Practical considerations to establish a validated platform for pooled detection of SARS-CoV-2 by droplet digital PCR

Detection of SARS-CoV-2 has created an enormous workload for laboratories worldwide resulting in a restriction at the time of massive testing. Pool testing is a strategy that reduces time and costs. However, beyond the detection of infectious diseases in blood banks, this approach is rarely implemented in routine laboratories. Therefore, what was learned from the SARS-CoV-2 pool testing should represent an opportunity to increase diagnostic capabilities. The present work, carried out in the context of a diagnostic laboratory of a public hospital during the COVID-19 pandemic, represents a contribution to this end. The main limitation of pool testing is the risk of false negatives that could have been identified by individual tests. These limitations are the dilution of samples with a low virus load during pooling and that the integrity of the sample may be affected by the quality of the sample collection. Fortunately, both limitations coincide with the main strengths of droplet digital PCR (ddPCR). ddPCR is a third-generation PCR that splits the amplification into thousands of droplets that work in parallel, increasing sensitivity and resistance to inhibitors. Therefore, ddPCR is particularly useful for pool testing. Here we show how to factor between test sensitivity and savings in test time and resources. We have identified and optimized critical parameters for pool testing. The present study, which analyzed 1000 nasopharyngeal samples, showed that the pool testing could detect even a single positive sample with a CT value of up to 30 in pools of 34 samples. This test was performed using three different standard extraction methods, the simplest being heating only, which resulted in substantial savings of extraction reagents in addition to PCR reagents. Moreover, we show that pooling can be extended to use saliva, which is less invasive and allows self-collection, reducing the risk for health personnel.