Gene function and cell surface protein association analysis based on single-cell multiomics data.

Single-cell transcriptomics provides researchers with a powerful tool to resolve the transcriptome heterogeneity of individual cells. However, this method falls short in revealing cellular heterogeneity at the protein level. Previous single-cell multiomics studies have focused on data integration rather than exploiting the full potential of multiomics data. Here we introduce a new analysis framework, gene function and protein association (GFPA), that mines reliable associations between gene function and cell surface protein from single-cell multimodal data. Applying GFPA to human peripheral blood mononuclear cells (PBMCs), we observe an association of epithelial mesenchymal transition (EMT) with the CD99 protein in CD4 T cells, which is consistent with previous findings. Our results show that GFPA is reliable across multiple cell subtypes and PBMC samples. The GFPA python packages and detailed tutorials are freely available at https://github.com/studentiz/GFPA.

An ultrasensitive ELISA to assay femtomolar level SARS-CoV-2 antigen based on specific peptide and tyramine signal amplification.

The SARS-CoV-2 spreading rapidly has aroused catastrophic public healthcare issues and economy crisis worldwide. It plays predominant role to rapidly and accurately diagnose the virus for effective prevention and treatment. As an abundant transmembrane protein, spike protein (SP) is one of the most valuable antigenic biomarkers for diagnosis of COVID-19. Herein a phage expression of WNLDLSQWLPPM peptide specific to SARS-CoV-2 SP was screened. Molecular docking revealed that the isolated peptide binds to major antigenic epitope locating at S2 subunit with hydrogen bonding. Taking the specific peptide as antigen sensing probe and tyramine signal amplification (TSA), an ultrasensitive "peptide-antigen-antibody" ELISA (p-ELISA) was explored, by which the limit of detection (LOD) was 14 fM and 2.8 fM SARS-CoV-2 SP antigen for first TSA and secondary TSA, respectively. Compared with the LOD by the p-ELISA by direct mode, the sensitivity with 2nd TSA enhanced 100 times. Further, the proposed p-ELISA method can detect SARS-CoV-2 pseudoviruses down to 10 and 3 TCID50/mL spiked in healthy nasal swab sample with 1st TSA and 2nd TSA, separately. Thus, the proposed p-ELISA method with TSA is expected to be a promising ultrasensitive tool for rapidly detecting SARS-CoV-2 antigen to help control the infectious disease.

Thymic tuft cells: potential "regulators" of non-mucosal tissue development and immune response.

As the leading central immune organ, the thymus is where T cells differentiate and mature, and plays an essential regulatory role in the adaptive immune response. Tuft cells, as chemosensory cells, were first found in rat tracheal epithelial, later gradually confirmed to exist in various mucosal and non-mucosal tissues. Although tuft cells are epithelial-derived, because of their wide heterogeneity, they show functions similar to cholinergic and immune cells in addition to chemosensory ability. As newly discovered non-mucosal tuft cells, thymic tuft cells have been demonstrated to be involved in and play vital roles in immune responses such as antigen presentation, immune tolerance, and type 2 immunity. In addition to their unique functions in the thymus, thymic tuft cells have the characteristics of peripheral tuft cells, so they may also participate in the process of tumorigenesis and virus infection. Here, we review tuft cells' characteristics, distribution, and potential functions. More importantly, the potential role of thymic tuft cells in immune response, tumorigenesis, and severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) infection was summarized and discussed.

Modeling and analyzing single-cell multimodal data with deep parametric inference.

The proliferation of single-cell multimodal sequencing technologies has enabled us to understand cellular heterogeneity with multiple views, providing novel and actionable biological insights into the disease-driving mechanisms. Here, we propose a comprehensive end-to-end single-cell multimodal analysis framework named Deep Parametric Inference (DPI). DPI transforms single-cell multimodal data into a multimodal parameter space by inferring individual modal parameters. Analysis of cord blood mononuclear cells (CBMC) reveals that the multimodal parameter space can characterize the heterogeneity of cells more comprehensively than individual modalities. Furthermore, comparisons with the state-of-the-art methods on multiple datasets show that DPI has superior performance. Additionally, DPI can reference and query cell types without batch effects. As a result, DPI can successfully analyze the progression of COVID-19 disease in peripheral blood mononuclear cells (PBMC). Notably, we further propose a cell state vector field and analyze the transformation pattern of bone marrow cells (BMC) states. In conclusion, DPI is a powerful single-cell multimodal analysis framework that can provide new biological insights into biomedical researchers. The python packages, datasets and user-friendly manuals of DPI are freely available at https://github.com/studentiz/dpi.

Integrating single-cell sequencing data with GWAS summary statistics reveals CD16+monocytes and memory CD8+T cells involved in severe COVID-19.

BACKGROUND: Understanding the host genetic architecture and viral immunity contributes to the development of effective vaccines and therapeutics for controlling the COVID-19 pandemic. Alterations of immune responses in peripheral blood mononuclear cells play a crucial role in the detrimental progression of COVID-19. However, the effects of host genetic factors on immune responses for severe COVID-19 remain largely unknown. METHODS: We constructed a computational framework to characterize the host genetics that influence immune cell subpopulations for severe COVID-19 by integrating GWAS summary statistics (N = 969,689 samples) with four independent scRNA-seq datasets containing healthy controls and patients with mild, moderate, and severe symptom (N = 606,534 cells). We collected 10 predefined gene sets including inflammatory and cytokine genes to calculate cell state score for evaluating the immunological features of individual immune cells. RESULTS: We found that 34 risk genes were significantly associated with severe COVID-19, and the number of highly expressed genes increased with the severity of COVID-19. Three cell subtypes that are CD16+monocytes, megakaryocytes, and memory CD8+T cells were significantly enriched by COVID-19-related genetic association signals. Notably, three causal risk genes of CCR1, CXCR6, and ABO were highly expressed in these three cell types, respectively. CCR1+CD16+monocytes and ABO+ megakaryocytes with significantly up-regulated genes, including S100A12, S100A8, S100A9, and IFITM1, confer higher risk to the dysregulated immune response among severe patients. CXCR6+ memory CD8+ T cells exhibit a notable polyfunctionality including elevation of proliferation, migration, and chemotaxis. Moreover, we observed an increase in cell-cell interactions of both CCR1+ CD16+monocytes and CXCR6+ memory CD8+T cells in severe patients compared to normal controls among both PBMCs and lung tissues. The enhanced interactions of CXCR6+ memory CD8+T cells with epithelial cells facilitate the recruitment of this specific population of T cells to airways, promoting CD8+T cell-mediated immunity against COVID-19 infection. CONCLUSIONS: We uncover a major genetics-modulated immunological shift between mild and severe infection, including an elevated expression of genetics-risk genes, increase in inflammatory cytokines, and of functional immune cell subsets aggravating disease severity, which provides novel insights into parsing the host genetic determinants that influence peripheral immune cells in severe COVID-19.

The mechanism and active compounds of semen armeniacae amarum treating coronavirus disease 2019 based on network pharmacology and molecular docking.

BACKGROUND: Coronavirus disease 2019 (COVID-19) outbreak is progressing rapidly, and poses significant threats to public health. A number of clinical practice results showed that traditional Chinese medicine (TCM) plays a significant role for COVID-19 treatment. OBJECTIVE: To explore the active components and molecular mechanism of semen armeniacae amarum treating COVID-19 by network pharmacology and molecular docking technology. METHODS: The active components and potential targets of semen armeniacae amarum were retrieved from traditional Chinese medicine systems pharmacology (TCMSP) database. Coronavirus disease 2019-associated targets were collected in the GeneCards, TTD, OMIM and PubChem database. Compound target, compound-target pathway and medicine-ingredient-target disease networks were constructed by Cytoscape 3.8.0. Protein-protein interaction (PPI) networks were drawn using the STRING database and Cytoscape 3.8.0 software. David database was used for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The main active components were verified by AutoDock Vina 1.1.2 software. A lipopolysaccharide (LPS)-induced lung inflammation model in Institute of Cancer Research (ICR) mice was constructed and treated with amygdalin to confirm effects of amygdalin on lung inflammation and its underlying mechanisms by western blot analyses and immunofluorescence. RESULTS: The network analysis revealed that nine key, active components regulated eight targets (Proto-oncogene tyrosine-protein kinase SRC (SRC), interleukin 6 (IL6), mitogen-activated protein kinase 1 (MAPK1), mitogen-activated protein kinase 3 (MAPK3), vascular endothelial growth factor A (VEGFA), epidermal growth factor receptor (EGFR), HRAS proto-oncogene (HRAS), caspase-3 (CASP3)). Gene ontology and KEGG enrichment analysis suggested that semen armeniacae amarum plays a role in COVID-19 by modulating 94 biological processes, 13 molecular functions, 15 cellular components and 80 potential pathways. Molecular docking indicated that amygdalin had better binding activity to key targets such as IL6, SRC, MAPK3, SARS coronavirus-2 3C-like protease (SARS-CoV-2 3CLpro) and SARS-CoV-2 angiotensin converting enzyme II (ACE2). Experimental validation revealed that the lung pathological injury and inflammatory injury were significantly increased in the model group and were improved in the amygdalin group. CONCLUSION: Amygdalin is a candidate compound for COVID-19 treatment by regulating IL6, SRC, MAPK1 EGFR and VEGFA to involve in PI3K-Akt signalling pathway, VEGF signalling pathway and MAPK signalling pathway. Meanwhile, amygdalin has a strong affinity for SARS-CoV-2 3CLpro and SARS-CoV-2 ACE2 and therefore prevents the virus transcription and dissemination.

Integrative genomics analysis reveals a 21q22.11 locus contributing risk to COVID-19.

The systematic identification of host genetic risk factors is essential for the understanding and treatment of coronavirus disease 2019 (COVID-19). By performing a meta-analysis of two independent genome-wide association summary datasets (N = 680 128), a novel locus at 21q22.11 was identified to be associated with COVID-19 infection (rs9976829 in IFNAR2-IL10RB, odds ratio = 1.16, 95% confidence interval = 1.09-1.23, P = 2.57 × 10-6). The rs9976829 represents a strong splicing quantitative trait locus for both IFNAR2 and IL10RB genes, especially in lung tissue (P = 1.8 × 10-24). Integrative genomics analysis of combining genome-wide association study with expression quantitative trait locus data showed the expression variations of IFNAR2 and IL10RB have prominent effects on COVID-19 in various types of tissues, especially in lung tissue. The majority of IFNAR2-expressing cells were dendritic cells (40%) and plasmacytoid dendritic cells (38.5%), and IL10RB-expressing cells were mainly nonclassical monocytes (29.6%). IFNAR2 and IL10RB are targeted by several interferons-related drugs. Together, our results uncover 21q22.11 as a novel susceptibility locus for COVID-19, in which individuals with G alleles of rs9976829 have a higher probability of COVID-19 susceptibility than those with non-G alleles.