A myeloid program associated with COVID-19 severity is decreased by therapeutic blockade of IL-6 signaling

Altered myeloid inflammation and lymphopenia are hallmarks of severe infections, including with SARS-CoV-2. Here, we identified a gene program, defined by correlation with EN-RAGE (S100A12) gene expression, which was up-regulated in airway and blood myeloid cells from COVID-19 patients. The EN-RAGE program was expressed in 7 cohorts and observed in patients with both COVID-19 and acute respiratory distress syndrome (ARDS) from other causes. This program was associated with greater clinical severity and predicted future mechanical ventilation and death. EN-RAGE+ myeloid cells express features consistent with suppressor cell functionality, with low HLA-DR and high PD-L1 surface expression and higher expression of T cell-suppressive genes. Sustained EN-RAGE signature expression in airway and blood myeloid cells correlated with clinical severity and increasing expression of T cell exhaustion markers, such as PD-1. IL-6 treatment of monocytes in vitro upregulated many of the severity-associated genes in the EN-RAGE gene program, along with potential mediators of T cell suppression, such as IL-10. Blockade of IL-6 signaling by tocilizumab in a placebo-controlled clinical trial led to a rapid normalization of ENRAGE and T cell gene expression. This identifies IL-6 as a key driver of myeloid dysregulation associated with worse clinical outcomes in COVID-19 patients and provides insights into shared pathophysiological mechanisms in non-COVID-19 ARDS.

Systematic functional interrogation of SARS-CoV-2 host factors using Perturb-seq

Numerous host factors of SARS-CoV-2 have been identified by screening approaches, but delineating their molecular roles during infection and whether they can be targeted for antiviral intervention remains a challenge. Here we use Perturb-seq, a single-cell CRISPR screening approach, to investigate how CRISPR interference of host factors changes the course of SARS-CoV-2 infection and the host response in human lung epithelial cells. Our data reveal two classes of host factors with pronounced phenotypes: factors required for the response to interferon and factors required for entry or early infection. Among the latter, we have characterized the NF-{kappa}B inhibitor I{kappa}B (NFKBIA), as well as the translation factors EIF4E2 and EIF4H as strong host dependency factors acting early in infection. Overall, our study provides high-throughput functional validation of host factors of SARS-CoV-2 and describes their roles during viral infection in both infected and bystander cells.

Tensor decomposition reveals coordinated multicellular patterns of transcriptional variation that distinguish and stratify disease individuals

Tissue- and organism-level biological processes often involve coordinated action of multiple distinct cell types. Current computational methods for the analysis of single-cell RNA-sequencing (scRNA-seq) data, however, are not designed to capture co-variation of cell states across samples, in part due to the low number of biological samples in most scRNA-seq datasets. Recent advances in sample multiplexing have enabled population-scale scRNA-seq measurements of tens to hundreds of samples. To take advantage of such datasets, here we introduce a computational approach called single-cell Interpretable Tensor Decomposition (scITD). This method extracts "multicellular gene expression patterns" that vary across different biological samples. These patterns capture how changes in one cell type are connected to changes in other cell types. The multicellular patterns can be further associated with known covariates (e.g., disease, treatment, or technical batch effects) and used to stratify heterogeneous samples. We first validated the performance of scITD using in vitro experimental data and simulations. We then applied scITD to scRNA-seq data on peripheral blood mononuclear cells (PBMCs) from 115 patients with systemic lupus erythematosus and 56 healthy controls. We recapitulated a well-established pan-cell-type signature of interferon-signaling that was associated with the presence of anti-dsDNA autoantibodies and a disease activity index. We further identified a novel multicellular pattern that appears to potentiate renal involvement for patients with anti-dsDNA autoantibodies. This pattern was characterized by an expansion of activated memory B cells along with helper T cell activation and is predicted to be mediated by an increase in ICOSLG-ICOS interaction between monocytes and helper T cells. Finally, we applied scITD to two PBMC datasets from patients with COVID-19 and identified reproducible multicellular patterns that stratify patients by disease severity. Overall, scITD is a flexible method for exploring co-variation of cell states in multi-sample single-cell datasets, which can yield new insights into complex non-cell-autonomous dependencies that define and stratify disease.

Improved COVID-19 Serology Test Performance by Integrating Multiple Lateral Flow Assays using Machine Learning

Mitigating transmission of SARS-CoV-2 has been complicated by the inaccessibility and, in some cases, inadequacy of testing options to detect present or past infection. Immunochromatographic lateral flow assays (LFAs) are a cheap and scalable modality for tracking viral transmission by testing for serological immunity, though systematic evaluations have revealed the low performance of some SARS-CoV-2 LFAs. Here, we re-analyzed existing data to present a proof-of-principle machine learning framework that may be used to inform the pairing of LFAs to achieve superior classification performance while enabling tunable False Positive Rates optimized for the estimated seroprevalence of the population being tested.

Test performance evaluation of SARS-CoV-2 serological assays

BackgroundSerological tests are crucial tools for assessments of SARS-CoV-2 exposure, infection and potential immunity. Their appropriate use and interpretation require accurate assay performance data. MethodWe conducted an evaluation of 10 lateral flow assays (LFAs) and two ELISAs to detect anti-SARS-CoV-2 antibodies. The specimen set comprised 128 plasma or serum samples from 79 symptomatic SARS-CoV-2 RT-PCR-positive individuals; 108 pre-COVID-19 negative controls; and 52 recent samples from individuals who underwent respiratory viral testing but were not diagnosed with Coronavirus Disease 2019 (COVID-19). Samples were blinded and LFA results were interpreted by two independent readers, using a standardized intensity scoring system. ResultsAmong specimens from SARS-CoV-2 RT-PCR-positive individuals, the percent seropositive increased with time interval, peaking at 81.8-100.0% in samples taken >20 days after symptom onset. Test specificity ranged from 84.3-100.0% in pre-COVID-19 specimens. Specificity was higher when weak LFA bands were considered negative, but this decreased sensitivity. IgM detection was more variable than IgG, and detection was highest when IgM and IgG results were combined. Agreement between ELISAs and LFAs ranged from 75.7-94.8%. No consistent cross-reactivity was observed. ConclusionOur evaluation showed heterogeneous assay performance. Reader training is key to reliable LFA performance, and can be tailored for survey goals. Informed use of serology will require evaluations covering the full spectrum of SARS-CoV-2 infections, from asymptomatic and mild infection to severe disease, and later convalescence. Well-designed studies to elucidate the mechanisms and serological correlates of protective immunity will be crucial to guide rational clinical and public health policies.