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Respiratory failure is the leading cause of death in patients with severe SARS-CoV-2 infection1,2, but the host response at the lung tissue level is poorly understood. Here we performed single-nucleus RNA sequencing of about 116,000 nuclei from the lungs of nineteen individuals who died of COVID-19 and underwent rapid autopsy and seven control individuals. Integrated analyses identified substantial alterations in cellular composition, transcriptional cell states, and cell-to-cell interactions, thereby providing insight into the biology of lethal COVID-19. The lungs from individuals with COVID-19 were highly inflamed, with dense infiltration of aberrantly activated monocyte-derived macrophages and alveolar macrophages, but had impaired T cell responses. Monocyte/macrophage-derived interleukin-1β and epithelial cell-derived interleukin-6 were unique features of SARS-CoV-2 infection compared to other viral and bacterial causes of pneumonia. Alveolar type 2 cells adopted an inflammation-associated transient progenitor cell state and failed to undergo full transition into alveolar type 1 cells, resulting in impaired lung regeneration. Furthermore, we identified expansion of recently described CTHRC1+ pathological fibroblasts3 contributing to rapidly ensuing pulmonary fibrosis in COVID-19. Inference of protein activity and ligand–receptor interactions identified putative drug targets to disrupt deleterious circuits. This atlas enables the dissection of lethal COVID-19, may inform our understanding of long-term complications of COVID-19 survivors, and provides an important resource for therapeutic development. Lung samples collected soon after death from COVID-19 are used to provide a single-cell atlas of SARS-CoV-2 infection and the ensuing molecular changes.

Immune response dynamics in COVID-19 and its severe manifestations have largely been studied in circulation. Here, we examined the relationship between immune processes in the respiratory tract and circulation through longitudinal phenotypic, transcriptomic and cytokine profiling of paired airway and blood samples from patients with severe COVID-19 relative to heathy controls. In COVID-19 airways, T cells exhibited activated, tissue-resident, and protective profiles; higher T cell frequencies correlated with survival and younger age. Myeloid cells in COVID-19 airways featured hyper-inflammatory signatures and higher frequencies of these cells correlated with mortality and older age. In COVID-19 blood, aberrant CD163+ monocytes predominated over conventional monocytes, and were found in corresponding airway samples and in damaged alveoli. High levels of myeloid chemoattractants in airways suggest recruitment of these cells through a CCL2-CCR2 chemokine axis. Our findings provide insights into immune processes driving COVID-19 lung pathology with therapeutic implications for targeting inflammation in the respiratory tract., Through longitudinal profiling of paired airway and blood from patients with severe COVID-19, Szabo et al. reveal airway immune responses that correlate with age and outcome. They further identify coordinate roles for T and myeloid cells in the respiratory tract and circulation in perpetuating lung pathology and disease pathogenesis.

Purpose: Intratumoral immunosuppression mediated by myeloid-derived suppressor cells (MDSC) and tumor-associated macrophages (TAM) represents a potential mechanism of immune checkpoint inhibitor (ICI) resistance in solid tumors. By promoting TAM and MDSC infiltration, IL1β may drive adaptive and innate immune resistance in renal cell carcinoma (RCC) and in other tumor types. Experimental Design: Using the RENCA model of RCC, we evaluated clinically relevant combinations of anti-IL1β plus either anti-PD-1 or the multitargeted tyrosine kinase inhibitor (TKI), cabozantinib. We performed comprehensive immune profiling of established RENCA tumors via multiparameter flow cytometry, tumor cytokine profiling, and single-cell RNA sequencing (RNA-seq). Similar analyses were extended to the MC38 tumor model. Results: Analyses via multiparameter flow cytometry, tumor cytokine profiling, and single-cell RNA-seq showed that anti-IL1β reduces infiltration of polymorphonuclear MDSCs and TAMs. Combination treatment with anti-IL1β plus anti-PD-1 or cabozantinib showed increased antitumor activity that was associated with decreases in immunosuppressive MDSCs and increases in M1-like TAMs. Conclusions: Single-cell RNA-seq analyses show that IL1β blockade and ICI or TKI remodel the myeloid compartment through nonredundant, relatively T-cell–independent mechanisms. IL1β is an upstream mediator of adaptive myeloid resistance and represents a potential target for kidney cancer immunotherapy.

Large-scale immune monitoring experiments (such as clinical trials) are a promising direction for biomarker discovery and responder stratification in immunotherapy. Mass cytometry is one of the tools in the immune monitoring arsenal. We propose a standardized workflow for the acquisition and analysis of large-scale mass cytometry experiments. The workflow includes two-tiered barcoding, a broad lyophilized panel, and the incorporation of a fully automated, cloud-based analysis platform. We applied the workflow to a large antibody staining screen using the LEGENDScreen kit, resulting in single-cell data for 350 antibodies over 71 profiling subsets. The screen recapitulates many known trends in the immune system and reveals potential markers for delineating MAIT cells. Additionally, we examine the effect of fixation on staining intensity and identify several markers where fixation leads to either gain or loss of signal. The standardized workflow can be seamlessly integrated into existing trials. Finally, the antibody staining data set is available as an online resource for researchers who are designing mass cytometry experiments in suspension and tissue.

High dimensional cytometry now allows measurement of over 50 parameters in a single sample, and is typically visualized using sophisticated dimensionality-reducing methods and analyzed with automated clustering algorithms. While these tools facilitate the identification and presentation of key findings, it remains challenging to effectively monitor and report the staining quality of individual markers. We present the Average Overlap Frequency (AOF), a simple and efficient metric to evaluate and quantify the robustness of staining and clustering quality in high-dimensional data. We leverage the AOF to compare and determine the optimal storage conditions for stained whole blood samples prior to mass cytometry analysis. We also show that the AOF can be easily incorporated as part of automated analysis pipelines in large scale immune monitoring studies and used to flag and exclude samples with poor staining quality. We propose that the AOF may be incorporated as an essential quality control metric to better identify and report the underlying sample quality in all CyTOF and other high-dimensional cytometry experiments.