Classification of real-time digital PCR amplification curves

Yuan-yuan LUO; Jia YAO; Dong-shu LI; Xun-hua ZHU; Shu-li LI; Lian-qun ZHOU; Zhen GUO

doi:10.37188/OPE.20212909.2178

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Classification of real-time digital PCR amplification curves

Micro/Nano Technology and Fine Mechanics | 更新时间：2021-10-08

- Classification of real-time digital PCR amplification curves
- Optics and Precision Engineering Vol. 29, Issue 9, Pages: 2178-2188(2021)
- 作者机构：
  
  1.中国科学技术大学，安徽合肥 230026
  2.中国科学院苏州生物医学工程技术研究所国科学院生物医学检验技术重点实验室，江苏苏州 215163
  3.苏州大学电子信息学院，江苏苏州 215006
  4.复旦大学儿科医院，上海 201102
  5.季华实验室，广东佛山 528200
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20212909.2178
  CLC： TP212.3
- Received：02 March 2021，
  
  Revised：09 April 2021，
  
  Published：15 September 2021
- 稿件说明：
移动端阅览
罗媛媛,姚佳,李东书等.实时数字PCR扩增曲线的分类[J].光学精密工程,2021,29(09):2178-2188.

LUO Yuan-yuan,YAO Jia,LI Dong-shu,et al.Classification of real-time digital PCR amplification curves[J].Optics and Precision Engineering,2021,29(09):2178-2188.
罗媛媛,姚佳,李东书等.实时数字PCR扩增曲线的分类[J].光学精密工程,2021,29(09):2178-2188. DOI： 10.37188/OPE.20212909.2178.

LUO Yuan-yuan,YAO Jia,LI Dong-shu,et al.Classification of real-time digital PCR amplification curves[J].Optics and Precision Engineering,2021,29(09):2178-2188. DOI： 10.37188/OPE.20212909.2178.

摘要

传统的数字PCR检测仅针对扩增终点的核酸样本执行终点式的荧光分析，依据反应后的荧光图像进行阴阳性统计及样本浓度计算，分析结果极易受假阳性及非特异性扩增影响。本文提出了一种基于过程的实时微孔式数字PCR芯片阴阳性分析方法，从时间维度对数字PCR结果进行定量分析，提高数字PCR检测的准确性。设计支持实时数字PCR分析的硬件系统并与终点式数字PCR仪进行对比，验证系统性能。在此系统上检测不同浓度的人类Ⅳ型疱疹病毒样本，获取实时扩增曲线，采用支持向量机算法对扩增曲线的特征进行学习并应用于检测曲线分类。实验结果表明，所设计的实时数字PCR系统与终点式数字PCR仪扩增结果具有高度一致性。本文所述的基于支持向量机的分类算法对扩增曲线的分类准确率达到98%以上，能准确识别假阳性及非特异性扩增微孔。相比于传统阈值分割法，本方法对阳性点识别的平均准确率提高了17.60%，并且目标模板拷贝数越低，效果越明显。与传统的终点式数字PCR相比，本文提出的基于实时数字PCR系统的数据分析方法具有准确性更高的优势，尤其在低拷贝数检测下可以获取更为精确的定量结果。

Abstract

Conventional digital PCR detection only carries out end-point fluorescence analysis for nucleic acid samples after amplification. Based on fluorescence images obtained after reactions are completed， negative and positive statistics and sample concentrations are calculated. Analysis results are easily affected by false-positives and non-specific amplification. In this paper， a real-time high-throughput digital PCR chip analysis method based on process tracking has been proposed， to quantitatively analyze digital PCR results incorporating the dimension of time， thereby improving the accuracy of digital PCR detection. A system supporting real-time digital PCR analysis was designed and compared with an end-point digital PCR instrument to verify system performance. Using this system， different concentrations of Epstein-Barr virus were detected， and real-time amplification curves were obtained. Support Vector Machine algorithms were used to learn amplification curve characteristics， and applied to classify the detection curves. The amplification results of the designed real-time digital PCR system were highly consistent with that obtained by the end-point digital PCR. The classification algorithm based on Support Vector Machine can achieve more than 98% amplification curve classification accuracy and accurately identify false-positives and non-specific amplification micro-wells. Compared with the traditional threshold segmentation method， the average accuracy of this method for positive recognition is increased by 17.60%. The lower the copy number of the target template， the more obvious this effect is. Compared with traditional end-point digital PCR， data analysis based on the real-time digital PCR system proposed in this paper offers the advantage of higher accuracy. Quantitative result accuracy is especially improved in cases of low copy number detection.

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references

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