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Review
. 2021 Mar:289:114044.
doi: 10.1016/j.jviromet.2020.114044. Epub 2020 Dec 11.

Sample pooling strategies for SARS-CoV-2 detection

Nefeli Lagopati  1 , Panagiota Tsioli  1 , Ioanna Mourkioti  1 , Aikaterini Polyzou  1 , Angelos Papaspyropoulos  1 , Alexandros Zafiropoulos  2 , Konstantinos Evangelou  1 , George Sourvinos  3 , Vassilis G Gorgoulis  4
Affiliations
Review

Sample pooling strategies for SARS-CoV-2 detection

Nefeli Lagopati et al. J Virol Methods. 2021 Mar.

Abstract

The worldwide COVID-19 pandemic outburst has caused a serious public health issue with increasing needs of accurate and rapid diagnostic and screening testing. This situation requires an optimized management of the chemical reagents, the consumables, and the human resources, in order to respond accurately and effectively, controlling the spread of the disease. Testing on pooled samples maximizes the number of tested samples, by minimizing the time and the lab supplies needed. The general conceptualization of the pooling method is based on mixing samples together in a batch. Individual testing is needed only if a specific pool exhibits a positive result. The development of alternative hybrid methods, based on "in house" protocols, utilizing commercially available consumables, in combination with a reliable pooling method would provide a solution, focusing on the better exploitation of the personnel and the lab supplies, allowing for rapid screening of a population in a reasonably short time.

Keywords: Array testing; Hierarchical algorithm; N genes; Pooling methods; SARS-CoV-2.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1
Fig. 1
Configuration for two-stage hierarchical testing with an initial pool N, followed by individual testing in the second stage of the algorithm. (a) Upon a negative result of the N, all the Xi can be declared healthy. (b) If the result is positive, then a second stage is required. All the Xi samples of the positive pool are going to be tested separately and the final result is that of this second step.
Fig. 2
Fig. 2
Configuration for three-stage hierarchical testing with an initial pool N. The number of the groups or the divisions of the N population is termed as Xi (1 < i < N). Yij is the number of the samples in each pool (1 < i < N, 1 < j < N) and is the same for all the pools. Xi pools are examined individually. Upon a negative result of the Xi, all the Yij can be declared healthy. If the result is positive, then sub-pools of its members are formed for a second stage of testing, to isolate those that contain positive samples. Consequently, if a sub-pool is positive, then individual testing of all its members is going to be implemented in the third and final stage.
Fig. 3
Fig. 3
Configuration for array testing with a N x M matrix development. Individuals are pooled by row and by column in the first stage of testing. The second stage of the 2-D array testing involves separately retesting specimens not classified as negative in the first stage. These are the samples that lie at intersections of positive rows and columns (in red). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
Fig. 4
Fig. 4
Configuration for array testing with a N x M matrix development in gradient (a) and spiral (b) mode. These approaches should be selected in case of samples with unequal disease probability. In a gradient array test, higher-risk individuals are put in the leftmost columns of the matrix. When the first column is filled, the second one is filled form the top to the bottom and so on. The lower-risk individual is placed in the cell (N, M). In the spiral methods the higher-risk individuals are put in a square sub-matrix within the master one. Typically, the highest-risk individual is assigned in the upper left-hand corner of the array. The common motif starts with the highest-risk individual at the (1, 1) cell, the next three highest-risk individuals are allocated to the (2, 1), (2, 2), and (1, 2) cells, respectively. Then, the samples are put in the cells of the hypothetical array in the same motif, in descending order of disease probability.
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