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Updated: Aug 4, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
Published on: November 10, 2023
Integrating Magnetic-Bead-Based Sample Extraction and Molecular Barcoding for the One-Step Pooled RT-qPCR Assay of
Xinyu Zhuang1, Zibin Zhao1, Xianzhen Feng1
1Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong 999077, China.
Abstract:
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.

