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. 2022 Oct 24;18(10):e1010629.
doi: 10.1371/journal.pcbi.1010629. eCollection 2022 Oct.

Use of compressed sensing to expedite high-throughput diagnostic testing for COVID-19 and beyond

Kody A Waldstein  1 , Jirong Yi  2 , Myung Cho  3 , Raghu Mudumbai  2 , Xiaodong Wu  2 , Steven M Varga  1   4   5 , Weiyu Xu  2
Affiliations

Use of compressed sensing to expedite high-throughput diagnostic testing for COVID-19 and beyond

Kody A Waldstein et al. PLoS Comput Biol. .

Abstract

The rapid spread of SARS-CoV-2 has placed a significant burden on public health systems to provide swift and accurate diagnostic testing highlighting the critical need for innovative testing approaches for future pandemics. In this study, we present a novel sample pooling procedure based on compressed sensing theory to accurately identify virally infected patients at high prevalence rates utilizing an innovative viral RNA extraction process to minimize sample dilution. At prevalence rates ranging from 0-14.3%, the number of tests required to identify the infection status of all patients was reduced by 69.26% as compared to conventional testing in primary human SARS-CoV-2 nasopharyngeal swabs and a coronavirus model system. Our method provided quantification of individual sample viral load within a pool as well as a binary positive-negative result. Additionally, our modified pooling and RNA extraction process minimized sample dilution which remained constant as pool sizes increased. Compressed sensing can be adapted to a wide variety of diagnostic testing applications to increase throughput for routine laboratory testing as well as a means to increase testing capacity to combat future pandemics.

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Conflict of interest statement

The authors are coinventors of a pending patent covering the use of compressed sensing in diagnostic testing applications.

Figures

Fig 1
Fig 1. Mathematical framework for pooling in the i -th stage in a multiple-stage pooling strategy.
Fig 2
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
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.
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