run with respect to RNA integrity, sample loading and inter-PCR variations. High accuracy and reproducibility (<2.5% variation) were reached in LightCycler PCR using the established mathematical model.