Fig 1.
Mathematical framework for pooling in the i -th stage in a multiple-stage pooling strategy.
Fig 2.
Optimized group testing mixing matrix design.
(A-C) Hamming code parity check pooling matrix design for N = 7, 15, and 31. (A) N = 7 numerical matrix with 3 pools (3x7). (B) N = 15 numerical matrix with 4 pools (4x15). (C) N = 31 pixel matrix with 5 pools (5x31). (D) Bipartite pooling matrix design optimized for high N and prevalence rates. N = 40 pixel matrix with 16 pools (16x40). (A,B,C,D) White pixel indicates a sample included in the pool. Black pixel indicates a sample not included in pool.
Fig 3.
Modified pooling protocol eliminates dilution effect of group testing.
(A) RNA extraction and qRT-PCR workflow in individual testing, traditional pooling (group testing), and the modified pooling protocol. Numerical examples are theoretical to display dilution effect and can be scaled to individual diagnostic testing facility protocols. (B) MHV-1 was used to generate individual samples of various viral loads (1x109-1x102 copy number/qRT-PCR reaction). qRT-PCR was performed on each sample to develop ground truth Ct values. Samples were then used in various pool sizes in traditional pooling and in the modified pooling protocol. Increases in sample Ct values from the ground truth values were calculated and plotted as ΔCt Value. Data are presented as the mean ± SEM from two combined independent experiments (n = 16). For statistical analysis a one-way ANOVA with a Tukey’s post hoc test was performed. ***p<0.001, ns = not significant. Created with BioRender.com.
Table 1.
N = 31 MHV-1 pooled testing qRT-PCR results.
Table 2.
Compressed sensing decoded pooled testing significantly decreases the number of tests required to identify infected samples.
Table 3.
Human COVID-19 sample pooled testing qRT-PCR results.
Table 4.
Compressed sensing decoded pooled testing accurately identifies positive human COVID-19 samples at real world prevalence rates.