从低生物量环境样品中扩增16S rRNA基因的数字聚合酶链反应验证。

IF 6.1 Q1 ECOLOGY
ISME communications Pub Date : 2025-07-09 eCollection Date: 2025-01-01 DOI:10.1093/ismeco/ycaf115
Veronika V Koziaeva, Katja Engel, Josh D Neufeld
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引用次数: 0

摘要

数字聚合酶链反应(dPCR)是一种DNA定量技术,提供DNA模板的绝对定量。在本研究中,我们利用常用的16S rRNA基因引物对,优化并验证了基于芯片的dPCR evgreen检测方法,并将其性能与实时荧光定量PCR (qPCR)进行了比较。我们使用新设计的合成DNA标准来比较低量模板DNA的测量,以评估精度,准确性和灵敏度。测试了优化方法,以最大限度地减少dPCR在真阳性和真阴性分区(所谓的“雨”)之间的中间荧光水平的分区。dPCR和qPCR都表现出相似的定量性能,在模板浓度小于30个拷贝μl-1的样品中,准确性的差异增加。两种测试的16S rRNA基因引物集扩增了qPCR和dPCR混合物中的非靶模板污染物,这些污染物无法通过紫外线或DNAse处理消除,并且对两种PCR检测的表观敏感性产生负面影响。与qPCR相比,数字PCR对常见的PCR抑制剂(如乙醇和腐植酸)的敏感性较低,但对单宁酸的抑制更敏感。这些结果证明了dPCR对低生物量环境样品中16S rRNA基因定量的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Validating digital polymerase chain reaction for 16S rRNA gene amplification from low biomass environmental samples.

Digital polymerase chain reaction (dPCR) is a DNA quantification technology that offers absolute quantification of DNA templates. In this study, we optimized and validated a chip-based dPCR EvaGreen assay with commonly used 16S rRNA gene primer pairs and compared its performance to quantitative real-time PCR (qPCR). We compared measurements of low amounts of template DNA using a newly designed synthetic DNA standard to assess precision, accuracy, and sensitivity. Optimization approaches were tested to minimize partitions with intermediate fluorescence levels between true positive and true negative partitions (so-called "rain") for dPCR. Both dPCR and qPCR demonstrated similar quantification performance, with variability in accuracy increasing for samples containing fewer than 30 copies μl-1 template concentrations. Both tested 16S rRNA gene primer sets amplified non-target template contaminants within both qPCR and dPCR mixtures, which could not be eliminated by ultraviolet light or DNAse treatment and negatively affected the apparent sensitivity of both PCR assays. Digital PCR was less susceptible to common PCR inhibitors, such as ethanol and humic acids, but was more susceptible to tannic acid inhibition than qPCR. These findings demonstrate the suitability of dPCR for 16S rRNA gene quantification of low biomass environmental samples.

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