Research Article: A novel data processing method CyC* for quantitative real time polymerase chain reaction minimizes cumulative error

Date Published: June 11, 2019

Publisher: Public Library of Science

Author(s): Linzhong Zhang, Rui Dong, Shu Wei, Han-Chen Zhou, Meng-Xian Zhang, Karthikeyan Alagarsamy, Baochuan Lin.


Quantitative real-time polymerase chain reaction (qPCR) is routinely conducted for DNA quantitative analysis using the cycle-threshold (Ct) method, which assumes uniform/optimum template amplification. In practice, amplification efficiencies vary from cycle to cycle in a PCR reaction, and often decline as the amplification proceeds, which results in substantial errors in measurement. This study reveals the cumulative error for quantification of initial template amounts, due to the difference between the assumed perfect amplification efficiency and actual one in each amplification cycle. The novel CyC* method involves determination of both the earliest amplification cycle detectable above background (“outlier” C*) and the amplification efficiency over the cycle range from C* to the next two amplification cycles; subsequent analysis allows the calculation of initial template amount with minimal cumulative error. Simulation tests indicated that the CyC* method resulted in significantly less variation in the predicted initial DNA level represented as fluorescence intensity F0 when the outlier cycle C* was advanced to an earlier cycle. Performance comparison revealed that CyC* was better than the majority of 13 established qPCR data analysis methods in terms of bias, linearity, reproducibility, and resolution. Actual PCR test also suggested that relative expression levels of nine genes in tea leaves obtained using CyC* were much closer to the real value than those obtained with the conventional 2-ΔΔCt method. Our data indicated that increasing the input of initial template was effective in advancing emergence of the earliest amplification cycle among the tested variants. A computer program (CyC* method) was compiled to perform the data processing. This novel method can minimize cumulative error over the amplification process, and thus, can improve qPCR analysis.

Partial Text

Quantitative real-time polymerase chain reaction (qPCR) employs fluorescent dyes such as SYBR Green or Taqman probe; these dyes intercalate into double strand DNA products to allow easy determination of amplified DNA amounts in each amplification cycle by detecting fluorescence intensity [1]. Because of its simplicity, efficiency and sensitivity [2], qPCR has become a routine technique in various biological studies and practical applications such as the noncoding small interfering RNA [3,4], differential gene expression [5,6], transgenic T-DNA tandem repeat analysis [7], virus titer evaluation [8] and diagnostic tools [9,10]. The two quantification methods often applied are absolute quantification and relative quantification. Absolute quantification is conducted based on an assumption that amplification efficiencies for both the target template and the standard template DNA used for calibration curve construction should be identical [11–13], and relative quantification determines relative transcript levels of a gene across multiple samples [12,14,15]. For a relative quantitative analysis, the comparative cycle-threshold (Ct) method [13] is widely accepted as a practical and feasible “golden method”. However, this method is based on the assumption that amplification efficiency for both target and reference genes is perfect (100%) or constant [12,15]. A slight PCR amplification efficiency decrease of about 4% could result in an error of up to 400% for a gene expression ratio [16].

In this study, the cumulative error in the qPCR process was revealed by kinetics analysis. Our study indicated that this error in the quantification of initial template amount could increase as amplification proceeded; this was due to both the difference in the actual template amount and estimates which were based on the assumption of perfect amplification efficiency. The conventional and widely employed threshold cycle (Ct) method requires assumed perfect amplification efficiency [13], which rarely occurs in practice [29,33]; this consequently leads to an inaccurate estimate of initial template amounts of both the genes of interest and of reference genes. To avoid amplification efficiency assumption, many investigations have been carried out to develop new approaches for transcript analysis. For example, the sigmoid curve fitting method (SCF) was developed to fit the sigmoid model so that the initial template amount can be deduced from the fluorescence (F0) without the need for standard curve [36]. BestKeeper was reported to determine transcript stability of tested gene using pair-wise correlations [25]. Another linear regression method, taking-difference between consecutive two cycles was recently developed to remove the background fluorescence interference [24]. However, the majority of previously reported data processing methods have largely ignored cumulative error.