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. 2022 Jun:304:114524.
doi: 10.1016/j.jviromet.2022.114524. Epub 2022 Mar 15.

Pooled sample testing for COVID-19 diagnosis: Evaluation of bi-directional matrix pooling strategies

Affiliations

Pooled sample testing for COVID-19 diagnosis: Evaluation of bi-directional matrix pooling strategies

Rajamani Barathidasan et al. J Virol Methods. 2022 Jun.

Abstract

In the on-going COVID-19 pandemic, pooled testing of samples by RT-PCR has been recommended at certain scenarios to increase labs' testing capacity and reduce cost of testing. This paper describes the evaluation of bi-directional matrix pooling strategies with clinical samples in a 5 × 5 and 10 × 10 matrix. Nasopharyngeal swab samples in viral transport medium (VTM) previously tested (positive or negative) by real time RT-PCR for SARS-CoV-2 were used for these experiments. Ten sets of 5 × 5 (250 samples) and ten sets of 10 × 10 (1000 samples) pooling of samples in both directions was done with known positive samples introduced at random positions. Extracted nucleic acid was tested for SARS-CoV-2 E-gene by RT-PCR. Sensitivity or concordance and feasibility of matrix pooling were assessed in comparison to direct RT-PCR testing. In comparison to direct testing, the overall concordance was 86.6% for 5 × 5 pooling, 73.3% for 10 × 10 with 200 µL extraction volume and 86.6% for 10 × 10 with 400 µL extraction volume. Bi-directional matrix pooling can be adopted with advantage over conventional direct or pool testing for COVID-19 by RT-PCR under the following conditions: i) sample positivity rate of ≤ 5%, ii) matrix pool size of 8-10 samples, iii) use of min. 40 µL VTM from each sample and iv) utilization of automated liquid handling equipment, if available, for sample addition to avoid human errors.

Keywords: COVID-19; Diagnosis; Pooling; RT-PCR; SARS-CoV-2; Screening.

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Conflict of interest statement

The authors declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
(a) 5×5 sample pool: A total of 25 samples were included in 5 × 5 matrix pool. Forty µL from each sample was added in two pools i.e row and column. e.g. 40 µL VTM from sample no. 1 was added to pool no. C1 and R1; Sample no.6 was added in pool no. C1 and R2, and so on. One to three positives were introduced at random positions in an operator- blinded manner; (b) 10 × 10 sample pool: A total of 100 samples were included in 10 × 10 pool. Twenty or Forty µL from each VTM was added in two pools. e.g. 40 µL VTM from Sample no. 1 was added in pool no. C1 and R1; Sample no.11 was added in pool no. C1 and R2, and so on. 1–3 positives were introduced at random positions in an operator-blinded manner [C- column, R-Row, numerical- sample nos.].
Fig. 2
Fig. 2
Identification of intersects of positive pools in 5-sample matrix. (a) 1 × 1 = 1 intersect (red) directly identified as positive; (b) 2 × 2 = 4 intersects (yellow) to be tested to identify true positives; (c) 3 × 3 = 9 intersects (yellow) to be tested to identify true positives.
Fig. 3
Fig. 3
Identification of intersects of positive pools in 10-sample matrix. 2 × 2 = 4 intersects (yellow) to be tested to identify true positives.

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