High Fidelity Machine-Learning-Assisted False Positive Discrimination in Loop-Mediated Isothermal Amplification Using Nanopore-Based Sizing and Counting
High Fidelity Machine-Learning-Assisted False Positive Discrimination in Loop-Mediated Isothermal Amplification Using Nanopore-Based Sizing and Counting
复制标题
DOI：
10.1021/acsnano.3c12053
复制
发表时间：
2024-02-23
期刊：
影响因子：
17.1
通讯作者：
Guan，Weihua
中科院分区：
文献类型：
--
作者：
Dong，Ming;Kshirsagar，Aneesh;Guan，Weihua
环介导的等温扩增(LAMP)是一种快速、灵敏、经济有效的方法，用于开发护理点核酸检测，因为它的等温性质。然而，LAMP可能会受到假阳性问题的影响，这可能会损害结果的特异性。LAMP假阳性通常是由于污染、非特异性放大和非特异性信号报告(嵌入染料、比色、混浊等)造成的。虽然染料标记的引物或探针已被引入用于多重检测和增强LAMP分析的特异性，但它们存在反应抑制的风险。这种抑制可能是由于带有荧光团或猝灭剂的标记引物和在反应过程中没有完全解离的探针造成的。这项工作展示了一种基于纳米孔的系统，通过使用扩增子大小和计数来实现无探针的灯读数，类似于电子版本的凝胶电泳。我们首先开发了一个模型来探索LAMP动力学，并通过凝胶电泳法验证了真阳性和假阳性之间的不同模式。随后，我们进行了纳米孔计数，并校准了事件电荷赤字(ECD)值和频率，以确保扩增子图谱的公平分析。该方法与机器学习相结合，达到了91.67%的误判准确率，提高了LAMP检测核酸的可靠性。
Loop-mediated isothermal amplification (LAMP) is a rapid, sensitive, and cost-effective method for developing point-of-care nucleic acid testing due to its isothermal nature. Yet, LAMP can suffer from the issue of false positives, which can compromise the specificity of the results. LAMP false positives typically arise due to contamination, nonspecific amplification, and nonspecific signal reporting (intercalating dyes, colorimetric, turbidity, etc.). While dye-labeled primers or probes have been introduced for multiplexed detection and enhanced specificity in LAMP assays, they carry the risk of reaction inhibition. This inhibition can result from the labeled primers with fluorophores or quenchers and probes that do not fully dissociate during reaction. This work demonstrated a nanopore-based system for probe-free LAMP readouts by employing amplicon sizing and counting, analogous to an electronic version of gel electrophoresis. We first developed a model to explore LAMP kinetics and verified distinct patterns between true and false positives via gel electrophoresis. Subsequently, we implemented nanopore sized counting and calibrated the event charge deficit (ECD) values and frequencies to ensure a fair analysis of amplicon profiles. This sized counting method, integrated with machine learning, achieved 91.67% accuracy for false positive discrimination, enhancing LAMP’s reliability for nucleic acid detection.