Robust Saliva-Based RNA Extraction-Free One-Step Nucleic Acid Amplification Test for Mass SARS-CoV-2 Monitoring.

Early diagnosis with rapid detection of the virus plays a key role in preventing the spread of infection and in treating patients effectively. In order to address the need for a straightforward detection of SARS-CoV-2 infection and assessment of viral spread, we developed rapid, sensitive, extraction-free one-step reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and reverse transcription loop-mediated isothermal amplification (RT-LAMP) tests for detecting SARS-CoV-2 in saliva. We analyzed over 700 matched pairs of saliva and nasopharyngeal swab (NSB) specimens from asymptomatic and symptomatic individuals. Saliva, as either an oral cavity swab or passive drool, was collected in an RNA stabilization buffer. The stabilized saliva specimens were heat-treated and directly analyzed without RNA extraction. The diagnostic sensitivity of saliva-based RT-qPCR was at least 95% in individuals with subclinical infection and outperformed RT-LAMP, which had at least 70% sensitivity when compared to NSBs analyzed with a clinical RT-qPCR test. The diagnostic sensitivity for passive drool saliva was higher than that of oral cavity swab specimens (95% and 87%, respectively). A rapid, sensitive one-step extraction-free RT-qPCR test for detecting SARS-CoV-2 in passive drool saliva is operationally simple and can be easily implemented using existing testing sites, thus allowing high-throughput, rapid, and repeated testing of large populations. Furthermore, saliva testing is adequate to detect individuals in an asymptomatic screening program and can help improve voluntary screening compliance for those individuals averse to various forms of nasal collections.

Digital PCR as an effective tool for GMO quantification in complex matrices.

The increased use of genetically modified organisms (GMOs) is accompanied by increased complexity of the matrices that contain GMOs. The most common DNA-based approach for GMO detection and quantification is real-time quantitative polymerase chain reaction (qPCR). However, as qPCR is sensitive to inhibitors and relies on standard curves for quantification, it has limited application in GMO quantification for complex matrices. To overcome this hurdle in DNA quantification, we present droplet digital PCR (ddPCR) assays that were designed to target 'Roundup Ready' soybean and the soybean reference gene. Three ddPCR assays were transferred from qPCR to QX100/QX200 ddPCR platforms and characterised. Together, the fitness-for-purpose study on four real-life samples and the use of a chamber-based PCR system, showed that dPCR has great potential to improve such measurements in GMO testing and monitoring of food authenticity.

Inter-laboratory analysis of selected genetically modified plant reference materials with digital PCR.

Digital PCR (dPCR), as a new technology in the field of genetically modified (GM) organism (GMO) testing, enables determination of absolute target copy numbers. The purpose of our study was to test the transferability of methods designed for quantitative PCR (qPCR) to dPCR and to carry out an inter-laboratory comparison of the performance of two different dPCR platforms when determining the absolute GM copy numbers and GM copy number ratio in reference materials certified for GM content in mass fraction. Overall results in terms of measured GM% were within acceptable variation limits for both tested dPCR systems. However, the determined absolute copy numbers for individual genes or events showed higher variability between laboratories in one third of the cases, most possibly due to variability in the technical work, droplet size variability, and analysis of the raw data. GMO quantification with dPCR and qPCR was comparable. As methods originally designed for qPCR performed well in dPCR systems, already validated qPCR assays can most generally be used for dPCR technology with the purpose of GMO detection. Graphical abstract The output of three different PCR-based platforms was assessed in an inter-laboratory comparison.

An international comparability study on quantification of mRNA gene expression ratios: CCQM-P103.1.

Measurement of RNA can be used to study and monitor a range of infectious and non-communicable diseases, with profiling of multiple gene expression mRNA transcripts being increasingly applied to cancer stratification and prognosis. An international comparison study (Consultative Committee for Amount of Substance (CCQM)-P103.1) was performed in order to evaluate the comparability of measurements of RNA copy number ratio for multiple gene targets between two samples. Six exogenous synthetic targets comprising of External RNA Control Consortium (ERCC) standards were measured alongside transcripts for three endogenous gene targets present in the background of human cell line RNA. The study was carried out under the auspices of the Nucleic Acids (formerly Bioanalysis) Working Group of the CCQM. It was coordinated by LGC (United Kingdom) with the support of National Institute of Standards and Technology (USA) and results were submitted from thirteen National Metrology Institutes and Designated Institutes. The majority of laboratories performed RNA measurements using RT-qPCR, with datasets also being submitted by two laboratories based on reverse transcription digital polymerase chain reaction and one laboratory using a next-generation sequencing method. In RT-qPCR analysis, the RNA copy number ratios between the two samples were quantified using either a standard curve or a relative quantification approach. In general, good agreement was observed between the reported results of ERCC RNA copy number ratio measurements. Measurements of the RNA copy number ratios for endogenous genes between the two samples were also consistent between the majority of laboratories. Some differences in the reported values and confidence intervals ('measurement uncertainties') were noted which may be attributable to choice of measurement method or quantification approach. This highlights the need for standardised practices for the calculation of fold change ratios and uncertainties in the area of gene expression profiling.