Single-cell transcriptomics reveal a unique memory-like NK cell subset that accumulates with ageing and correlates with disease severity in COVID-19.

BACKGROUND: Natural killer (NK) cells are innate lymphoid cells that mediate antitumour and antiviral responses. However, very little is known about how ageing influences human NK cells, especially at the single-cell level. METHODS: We applied single-cell sequencing (scRNA-seq) to human lymphocytes and NK cells from 4 young and 4 elderly individuals and then analysed the transcriptome data using Seurat. We detected the proportion and phenotype of NK cell subsets in peripheral blood samples from a total of 62 young and 52 elderly healthy donors by flow cytometry. We also used flow cytometry to examine the effector functions of NK cell subsets upon IFN-α/IL-12+IL-15/K562/IL-2 stimulation in vitro in peripheral blood samples from a total of 64 young and 63 elderly healthy donors. We finally studied and integrated single-cell transcriptomes of NK cells from 15 young and 41 elderly COVID-19 patients with those from 12 young and 6 elderly healthy control individuals to investigate the impacts of ageing on NK cell subsets in COVID-19 disease. RESULTS: We discovered a memory-like NK subpopulation (NK2) exhibiting the largest distribution change between elderly and young individuals among lymphocytes. Notably, we discovered a unique NK subset that was predominantly CD52+ NK2 cells (NK2.1). These memory-like NK2.1 cells accumulated with age, exhibited proinflammatory characteristics, and displayed a type I interferon response state. Integrative analyses of a large-cohort COVID-19 dataset and our datasets revealed that NK2.1 cells from elderly COVID-19 patients are enriched for type I interferon signalling, which is positively correlated with disease severity in COVID-19. CONCLUSIONS: We identified a unique memory-like NK cell subset that accumulates with ageing and correlates with disease severity in COVID-19. Our results identify memory-like NK2.1 cells as a potential target for developing immunotherapies for infectious diseases and for addressing age-related dysfunctions of the immune system.

Single-cell analysis of COVID-19, sepsis, and HIV infection reveals hyperinflammatory and immunosuppressive signatures in monocytes.

The mortality risk of coronavirus disease 2019 (COVID-19) patients has been linked to the cytokine storm caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Understanding the inflammatory responses shared between COVID-19 and other infectious diseases that feature cytokine storms may therefore help in developing improved therapeutic strategies. Here, we use integrative analysis of single-cell transcriptomes to characterize the inflammatory signatures of peripheral blood mononuclear cells from patients with COVID-19, sepsis, and HIV infection. We identify ten hyperinflammatory cell subtypes in which monocytes are the main contributors to the transcriptional differences in these infections. Monocytes from COVID-19 patients share hyperinflammatory signatures with HIV infection and immunosuppressive signatures with sepsis. Finally, we construct a "three-stage" model of heterogeneity among COVID-19 patients, related to the hyperinflammatory and immunosuppressive signatures in monocytes. Our study thus reveals cellular and molecular insights about inflammatory responses to SARS-CoV-2 infection and provides therapeutic guidance to improve treatments for subsets of COVID-19 patients.

Profiling of the immune repertoire in COVID-19 patients with mild, severe, convalescent, or retesting-positive status.

Forty-seven samples of peripheral blood mononuclear cells from four groups of coronavirus disease (COVID)-19 patients (mild, severe, convalescent, retesting-positive) and healthy controls were applied to profile the immune repertoire of COVID-19 patients in acute infection or convalescence by transcriptome sequencing and immune-receptor repertoire (IRR) sequencing. Transcriptome analyses showed that genes within principal component group 1 (PC1) were associated with infection and disease severity whereas genes within PC2 were associated with recovery from COVID-19. A "dual-injury mechanism" of COVID-19 severity was related to an increased number of proinflammatory pathways and activated hypercoagulable pathways. A machine-learning model based on the genes associated with inflammatory and hypercoagulable pathways had the potential to be employed to monitor COVID-19 severity. Signature analyses of B-cell receptors (BCRs) and T-cell receptors (TCRs) revealed the dominant selection of longer V-J pairs (e.g., IGHV3-9-IGHJ6 and IGHV3-23-IGHJ6) and continuous tyrosine motifs in BCRs and lower diversity of TCRs. These findings provide potential predictors for COVID-19 outcomes, and new potential targets for COVID-19 treatment.

Single-cell analysis of two severe COVID-19 patients reveals a monocyte-associated and tocilizumab-responding cytokine storm.

Several studies show that the immunosuppressive drugs targeting the interleukin-6 (IL-6) receptor, including tocilizumab, ameliorate lethal inflammatory responses in COVID-19 patients infected with SARS-CoV-2. Here, by employing single-cell analysis of the immune cell composition of two severe-stage COVID-19 patients prior to and following tocilizumab-induced remission, we identify a monocyte subpopulation that contributes to the inflammatory cytokine storms. Furthermore, although tocilizumab treatment attenuates the inflammation, immune cells, including plasma B cells and CD8+ T cells, still exhibit robust humoral and cellular antiviral immune responses. Thus, in addition to providing a high-dimensional dataset on the immune cell distribution at multiple stages of the COVID-19, our work also provides insights into the therapeutic effects of tocilizumab, and identifies potential target cell populations for treating COVID-19-related cytokine storms.