Your browser doesn't support javascript.
loading
Self-correction of cycle threshold values by a normal distribution-based process to improve accuracy of quantification in real-time digital PCR.
Zang, Peilin; Xu, Qi; Li, Chuanyu; Tao, Mingli; Zhang, Zhiqi; Li, Jinze; Zhang, Wei; Li, Shuli; Li, Chao; Yang, Qi; Guo, Zhen; Yao, Jia; Zhou, Lianqun.
Afiliação
  • Zang P; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
  • Xu Q; CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
  • Li C; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
  • Tao M; CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
  • Zhang Z; CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
  • Li J; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
  • Zhang W; CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
  • Li S; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
  • Li C; CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
  • Yang Q; Suzhou CASENS Co., Ltd, Suzhou, 215163, China.
  • Guo Z; CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
  • Yao J; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
  • Zhou L; CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
Anal Bioanal Chem ; 416(10): 2453-2464, 2024 Apr.
Article em En | MEDLINE | ID: mdl-38400940
ABSTRACT
The digital polymerase chain reaction (dPCR) is a new and developing nucleic acid detection technology with high sensitivity that can realize the absolute quantitative analysis of samples. In order to improve the accuracy of quantitative results, real-time digital PCR emphasizes the kinetic information during amplification to identify prominent abnormal data. However, it is challenging to use a unified standard to accurately classify the amplification curve of each well as negative and positive, due to the interference caused by various factors in the experiment. In this work, a normal distribution-based cycle threshold value self-correcting model (NCSM) was established, which focused on the feature of the cycle threshold values in amplification curves and conducted continuous detection and correction on the whole. The cycle threshold value distribution was closer to the ideal normal distribution to avoid the influence of interference. Thus, the model achieves a more accurate classification between positive and negative results. The corrective process was applied to plasmid samples and resulted in an accuracy improvement from 92 to 99%. The coefficient of variation was below 5% when considering the quantitation of a range between 100 and 10,000 copies. At the same time, by utilizing this model, the distribution of cycle threshold values at the endpoint can be predicted with fewer thermal cycles, which can reduce the cycling time by around 25% while maintaining a consistency of more than 98%. Therefore, using the NCSM can effectively enhance the quantitative accuracy and increase the detection efficiency based on the real-time dPCR platform.
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Distribuição Normal Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Distribuição Normal Idioma: En Ano de publicação: 2024 Tipo de documento: Article