Evaluation of the analytical sensitivity of ACON and LumiraDx SARS-CoV-2 rapid antigen tests using samples with presumed Omicron variant.

BACKGROUND: Analytical sensitivity of 2 rapid antigen tests was evaluated for detection of presumed SARS-CoV-2 Omicron variants and earlier variants of concern. METHODS: A total of 152 SARS-CoV-2 RNA positive samples (N and ORF1ab positive but S gene negative) were tested for SARS-CoV-2 antigen by ACON lateral flow and LumiraDx fluorescence immunoassays. Sensitivity within 3 viral load ranges was compared among these 152 samples and 194 similarly characterized samples collected prior to the circulation of the Delta variant (pre-Delta). RESULTS: Antigen was detected in >95% of pre-Delta and presumed Omicron samples for both tests at viral loads >500,000 copies/mL, and 65 to 85% of samples with 50,000-500,000 copies/mL. At viral load <50,000 copies/mL, antigen tests showed better sensitivity in detecting pre-Delta compared to Omicron variants. LumiraDx was more sensitive than ACON at low viral load. CONCLUSIONS: Antigen tests had decreased sensitivity for detecting presumed Omicron compared to pre-Delta variants at low viral load.

Authorized SARS-CoV-2 molecular methods show wide variability in the limit of detection.

On February 29th, 2020, the U.S. Food and Drug Administration issued the first Emergency Use Authorization (EUA) for a SARS-CoV-2 assay outside of the U.S. Centers for Disease Control and Prevention. As of May 3rd, 2021, 289 total EUAs have been granted. Like influenza, there is no standard for defining limit of detection (LoD), but rather guidance that analytical sensitivity/LoD be established as the level that gives a 95% detection rate in at least 20 replicates. Here we compare the performance characteristics of SARS-CoV-2 tests receiving EUA by standardizing sensitivity to a common unit of measure and assess the variability in LoD between tests. Additionally, we looked at factors that may impact sensitivities due to lack of standardization of the test development process and compare results for a standardized reference panel for comparative analysis within a subset of EUA tests offered by the U.S. Food and Drug Administration.

Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study.

BACKGROUND: COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who are more likely to have adverse outcomes. Thus, it is crucial to discover predictive markers of serious complications. METHODS: In this retrospective cohort study, we analysed samples from 455 participants with COVID-19 who had had a positive SARS-CoV-2 RT-PCR result between April 14, 2020, and Dec 1, 2020 and who had visited one of three Mayo Clinic sites in the USA (Minnesota, Arizona, or Florida) in the same period. These participants were assigned to three subgroups depending on disease severity as defined by the WHO ordinal scale of clinical improvement (outpatient, severe, or critical). Our control cohort comprised of 182 anonymised age-matched and sex-matched plasma samples that were available from the Mayo Clinic Biorepository and banked before the COVID-19 pandemic. We did a deep profiling of circulatory cytokines and other proteins, lipids, and metabolites from both cohorts. Most patient samples were collected before, or around the time of, hospital admission, representing ideal samples for predictive biomarker discovery. We used proximity extension assays to quantify cytokines and circulatory proteins and tandem mass spectrometry to measure lipids and metabolites. Biomarker discovery was done by applying an AutoGluon-tabular classifier to a multiomics dataset, producing a stacked ensemble of cutting-edge machine learning algorithms. Global proteomics and glycoproteomics on a subset of patient samples with matched pre-COVID-19 plasma samples was also done. FINDINGS: We quantified 1463 cytokines and circulatory proteins, along with 902 lipids and 1018 metabolites. By developing a machine-learning-based prediction model, a set of 102 biomarkers, which predicted severe and clinical COVID-19 outcomes better than the traditional set of cytokines, were discovered. These predictive biomarkers included several novel cytokines and other proteins, lipids, and metabolites. For example, altered amounts of C-type lectin domain family 6 member A (CLEC6A), ether phosphatidylethanolamine (P-18:1/18:1), and 2-hydroxydecanoate, as reported here, have not previously been associated with severity in COVID-19. Patient samples with matched pre-COVID-19 plasma samples showed similar trends in muti-omics signatures along with differences in glycoproteomics profile. INTERPRETATION: A multiomic molecular signature in the plasma of patients with COVID-19 before being admitted to hospital can be exploited to predict a more severe course of disease. Machine learning approaches can be applied to highly complex and multidimensional profiling data to reveal novel signatures of clinical use. The absence of validation in an independent cohort remains a major limitation of the study. FUNDING: Eric and Wendy Schmidt.

SARS-CoV-2 RNA detection in Formalin-Fixed Paraffin-Embedded (FFPE) tissue by droplet digital PCR (ddPCR).

BACKGROUND: SARS-CoV-2 is an RNA virus that primarily causes respiratory disease; however, infection of other tissue has been reported. Evaluation of SARS-CoV-2 in tissue specimens may increase understanding of SARS-CoV-2 pathobiology. MATERIALS AND METHODS: A qualitative test for detection of SARS-CoV-2 in formalin-fixed paraffin-embedded (FFPE) tissues was developed and validated using droplet digital PCR (ddPCR), which has a lower limit of detection than reverse transcription (RT)-qPCR. After extraction of total RNA from unstained FFPE tissue, SARS-CoV-2 nucleocapsid (N1, N2) target sequences were amplified and quantified, along with human RPP30 as a control using the Bio-Rad SARS-CoV-2 ddPCR kit. RESULTS: SARS-CoV-2 was detected in all 21 known positive samples and none of the 16 negative samples. As few as approximately 5 viral copies were reliably detected. Since January 2021, many tissue types have been clinically tested. Of the 195 clinical specimens, the positivity rate was 35% with placenta and fetal tissue showing the highest percentage of positive cases. CONCLUSION: This sensitive FFPE-based assay has broad clinical utility with applications as diverse as pregnancy loss and evaluation of liver transplant rejection. This assay will aid in understanding atypical presentations of COVID-19 as well as long-term sequelae.

A reverse-transcription droplet digital PCR assay to detect and quantify SARS-CoV-2 RNA in upper respiratory tract specimens.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a positive-sense, single-stranded RNA virus that causes coronavirus disease 2019 (COVID-19). Symptoms are variable and range from asymptomatic or mild to severe (i.e., pneumonia) in both healthy and immunocompromised patients. We developed a reverse-transcription droplet digital PCR (RT-ddPCR) assay for quantification of SARS-CoV-2 RNA in clinical nasopharyngeal and oropharyngeal swab specimens and evaluated the assay, including reproducibility, agreement of results, analytical measurement range, linearity, analytical sensitivity, and analytical specificity. This quantitative assay had a LoD of 218 copies/mL of viral transport media, with a linear quantification range from 500 to 5,000,000 copies/mL (R2 of 0.9817 and 0.9853 for N1 and N2 targets, respectively). Qualitative agreement of categorical results was 90.5% (57/63) between the reference and RT-ddPCR assays. Quantitative agreement between the two assays showed correlation, with R2 of 0.9726 and 0.9713 for N1 and N2 targets, respectively. No cross-reactivity with common coronavirus strains was detected. This SARS-CoV-2 quantitative RT-ddPCR assay may be a useful tool for a variety of applications including identification of patients with low viral load and serial monitoring of viral load in respiratory tracts specimens of patients for evaluation of the efficacy of therapy for COVID-19.

SARS-CoV-2 RNA Detection in Formalin-Fixed Paraffin-Embedded (FFPE) Tissue by Droplet Digital PCR (ddPCR)

Background SARS-CoV-2 is an RNA virus that causes primarily causes respiratory disease;however, infection of other tissue has been reported. Evaluation of SARS-CoV-2 in tissue specimens may increase understanding of SARS-CoV-2 pathobiology. Materials and Methods A qualitative test for detection of SARS-CoV-2 in formalin-fixed paraffin-embedded (FFPE) tissues was developed and validated using droplet digital PCR (ddPCR), which has a lower limit of detection than reverse transcription (RT)-qPCR. After extraction of total RNA from unstained FFPE tissue, SARS-CoV-2 nucleocapsid (N1, N2) target sequences were amplified and quantified, along with human RPP30 as a control using the Bio-Rad SARS-CoV-2 ddPCR kit. Results SARS-CoV-2 was detected in all 21 known positive samples and none of the 16 negative samples. As few as approximately 5 viral copies were reliably detected. Since January 2021, many tissue types have been clinically tested. Of the 195 clinical specimens, the positivity rate was 35% with placenta and fetal tissue showing the highest percentage of positive cases. Conclusion This sensitive FFPE-based assay has broad clinical utility with applications as diverse as pregnancy loss and evaluation of liver transplant rejection. This assay will aid in understanding atypical presentations of COVID-19 as well as long-term sequelae.

Targeted Detection of SARS-CoV-2 Nucleocapsid Sequence Variants by Mass Spectrometric Analysis of Tryptic Peptides.

COVID-19 vaccines are becoming more widely available, but accurate and rapid testing remains a crucial tool for slowing the spread of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus. Although the quantitative reverse transcription-polymerase chain reaction (qRT-PCR) remains the most prevalent testing methodology, numerous tests have been developed that are predicated on detection of the SARS-CoV-2 nucleocapsid protein, including liquid chromatography-tandem mass spectrometry (LC-MS/MS) and immunoassay-based approaches. The continuing emergence of SARS-CoV-2 variants has complicated these approaches, as both qRT-PCR and antigen detection methods can be prone to missing viral variants. In this study, we describe several COVID-19 cases where we were unable to detect the expected peptide targets from clinical nasopharyngeal swabs. Whole genome sequencing revealed that single nucleotide polymorphisms in the gene encoding the viral nucleocapsid protein led to sequence variants that were not monitored in the targeted assay. Minor modifications to the LC-MS/MS method ensured detection of the variants of the target peptide. Additional nucleocapsid variants could be detected by performing the bottom-up proteomic analysis of whole viral genome-sequenced samples. This study demonstrates the importance of considering variants of SARS-CoV-2 in the assay design and highlights the flexibility of mass spectrometry-based approaches to detect variants as they evolve.

A SISCAPA-based approach for detection of SARS-CoV-2 viral antigens from clinical samples.

SARS-CoV-2, a novel human coronavirus, has created a global disease burden infecting > 100 million humans in just over a year. RT-PCR is currently the predominant method of diagnosing this viral infection although a variety of tests to detect viral antigens have also been developed. In this study, we adopted a SISCAPA-based enrichment approach using anti-peptide antibodies generated against peptides from the nucleocapsid protein of SARS-CoV-2. We developed a targeted workflow in which nasopharyngeal swab samples were digested followed by enrichment of viral peptides using the anti-peptide antibodies and targeted parallel reaction monitoring (PRM) analysis using a high-resolution mass spectrometer. This workflow was applied to 41 RT-PCR-confirmed clinical SARS-CoV-2 positive nasopharyngeal swab samples and 30 negative samples. The workflow employed was highly specific as none of the target peptides were detected in negative samples. Further, the detected peptides showed a positive correlation with the viral loads as measured by RT-PCR Ct values. The SISCAPA-based platform described in the current study can serve as an alternative method for SARS-CoV-2 viral detection and can also be applied for detecting other microbial pathogens directly from clinical samples.

A mass spectrometry-based targeted assay for detection of SARS-CoV-2 antigen from clinical specimens.

BACKGROUND: The COVID-19 pandemic caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has overwhelmed health systems worldwide and highlighted limitations of diagnostic testing. Several types of diagnostic tests including RT-PCR-based assays and antigen detection by lateral flow assays, each with their own strengths and weaknesses, have been developed and deployed in a short time. METHODS: Here, we describe an immunoaffinity purification approach followed a by high resolution mass spectrometry-based targeted qualitative assay capable of detecting SARS-CoV-2 viral antigen from nasopharyngeal swab samples. Based on our discovery experiments using purified virus, recombinant viral protein and nasopharyngeal swab samples from COVID-19 positive patients, nucleocapsid protein was selected as a target antigen. We then developed an automated antibody capture-based workflow coupled to targeted high-field asymmetric waveform ion mobility spectrometry (FAIMS) - parallel reaction monitoring (PRM) assay on an Orbitrap Exploris 480 mass spectrometer. An ensemble machine learning-based model for determining COVID-19 positive samples was developed using fragment ion intensities from the PRM data. FINDINGS: The optimized targeted assay, which was used to analyze 88 positive and 88 negative nasopharyngeal swab samples for validation, resulted in 98% (95% CI = 0.922-0.997) (86/88) sensitivity and 100% (95% CI = 0.958-1.000) (88/88) specificity using RT-PCR-based molecular testing as the reference method. INTERPRETATION: Our results demonstrate that direct detection of infectious agents from clinical samples by tandem mass spectrometry-based assays have potential to be deployed as diagnostic assays in clinical laboratories, which has hitherto been limited to analysis of pure microbial cultures. FUNDING: This study was supported by DBT/Wellcome Trust India Alliance Margdarshi Fellowship grant IA/M/15/1/502023 awarded to AP and the generosity of Eric and Wendy Schmidt.

Comparison of In Situ Hybridization, Immunohistochemistry, and Reverse Transcription-Droplet Digital Polymerase Chain Reaction for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Testing in Tissue.

CONTEXT.­: Small case series have evaluated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection in formalin-fixed, paraffin-embedded tissue using reverse transcription-polymerase chain reaction, immunohistochemistry (IHC), and/or RNA in situ hybridization (RNAish). OBJECTIVE.­: To compare droplet digital polymerase chain reaction, IHC, and RNAish to detect SARS-CoV-2 in formalin-fixed, paraffin-embedded tissue in a large series of lung specimens from coronavirus disease 2019 (COVID-19) patients. DESIGN.­: Droplet digital polymerase chain reaction and RNAish used commercially available probes; IHC used clone 1A9. Twenty-six autopsies of COVID-19 patients with formalin-fixed, paraffin-embedded tissue blocks of 62 lung specimens, 22 heart specimens, 2 brain specimens, and 1 liver, and 1 umbilical cord were included. Control cases included 9 autopsy lungs from patients with other infections/inflammation and virus-infected tissue or cell lines. RESULTS.­: Droplet digital polymerase chain reaction had the highest sensitivity for SARS-CoV-2 (96%) when compared with IHC (31%) and RNAish (36%). All 3 tests had a specificity of 100%. Agreement between droplet digital polymerase chain reaction and IHC or RNAish was fair (κ = 0.23 and κ = 0.35, respectively). Agreement between IHC and in situ hybridization was substantial (κ = 0.75). Interobserver reliability was almost perfect for IHC (κ = 0.91) and fair to moderate for RNAish (κ = 0.38-0.59). Lung tissues from patients who died earlier after onset of symptoms revealed higher copy numbers by droplet digital polymerase chain reaction (P = .03, Pearson correlation = -0.65) and were more likely to be positive by RNAish (P = .02) than lungs from patients who died later. We identified SARS-CoV-2 in hyaline membranes, in pneumocytes, and rarely in respiratory epithelium. Droplet digital polymerase chain reaction showed low copy numbers in 7 autopsy hearts from ProteoGenex Inc. All other extrapulmonary tissues were negative. CONCLUSIONS.­: Droplet digital polymerase chain reaction was the most sensitive and highly specific test to identify SARS-CoV-2 in lung specimens from COVID-19 patients.

COVID-19-Associated Nonocclusive Fibrin Microthrombi in the Heart.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its resultant clinical presentation, coronavirus disease 2019 (COVID-19), is an emergent cause of mortality worldwide. Cardiac complications secondary to this infection are common; however, the underlying mechanisms of such remain unclear. A detailed cardiac evaluation of a series of individuals with COVID-19 undergoing postmortem evaluation is provided, with 4 aims: (1) describe the pathological spectrum of the myocardium; (2) compare with an alternate viral illness; (3) investigate angiotensin-converting enzyme 2 expression; and (4) provide the first description of the cardiac findings in patients with cleared infection. METHODS: Study cases were identified from institutional files and included COVID-19 (n=15: 12 active, 3 cleared), influenza A/B (n=6), and nonvirally mediated deaths (n=6). Salient information was abstracted from the medical record. Light microscopic findings were recorded. An angiotensin-converting enzyme 2 immunohistochemical H-score was compared across cases. Viral detection encompassed SARS-CoV-2 immunohistochemistry, ultrastructural examination, and droplet digital polymerase chain reaction. RESULTS: Male sex was more common in the COVID-19 group (P=0.05). Nonocclusive fibrin microthrombi (without ischemic injury) were identified in 16 cases (12 COVID-19, 2 influenza, and 2 controls) and were more common in the active COVID-19 cohort (P=0.006). Four active COVID-19 cases showed focal myocarditis, whereas 1 case of cleared COVID-19 showed extensive disease. Arteriolar angiotensin-converting enzyme 2 endothelial expression was lower in COVID-19 cases than in controls (P=0.004). Angiotensin-converting enzyme 2 myocardial expression did not differ by disease category, sex, age, or number of patient comorbidities (P=0.69, P=1.00, P=0.46, P=0.65, respectively). SARS-CoV-2 immunohistochemistry showed nonspecific staining, whereas ultrastructural examination and droplet digital polymerase chain reaction were negative for viral presence. Four patients (26.7%) with COVID-19 had underlying cardiac amyloidosis. Cases with cleared infection had variable presentations. CONCLUSIONS: This detailed histopathologic, immunohistochemical, ultrastructural, and molecular cardiac series showed no definitive evidence of direct myocardial infection. COVID-19 cases frequently have cardiac fibrin microthrombi, without universal acute ischemic injury. Moreover, myocarditis is present in 33.3% of patients with active and cleared COVID-19 but is usually limited in extent. Histological features of resolved infection are variable. Cardiac amyloidosis may be an additional risk factor for severe disease.