Our understanding of protective versus pathological immune responses to SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), is limited by inadequate profiling of patients at the extremes of the disease severity spectrum. Here, we performed multi-omic single-cell immune profiling of 64 COVID-19 patients across the full range of disease severity, from outpatients with mild disease to fatal cases. Our transcriptomic, epigenomic, and proteomic analyses revealed widespread dysfunction of peripheral innate immunity in severe and fatal COVID-19, including prominent hyperactivation signatures in neutrophils and NK cells. We also identified chromatin accessibility changes at NF-κB binding sites within cytokine gene loci as a potential mechanism for the striking lack of pro-inflammatory cytokine production observed in monocytes in severe and fatal COVID-19. We further demonstrated that emergency myelopoiesis is a prominent feature of fatal COVID-19. Collectively, our results reveal disease severity–associated immune phenotypes in COVID-19 and identify pathogenesis-associated pathways that are potential targets for therapeutic intervention.

Background The determinants of COVID-19 disease severity and extrapulmonary complications (EPCs) are poorly understood. We characterized relationships between SARS-CoV-2 RNAemia and disease severity, clinical deterioration, and specific EPCs. Methods We used quantitative (qPCR) and digital (dPCR) PCR to quantify SARS-CoV-2 RNA from plasma in 191 patients presenting to the Emergency Department (ED) with COVID-19. We recorded patient symptoms, laboratory markers, and clinical outcomes, with a focus on oxygen requirements over time. We collected longitudinal plasma samples from a subset of patients. We characterized the role of RNAemia in predicting clinical severity and EPCs using elastic net regression. Results 23.0% (44/191) of SARS-CoV-2 positive patients had viral RNA detected in plasma by dPCR, compared to 1.4% (2/147) by qPCR. Most patients with serial measurements had undetectable RNAemia within 10 days of symptom onset, reached maximum clinical severity within 16 days, and symptom resolution within 33 days. Initially RNAaemic patients were more likely to manifest severe disease (OR 6.72 [95% CI, 2.45 – 19.79]), worsening of disease severity (OR 2.43 [95% CI, 1.07 – 5.38]), and EPCs (OR 2.81 [95% CI, 1.26 – 6.36]). RNA load correlated with maximum severity (r = 0.47 [95% CI, 0.20 – 0.67]). Conclusions dPCR is more sensitive than qPCR for the detection of SARS-CoV-2 RNAemia, which is a robust predictor of eventual COVID-19 severity and oxygen requirements, as well as EPCs. Since many COVID-19 therapies are initiated on the basis of oxygen requirements, RNAemia on presentation might serve to direct early initiation of appropriate therapies for the patients most likely to deteriorate.

BackgroundThe determinants of COVID-19 disease severity and extrapulmonary complications (EPCs) are poorly understood. We characterise the relationships between SARS-CoV-2 RNAaemia and disease severity, clinical deterioration, and specific EPCs.MethodsWe used quantitative (qPCR) and digital (dPCR) PCR to quantify SARS-CoV-2 RNA from nasopharyngeal swabs and plasma in 191 patients presenting to the Emergency Department (ED) with COVID-19. We recorded patient symptoms, laboratory markers, and clinical outcomes, with a focus on oxygen requirements over time. We collected longitudinal plasma samples from a subset of patients. We characterised the role of RNAaemia in predicting clinical severity and EPCs using elastic net regression.Findings23·0% (44/191) of SARS-CoV-2 positive patients had viral RNA detected in plasma by dPCR, compared to 1·4% (2/147) by qPCR. Most patients with serial measurements had undetectable RNAaemia 10 days after onset of symptoms, but took 16 days to reach maximum severity, and 33 days for symptoms to resolve. Initially RNAaemic patients were more likely to manifest severe disease (OR 6·72 [95% CI, 2·45 – 19·79]), worsening of disease severity (OR 2·43 [95% CI, 1·07 - 5·38]), and EPCs (OR 2·81 [95% CI, 1·26 – 6·36]). RNA load correlated with maximum severity (r = 0·47 [95% CI, 0·20 - 0·67]).InterpretationdPCR is more sensitive than qPCR for the detection of SARS-CoV-2 RNAaemia, which is a robust predictor of eventual COVID-19 severity and oxygen requirements, as well as EPCs. Since many COVID-19 therapies are initiated on the basis of oxygen requirements, RNAaemia on presentation might serve to direct early initiation of appropriate therapies for the patients most likely to deteriorate.FundingNIH/NIAID (Grants R01A153133, R01AI137272, and 3U19AI057229 – 17W1 COVID SUPP #2) and a donation from Eva Grove.Research in contextEvidence before this studyThe varied clinical manifestations of COVID-19 have directed attention to the distribution of SARS-CoV-2 in the body. Although most concentrated and tested for in the nasopharynx, SARS-CoV-2 RNA has been found in blood, stool, and numerous tissues, raising questions about dissemination of viral RNA throughout the body, and the role of this process in disease severity and extrapulmonary complications. Recent studies have detected low levels of SARS-CoV-2 RNA in blood using either quantitative reverse transcriptase real-time PCR (qPCR) or droplet digital PCR (dPCR), and have associated RNAaemia with disease severity and biomarkers of dysregulated immune response.Added value of this studyWe quantified SARS-CoV-2 RNA in the nasopharynx and plasma of patients presenting to the Emergency Department with COVID-19, and found an array-based dPCR platform to be markedly more sensitive than qPCR for detection of SARS-CoV-2 RNA, with a simplified workflow well-suited to clinical adoption. We collected serial plasma samples during patients’ course of illness, and showed that SARS-CoV-2 RNAaemia peaks early, while clinical condition often continues to worsen. Our findings confirm the association between RNAaemia and disease severity, and additionally demonstrate a role for RNAaemia in predicting future deterioration and specific extrapulmonary complications.Implications of all the available evidenceVariation in SARS-CoV-2 RNAaemia may help explain disparities in disease severity and extrapulmonary complications from COVID-19. Testing for RNAaemia with dPCR early in the course of illness may help guide patient triage and management.

Purpose Determine the efficacy of a 45-amino acid Gp2 domain, engineered to bind to epidermal growth factor receptor (EGFR), as a positron emission tomography (PET) probe of EGFR in a xenograft mouse model. Methods The EGFR-targeted Gp2 (Gp2-EGFR) and a non-binding control were site-specifically labeled with 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) chelator. Binding affinity was tested towards human EGFR and mouse EGFR. Biological activity on downstream EGFR signaling was examined in cell culture. DOTA-Gp2 molecules were labeled with 64Cu and intravenously injected (0.6–2.3 MBq) into mice bearing EGFRhigh (n=7) and EGFRlow (n=4) xenografted tumors. PET/computed tomography (CT) images were acquired at 45 min, 2 h, and 24 h. Dynamic PET (25 min) was also acquired. Tomography results were verified with gamma counting of resected tissues. Two-tailed t tests with unequal variances provided statistical comparison. Results DOTA-Gp2-EGFR bound strongly to human (KD = 7 ± 5 nM) and murine (KD = 29 ± 6 nM) EGFR, and non-targeted Gp2 had no detectable binding. Gp2-EGFR did not agonize EGFR nor antagonize EGF-EGFR. 64Cu-Gp2-EGFR tracer effectively localized to EGFRhigh tumors at 45 minutes (3.2 ± 0.5 %ID/g). High specificity was observed with significantly lower uptake in EGFRlow tumors (0.9 ± 0.3 %ID/g, p < 0.001), high tumor-to-background ratios (11 ± 6 tumor:muscle, p < 0.001). Non-targeted Gp2 tracer had low uptake in EGFRhigh tumors (0.5 ± 0.3 %ID/g, p < 0.001). Similar data was observed at 2 h and tumor signal was retained at 24 h (2.9 ± 0.3 %ID/g). Conclusion An engineered Gp2 PET imaging probe exhibited low background and target-specific EGFRhigh tumor uptake at 45 min, with tumor signal retained at 24 h post-injection, and compared favorably with published EGFR PET probes for alternative protein scaffolds. These beneficial in vivo characteristics, combined with thermal stability, efficient evolution, and small size of the Gp2 domain validate its use as a future class of molecular imaging agents.

ObjectiveClinicians in the emergency department (ED) face challenges in concurrently assessing patients with suspected COVID-19 infection, detecting bacterial co-infection, and determining illness severity since current practices require separate workflows. Here we explore the accuracy of the IMX-BVN-3/IMX-SEV-3 29 mRNA host response classifiers in simultaneously detecting SARS-CoV-2 infection, bacterial co-infections, and predicting clinical severity of COVID-19.Methods161 patients with PCR-confirmed COVID-19 (52.2% female, median age 50.0 years, 51% hospitalized, 5.6% deaths) were enrolled at the Stanford Hospital ED. RNA was extracted (2.5 mL whole blood in PAXgene Blood RNA) and 29 host mRNAs in response to the infection were quantified using Nanostring nCounter.ResultsThe IMX-BVN-3 classifier identified SARS-CoV-2 infection in 151 patients with a sensitivity of 93.8%. Six of 10 patients undetected by the classifier had positive COVID tests more than 9 days prior to enrolment and the remaining oscillated between positive and negative results in subsequent tests. The classifier also predicted that 6 (3.7%) patients had a bacterial co-infection. Clinical adjudication confirmed that 5/6 (83.3%) of the patients had bacterial infections, i.e. Clostridioides difficile colitis (n=1), urinary tract infection (n=1), and clinically diagnosed bacterial infections (n=3) for a specificity of 99.4%. 2/101 (2.8%) patients in the IMX-SEV-3 Low and 7/60 (11.7%) in the Moderate severity classifications died within thirty days of enrollment.ConclusionsIMX-BVN-3/IMX-SEV-3 classifiers accurately identified patients with COVID-19, bacterial co-infections, and predicted patients’ risk of death. A point-of-care version of these classifiers, under development, could improve ED patient management including more accurate treatment decisions and optimized resource utilization.