Identification of florescent droplets at low concentrations for droplet digital PCR

Cong LIU; Wen-fei DONG; Tao ZHANG; Wu-ping ZHOU; Ke-ming JIANG; Hai-wen LI

doi:10.3788/OPE.20182603.0647

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Identification of florescent droplets at low concentrations for droplet digital PCR

Micro/Nano Technology and Fine Mechanics | 更新时间：2020-07-05

- Identification of florescent droplets at low concentrations for droplet digital PCR
- Optics and Precision Engineering Vol. 26, Issue 3, Pages: 647-653(2018)
- 作者机构：
  
  1.中国科学院苏州生物医学工程技术研究所, 江苏苏州 215163
  2.中国科学院大学, 北京 100039
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20182603.0647
  CLC： TP274.6
- Received：05 June 2017，
  
  Accepted：05 August 2017，
  
  Published：25 March 2018
- 稿件说明：
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Cong LIU, Wen-fei DONG, Tao ZHANG, et al. Identification of florescent droplets at low concentrations for droplet digital PCR[J]. Optics and precision engineering, 2018, 26(3): 647-653.
DOI：

Cong LIU, Wen-fei DONG, Tao ZHANG, et al. Identification of florescent droplets at low concentrations for droplet digital PCR[J]. Optics and precision engineering, 2018, 26(3): 647-653. DOI： 10.3788/OPE.20182603.0647.

摘要

数字PCR检测过程中，确定微滴是否为阳性直接影响最终浓度，也是影响仪器准确度的重要因素之一。目前的自动分类方法并未考虑到数字PCR技术中浓度对结果误差的影响，导致在低浓度下自动设置的方法与实际结果偏差较大。本文设计了一种基于广义帕累托分布的荧光微滴分类方法，讨论了不同浓度下误分类对结果可能的影响，据此确定了分布模型参数，并在自行研制的微滴式数字PCR仪上进行验证。实验结果显示:经本文方法分类后，样品中目标拷贝数在5~5 000的范围内线性回归的

=0.995 6，这意味着广义帕累托分布较好地拟合了微滴荧光强度边界分布，本文提出的荧光微滴自动分类方法在低浓度下取得了较好的效果。

Abstract

In the digital Polymerase Chain Reaction(dPCR) detection process

discriminating positive droplets from negative ones directly affect the final concentration

which is one of the important factors affecting the accuracy of the instrument. Current methods do not take into account the influence of sample concentration on the result error

resulting in a larger deviation from the actual results at a low concentration. In this paper

a florescent droplets classification method was designed based on generalized Pareto distribution. It was discussed that the possible effects of misclassification at different concentrations on the results

determined the high quantiles of generalized Pareto distribution

and verified the proposed method on the self-made droplet digital PCR. Experimental results showed that for the method proposed

the linear regression of samples with target copies from 5 to 5 000 got an

=0.995 6 and a detection limit of less than 5 copies/samples

while that of the comparison method was less than 50 copies/sample. These results indicate that the proposed method improves the lower detection limit of the droplet digital PCR by oneorder

and can achieve automated droplet classification at ultra-low concentration.

关键词

Keywords

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