Optimization of SARS-CoV-2 culture from clinical samples for clinical trial applications

Abstract

Clinical trials of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) therapeutics often include virological secondary endpoints to compare viral clearance and viral load reduction between treatment and placebo arms. This is typically achieved using quantitative reverse-transcriptase PCR (RT-qPCR), which cannot differentiate replicant competent virus from non-viable virus or free RNA, limiting its utility as an endpoint. Culture-based methods for SARS-CoV-2 exist; however, these are often insensitive and poorly standardized for use as clinical trial endpoints. We report optimization of a culture-based approach evaluating three cell lines, three detection methods, and key culture parameters. We show that Vero-angiotensin-converting enzyme 2-transmembrane serine protease 2 cells in combination with RT-qPCR of culture supernatants from the first passage provides the greatest overall detection of Delta viral replication (22 of 32, 68.8%), being able to identify viable virus in 83.3% (20 of 24) of clinical samples with initial Ct values of <30. Likewise, we demonstrate that RT-qPCR using culture supernatants from the first passage of Vero human signaling lymphocytic activation molecule cells provides the highest overall detection of Omicron viral replication (9 of 31, 29%), detecting live virus in 39.1% (9 of 23) of clinical samples with initial Ct values of <25. This assessment demonstrates that combining RT-qPCR with virological endpoint analysis has utility in clinical trials of therapeutics for SARS-CoV-2; however, techniques may require optimization based on dominant circulating strain.

Importance: RT-qPCR is commonly used for virological endpoints during clinical trials for antiviral therapy to determine the quantity and presence of virus in a sample. However, RT-qPCR identifies viral RNA and cannot determine if viable virus is present. Existing culture-based techniques for SARS-CoV-2 are insensitive and not sufficiently standardized to be employed as clinical study endpoints. The use of a culture system to monitor replicating viruses could mitigate the possibility of molecular techniques identifying viral RNA from inactive or lysed viral particles. The methodology optimized in this study for detecting infectious viruses may have application as a secondary virological endpoint in clinical trials of therapeutics for SARS-CoV-2 in addition to numerous research processes.

Keywords: COVID-19; clinical trials; molecular diagnostics.

Fig 1

Experimental design for the optimization of SARS-CoV-2 culture from clinical samples. Scheme of the initial experiments performed using different parameters before the optimization of detection methods using clinical samples. Samples during the Delta and Omicron outbreak were cultured in Vero E6, VAT, and/or Vero hSLAM for 3 days. Supernatants were collected to perform reverse-transcriptase PCR for Delta and Omicron, respectively, and CPE imaging and plaque assays were performed for Delta only.

Fig 2

Percentage of positive results by cell type and passage (P1, P2, and P3 refer to passages 1, 2, and 3, respectively) by each detection methodology tested using RT-qPCR-positive samples. PCR denotes RT-qPCR; plaque denotes plaque assay. CPE, cytopathic effect.

Fig 3

The percentage of positive results according to the different Ct ranges in viral passage 1 using the three detection methods. (A) CPE, (B) plaque assay, and (C) RT-qPCR. CPE, cytopathic effect.

Fig 4

Positive and negative results by original Ct values using the different detection methods with (A) Delta and (B) Omicron samples, respectively. Each dot or triangle represents an individual sample. Most positive results were identified when samples had Ct values of <25 regardless of the variant used. Plaque denotes plaque assay and PCR denotes RT-qPCR. CPE, cytopathic effect.

Fig 5

Percentage of Omicron-positive results detected by RT-qPCR according to Ct range. Positive results are presented based on the four different Ct categories for each cell line. Solid colored lines show the cumulative percentage values for each category.