Elsevier

Analytical Biochemistry

Volume 373, Issue 2, 15 February 2008, Pages 370-376
Analytical Biochemistry

Revisiting the sigmoidal curve fitting applied to quantitative real-time PCR data

https://doi.org/10.1016/j.ab.2007.10.019Get rights and content

Abstract

Amplification of a cDNA product by quantitative polymerase chain reaction (qPCR) gives rise to fluorescence sigmoidal curves from which absolute or relative target gene content of the sample is inferred. Besides comparative Ct methods that require the construction of a reference standard curve, other methods that focus on the analysis of the sole amplification curve have been proposed more recently. Among them, the so-called sigmoidal curve fitting (SCF) method rests on the fitting of an empirical sigmoidal model to the experimental amplification data points, leading to the prediction of the amplification efficiency and to the calculation of the initial copy number in the sample. The implicit assumption of this method is that the sigmoidal model may describe an amplification curve quantitatively even in the portion of the curve where the fluorescence signal is hidden in the noise band. The theoretical basis of the SCF method was revisited here for defining the class of experimental amplification curves for which the method might be relevant. Applying the SCF method to six well-characterized different PCR assays illustrated possible pitfalls leading to biased estimates of the amplification efficiency and, thus, of the target gene content of a sample.

Section snippets

Materials and methods

Six gene targets were submitted to qPCR amplification. Three of them (ETS1, PMP22, and TACC1) were amplified in the presence of the SYBR Green dye (B. Dessars et al., in preparation). The PCR amplification of the other three targets (fusion genes E2APBX1, mBCRABL, and TELAML1) was tracked by the fluorescence of a TaqMan probe [12]. All experiments were performed on the 7500 Fast Real-Time PCR System (Applied Biosystems, Foster City, CA, USA), and the results were processed using the SDS

Results

To revisit the SCF methodology, first general concepts and notations are recalled.

Discussion

The widespread use of qPCR and the subsequent high-throughput measurement of gene expression have appealed for some kind of automated algorithm. In this context, the SCF method would be an appropriate choice because it is theoretically able to estimate both relative and absolute gene contents by analyzing the sole amplification curve, thereby avoiding the construction of a standard dilution curve [6], [10]. As for any other method based on an empirical model, the reliability of the results is

Acknowledgments

This study was supported by the Interuniversity Attraction Poles Programme (P6/14), Belgian State, Belgian Science Policy, and by grants from the Fonds de la Recherche Scientifique Médicale and Fonds Erasme to Gilbert Vassart.

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