OncoRNALab, Cancer Research Institute Ghent, Corneel Heymanslaan 10, 9000, Ghent, Belgium.
Department of Biomolecular Medicine, Ghent University, Corneel Heymanslaan 10, 9000, Ghent, Belgium.
Sci Rep. 2022 Apr 22;12(1):6603. doi: 10.1038/s41598-022-10581-6.
To increase the throughput, lower the cost, and save scarce test reagents, laboratories can pool patient samples before SARS-CoV-2 RT-qPCR testing. While different sample pooling methods have been proposed and effectively implemented in some laboratories, no systematic and large-scale evaluations exist using real-life quantitative data gathered throughout the different epidemiological stages. Here, we use anonymous data from 9673 positive cases to model, simulate and compare 1D and 2D pooling strategies. We show that the optimal choice of pooling method and pool size is an intricate decision with a testing population-dependent efficiency-sensitivity trade-off and present an online tool to provide the reader with custom real-time 1D pooling strategy recommendations.
为了提高通量、降低成本并节省稀缺的检测试剂,实验室可以在进行 SARS-CoV-2 RT-qPCR 检测之前对患者样本进行汇集。虽然已经提出并在一些实验室中有效实施了不同的样本汇集方法,但使用在不同流行病学阶段收集的真实定量数据进行系统的和大规模的评估尚未存在。在这里,我们使用来自 9673 例阳性病例的匿名数据来建模、模拟和比较 1D 和 2D 汇集策略。我们表明,汇集方法和汇集大小的最佳选择是一个复杂的决策,具有基于检测人群的效率-敏感性权衡,并提供了一个在线工具,为读者提供定制的实时 1D 汇集策略建议。