Dataset Information

Multicenter study evaluating novel multi-specimen pooling assay for the detection of SARS-CoV-2: High sensitivity and high throughput testing.

ABSTRACT:

Background/purpose

Mass screening for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is important to prevent the spread of coronavirus disease 2019 (COVID-19). Pooling samples can increase the number of tests processed. LabTurbo AIO 48 is an automated platform that allows ribonucleic acid extraction and sample analysis on the same instrument. We created a novel pooling assay on this platform for SARS-CoV-2 detection and demonstrated that the pooling strategy increases testing capacity without affecting accuracy and sensitivity.

Methods

Comparative limit of detection (LoD) assessment was performed on the LabTurbo AIO 48 platform and the current standard detection system based on real-time reverse transcription polymerase chain reaction (rRT-PCR) using 55 clinically positive samples. An additional 330 primary clinical samples were assessed.

Results

Six samples pooled into one reaction tube were detected in approximately 2.5 h using the World Health Organization rRT-PCR protocol. LabTurbo AIO 48 also demonstrated a higher throughput than our reference rRT-PCR assay, with an LoD of 1000 copies/mL. The overall percentage agreement between the methods for the 330 samples was 100%.

Conclusion

We created a novel multi-specimen pooling assay using LabTurbo AIO 48 for the robust detection of SARS-CoV-2, allowing high-throughput results; this assay will aid in better control and prevention of COVID-19. The diagnostic assay was cost-effective and time-efficient; thus, the pooling strategy is a practical and effective method for diagnosing large quantities of specimens without compromising precision.

SUBMITTER: Chung HY

PROVIDER: S-EPMC8411585 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Data from a high-throughput human adrenocortical carcinoma assay (HT-H295R) for steroid hormone biosynthesis are available for >2000 chemicals in single concentration and 654 chemicals in multi-concentration (mc). Previously, a metric describing the effect size of a chemical on the biosynthesis of 11 hormones was derived using mc data referred to as the maximum mean Mahalanobis distance (maxmMd). However, mc HT-H295R assay data remain unavailable for many chemicals. This work leverages existing HT-H295R assay data by constructing structure-activity relationships to make predictions for data-poor chemicals, including: (1) identification of individual structural descriptors, known as ToxPrint chemotypes, associated with increased odds of affecting estrogen or androgen synthesis; (2) a random forest (RF) classifier using physicochemical property descriptors to predict HT-H295R maxmMd binary (positive or negative) outcomes; and, (3) a local approach to predict maxmMd binary outcomes using nearest neighbors (NNs) based on two types of chemical fingerprints (chemotype or Morgan). Individual chemotypes demonstrated high specificity (85-98%) for modulators of estrogen and androgen synthesis but with low sensitivity. The best RF model for maxmMd classification included 13 predicted physicochemical descriptors, yielding a balanced accuracy (BA) of 71% with only modest improvement when hundreds of structural features were added. The best two NN models for binary maxmMd prediction demonstrated BAs of 85 and 81% using chemotype and Morgan fingerprints, respectively. Using an external test set of 6302 chemicals (lacking HT-H295R data), 1241 were identified as putative estrogen and androgen modulators. Combined results across the three classification models (global RF model and two local NN models) predict that 1033 of the 6302 chemicals would be more likely to affect HT-H295R bioactivity. Together, these in silico approaches can efficiently prioritize thousands of untested chemicals for screening to further evaluate their effects on steroid biosynthesis.