Integrating Magnetic-Bead-Based Sample Extraction and Molecular Barcoding for the One-Step Pooled RT-qPCR Assay of Viral Pathogens without Retesting

联营 熔化曲线分析 计算生物学 化学 多路复用 RNA提取 色谱法 实时聚合酶链反应 复制 再现性 逆转录聚合酶链式反应 生物系统 核酸 逆转录酶 聚合酶链反应 核糖核酸 生物 计算机科学 统计 生物信息学 信使核糖核酸 数学 人工智能 生物化学 基因
作者
Xinyu Zhuang,Zibin Zhao,Xianzhen Feng,Grace Lui,Denise Pui Chung Chan,Shui Shan Lee,I‐Ming Hsing
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:95 (14): 6182-6190 被引量:4
标识
DOI:10.1021/acs.analchem.3c00885
摘要

Pooling multiple samples prior to real-time reverse-transcription polymerase chain reaction (RT-PCR) analysis has been proposed as a strategy to minimize expenses and boost test throughput during the COVID-19 pandemic. Nevertheless, the traditional pooling approach cannot be effectively deployed in high-prevalence settings due to the need for secondary tests in the case of a positive pool. In this study, we present a pooling test platform with high adaptability and simplicity that allows sample-specific detection of multiple-tagged samples in a single run without the need for retesting. This was accomplished by labeling distinct samples with predefined ID-Primers and identifying tagged pooled samples using one-step RT-PCR followed by melting curve analysis with rationally designed universal fluorescence- and quencher-tagged oligo probes. Using magnetic beads (MBs), nucleic acid targets from different individuals can be tagged and extracted concurrently and then pooled before RT, eliminating the need for extra RNA extraction and separate RT and enzyme digestion steps in the recently developed barcoding strategies. Pools of six samples (positive and negative) were successfully identified by melting temperature values under two fluorescent channels, with a detection sensitivity of 5 copies/μL. We validated the reproducibility of this assay by running it on 40 clinical samples with a hypothetical infection rate of 15%. In addition, to aid the scenario of large-scale pooling tests, we constructed a melting curve autoreadout system (MCARS) for statistical analysis of melting curve plots to eliminate error-prone manual result readout. Our results suggest that this strategy could be a simple and adaptable tool for alleviating existing bottlenecks in diagnostic pooling testing.
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