It is a challenge for many labs to comply with the demands to become more efficient when handling SARS-CoV-2 tests in the lab.
According to Worldometer.com in the USA over 422 million COVID-19 tests have been performed (data from April 14 2021)
– All in need for analysis by the very same laboratory professionals that already conduct an estimated 13 billion laboratory tests every year.
As labs globally are struggling with supply chain issues around molecular testing for SARS-CoV-2, pooling testing has been put forth as a solution for these challenges.
Sample Pooling testing is already known as an approach used in applications like blood biobanking.
The principle is simple: you run multiple samples in a single reaction.
Studies suggest that sample pooling can present a the solution to the pressure many labs experience due to the increased demand for processing SARS-CoV-2 tests.
What is sample pooling?
In the simplest form of sample pooling, known as the Dorfman pooling, a set of samples get tested together in a single run. If the pooled sample test has no response for the virus, no further testing is needed, and multiple samples have been tested faster and cheaper (in terms of reagents and instrument time). However, if the pooled sample tests positive, the lab must retest every sample individually to determine, which sample caused the positive result, hence a bit more expensive and time consuming. The balance is dependent on the positive rate but can be calculated up front.
There are also more complex pooling schemes available, called multiplexing, where samples in multiple, overlapping pools get combined. If a positive result is detected, the positive sample can be determined by looking at which combined pools were negative and which were positive. This allow the researchers to skip the second round of testing or at least reduce the number of samples that must be tested a second time.
But now back to the studies and how this could be used for SARS-CoV-2 tests. 2 studies were recently published in Science Translational Medicine.
The Hebrew University study
One study was conducted by a team led by researchers at Hebrew University of Jerusalem. At the institution they had been using pooling to run more than 133,000 samples between April and September 2020.
This study focused primarily on Dorfman approaches as they found it much simpler to pool and interpret.
As pooled samples must be broken apart and tested separately in the case of a positive result, the optimal size of a given pool will depend on the prevalence rate in a community, with smaller pools being more efficient.
The Hebrew University team used either 8-sample or 5-sample pools, however they had also tried using pools as large as 16 samples. In total, they managed to test all 133,816 specimens using 32,466 tests. This is 76% fewer tests than they would have under a non-pooled scheme.
In addition to efficiency, sensitivity is a major concern with sample pooling, as samples are diluted when pooled, meaning that, for instance, an 8-sample pool with one positive sample would have one-eighth the amount of virus as the positive sample tested individually. The Hebrew University researchers noted, though, that pooled testing exceeded their sensitivity estimates, attributing this to what they called a “hitchhiker phenomenon” in which “strongly positive samples lead to individual testing of all samples in the pool, revealing weakly positive ‘hitchhikers”.
The Broad Institute and Harvard University study
This study led by researchers at the Broad Institute and Harvard University present methods for optimizing pooled testing by accounting for patient viral load and the trajectory of the outbreak within the population being tested.
The researchers observed that samples with lower viral loads, which would otherwise be missed due to dilution are… ‘rescued’ by coexisting in the same pool with high viral load samples and thus ultimately get individually retested.
They made this observation as part of a larger analysis of the influence of the dynamics of an outbreak on pooling effectiveness.
They found out that the distribution of viral loads among infected people changes over the course of a pandemic and it is not just because more or fewer people are infected, but also how many people have high versus low viral loads that differs when the epidemic is declining or growing, even at the same prevalence.
The intuition is that, when the pandemic is increasing, more infections are typically recent and have higher viral loads. When the pandemic is declining, more infections are typically older with lower viral loads. This is important to consider when evaluating test sensitivity at different stages of the pandemic. If we expect more of our samples to have low viral loads, then diluting these samples through pooling may have a bigger impact on sensitivity. If more samples have high viral loads, then we can get away with greater pool sizes and not expect to lose sensitivity.
An understanding of an outbreak’s dynamics can also inform the sensitivity a lab should aim for in its pooling strategies, if most missed cases are from patients with low viral loads at the end of their infections, this may not have a meaningful clinical or epidemiological impact.
Even if pooling reduces sensitivity because it dilutes low viral load samples, we see that most of those low viral load individuals are well past the peak of their infection and therefore likely to be no longer infectious.
By taking into account the epidemic dynamics and projecting how we expect prevalence to change over time, we can find strategies that will remain effective for weeks or even months.
Both studies found that pooling could significantly improve testing efficiency while maintaining sufficient sensitivity.
This of course raises a new question: can my liquid handling system facilitate pooling? If you use our flowbot™ ONE the short answer is yes as the flexibility of the robot make pre-analytic liquid handling simple, easy and accessible.
This article is written with inspiration from an article on 360DXvv