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 tocilizumabinduced 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 highdimensional 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.

Maintaining homeostasis of the decidual immune microenvironment at the maternal–fetal interface is essential for placentation and reproductive success. Although distinct decidual immune cell subpopulations have been identified under normal conditions, systematic understanding of the spectrum and heterogeneity of leukocytes under recurrent miscarriage in human deciduas remains unclear. To address this, we profiled the respective transcriptomes of 18,646 primary human decidual immune cells isolated from patients with recurrent pregnancy loss (RPL) and healthy controls at single-cell resolution. We discovered dramatic differential distributions of immune cell subsets in RPL patients compared with the normal decidual immune microenvironment. Furthermore, we found a subset of decidual natural killer (NK) cells that support embryo growth were diminished in proportion due to abnormal NK cell development in RPL patients. We also elucidated the altered cellular interactions between the decidual immune cell subsets in the microenvironment and those of the immune cells with stromal cells and extravillous trophoblast under disease state. These results provided deeper insights into the RPL decidual immune microenvironment disorder that are potentially applicable to improve the diagnosis and therapeutics of this disease.

1 inflammatory storm incited by monocyte centric immune interactions 2 revealed by single-cell analysis 3 4 ABSTRACT 40 41Coronavirus disease 2019 has caused more than 40,000 deaths 42 worldwide 1 . Approximately 14% of patients with COVID-19 experienced severe 43 disease and 5% were critically ill 2 . Studies have shown that dysregulation of the 44 COVID-19 patients' immune system may lead to inflammatory storm and cause severe 45 illness and even death 3,4 . Tocilizumab treatment targeting interleukin 6 receptor has 46shown inspiring clinical results of severe COVID-19 patients 5 . However, the immune 47 network with Tocilizumab treatment at single cell resolution has not been uncovered. 48Here, we profiled the single-cell transcriptomes of 13,289 peripheral blood 49 mononuclear cells isolated at three longitudinal stages from two severe COVID-19 50 patients treated with Tocilizumab. We identified a severe stage-specific monocyte 51 subpopulation and these cells centric immune cell interaction network connected by the 52 inflammatory cytokines and their receptors. The over-activated inflammatory immune 53 response was attenuated after Tocilizumab treatment, yet immune cells including 54 plasma B cells and CD8 + T cells still exhibited an intense humoral and cell-mediated 55 anti-virus immune response in recovered COVID-19 patients. These results provided 56 critical insights into the immunopathogenesis of severe COVID-19 and revealed 57 fundamentals of effectiveness in Tocilizumab treatment. 58 59 60 61Keywords ： Coronavirus disease 2019 (COVID-19); Severe acute respiratory 62 syndrome coronavirus 2 (SARS-CoV-2); Tocilizumab; Single-cell RNA sequencing 63(scRNA-seq); Inflammatory storm; Monocyte 64 65 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

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The development of sequencing technologies has promoted the survey of genomewide chromatin accessibility at single-cell resolution. However, comprehensive analysis of single-cell epigenomic profiles remains a challenge. Here, we introduce an accessibility pattern-based epigenomic clustering (APEC) method, which classifies each cell by groups of accessible regions with synergistic signal patterns termed "accessons". This python-based package greatly improves the accuracy of unsupervised single-cell clustering for many public datasets. It also predicts gene expression, identifies enriched motifs, discovers super-enhancers, and projects pseudotime trajectories. APEC is available at https://github.com/QuKunLab/APEC.