Amplification Curve Analysis: Data-Driven Multiplexing Using Real-Time Digital PCR

数字聚合酶链反应 多路复用 化学 熔化曲线分析 枚举 生物系统 计算机科学 实时聚合酶链反应 数学 聚合酶链反应 基因 生物化学 电信 生物 组合数学
作者
Ahmad Moniri,Luca Miglietta,Kenny Malpartida-Cardenas,Ivana Pennisi,Miguel Cacho-Soblechero,Nicolas Moser,Alison Holmes,Pantelis Georgiou,Jesús Rodríguez-Manzano
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:92 (19): 13134-13143 被引量:44
标识
DOI:10.1021/acs.analchem.0c02253
摘要

Information about the kinetics of PCR reactions is encoded in the amplification curve. However, in digital PCR (dPCR), this information is typically neglected by collapsing each amplification curve into a binary output (positive/negative). Here, we demonstrate that the large volume of raw data obtained from real-time dPCR instruments can be exploited to perform data-driven multiplexing in a single fluorescent channel using machine learning methods, by virtue of the information in the amplification curve. This new approach, referred to as amplification curve analysis (ACA), was shown using an intercalating dye (EvaGreen), reducing the cost and complexity of the assay and enabling the use of melting curve analysis for validation. As a case study, we multiplexed 3 carbapenem-resistant genes to show the impact of this approach on global challenges such as antimicrobial resistance. In the presence of single targets, we report a classification accuracy of 99.1% (N = 16188), which represents a 19.7% increase compared to multiplexing based on the final fluorescent intensity. Considering all combinations of amplification events (including coamplifications), the accuracy was shown to be 92.9% (N = 10383). To support the analysis, we derived a formula to estimate the occurrence of coamplification in dPCR based on multivariate Poisson statistics and suggest reducing the digital occupancy in the case of multiple targets in the same digital panel. The ACA approach takes a step toward maximizing the capabilities of existing real-time dPCR instruments and chemistries, by extracting more information from data to enable data-driven multiplexing with high accuracy. Furthermore, we expect that combining this method with existing probe-based assays will increase multiplexing capabilities significantly. We envision that once emerging point-of-care technologies can reliably capture real-time data from isothermal chemistries, the ACA method will facilitate the implementation of dPCR outside of the lab.
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