Classification of "Near-patient" and "Point-of-Care" SARS-CoV-2 Nucleic Acid Amplification Test Systems and a first approach to evaluate their analytical independence of operator activities.

BACKGROUND: European legislation defines as "near-patient testing" (NPT) what is popularly and in other legislations specified as "point-of-care testing" (POCT). Systems intended for NPT/POCT use must be characterized by independence from operator activities during the analytic procedure. However, tools for evaluating this are lacking. We hypothesized that the variability of measurement results obtained from identical samples with a larger number of identical devices by different operators, expressed as the method-specific reproducibility of measurement results reported in External Quality Assessment (EQA) schemes, is an indicator for this characteristic. MATERIALS AND METHODS: Legal frameworks in the EU, the USA and Australia were evaluated about their requirements for NPT/POCT. EQA reproducibility of seven SARS-CoV-2-NAAT systems, all but one designated as "POCT", was calculated from variabilities in Ct values obtained from the respective device types in three different EQA schemes for virus genome detection. RESULTS: A matrix for characterizing test systems based on their technical complexity and the required operator competence was derived from requirements of the European In Vitro Diagnostic Regulation (IVDR) 2017/746. Good EQA reproducibility of the measurement results of the test systems investigated implies that different users in different locations have no recognizable influence on their measurement results. CONCLUSION: The fundamental suitability of test systems for NPT/POCT use according to IVDR can be easily verified using the evaluation matrix presented. EQA reproducibility is a specific characteristic indicating independence from operator activities of NPT/POCT assays. EQA reproducibility of other systems than those investigated here remains to be determined.

Strategy and Performance Evaluation of Low-Frequency Variant Calling for SARS-CoV-2 Using Targeted Deep Illumina Sequencing.

The ongoing COVID-19 pandemic, caused by SARS-CoV-2, constitutes a tremendous global health issue. Continuous monitoring of the virus has become a cornerstone to make rational decisions on implementing societal and sanitary measures to curtail the virus spread. Additionally, emerging SARS-CoV-2 variants have increased the need for genomic surveillance to detect particular strains because of their potentially increased transmissibility, pathogenicity and immune escape. Targeted SARS-CoV-2 sequencing of diagnostic and wastewater samples has been explored as an epidemiological surveillance method for the competent authorities. Currently, only the consensus genome sequence of the most abundant strain is taken into consideration for analysis, but multiple variant strains are now circulating in the population. Consequently, in diagnostic samples, potential co-infection(s) by several different variants can occur or quasispecies can develop during an infection in an individual. In wastewater samples, multiple variant strains will often be simultaneously present. Currently, quality criteria are mainly available for constructing the consensus genome sequence, and some guidelines exist for the detection of co-infections and quasispecies in diagnostic samples. The performance of detection and quantification of low-frequency variants using whole genome sequencing (WGS) of SARS-CoV-2 remains largely unknown. Here, we evaluated the detection and quantification of mutations present at low abundances using the mutations defining the SARS-CoV-2 lineage B.1.1.7 (alpha variant) as a case study. Real sequencing data were in silico modified by introducing mutations of interest into raw wild-type sequencing data, or by mixing wild-type and mutant raw sequencing data, to construct mixed samples subjected to WGS using a tiling amplicon-based targeted metagenomics approach and Illumina sequencing. As anticipated, higher variation and lower sensitivity were observed at lower coverages and allelic frequencies. We found that detection of all low-frequency variants at an abundance of 10, 5, 3, and 1%, requires at least a sequencing coverage of 250, 500, 1500, and 10,000×, respectively. Although increasing variability of estimated allelic frequencies at decreasing coverages and lower allelic frequencies was observed, its impact on reliable quantification was limited. This study provides a highly sensitive low-frequency variant detection approach, which is publicly available at https://galaxy.sciensano.be, and specific recommendations for minimum sequencing coverages to detect clade-defining mutations at certain allelic frequencies. This approach will be useful to detect and quantify low-frequency variants in both diagnostic (e.g., co-infections and quasispecies) and wastewater [e.g., multiple variants of concern (VOCs)] samples.