Identifying SARS-CoV-2 Variants of Concern through Saliva-Based RT-qPCR by Targeting Recurrent Mutation Sites

Biolayer-Interferometry-Guided Functionalization of Screen-Printed Graphene for Label-Free Electrochemical Virus Detection

Additive manufacturing holds promise for rapid prototyping and low-cost production of biosensors for diverse pathogens. Among additive manufacturing methods, screen printing is particularly desirable for high-throughput production of sensing platforms. However, this technique needs to be combined with carefully formulated inks, rapid postprocessing, and selective functionalization to meet all requirements for high-performance biosensing applications. Here, we present screen-printed graphene electrodes that are processed with thermal annealing to achieve high surface area and electrical conductivity for sensitive biodetection via electrochemical impedance spectroscopy. As a proof-of-concept, this biosensing platform is utilized for electrochemical detection of SARS-CoV-2. To ensure reliable specificity in the presence of multiple variants, biolayer interferometry (BLI) is used as a label-free and dynamic screening method to identify optimal antibodies for concurrent affinity to the Spike S1 proteins of Delta, Omicron, and Wild Type SARS-CoV-2 variants while maintaining low affinity to competing pathogens such as Influenza H1N1. The BLI-identified antibodies are robustly bound to the graphene electrode surface via oxygen moieties that are introduced during the thermal annealing process. The resulting electrochemical immunosensors achieve superior metrics including rapid detection (55 s readout following 15 min of incubation), low limits of detection (approaching 500 ag/mL for the Omicron variant), and high selectivity toward multiple variants. Importantly, the sensors perform well on clinical saliva samples detecting as few as 103 copies/mL of SARS-CoV-2 Omicron, following CDC protocols. The combination of the screen-printed graphene sensing platform and effective antibody selection using BLI can be generalized to a wide range of point-of-care immunosensors.

Repurposing a SARS-CoV-2 surveillance program for infectious respiratory diseases in a university setting

Standard multiplex RT-qPCR diagnostic tests use nasopharyngeal swabs to simultaneously detect a variety of infections, but commercially available kits can be expensive and have limited throughput. Previously, we clinically validated a saliva-based RT-qPCR diagnostic test for SARS-CoV-2 to provide low-cost testing with high throughput and low turnaround time on a university campus. Here, we developed a respiratory diagnostic panel to detect SARS-CoV-2, influenza A and B within a single saliva sample. When compared to clinical results, our assay demonstrated 93.5% accuracy for influenza A samples (43/46 concordant results) with no effect on SARS-CoV-2 accuracy or limit of detection. In addition, our assay can detect simulated coinfections at varying virus concentrations generated from synthetic RNA controls. We also confirmed the stability of influenza A in saliva at room temperature for up to 5 days. The cost of the assay is lower than standard nasopharyngeal swab respiratory panel tests as saliva collection does not require specialized swabs or trained clinical personnel. By repurposing the lab infrastructure developed for the COVID-19 pandemic, our multiplex assay can be used to provide expanded access to respiratory disease diagnostics, especially for community, school, or university testing applications where saliva testing was effectively utilized during the COVID-19 pandemic.

Predicting COVID-19 Infected Individuals in a Defined Population from Wastewater RNA Data

Wastewater surveillance of SARS-CoV-2 RNA has become an important tool for tracking the presence of the virus and serving as an early indicator for the onset of rapid transmission. Nevertheless, wastewater data are still not commonly used to predict the number of infected individuals in a sewershed. The main objective of this study was to calibrate a susceptible-exposed-infectious-recovered (SEIR) model using RNA copy rates in sewage (i.e., gene copies per liter times flow rate) and the number of SARS-CoV-2 saliva-test-positive infected individuals in a university student population that was subject to repeated weekly testing during the Spring 2021 semester. A strong correlation was observed between the RNA copy rates and the number of infected individuals. The parameter in the SEIR model that had the largest impact on calibration was the maximum shedding rate, resulting in a mean value of 7.72 log10 genome copies per gram of feces. Regressing the saliva-test-positive infected individuals on predictions from the SEIR model based on the RNA copy rates yielded a slope of 0.87 (SE=0.11), which is statistically consistent with a 1:1 relationship between the two. These findings demonstrate that wastewater surveillance of SARS-CoV-2 can be used to estimate the number of infected individuals in a sewershed.

Differentiation of SARS-CoV-2 Variants Using RT-qPCRs by Targeting Recurrent Mutation Sites: A Diagnostic Laboratory Experience from Multi-Center Regional Study, August 2020-December 2021, Poland

Rapid identification of SARS-CoV-2 variants is essential for epidemiological surveillance. RT-qPCR-based variant differentiation tests can be used to quickly screen large sets of samples for relevant variants of concern/interest; this study was conducted on specimens collected at 11 centers located in Poland during routine SARS-CoV-2 diagnostics between August 2020 and December 2021. A total of 1096 samples (with CT < 30) were screened for Alpha, Beta, Delta, Kappa and Omicron variants using commercial assays targeting repeat mutation sites. Variants were assigned to 434 (39.6%) specimens; the remaining 662 (60.4%) samples were not classified (no tested mutations detected). Alpha (n = 289; 66.59%), Delta (n = 115; 26.5%), Kappa (n = 30; 6.91%) and Omicron (n = 2; 0.46%) variants were identified and their distribution changed over time. The first Alpha variant appeared in October 2020, and it began to gradually increase its proportion of the virus population by June 2021. In July 2021, it was replaced by the Delta variant, which already dominated by the end of the year. The first Kappa was detected in October 2021, while Omicron was found in December 2021. The screening of samples allowed the determination of epidemiological trends over a time interval reflecting the national COVID-19 waves.