Pericytes may facilitate SARS-CoV-2 entry into the nervous system

Objective: Although the neurological and olfactory symptoms of coronavirus disease 2019 have been identified, the neurotropic properties of the causative virus, severe acute respiratory syndrome-associated coronavirus 2 (SARS-CoV-2), remain unknown. We sought to identify the susceptible cell types and potential routes of SARS-CoV-2 entry into the central nervous system, olfactory system, and respiratory system. Method(s): We collected single-cell RNA data from normal brain and nasal epithelium specimens, along with bronchial, tracheal, and lung specimens in public datasets. The susceptible cell types that express SARS-CoV-2 entry genes were identified using single-cell RNA sequencing and the expression of the key genes at protein levels was verified by immunohistochemistry. We compared the coexpression patterns of the entry receptor angiotensin-converting enzyme 2 (ACE2) and the spike protein priming enzyme transmembrane serine protease (TMPRSS)/cathepsin L among the specimens. Result(s): The SARS-CoV-2 entry receptor ACE2 and the spike protein priming enzyme TMPRSS/cathepsin L were coexpressed by pericytes in brain tissue;this coexpression was confirmed by immunohistochemistry. In the nasal epithelium, ciliated cells and sustentacular cells exhibited strong coexpression of ACE2 and TMPRSS. Neurons and glia in the brain and nasal epithelium did not exhibit coexpression of ACE2 and TMPRSS. However, coexpression was present in ciliated cells, vascular smooth muscle cells, and fibroblasts in tracheal tissue;ciliated cells and goblet cells in bronchial tissue;and alveolar epithelium type 1 cells, AT2 cells, and ciliated cells in lung tissue. Conclusion(s): Neurological symptoms in patients with coronavirus disease 2019 could be associated with SARS-CoV-2 invasion across the blood-brain barrier via pericytes. Additionally, SARS-CoV-2-induced olfactory disorders could be the result of localized cell damage in the nasal epithelium.Copyright © Wolters Kluwer Health, Inc. All rights reserved.

Application of single-cell RNA sequencing on human testicular samples: a comprehensive review.

So far there has been no comprehensive review using systematic literature search strategies to show the application of single-cell RNA sequencing (scRNA-seq) in the human testis of the whole life cycle (from embryos to aging males). Here, we summarized the application of scRNA-seq analyses on various human testicular biological samples. A systematic search was conducted in PubMed and Gene Expression Omnibus (GEO), focusing on English researches published after 2009. Articles related to GEO data-series were also retrieved in PubMed or BioRxiv. 81 full-length studies were finally included in the review. ScRNA-seq has been widely used on different human testicular samples with various library strategies, and new cell subtypes such as State 0 spermatogonial stem cells (SSC) and stage_a/b/c Sertoli cells (SC) were identified. For the development of normal testes, scRNA-seq-based evidence showed dynamic transcriptional changes of both germ cells and somatic cells from embryos to adults. And dysregulated metabolic signaling or hedgehog signaling were revealed by scRNA-seq in aged SC or Leydig cells (LC), respectively. For infertile males, scRNA-seq studies revealed profound changes of testes, such as the increased proportion of immature SC/LC of Klinefelter syndrome, the somatic immaturity and altered germline autophagy of patients with non-obstructive azoospermia, and the repressed differentiation of SSC in trans-females receiving testosterone inhibition therapy. Besides, the re-analyzing of public scRNA-seq data made further discoveries such as the potential vulnerability of testicular SARS-CoV-2 infection, and both evolutionary conservatism and divergence among species. ScRNA-seq analyses would unveil mechanisms of testes' development and changes so as to help developing novel treatments for male infertility.

Single cell meta-analysis of EndMT and EMT state in COVID-19.

COVID-19 prognoses suggests that a proportion of patients develop fibrosis, but there is no evidence to indicate whether patients have progression of mesenchymal transition (MT) in the lungs. The role of MT during the COVID-19 pandemic remains poorly understood. Using single-cell RNA sequencing, we profiled the transcriptomes of cells from the lungs of healthy individuals (n = 45), COVID-19 patients (n = 58), and idiopathic pulmonary fibrosis (IPF) patients (n = 64) human lungs to map the entire MT change. This analysis enabled us to map all high-resolution matrix-producing cells and identify distinct subpopulations of endothelial cells (ECs) and epithelial cells as the primary cellular sources of MT clusters during COVID-19. For the first time, we have identied early and late subgroups of endothelial mesenchymal transition (EndMT) and epithelial-mesenchymal transition (EMT) using analysis of public databases for single-cell sequencing. We assessed epithelial subgroups by age, smoking status, and gender, and the data suggest that the proportional changes in EMT in COVID-19 are statistically significant. Further enumeration of early and late EMT suggests a correlation between invasive genes and COVID-19. Finally, EndMT is upregulated in COVID-19 patients and enriched for more inflammatory cytokines. Further, by classifying EndMT as early or late stages, we found that early EndMT was positively correlated with entry factors but this was not true for late EndMT. Exploring the MT state of may help to mitigate the fibrosis impact of SARS-CoV-2 infection.

Single-cell sequencing of PBMC characterizes the altered transcriptomic landscape of classical monocytes in BNT162b2-induced myocarditis.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been the most dangerous threat to public health worldwide for the last few years, which led to the development of the novel mRNA vaccine (BNT162b2). However, BNT162b2 vaccination is known to be associated with myocarditis. Here, as an attempt to determine the pathogenesis of the disease and to develop biomarkers to determine whether subjects likely proceed to myocarditis after vaccination, we conducted a time series analysis of peripheral blood mononuclear cells of a patient with BNT162b2-induced myocarditis. Single-cell RNA sequence analysis identified monocytes as the cell clusters with the most dynamic changes. To identify distinct gene expression signatures, we compared monocytes of BNT162b2-induced myocarditis with monocytes under various conditions, including SARS-CoV-2 infection, BNT162b2 vaccination, and Kawasaki disease, a disease similar to myocarditis. Representative changes in the transcriptomic profile of classical monocytes include the upregulation of genes related to fatty acid metabolism and downregulation of transcription factor AP-1 activity. This study provides, for the first time, the importance of classical monocytes in the pathogenesis of myocarditis following BNT162b2 vaccination and presents the possibility that vaccination affects monocytes, further inducing their differentiation and infiltration into the heart.

Single cell sequencing of neutrophils demonstrates phenotypic heterogeneity and functional plasticity in health, disease, and cancer.

A vital constituent of innate immunity, neutrophils had previously been considered functionally rigid with a fixed, defined role in host pathogen response, in part due to their fleeting lifespan. However, that consensus opinion has changed with evidence of functional neutrophil plasticity in a range of diseases including cancer. Typically difficult to sequence due to their low level of transcriptomic activity, advances in single cell RNA sequencing has allowed for closer examination of the neutrophil transcriptome in humans and mouse models and their interaction with other immune system constituents, both in health and disease, allowing for description of neutrophil phenotypes beyond previous descriptions reliant upon microscopic appearance, surface marker expression, and function. Transcriptomic analysis shows that neutrophils develop and mature along a fixed trajectory, but their transcriptome varies based on maturity, the insult that has provoked release from the bone marrow, and the tissue to which they are recruited. Thus neutrophil heterogeneity increases with maturity, with immature neutrophils being more transcriptomically rigid. Here, we review work done in neutrophil single cell RNA sequencing in mice and humans in health and a range of disease states including coronavirus disease 2019 (COVID-19) infection, and solid cancers to provide a template for understanding neutrophil biology in context.

Single-cell RNA sequencing analysis of a COVID-19-associated maculopapular rash in a patient with psoriasis treated with ustekinumab.

Coronavirus disease 2019 (COVID-19) primarily affects the respiratory system but extrapulmonary manifestations, including the skin, have been well documented. However, transcriptomic profiles of skin lesions have not been performed thus far. Here, we present a single-cell RNA sequencing analysis in a patient with COVID-19 infection with a maculopapular skin rash while on treatment with the interleukin (IL)-12/IL-23 blocker ustekinumab for his underlying psoriasis. Results were compared with healthy controls and untreated psoriasis lesions. We found the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral entry receptors ACE2 and TMPRSS2 in keratinocytes of the patient with COVID-19, while ACE2 expression was low to undetectable in psoriasis lesions and healthy skin. Among all cell types, ACE2+ keratinocyte clusters showed the highest levels of transcriptomic dysregulation in COVID-19, expressing type 1-associated immune markers such as CXCL9 and CXCL10. In line with a generally type 1-skewed immune microenvironment, cytotoxic lymphocytes showed increased expression of the IFNG gene and other T-cell effector genes, while type 2, type 17, or type 22 T-cell activation was largely absent. Conversely, downregulation of several anti-inflammatory mediators was observed. This first transcriptomic description of a COVID-19-associated rash identifies ACE2+ keratinocytes displaying profound transcriptional changes, and inflammatory immune cells that might help to improve the understanding of SARS-CoV-2-associated skin conditions.

A single-cell atlas reveals shared and distinct immune responses and metabolic profiles in SARS-CoV-2 and HIV-1 infections.

Introduction: Within the inflammatory immune response to viral infection, the distribution and cell type-specific profiles of immune cell populations and the immune-mediated viral clearance pathways vary according to the specific virus. Uncovering the immunological similarities and differences between viral infections is critical to understanding disease progression and developing effective vaccines and therapies. Insight into COVID-19 disease progression has been bolstered by the integration of single-cell (sc)RNA-seq data from COVID-19 patients with data from related viruses to compare immune responses. Expanding this concept, we propose that a high-resolution, systematic comparison between immune cells from SARS-CoV-2 infection and an inflammatory infectious disease with a different pathophysiology will provide a more comprehensive picture of the viral clearance pathways that underscore immunological and clinical differences between infections. Methods: Using a novel consensus single-cell annotation method, we integrate previously published scRNA-seq data from 111,566 single PBMCs from 7 COVID-19, 10 HIV-1+, and 3 healthy patients into a unified cellular atlas. We compare in detail the phenotypic features and regulatory pathways in the major immune cell clusters. Results: While immune cells in both COVID-19 and HIV-1+ cohorts show shared inflammation and disrupted mitochondrial function, COVID-19 patients exhibit stronger humoral immunity, broader IFN-I signaling, elevated Rho GTPase and mTOR pathway activity, and downregulated mitophagy. Discussion: Our results indicate that differential IFN-I signaling regulates the distinct immune responses in the two diseases, revealing insight into fundamental disease biology and potential therapeutic candidates.

Characterizing the cellular and molecular variabilities of peripheral immune cells in healthy recipients of BBIBP-CorV inactivated SARS-CoV-2 vaccine by single-cell RNA sequencing.

Over 3 billion doses of inactivated vaccines for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been administered globally. However, our understanding of the immune cell functional transcription and T cell receptor (TCR)/B cell receptor (BCR) repertoire dynamics following inactivated SARS-CoV-2 vaccination remains poorly understood. Here, we performed single-cell RNA and TCR/BCR sequencing on peripheral blood mononuclear cells at four time points after immunization with the inactivated SARS-CoV-2 vaccine BBIBP-CorV. Our analysis revealed an enrichment of monocytes, central memory CD4+ T cells, type 2 helper T cells and memory B cells following vaccination. Single-cell TCR-seq and RNA-seq comminating analysis identified a clonal expansion of CD4+ T cells (but not CD8+ T cells) following a booster vaccination that corresponded to a decrease in the TCR diversity of central memory CD4+ T cells and type 2 helper T cells. Importantly, these TCR repertoire changes and CD4+ T cell differentiation were correlated with the biased VJ gene usage of BCR and the antibody-producing function of B cells post-vaccination. Finally, we compared the functional transcription and repertoire dynamics in immune cells elicited by vaccination and SARS-CoV-2 infection to explore the immune responses under different stimuli. Our data provide novel molecular and cellular evidence for the CD4+ T cell-dependent antibody response induced by inactivated vaccine BBIBP-CorV. This information is urgently needed to develop new prevention and control strategies for SARS-CoV-2 infection. (ClinicalTrials.gov Identifier: NCT04871932).

Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review.

Single-cell RNA sequencing (scRNA-seq) has become a routinely used technique to quantify the gene expression profile of thousands of single cells simultaneously. Analysis of scRNA-seq data plays an important role in the study of cell states and phenotypes, and has helped elucidate biological processes, such as those occurring during the development of complex organisms, and improved our understanding of disease states, such as cancer, diabetes, and coronavirus disease 2019 (COVID-19). Deep learning, a recent advance of artificial intelligence that has been used to address many problems involving large datasets, has also emerged as a promising tool for scRNA-seq data analysis, as it has a capacity to extract informative and compact features from noisy, heterogeneous, and high-dimensional scRNA-seq data to improve downstream analysis. The present review aims at surveying recently developed deep learning techniques in scRNA-seq data analysis, identifying key steps within the scRNA-seq data analysis pipeline that have been advanced by deep learning, and explaining the benefits of deep learning over more conventional analytic tools. Finally, we summarize the challenges in current deep learning approaches faced within scRNA-seq data and discuss potential directions for improvements in deep learning algorithms for scRNA-seq data analysis.

Role of lymphoid lineage cells aberrantly expressing alarmins S100A8/A9 in determining the severity of COVID-19.

BACKGROUND: Alarmins S100A8 and S100A9 are recognized as hallmarks of severe COVID-19 and are primarily produced in myeloid cells, such as monocytes and neutrophils. As single-cell RNA-sequencing (scRNA-seq) data from patients with COVID-19 revealed the expression of S100A8/A9 in lymphoid cells in patients with severe COVID-19. OBJECTIVE: We investigated the characteristics of lymphoid cells expressing S100A8/A9 in COVID-19 patients. METHODS: Publicly available scRNA-seq data from patients with mild (N = 12) or severe (N = 7) COVID-19 were reanalyzed. The data were further divided into the following two groups based on the time of sample collection (from infection-onset): within 6 days (early phase) and after 6 days (late phase). Differential expression and gene set enrichment analyses were performed between S100A8/A9High and S100A8/A9Low lymphoid cells. Finally, cell-cell interaction analysis was performed to investigate the role of lymphoid cells expressing high levels of S100A8/A9 in COVID-19. RESULTS: S100A8/A9 overexpression was observed in lymphoid cells, including B cells, T cells, and NK cells, in patients with severe COVID-19 (compared to patients with mild COVID-19). Cells exhibiting strong interferon/cytokine responses were found to be associated with the severity of COVID-19. Furthermore, differences in S100A8/A9-TLR4/RAGE interactions were confirmed between patients with severe and mild disease. CONCLUSIONS: Lymphoid cells overexpressing S100A8/A9 contribute to the dysregulation of the innate immune response in patients with severe COVID-19, specifically during the early phase of infection. This study fosters a better understanding of the hyper-induction of pro-inflammatory cytokine expression and the generation of a cytokine storm in response to COVID-19 infection.

Single-cell RNA-seq methods to interrogate virus-host interactions.

The twenty-first century has seen the emergence of many epidemic and pandemic viruses, with the most recent being the SARS-CoV-2-driven COVID-19 pandemic. As obligate intracellular parasites, viruses rely on host cells to replicate and produce progeny, resulting in complex virus and host dynamics during an infection. Single-cell RNA sequencing (scRNA-seq), by enabling broad and simultaneous profiling of both host and virus transcripts, represents a powerful technology to unravel the delicate balance between host and virus. In this review, we summarize technological and methodological advances in scRNA-seq and their applications to antiviral immunity. We highlight key scRNA-seq applications that have enabled the understanding of viral genomic and host response heterogeneity, differential responses of infected versus bystander cells, and intercellular communication networks. We expect further development of scRNA-seq technologies and analytical methods, combined with measurements of additional multi-omic modalities and increased availability of publicly accessible scRNA-seq datasets, to enable a better understanding of viral pathogenesis and enhance the development of antiviral therapeutics strategies.

Multiple Fibroblast Subtypes Contribute to Matrix Deposition in Pulmonary Fibrosis.

Progressive pulmonary fibrosis results from a dysfunctional tissue repair response and is characterized by fibroblast proliferation, activation, and invasion and extracellular matrix accumulation. Lung fibroblast heterogeneity is well recognized. With single-cell RNA sequencing, fibroblast subtypes have been reported by recent studies. However, the roles of fibroblast subtypes in effector functions in lung fibrosis are not well understood. In this study, we incorporated the recently published single-cell RNA-sequencing datasets on murine lung samples of fibrosis models and human lung samples of fibrotic diseases and analyzed fibroblast gene signatures. We identified and confirmed the novel fibroblast subtypes we reported recently across all samples of both mouse models and human lung fibrotic diseases, including idiopathic pulmonary fibrosis, systemic sclerosis-associated interstitial lung disease, and coronavirus disease (COVID-19). Furthermore, we identified specific cell surface proteins for each fibroblast subtype through differential gene expression analysis, which enabled us to isolate primary cells representing distinct fibroblast subtypes by flow cytometry sorting. We compared matrix production, including fibronectin, collagen, and hyaluronan, after profibrotic factor stimulation and assessed the invasive capacity of each fibroblast subtype. Our results suggest that in addition to myofibroblasts, lipofibroblasts and Ebf1+ (Ebf transcription factor 1+) fibroblasts are two important fibroblast subtypes that contribute to matrix deposition and also have enhanced invasive, proliferative, and contraction phenotypes. The histological locations of fibroblast subtypes are identified in healthy and fibrotic lungs by these cell surface proteins. This study provides new insights to inform approaches to targeting lung fibroblast subtypes to promote the development of therapeutics for lung fibrosis.

Recent Dimensionality Reduction Techniques for High-Dimensional COVID-19 Data

We are going through the last years of the COVID-19 pandemic, where almost the entire research community has focused on the challenges that constantly arise. From the computational and mathematical perspective, we have to deal with a dataset with ultra-high volume and ultra-high dimensionality in several experimental studies. An indicative example is DNA sequencing technologies, which offer a more realistic picture of human diseases at the molecular biology level. However, these technologies produce data with high complexity and ultra-high dimensionality. On the other hand, dimensionality reduction techniques are the first choice to address this complexity, revealing the hidden data structure in the original multidimensional space. Also, such techniques can improve the efficiency of machine learning tasks such as classification and clustering. Towards this direction, we study the behavior of seven well-known and cutting-edge dimensionality reduction techniques tailored for RNA-sequencing data. Along with the study of the effect of these algorithms, we propose the extension of the Random projection and Geodesic distance t-Stochastic Neighbor Embedding (RGt-SNE) algorithm, a recent t-Stochastic Neighbor Embedding (t-SNE) improvement. We suggest a new distance criterion for the kernel matrix construction. Our results show the potential of the proposed algorithm and, at the same time, highlight the complexity of the COVID-19 data, which are not separable, creating a significant challenge that the Machine Learning field will have to face. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications.

The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.

The challenges and opportunities of scRNA-seq in COVID-19 research and clinical translation.

The application of single-cell RNA sequencing in COVID-19 research has greatly improved our understanding of COVID-19 pathogenesis and immunological characteristics. In this commentary, we discuss the current challenges, limitations, and perspectives in harnessing the power of single-cell RNA sequencing to accelerate both basic research and therapeutic development for COVID-19 and other emerging infectious diseases.

Genome-wide Mendelian randomization and single-cell RNA sequencing analyses identify the causal effects of COVID-19 on 41 cytokines.

The elevated levels of inflammatory cytokines have attracted much attention during the treatment of COVID-19 patients. The conclusions of current observational studies are often controversial in terms of the causal effects of COVID-19 on various cytokines because of the confounding factors involving underlying diseases. To resolve this problem, we conducted a Mendelian randomization analysis by integrating the GWAS data of COVID-19 and 41 cytokines. As a result, the levels of 2 cytokines were identified to be promoted by COVID-19 and had unsignificant pleiotropy. In comparison, the levels of 10 cytokines were found to be inhibited and had unsignificant pleiotropy. Among down-regulated cytokines, CCL2, CCL3 and CCL7 were members of CC chemokine family. We then explored the potential molecular mechanism for a significant causal association at a single cell resolution based on single-cell RNA data, and discovered the suppression of CCL3 and the inhibition of CCL3-CCR1 interaction in classical monocytes (CMs) of COVID-19 patients. Our findings may indicate that the capability of COVID-19 in decreasing the chemotaxis of lymphocytes by inhibiting the CCL3-CCR1 interaction in CMs.

Single-cell RNA sequencing to decipher the immunogenicity of ChAdOx1 nCoV-19/AZD1222 and mRNA-1273 vaccines in patients with autoimmune rheumatic diseases.

Objectives: To investigate the differences between the vector vaccine ChAdOx1 nCoV-19/AZD1222 (Oxford-AstraZeneca) and mRNA-based vaccine mRNA-1273 (Moderna) in patients with autoimmune rheumatic diseases (AIRD), and to explore the cell-cell interactions between high and low anti-SARS-CoV-2 IgG levels in patients with rheumatic arthritis (RA) using single-cell RNA sequencing (scRNA-seq). Methods: From September 16 to December 10, 2021, we consecutively enrolled 445 participants (389 patients with AIRD and 56 healthy controls), of whom 236 were immunized with AZD1222 and 209 with mRNA-1273. The serum IgG antibodies to the SARS-CoV-2 receptor-binding domain was quantified by electrochemiluminescence immunoassay at 4-6 weeks after vaccination. Moreover, peripheral blood mononuclear cells (PBMCs) were isolated from RA patients at 4-6 weeks after vaccination for scRNA-seq and further analyzed by CellChat. ScRNA-seq of PBMCs samples from GSE201534 in the Gene Expression Omnibus (GEO) database were also extracted for analysis. Results: The anti-SARS-CoV-2 IgG seropositivity rate was 85.34% for AIRD patients and 98.20% for healthy controls. The anti-SARS-CoV-2 IgG level was higher in patients receiving mRNA-1273 than those receiving AZD1222 (ß: 35.25, 95% CI: 14.81-55.68, p=0.001). Prednisolone-equivalent dose >5 mg/day and methotrexate use in AIRD patients, and non-anti-tumor necrosis factor-α biologics and Janus kinase inhibitor use in RA patients were associated with inferior immunogenicity. ScRNA-seq revealed CD16-monocytes were predominant in RA patients with high anti-SARS-CoV2-IgG antibodies, and enriched pathways related to antigen presentation via MHC class II were found. HLA-DRA and CD4 interaction was enhanced in high anti-SARS-CoV2-IgG group. Conclusions: mRNA-1273 and AZD1222 vaccines exhibited differential immunogenicity in AIRD patients. Enriched pathways related to antigen presentation via MHC class II in CD16-monocytes might be associated with higher anti-SARS-CoV2-IgG level in RA patients and further study is warranted.

Defining mast cell differentiation and heterogeneity through single-cell transcriptomics analysis.

Mast cells (MCs) are widely recognized as central effector cells during type 2 inflammatory reactions and thought to also play a role in innate immune responses, wound healing, and potentially cancer. Circulating progenitor cells mature to MCs in peripheral tissues, where they exhibit phenotypic and functional heterogeneity. This diversity likely originates from differences in MC development imprinted by microenvironmental signals. The advent of single-cell transcriptomics reveals MC diversity beyond differences in proteases that were classically used to identify MC phenotypes. Here, we provide an overview of the current knowledge on MC progenitor differentiation and characteristics, and MC heterogeneity seen in health versus disease, that are drastically advanced through single-cell profiling technologies. This powerful approach can provide detailed cellular maps of tissues to decipher the complex cellular functions and interactions that may lead to identifying candidate factors to target in therapies.

Comparable bidirectional neutrophil immune dysregulation between Kawasaki disease and severe COVID-19.

Kawasaki disease (KD), a multisystem inflammatory syndrome that occurs in children, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 or COVID-19) may share some overlapping mechanisms. The purpose of this study was to analyze the differences in single-cell RNA sequencing between KD and COVID-19. We performed single-cell RNA sequencing in KD patients (within 24 hours before IVIG treatment) and age-matched fever controls. The single-cell RNA sequencing data of COVID-19, influenza, and health controls were downloaded from the Sequence Read Archive (GSE149689/PRJNA629752). In total, 22 single-cell RNA sequencing data with 102,355 nuclei were enrolled in this study. After performing hierarchical and functional clustering analyses, two enriched gene clusters demonstrated similar patterns in severe COVID-19 and KD, heightened neutrophil activation, and decreased MHC class II expression. Furthermore, comparable dysregulation of neutrophilic granulopoiesis representing two pronounced hyperinflammatory states was demonstrated, which play a critical role in the overactivated and defective aging program of granulocytes, in patients with KD as well as those with severe COVID-19. In conclusion, both neutrophil activation and MHC class II reduction play a crucial role and thus may provide potential treatment targets for KD and severe COVID-19.

Effect of invasive mechanical ventilation on the diversity of the pulmonary microbiota.

Pulmonary microbial diversity may be influenced by biotic or abiotic conditions (e.g., disease, smoking, invasive mechanical ventilation (MV), etc.). Specially, invasive MV may trigger structural and physiological changes in both tissue and microbiota of lung, due to gastric and oral microaspiration, altered body posture, high O2 inhalation-induced O2 toxicity in hypoxemic patients, impaired airway clearance and ventilator-induced lung injury (VILI), which in turn reduce the diversity of the pulmonary microbiota and may ultimately lead to poor prognosis. Furthermore, changes in (local) O2 concentration can reduce the diversity of the pulmonary microbiota by affecting the local immune microenvironment of lung. In conclusion, systematic literature studies have found that invasive MV reduces pulmonary microbiota diversity, and future rational regulation of pulmonary microbiota diversity by existing or novel clinical tools (e.g., lung probiotics, drugs) may improve the prognosis of invasive MV treatment and lead to more effective treatment of lung diseases with precision.