Adrian Viehweger (1, 4, *), Felix Kühnl (2, *), Christian Brandt (3, 4), Brigitte König (1)
* These authors contributed equally.
(1) Medical Microbiology, University Hospital Leipzig
(2) Bioinformatics, University Leipzig
(3) Infection Medicine, University Hospital Jena
(4) nanozoo GmbH, Leipzig
Effective public health response to viral outbreaks such as SARS-CoV-2 is often informed by real-time PCR screening of large populations. Pooling samples can increase screening capacity. However, when a traditional pool is tested positive, all samples in the pool need individual retesting, which becomes ineffective at a higher proportion of positive samples. Here, we report a new pooling protocol that mitigates this problem by replicating samples across multiple pools. The resulting pool set allows the sample status to be resolved more often than with traditional pooling. At 2% prevalence and 20 samples per pool, our protocol increases screening capacity by factors of 5 and 2 compared to individual testing and traditional pooling, respectively. The corresponding software to layout and resolve samples is freely available under a BSD license (https://github.com/phiweger/clonepool).
Figure 1: Illustration of the clonepool algorithm. Circles denote the wells, each containing a pool of samples (small squares). A distinct color marks all replicates of a single sample. Positive samples are flagged with "+", negative ones remain empty. Positive pools are shaded in grey, negative ones in white. In a first phase, all samples that have at least one replicate in a negative pool are identified as negative (blue, green). In the second phase, samples that only occur in positive pools and where at least one replicate is in a pool where all other samples are negative, are recognized as positive (red, orange). All other samples cannot be resolved and have to be retested individually (yellow).
Figure 2: Simulation results for different percentages of positive samples (x-axis), replicates (colors), and pool sizes (panels). The target metric is the effective number of samples per PCR reaction, which includes the individual retesting of samples that cannot be resolved in the first pooling run.
clonepool is available as an interactive Google Colaboratory notebook, which can be used without installing any code locally.
We recommend using Conda to provide an environment for clonepool and its dependencies. Miniconda is a minimal installer for Conda.
# Check out the code from the Git repository.
git clone https://github.com/phiweger/clonepool.git
cd clonepool
# Create a Conda environment including all dependencies.
conda env create --file conda_env.yml
conda activate clonepool
# Install clonepool.
pip install .# Make sure the Conda environment is active.
conda activate clonepool
# Generate layout ...
clonepool layout --pool-size 5 data/layout.csv
# and add positive pools after experiment (- -> +) or simulate experiment.
clonepool simulate --layout data/layout.csv data/simulation.csv
# Finally, resolve which samples are positive, negative or incertain (NA).
clonepool resolve --layout data/simulation.csv data/results.csv