Post-infectious inflammatory disease in MIS-C features elevated cytotoxicity signatures and autoreactivity that correlates with severity

Multisystem inflammatory syndrome in children (MIS-C) is a life-threatening post-infectious complication occurring unpredictably weeks after mild or asymptomatic SARS-CoV2 infection in otherwise healthy children. Here, we define immune abnormalities in MIS-C compared to adult COVID-19 and pediatric/adult healthy controls using single-cell RNA sequencing, antigen receptor repertoire analysis, unbiased serum proteomics, and in vitro assays. Despite no evidence of active infection, we uncover elevated S100A-family alarmins in myeloid cells and marked enrichment of serum proteins that map to myeloid cells and pathways including cytokines, complement/coagulation, and fluid shear stress in MIS-C patients. Moreover, NK and CD8 T cell cytotoxicity genes are elevated, and plasmablasts harboring IgG1 and IgG3 are expanded. Consistently, we detect elevated binding of serum IgG from severe MIS-C patients to activated human cardiac microvascular endothelial cells in culture. Thus, we define immunopathology features of MIS-C with implications for predicting and managing this SARS-CoV2-induced critical illness in children.

Single-Cell Omics Reveals Dyssynchrony of the Innate and Adaptive Immune System in Progressive COVID-19

A dysregulated immune response against the SARS-CoV-2 virus plays a critical role in severe COVID-19. However, the molecular and cellular mechanisms by which the virus causes lethal immunopathology are poorly understood. Here, we utilize multiomics single-cell analysis to probe dynamic immune responses in patients with stable or progressive manifestations of COVID-19, and assess the effects of tocilizumab, an anti-IL-6 receptor monoclonal antibody. Coordinated profiling of gene expression and cell lineage protein markers reveals a prominent type-1 interferon response across all immune cells, especially in progressive patients. An anti-inflammatory innate immune response and a pre-exhaustion phenotype in activated T cells are hallmarks of progressive disease. Skewed T cell receptor repertoires in CD8+ T cells and uniquely enriched V(D)J sequences are also identified in COVID-19 patients. B cell repertoire and somatic hypermutation analysis are consistent with a primary immune response, with possible contribution from memory B cells. Our in-depth immune profiling reveals dyssynchrony of the innate and adaptive immune interaction in progressive COVID-19, which may contribute to delayed virus clearance and has implications for therapeutic intervention.

Single-cell longitudinal analysis of SARS-CoV-2 infection in human bronchial epithelial cells

SARS-CoV-2, the causative agent of COVID-19, has tragically burdened individuals and institutions around the world. There are currently no approved drugs or vaccines for the treatment or prevention of COVID-19. Enhanced understanding of SARS-CoV-2 infection and pathogenesis is critical for the development of therapeutics. To reveal insight into viral replication, cell tropism, and host-viral interactions of SARS-CoV-2 we performed single-cell RNA sequencing of experimentally infected human bronchial epithelial cells (HBECs) in air-liquid interface cultures over a time-course. This revealed novel polyadenylated viral transcripts and highlighted ciliated cells as a major target of infection, which we confirmed by electron microscopy. Over the course of infection, cell tropism of SARS-CoV-2 expands to other epithelial cell types including basal and club cells. Infection induces cell-intrinsic expression of type I and type III IFNs and IL6 but not IL1. This results in expression of interferon-stimulated genes in both infected and bystander cells. We observe similar gene expression changes from a COVID-19 patient ex vivo. In addition, we developed a new computational method termed CONditional DENSity Embedding (CONDENSE) to characterize and compare temporal gene dynamics in response to infection, which revealed genes relating to endothelin, angiogenesis, interferon, and inflammation-causing signaling pathways. In this study, we conducted an in-depth analysis of SARS-CoV-2 infection in HBECs and a COVID-19 patient and revealed genes, cell types, and cell state changes associated with infection.

Development and validation of the COVID-19 severity index (CSI): a prognostic tool for early respiratory decompensation

ObjectiveThe goal of this study was to create a predictive model of early hospital respiratory decompensation among patients with COVID-19. DesignObservational, retrospective cohort study. SettingNine-hospital health system within the Northeastern United States. PopulationsAdult patients ([≥] 18 years) admitted from the emergency department who tested positive for SARS-CoV-2 (COVID-19) up to 24 hours after initial presentation. Patients meeting criteria for respiratory critical illness within 4 hours of arrival were excluded. Main outcome and performance measuresWe used a composite endpoint of critical illness as defined by oxygen requirement (greater than 10 L/min by low-flow device, high-flow device, non-invasive, or invasive ventilation) or death within the first 24 hours of hospitalization. We developed models predicting our composite endpoint using patient demographic and clinical data available within the first four hours of arrival. Eight hospitals (n = 932) were used for model development and one hospital (n = 240) was held out for external validation. Area under receiver operating characteristic (AU-ROC), precision-recall curves (AU-PRC), and calibration metrics were used to compare predictive models to three illness scoring systems: Elixhauser comorbidity index, qSOFA, and CURB-65. ResultsDuring the study period from March 1, 2020 to April 27,2020, 1,792 patients were admitted with COVID-19. Six-hundred and twenty patients were excluded based on age or critical illness within the first 4 hours, yielding 1,172 patients in the final cohort. Of these patients, 144 (12.3%) met the composite endpoint within the first 24 hours. We first developed a bedside quick COVID-19 severity index (qCSI), a twelve-point scale using nasal cannula flow rate, respiratory rate, and minimum documented pulse oximetry. We then created a machine-learning gradient boosting model, the COVID-19 severity index (CSI), using twelve additional variables including inflammatory markers and liver chemistries. Both the qCSI (AU-ROC mean [95% CI]: 0.90 [0.85-0.96]) and CSI (AU-ROC: 0.91 [0.86-0.97]) outperformed the comparator models (qSOFA: 0.76 [0.69-0.85]; Elixhauser: 0.70 [0.62-0.80]; CURB-65: AU-ROC 0.66 [0.58-0.77]) on cross-validation and performed well on external validation (qCSI: 0.82, CSI: 0.76, CURB-65: 0.50, qSOFA: 0.59, Elixhauser: 0.61). We find that a qCSI score of 0-3 is associated with a less than 5% risk of critical respiratory illness, while a score of 9-12 is associated with a 57% risk of progression to critical illness. ConclusionsA significant proportion of admitted COVID-19 patients decompensate within 24 hours of hospital presentation and these events are accurately predicted using bedside respiratory exam findings within a simple scoring system.