The development of real-time digital PCR technology using an improved data classification method

Jia Yao; Yuanyuan Luo; Zhiqi Zhang; Jinze Li; Chuanyu Li; Chao Li; Zhen Guo; Lirong Wang; Wei Zhang; Heming Zhao; Lianqun Zhou

doi:10.1016/j.bios.2021.113873

The development of real-time digital PCR technology using an improved data classification method

Biosens Bioelectron. 2022 Mar 1:199:113873. doi: 10.1016/j.bios.2021.113873. Epub 2021 Dec 8.

Authors

Jia Yao¹, Yuanyuan Luo², Zhiqi Zhang², Jinze Li², Chuanyu Li², Chao Li², Zhen Guo², Lirong Wang², Wei Zhang³, Heming Zhao⁴, Lianqun Zhou⁵

Affiliations

¹ School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, China; CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
² CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
³ CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China. Electronic address: zhangw@sibet.ac.cn.
⁴ School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, China. Electronic address: hmzhao@suda.edu.cn.
⁵ CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China. Electronic address: zhoulq@sibet.ac.cn.

PMID: 34953301
DOI: 10.1016/j.bios.2021.113873

Abstract

For digital polymerase chain reaction (PCR), data classification is always a crucial task. The dynamic real-time amplification process information of each partition is always ignored in typical digital PCR analysis, which can easily lead to inaccurate outcomes. In this work, an integrated device that offers real-time chip-based digital PCR analysis was established. In addition, an enhanced process-based classification model (PAM) was built and trained. And then the device and the analytical model were employed in classification tasks for different concentrations of Epstein-Barr Virus (EBV) plasmid quantification assays. The results indicated that the real-time analysis device achieved a linearity of 0.97, the classification method was able to distinguish the false-positive curves, and the recognition error of positive wells was decreased by 64.4% compared with typical static analysis techniques when low concentrations of samples were tested. With these advantages, it is supposed that the real-time digital PCR analysis apparatus and the improved classification method can be employed to enhance the performance of digital PCR technology.

Keywords: Data classification; Digital PCR; Dynamic process information; Real-time.

MeSH terms

Biosensing Techniques*
Epstein-Barr Virus Infections*
Herpesvirus 4, Human / genetics
Humans
Real-Time Polymerase Chain Reaction
Technology