Although critical for host defense, innate immune cells are also pathologic drivers of acute respiratory distress syndrome (ARDS). Innate immune dynamics during Coronavirus Disease 2019 (COVID-19) ARDS, compared to ARDS from other respiratory pathogens, is unclear. Moreover, mechanisms underlying the beneficial effects of dexamethasone during severe COVID-19 remain elusive. Using single-cell RNA sequencing and plasma proteomics, we discovered that, compared to bacterial ARDS, COVID-19 was associated with expansion of distinct neutrophil states characterized by interferon (IFN) and prostaglandin signaling. Dexamethasone during severe COVID-19 affected circulating neutrophils, altered IFNactive neutrophils, downregulated interferon-stimulated genes and activated IL-1R2+ neutrophils. Dexamethasone also expanded immunosuppressive immature neutrophils and remodeled cellular interactions by changing neutrophils from information receivers into information providers. Male patients had higher proportions of IFNactive neutrophils and preferential steroid-induced immature neutrophil expansion, potentially affecting outcomes. Our single-cell atlas (see ‘Data availability’ section) defines COVID-19-enriched neutrophil states and molecular mechanisms of dexamethasone action to develop targeted immunotherapies for severe COVID-19.

25Resolving the COVID-19 pandemic requires diagnostic testing to determine which individuals 26 are infected and which are not. The current gold standard is to perform RT-PCR on 27 nasopharyngeal samples. Best-in-class assays demonstrate a limit of detection (LoD) of 100 28 copies of viral RNA per milliliter of transport media. However, LoDs of currently approved 29 assays vary over 10,000-fold. Assays with higher LoDs will miss more infected patients, 30 resulting in more false negatives. However, the false-negative rate for a given LoD remains 31 unknown. Here we address this question using over 27,500 test results for patients from across 32 our healthcare network tested using the Abbott RealTime SARS-CoV-2 EUA. These results 33suggest that each 10-fold increase in LoD is expected to increase the false negative rate by 34 13%, missing an additional one in eight infected patients. The highest LoDs on the market will 35 miss a majority of infected patients, with false negative rates as high as 70%. These results 36 suggest that choice of assay has meaningful clinical and epidemiological consequences. The 37 limit of detection matters. 38 39 102 Efficiency was measured from plots of fluorescence intensity vs. cycle number for 50 positive 103 samples chosen at random, yielding an expression for viral load in copies/mL as a function of Ct 104 (Eq. 6, Supplementary Methods). Per this expression, the expected negative cutoff corresponds 105 to 9.2 copies per mL or ~2 virions per RT-PCR reaction volume (0.5mL), supporting the validity 106 of our parameter estimation.107 We used Python (v3.6) and its NumPy, SciPy, Matplotlib, and Pandas libraries to plot linear 108 regression and Theil-Sen slopes with 95% confidence intervals on repeat positives; a 109 normalized cumulative distribution (histogram) of positive results (with reversed x-axis for ease 110 of interpretation); binned histogram by 0.5 log10 units, and linear regression on log10-111 transformed data. 112 Results 113 Of the 27,098 tests performed on 20,076 patients over the testing period, 6,037 tests were 114 positive (22%), representing 4,774 unique patients. Analysis of repeats within 6 or 12 hours of 115 each other (7) demonstrated high repeatability of Ct values over these short time windows (R 2 116 0.70 and 0.63, n=25 and 51, respectively), supporting the validity of this quantitative measure as 117 a basis for assessment of viral load in patients (Fig. 1). We used basic principles of PCR and 118 detailed measurements of PCR efficiency on 50 randomly chosen positive samples to convert 119from Ct values to viral load, in units of copies of viral RNA per mL of viral transport medium. In 120 order to study the patient population upon presentation without confounding by repeat 121 measurements on the same patients, the remainder of the analysis was on the first positive 122 value for the above 4,774 unique patients.

Highlights d Hair follicle (HF) dermal stem cells make minor contributions to skin and HF neogenesis d Extrafollicular Hic1 + progenitors regenerate injured dermis and populate neogenic HFs d Distinct transcriptional and epigenetic changes mediate fibroblast heterogeneity d Runx1, retinoic acid, and Hic1 control mesenchymal regenerative competence

Background Resolving the coronavirus disease 2019 (COVID-19) pandemic requires diagnostic testing to determine which individuals are infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The current gold standard is to perform reverse-transcription polymerase chain reaction (PCR) on nasopharyngeal samples. Best-in-class assays demonstrate a limit of detection (LoD) of approximately 100 copies of viral RNA per milliliter of transport media. However, LoDs of currently approved assays vary over 10,000-fold. Assays with higher LoDs will miss infected patients. However, the relative clinical sensitivity of these assays remains unknown. Methods Here we model the clinical sensitivities of assays based on their LoD. Cycle threshold (Ct) values were obtained from 4700 first-time positive patients using the Abbott RealTime SARS-CoV-2 Emergency Use Authorization test. We derived viral loads from Ct based on PCR principles and empiric analysis. A sliding scale relationship for predicting clinical sensitivity was developed from analysis of viral load distribution relative to assay LoD. Results Ct values were reliably repeatable over short time testing windows, providing support for use as a tool to estimate viral load. Viral load was found to be relatively evenly distributed across log10 bins of incremental viral load. Based on these data, each 10-fold increase in LoD is expected to lower assay sensitivity by approximately 13%. Conclusions The assay LoD meaningfully impacts clinical performance of SARS-CoV-2 tests. The highest LoDs on the market will miss a majority of infected patients. Assays should therefore be benchmarked against a universal standard to allow cross-comparison of SARS-CoV-2 detection methods.