Gong Liang, Yao Siyue, He Yidong, Liu Chengliang
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P. R. China.
Anal Chem. 2023 Mar 28;95(12):5402-5410. doi: 10.1021/acs.analchem.3c00061. Epub 2023 Mar 13.
Quantitative real-time PCR (qPCR) is a method extensively used in nucleic acid testing for plants and animals. During the coronavirus disease 2019 (COVID-19) pandemic, high-precision qPCR analysis was urgently needed since quantitative results obtained from conventional qPCR methods were not accurate and precise, causing misdiagnoses and high rates of false-negative. To achieve more accurate results, we propose a new qPCR data analysis method with an amplification efficiency-aware reaction kinetics model (AERKM). Our reaction kinetics model (RKM) mathematically describes the tendency of the amplification efficiency during the whole qPCR process inferred by biochemical reaction dynamics. Amplification efficiency (AE) was introduced to rectify the fitted data so as to match the real reaction process for individual tests, thus reducing errors. The 5-point 10-fold gradient qPCR tests of 63 genes have been verified. The results of a 0.9% slope bias and an 8.2% ratio bias using AERKM exceed 4.1 and 39.4%, respectively, of the best performance of existing models, which demonstrates higher precision, less fluctuation, and better robustness among different nucleic acids. AERKM also provides a better understanding of the real qPCR process and gives insights into the detection, treatment, and prevention of severe diseases.
定量实时聚合酶链反应（qPCR）是一种广泛应用于动植物核酸检测的方法。在2019年冠状病毒病（COVID-19）大流行期间，由于传统qPCR方法获得的定量结果不准确、不精确，导致误诊和高假阴性率，因此迫切需要高精度的qPCR分析。为了获得更准确的结果，我们提出了一种新的qPCR数据分析方法，即具有扩增效率感知反应动力学模型（AERKM）。我们的反应动力学模型（RKM）通过生化反应动力学推断出整个qPCR过程中扩增效率的变化趋势，并进行数学描述。引入扩增效率（AE）对拟合数据进行校正，以匹配单个测试的实际反应过程，从而减少误差。对63个基因进行了5点10倍梯度qPCR测试验证。使用AERKM的结果显示，斜率偏差为0.9%，比率偏差为8.2%，分别超过现有模型最佳性能的4.1%和39.4%这表明在不同核酸之间具有更高的精度、更小的波动和更好的稳健性。AERKM还能更好地理解实际的qPCR过程，并为严重疾病的检测、治疗和预防提供见解。
