Rapid and Accurate Identification of SARS-CoV-2 Omicron Variants Using Droplet Digital PCR (RT-ddPCR)

Background Mutations in the receptor binding domain of the SARS-CoV-2 Spike protein are associated with increased transmission or substantial reductions in vaccine efficacy, including in the recently described Omicron variant. The changing frequencies of these mutations combined with their differing susceptibility to available therapies have posed significant problems for clinicians and public health professionals. Objective To develop an assay capable of rapidly and accurately identifying variants including Omicron in clinical specimens to enable case tracking and/or selection of appropriate clinical treatment. Study Design Using three duplex RT-ddPCR reactions targeting four amino acids, we tested 419 positive clinical specimens from February to December 2021 during a period of rapidly shifting variant prevalences and compared genotyping results to genome sequences for each sample, determining the sensitivity and specificity of the assay for each variant. Results Mutation determinations for 99.7% of detected samples agree with NGS data for those samples, and are accurate despite wide variation in RNA concentration and potential confounding factors like transport medium, presence of additional respiratory viruses, and additional mutations in primer and probe sequences. The assay accurately identified the first 15 Omicron variants in our laboratory including the first Omicron in Washington State and discriminated against S-gene dropout Delta specimen. Conclusion We describe an accurate, precise, and specific RT-ddPCR assay for variant detection that remains robust despite being designed prior the emergence of Delta and Omicron variants. The assay can quickly identify mutations in current and past SARS-CoV-2 variants, and can be adapted to future mutations.

Purine Auxotrophic Starvation Evokes Phenotype Similar to Stationary Phase Cells in Budding Yeast

Purine auxotrophy is an abundant trait among eukaryotic parasites and a typical marker for many budding yeast strains. Supplementation with an additional purine source (such as adenine) is necessary to cultivate these strains. If not supplied in adequate amounts, purine starvation sets in. We explored purine starvation effects in a model organism, a budding yeast Saccharomyces cerevisiae ade8 knockout, at the level of cellular morphology, central carbon metabolism, and global transcriptome. We observed that purine-starved cells stopped their cycle in G1/G0 state and accumulated trehalose, and the intracellular concentration of AXP decreased, but adenylate charge remained stable. Cells became tolerant to severe environmental stresses. Intracellular RNA concentration decreased, and massive downregulation of ribosomal biosynthesis genes occurred. We proved that the expression of new proteins during purine starvation is critical for cells to attain stress tolerance phenotype Msn2/4p targets are upregulated in purine-starved cells when compared to cells cultivated in purine-rich media. The overall transcriptomic response to purine starvation resembles that of stationary phase cells. Our results demonstrate that the induction of a strong stress resistance phenotype in budding yeast can be caused not only by natural starvation, but also starvation for metabolic intermediates, such as purines.

DropletQC: improved identification of empty droplets and damaged cells in single-cell RNA-seq data

Background Advances in droplet-based single-cell RNA-sequencing (scRNA-seq) have dramatically increased throughput, allowing tens of thousands of cells to be routinely sequenced in a single experiment. In addition to cells, droplets capture cell-free “ambient” RNA predominantly caused by lysis of cells during sample preparation. Samples with high ambient RNA concentration can create challenges in accurately distinguishing cell-containing droplets and droplets containing ambient RNA. Current methods to separate these groups often retain a significant number of droplets that do not contain cells or empty droplets. Additionally, there are currently no methods available to detect droplets containing damaged cells, which comprise partially lysed cells, the original source of the ambient RNA. Results Here, we describe DropletQC, a new method that is able to detect empty droplets, damaged, and intact cells, and accurately distinguish them from one another. This approach is based on a novel quality control metric, the nuclear fraction, which quantifies for each droplet the fraction of RNA originating from unspliced, nuclear pre-mRNA. We demonstrate how DropletQC provides a powerful extension to existing computational methods for identifying empty droplets such as EmptyDrops. Conclusions We implement DropletQC as an R package, which can be easily integrated into existing single-cell analysis workflows.

Reliability of Wastewater Analysis for Monitoring COVID-19 Incidence Revealed by a Long-Term Follow-Up Study

Wastewater-based epidemiology has been used for monitoring human activities and waterborne pathogens. Although wastewaters can also be used for tracking SARS-CoV-2 at the population level, the reliability of this approach remains to be established, especially for early warning of outbreaks. We collected 377 samples from different treatment plants processing wastewaters of >1 million inhabitants in Valencia, Spain, between April 2020 and March 2021. Samples were cleaned, concentrated, and subjected to RT-qPCR to determine SARS-CoV-2 concentrations. These data were compared with cumulative disease notification rates over 7 and 14 day periods. We amplified SARS-CoV-2 RNA in 75% of the RT-qPCRs, with an estimated detection limit of 100 viral genome copies per liter (gc/L). SARS-CoV-2 RNA concentration correlated strongly with disease notification rates over 14-day periods (Pearson r = 0.962, P < 0.001). A concentration >1000 gc/L showed >95% sensitivity and specificity as an indicator of more than 25 new cases per 100,000 inhabitants. Albeit with slightly higher uncertainty, these figures were reproduced using a 7-day period. Time series were similar for wastewaters data and declared cases, but wastewater RNA concentrations exhibited transient peaks that were not observed in declared cases and preceded major outbreaks by several weeks. In conclusion, wastewater analysis provides a reliable tool for monitoring COVID-19, particularly at low incidence values, and is not biased by asymptomatic cases. Moreover, this approach might reveal previously unrecognized features of COVID-19 transmission.

A Portable Device for Simple Exosome Separation from Biological Samples

Exosomes are membrane-bound nanovesicles secreted by most types of cells, which contain a series of biologically important molecules, such as miRNAs, proteins, and lipids, etc. Emerging evidence show that exosomes can affect the physiological status of cells and are involved in various pathological processes. However, due to their small size and density close to body fluids, it is challenging to separate exosomes from a small volume of biological samples in a simple manner. Herein, we propose a new strategy for isolating circulating exosomes from biological samples in a portable device. This method synergistically integrates chitosan electrostatic-adsorption, scaffold substrates, and shuttle flow to enable the highly effective capture of circulating exosomes with a recovery rate of over 80% within 20 minutes, which is much better than the performance of traditional ultracentrifugation (5–25%, 3 h). Besides, the isolated exosomes from samples could be lysed in situ and further subjected to RNA concentration detection and protein analysis. In particular, all the necessary procedures for exosome separation could be integrated into a single device without the need for bulky equipment. This established device is portable and easy to operate, which provides a promising platform for the study of exosome biology and clinical diagnosis.

Daily monitoring of viral load measured as SARS-CoV-2 antigen and RNA in blood, IL-6, CRP and complement C3d predicts outcome in patients hospitalized with COVID-19

Objectives We hypothesized that the amount of antigen produced in the body during a COVID-19 infection might differ between patients, and that maximum concentrations would predict the degree of both inflammation and outcome for patients. Methods Eighty-four hospitalized and SARS-CoV-2 PCR swab-positive patients, were followed with blood sampling every day until discharge or death. A total of 444 serial EDTA plasma samples were analyzed for a range of biomarkers: SARS-CoV-2 nuclear antigen and RNA concentration, complement activation as well as several inflammatory markers, and KL-6 as a lung marker. The patients were divided into outcome groups depending on need of respiratory support and death/survival. Results Circulating SARS-CoV-2 nuclear antigen levels were above the detection limit in blood in 65 out of 84 COVID-19 PCR swab-positive patients on day one of hospitalization, as was viral RNA in plasma in 30 out of 84. In all patients, complete antigen clearance was observed within 24 days. There were definite statistically significant differences between the groups depending on their biomarkers, showing that the concentrations of virus RNA and antigen were correlated to the inflammatory biomarker levels, respiratory treatment and death. Conclusions Viral antigen is cleared in parallel with the virus RNA levels. The levels of antigens and SARS-CoV-2 RNA in the blood correlates with the level of IL-6, inflammation, respiratory failure and death. We propose that the antigens levels together with RNA in blood can be used to predict the severity of disease, outcome, and the clearance of the virus from the body.

DropletQC: improved identification of empty droplets and damaged cells in single-cell RNA-seq data

Advances in droplet-based single cell RNA-sequencing (scRNA-seq) have dramatically increased throughput, allowing tens of thousands of cells to be routinely sequenced in a single experiment. In addition to cells, droplets capture cell-free 'ambient' RNA predominately caused by lysis of cells during sample preparation. Samples with high ambient RNA concentration can create challenges in accurately distinguishing cell containing droplets and droplets containing ambient RNA. Current methods to separate these groups often retain a significant number of droplets that do not contain cells, so called empty droplets. Additional to the challenge of identifying empty drops, there are currently no methods available to detect droplets containing damaged cells, which comprise of partially lysed cells, the original source of the ambient RNA. Here we describe DropletQC, a new method that is able to detect empty droplets, damaged, and intact cells, and accurately distinguish from one another. This approach is based on a novel quality control metric, the nuclear fraction, which quantifies for each droplet the fraction of RNA originating from unspliced, nuclear pre-mRNA. We demonstrate how DropletQC provides a powerful extension to existing computational methods for identifying empty droplets such as EmptyDrops. We have implemented DropletQC as an R package, which can be easily integrated into existing single cell analysis workflows.

Norovirus Persistence in Oysters to Prolonged Commercial Purification

Depuration is generally the main treatment employed for bivalve mollusks harvested from contaminated sites. Commercial depuration has demonstrated to be effective for removal of bacterial pathogens, although it probably provides only limited efficacy against human enteric viruses. We evaluated the quantitative reduction of norovirus (NoV) genogroups I and II in naturally contaminated oysters after 1, 4, and 9 days of depuration. The process was conducted in an authorized depuration plant, and NoV concentration was determined by RT-qPCR according to ISO 15216-1:2017 method. Regardless of the NoV genogroup, our results showed no significant reduction in NoV concentration after 1 day of depuration. Higher mean reduction (68%) was obtained after 4 days of treatment, while no further increase was observed after 9 days. Overall, reduction was highly variable, and none of the trials showed statistically significant reduction in NoV RNA concentration at the end of each depuration period. Indeed, NoV concentration remained high in 70% of samples even after 9 days of depuration, with values ranging between 4.0 × 102 and 2.3 × 104 g.c./g. These results indicate that an extension of commercial depuration time does not appear to be effective for reducing or eliminating NoV in oysters.