Next-generation molecular diagnostics: Leveraging digital technologies to enhance multiplexing in real-time PCR

多路复用 多路复用 计算机科学 寡核苷酸 数字聚合酶链反应 吞吐量 核酸 杠杆(统计) 计算生物学 聚合酶链反应 DNA 生物 生物信息学 机器学习 无线 遗传学 基因 电信
作者
Louis Kreitmann,Luca Miglietta,Ke Xu,Kenny Malpartida-Cardenas,Giselle D’Souza,Myrsini Kaforou,Karen Brengel‐Pesce,Laurent Drazek,Alison Holmes,Jesús Rodríguez-Manzano
出处
期刊:Trends in Analytical Chemistry [Elsevier]
卷期号:160: 116963-116963 被引量:19
标识
DOI:10.1016/j.trac.2023.116963
摘要

Real-time polymerase chain reaction (qPCR) enables accurate detection and quantification of nucleic acids and has become a fundamental tool in biological sciences, bioengineering and medicine. By combining multiple primer sets in one reaction, it is possible to detect several DNA or RNA targets simultaneously, a process called multiplex PCR (mPCR) which is key to attaining optimal throughput, cost-effectiveness and efficiency in molecular diagnostics, particularly in infectious diseases. Multiple solutions have been devised to increase multiplexing in qPCR, including single-well techniques, using target-specific fluorescent oligonucleotide probes, and spatial multiplexing, where segregation of the sample enables parallel amplification of multiple targets. However, these solutions are mostly limited to three or four targets, or highly sophisticated and expensive instrumentation. There is a need for innovations that will push forward the multiplexing field in qPCR, enabling for a next generation of diagnostic tools which could accommodate high throughput in an affordable manner. To this end, the use of machine learning (ML) algorithms (data-driven solutions) has recently emerged to leverage information contained in amplification and melting curves (AC and MC, respectively) - two of the most standard bio-signals emitted during qPCR - for accurate classification of multiple nucleic acid targets in a single reaction. Therefore, this review aims to demonstrate and illustrate that data-driven solutions can be successfully coupled with state-of-the-art and common qPCR platforms using a variety of amplification chemistries to enhance multiplexing in qPCR. Further, because both ACs and MCs can be predicted from sequence data using thermodynamic databases, it has also become possible to use computer simulation to rationalize and optimize the design of mPCR assays where target detection is supported by data-driven technologies. Thus, this review also discusses recent work converging towards the development of an end-to-end framework where knowledge-based and data-driven software solutions are integrated to streamline assay design, and increase the accuracy of target detection and quantification in the multiplex setting. We envision that concerted efforts by academic and industry scientists will help advance these technologies, to a point where they become mature and robust enough to bring about major improvements in the detection of nucleic acids across many fields.
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