Single-cell immunology of SARS-CoV-2 infection

Primate-specific BTN3A2 protects against SARS-CoV-2 infection by interacting with and reducing ACE2

BACKGROUND

Coronavirus disease 2019 (COVID-19) is an immune-related disorder caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The complete pathogenesis of the virus remains to be determined. Unraveling the molecular mechanisms governing SARS-CoV-2 interactions with host cells is crucial for the formulation of effective prophylactic measures and the advancement of COVID-19 therapeutics.

METHODS

We analyzed human lung single-cell RNA sequencing dataset to discern the association of butyrophilin subfamily 3 member A2 (BTN3A2) expression with COVID-19. The BTN3A2 gene edited cell lines and transgenic mice were infected by live SARS-CoV-2 in a biosafety level 3 (BSL-3) laboratory. Immunoprecipitation, flow cytometry, biolayer interferometry and competition ELISA assays were performed in BTN3A2 gene edited cells. We performed quantitative real-time PCR, histological and/or immunohistochemical analyses for tissue samples from mice with or without SARS-CoV-2 infection.

FINDINGS

The BTN3A2 mRNA level was correlated with COVID-19 severity. BTN3A2 expression was predominantly identified in epithelial cells, elevated in pathological epithelial cells from COVID-19 patients and co-occurred with ACE2 expression in the same lung cell subtypes. BTN3A2 targeted the early stage of the viral life cycle by inhibiting SARS-CoV-2 attachment through interactions with the receptor-binding domain (RBD) of the Spike protein and ACE2. BTN3A2 inhibited ACE2-mediated SARS-CoV-2 infection by reducing ACE2 in vitro and in vivo.

INTERPRETATION

These results reveal a key role of BTN3A2 in the fight against COVID-19. Identifying potential monoclonal antibodies which mimic BTN3A2 may facilitate disruption of SARS-CoV-2 infection, providing a therapeutic avenue for COVID-19.

FUNDING

This study was supported by the National Natural Science Foundation of China (32070569, U1902215, and 32371017), the CAS "Light of West China" Program, and Yunnan Province (202305AH340006).

Interferon-responsive neutrophils and macrophages extricate SARS-CoV-2 Omicron critical patients from the nasty fate of sepsis

The SARS-CoV-2 Omicron variant is characterized by its high transmissibility, which has caused a worldwide epidemiological event. Yet, it turns ominous once the disease progression degenerates into severe pneumonia and sepsis, presenting a horrendous lethality. To elucidate the alveolar immune or inflammatory landscapes of Omicron critical-ill patients, we performed single-cell RNA-sequencing (scRNA-seq) of bronchoalveolar lavage fluid (BALF) from the patients with critical pneumonia caused by Omicron infection, and analyzed the correlation between the clinical severity scores and different immune cell subpopulations. In the BALF of Omicron critical patients, the alveolar violent myeloid inflammatory environment was determined. ISG15+ neutrophils and CXCL10+ macrophages, both expressed the interferon-stimulated genes (ISGs), were negatively correlated with clinical pulmonary infection score, while septic CST7+ neutrophils and inflammatory VCAN+ macrophages were positively correlated with sequential organ failure assessment. The percentages of ISG15+ neutrophils were associated with more protective alveolar epithelial cells, and may reshape CD4+ T cells to the exhaustive phenotype, thus preventing immune injuries. The CXCL10+ macrophages may promote plasmablast/plasma cell survival and activation as well as the production of specific antibodies. As compared to the previous BALF scRNA-seq data from SARS-CoV-2 wild-type/Alpha critical patients, the subsets of neutrophils and macrophages with pro-inflammatory and immunoregulatory features presented obvious distinctions, suggesting an immune disparity in Omicron variants. Overall, this study provides a BALF single-cell atlas of Omicron critical patients, and suggests that alveolar interferon-responsive neutrophils and macrophages may extricate SARS-CoV-2 Omicron critical patients from the nasty fate of sepsis.

Spatial metatranscriptomics resolves host-bacteria-fungi interactomes

The interactions of microorganisms among themselves and with their multicellular host take place at the microscale, forming complex networks and spatial patterns. Existing technology does not allow the simultaneous investigation of spatial interactions between a host and the multitude of its colonizing microorganisms, which limits our understanding of host-microorganism interactions within a plant or animal tissue. Here we present spatial metatranscriptomics (SmT), a sequencing-based approach that leverages 16S/18S/ITS/poly-d(T) multimodal arrays for simultaneous host transcriptome- and microbiome-wide characterization of tissues at 55-µm resolution. We showcase SmT in outdoor-grown Arabidopsis thaliana leaves as a model system, and find tissue-scale bacterial and fungal hotspots. By network analysis, we study inter- and intrakingdom spatial interactions among microorganisms, as well as the host response to microbial hotspots. SmT provides an approach for answering fundamental questions on host-microbiome interplay.

Leveraging artificial intelligence in vaccine development: A narrative review

Vaccine development stands as a cornerstone of public health efforts, pivotal in curbing infectious diseases and reducing global morbidity and mortality. However, traditional vaccine development methods are often time-consuming, costly, and inefficient. The advent of artificial intelligence (AI) has ushered in a new era in vaccine design, offering unprecedented opportunities to expedite the process. This narrative review explores the role of AI in vaccine development, focusing on antigen selection, epitope prediction, adjuvant identification, and optimization strategies. AI algorithms, including machine learning and deep learning, leverage genomic data, protein structures, and immune system interactions to predict antigenic epitopes, assess immunogenicity, and prioritize antigens for experimentation. Furthermore, AI-driven approaches facilitate the rational design of immunogens and the identification of novel adjuvant candidates with optimal safety and efficacy profiles. Challenges such as data heterogeneity, model interpretability, and regulatory considerations must be addressed to realize the full potential of AI in vaccine development. Integrating emerging technologies, such as single-cell omics and synthetic biology, promises to enhance vaccine design precision and scalability. This review underscores the transformative impact of AI on vaccine development and highlights the need for interdisciplinary collaborations and regulatory harmonization to accelerate the delivery of safe and effective vaccines against infectious diseases.

An interactive web application for exploring systemic lupus erythematosus blood transcriptomic diversity

In the field of complex autoimmune diseases such as systemic lupus erythematosus (SLE), systems immunology approaches have proven invaluable in translational research settings. Large-scale datasets of transcriptome profiling have been collected and made available to the research community in public repositories, but remain poorly accessible and usable by mainstream researchers. Enabling tools and technologies facilitating investigators' interaction with large-scale datasets such as user-friendly web applications could promote data reuse and foster knowledge discovery. Microarray blood transcriptomic data from the LUPUCE cohort (publicly available on Gene Expression Omnibus, GSE49454), which comprised 157 samples from 62 adult SLE patients, were analyzed with the third-generation (BloodGen3) module repertoire framework, which comprises modules and module aggregates. These well-characterized samples corresponded to different levels of disease activity, different types of flares (including biopsy-proven lupus nephritis), different auto-antibody profiles and different levels of interferon signatures. A web application was deployed to present the aggregate-level, module-level and gene-level analysis results from LUPUCE dataset. Users can explore the similarities and heterogeneity of SLE samples, navigate through different levels of analysis, test hypotheses and generate custom fingerprint grids and heatmaps, which may be used in reports or manuscripts. This resource is available via this link: https://immunology-research.shinyapps.io/LUPUCE/. This web application can be employed as a stand-alone resource to explore changes in blood transcript profiles in SLE, and their relation to clinical and immunological parameters, to generate new research hypotheses.

Conventional and unconventional T-cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients

Sepsis is characterized by a dysfunctional host response to infection culminating in life-threatening organ failure that requires complex patient management and rapid intervention. Timely diagnosis of the underlying cause of sepsis is crucial, and identifying those at risk of complications and death is imperative for triaging treatment and resource allocation. Here, we explored the potential of explainable machine learning models to predict mortality and causative pathogen in sepsis patients. By using a modelling pipeline employing multiple feature selection algorithms, we demonstrate the feasibility of identifying integrative patterns from clinical parameters, plasma biomarkers, and extensive phenotyping of blood immune cells. While no single variable had sufficient predictive power, models that combined five and more features showed a macro area under the curve (AUC) of 0.85 to predict 90-day mortality after sepsis diagnosis, and a macro AUC of 0.86 to discriminate between Gram-positive and Gram-negative bacterial infections. Parameters associated with the cellular immune response contributed the most to models predictive of 90-day mortality, most notably, the proportion of T cells among PBMCs, together with expression of CXCR3 by CD4+ T cells and CD25 by mucosal-associated invariant T (MAIT) cells. Frequencies of Vδ2+ γδ T cells had the most profound impact on the prediction of Gram-negative infections, alongside other T-cell-related variables and total neutrophil count. Overall, our findings highlight the added value of measuring the proportion and activation patterns of conventional and unconventional T cells in the blood of sepsis patients in combination with other immunological, biochemical, and clinical parameters.

The future of rapid and automated single-cell data analysis using reference mapping

As the number of single-cell datasets continues to grow rapidly, workflows that map new data to well-curated reference atlases offer enormous promise for the biological community. In this perspective, we discuss key computational challenges and opportunities for single-cell reference-mapping algorithms. We discuss how mapping algorithms will enable the integration of diverse datasets across disease states, molecular modalities, genetic perturbations, and diverse species and will eventually replace manual and laborious unsupervised clustering pipelines.

From Detection to Protection: Antibodies and Their Crucial Role in Diagnosing and Combatting SARS-CoV-2

Understanding the antibody response to SARS-CoV-2, the virus responsible for COVID-19, is crucial to comprehending disease progression and the significance of vaccine and therapeutic development. The emergence of highly contagious variants poses a significant challenge to humoral immunity, underscoring the necessity of grasping the intricacies of specific antibodies. This review emphasizes the pivotal role of antibodies in shaping immune responses and their implications for diagnosing, preventing, and treating SARS-CoV-2 infection. It delves into the kinetics and characteristics of the antibody response to SARS-CoV-2 and explores current antibody-based diagnostics, discussing their strengths, clinical utility, and limitations. Furthermore, we underscore the therapeutic potential of SARS-CoV-2-specific antibodies, discussing various antibody-based therapies such as monoclonal antibodies, polyclonal antibodies, anti-cytokines, convalescent plasma, and hyperimmunoglobulin-based therapies. Moreover, we offer insights into antibody responses to SARS-CoV-2 vaccines, emphasizing the significance of neutralizing antibodies in order to confer immunity to SARS-CoV-2, along with emerging variants of concern (VOCs) and circulating Omicron subvariants. We also highlight challenges in the field, such as the risks of antibody-dependent enhancement (ADE) for SARS-CoV-2 antibodies, and shed light on the challenges associated with the original antigenic sin (OAS) effect and long COVID. Overall, this review intends to provide valuable insights, which are crucial to advancing sensitive diagnostic tools, identifying efficient antibody-based therapeutics, and developing effective vaccines to combat the evolving threat of SARS-CoV-2 variants on a global scale.

A prognostic model for SARS-CoV-2 breakthrough infection: Analyzing a prospective cellular immunity cohort

BACKGROUND

Following the COVID-19 pandemic, studies have identified several prevalent characteristics, especially related to lymphocyte subsets. However, limited research is available on the focus of this study, namely, the specific memory cell subsets among individuals who received COVID-19 vaccine boosters and subsequently experienced a SARS-CoV-2 breakthrough infection.

METHODS

Flow cytometry (FCM) was employed to investigate the early and longitudinal pattern changes of cellular immunity in patients with SARS-CoV-2 breakthrough infections following COVID-19 vaccine boosters. XGBoost (a machine learning algorithm) was employed to analyze cellular immunity prior to SARS-CoV-2 breakthrough, aiming to establish a prognostic model for SARS-CoV-2 breakthrough infections.

RESULTS

Following SARS-CoV-2 breakthrough infection, naïve T cells and TEMRA subsets increased while the percentage of TCM and TEM cells decreased. Naïve and non-switched memory B cells increased while switched and double-negative memory B cells decreased. The XGBoost model achieved an area under the curve (AUC) of 0.78, with an accuracy rate of 81.8 %, a sensitivity of 75 %, and specificity of 85.7 %. TNF-α, CD27-CD19+cells, and TEMRA subsets were identified as high predictors. An increase in TNF-α, cTfh, double-negative memory B cells, IL-6, IL-10, and IFN-γ prior to SARS-CoV-2 infection was associated with enduring clinical symptoms; conversely, an increase in CD3+ T cells, CD4+ T cells, and IL-2 was associated with clinical with non-enduring clinical symptoms.

CONCLUSION

SARS-CoV-2 breakthrough infection leads to disturbances in cellular immunity. Assessing cellular immunity prior to breakthrough infection serves as a valuable prognostic tool for SARS-CoV-2 infection, which facilitates clinical decision-making.

Single Cell Atlas: a single-cell multi-omics human cell encyclopedia

Single-cell sequencing datasets are key in biology and medicine for unraveling insights into heterogeneous cell populations with unprecedented resolution. Here, we construct a single-cell multi-omics map of human tissues through in-depth characterizations of datasets from five single-cell omics, spatial transcriptomics, and two bulk omics across 125 healthy adult and fetal tissues. We construct its complement web-based platform, the Single Cell Atlas (SCA, www.singlecellatlas.org ), to enable vast interactive data exploration of deep multi-omics signatures across human fetal and adult tissues. The atlas resources and database queries aspire to serve as a one-stop, comprehensive, and time-effective resource for various omics studies.

PreTSA: computationally efficient modeling of temporal and spatial gene expression patterns

Modeling temporal and spatial gene expression patterns in large-scale single-cell and spatial transcriptomics data is a computationally intensive task. We present PreTSA, a method that offers computational efficiency in modeling these patterns and is applicable to single-cell and spatial transcriptomics data comprising millions of cells. PreTSA consistently matches the results of state-of-the-art methods while significantly reducing computational time. PreTSA provides a unique solution for studying gene expression patterns in extremely large datasets.

Peripheral priming induces plastic transcriptomic and proteomic responses in circulating neutrophils required for pathogen containment

Neutrophils rapidly respond to inflammation and infection, but to which degree their functional trajectories after mobilization from the bone marrow are shaped within the circulation remains vague. Experimental limitations have so far hampered neutrophil research in human disease. Here, using innovative fixation and single-cell-based toolsets, we profile human and murine neutrophil transcriptomes and proteomes during steady state and bacterial infection. We find that peripheral priming of circulating neutrophils leads to dynamic shifts dominated by conserved up-regulation of antimicrobial genes across neutrophil substates, facilitating pathogen containment. We show the TLR4/NF-κB signaling-dependent up-regulation of canonical neutrophil activation markers like CD177/NB-1 during acute inflammation, resulting in functional shifts in vivo. Blocking de novo RNA synthesis in circulating neutrophils abrogates these plastic shifts and prevents the adaptation of antibacterial neutrophil programs by up-regulation of distinct effector molecules upon infection. These data underline transcriptional plasticity as a relevant mechanism of functional neutrophil reprogramming during acute infection to foster bacterial containment within the circulation.

Microfluidic platform for omics analysis on single cells with diverse morphology and size: A review

BACKGROUND

Microfluidic techniques have emerged as powerful tools in single-cell research, facilitating the exploration of omics information from individual cells. Cell morphology is crucial for gene expression and physiological processes. However, there is currently a lack of integrated analysis of morphology and single-cell omics information. A critical challenge remains: what platform technologies are the best option to decode omics data of cells that are complex in morphology and size?

RESULTS

This review highlights achievements in microfluidic-based single-cell omics and isolation of cells based on morphology, along with other cell sorting methods based on physical characteristics. Various microfluidic platforms for single-cell isolation are systematically presented, showcasing their diversity and adaptability. The discussion focuses on microfluidic devices tailored to the distinct single-cell isolation requirements in plants and animals, emphasizing the significance of considering cell morphology and cell size in optimizing single-cell omics strategies. Simultaneously, it explores the application of microfluidic single-cell sorting technologies to single-cell sequencing, aiming to effectively integrate information about cell shape and size.

SIGNIFICANCE AND NOVELTY

The novelty lies in presenting a comprehensive overview of recent accomplishments in microfluidic-based single-cell omics, emphasizing the integration of different microfluidic platforms and their implications for cell morphology-based isolation. By underscoring the pivotal role of the specialized morphology of different cells in single-cell research, this review provides robust support for delving deeper into the exploration of single-cell omics data.

Serological surveillance reveals a high exposure to SARS-CoV-2 and altered immune response among COVID-19 unvaccinated Cameroonian individuals

Surveillance of COVID-19/SARS-CoV-2 dynamics is crucial to understanding natural history and providing insights into the population's exposure risk and specific susceptibilities. This study investigated the seroprevalence of SARS-CoV-2 antibodies, its predictors, and immunological status among unvaccinated patients in Cameroon. A multicentre cross-sectional study was conducted between January and September 2022 in the town of Douala. Patients were consecutively recruited, and data of interest were collected using a questionnaire. Blood samples were collected to determine Immunoglobin titres (IgM and IgG), interferon gamma (IFN- γ) and interleukin-6 (IL-6) by ELISA, and CD4+ cells by flow cytometry. A total of 342 patients aged 41.5 ± 13.9 years were included. Most participants (75.8%) were asymptomatic. The overall crude prevalence of IgM and IgG was 49.1% and 88.9%, respectively. After adjustment, the seroprevalence values were 51% for IgM and 93% for IgM. Ageusia and anosmia have displayed the highest positive predictive values (90.9% and 82.4%) and specificity (98.9% and 98.3%). The predictors of IgM seropositivity were being diabetic (aOR = 0.23, p = 0.01), frequently seeking healthcare (aOR = 1.97, p = 0.03), and diagnosed with ageusia (aOR = 20.63, p = 0.005), whereas those of IgG seropositivity included health facility (aOR = 0.15, p = 0.01), age of 40-50 years (aOR = 8.78, p = 0.01), married (aOR = 0.21, p = 0.02), fever (aOR = 0.08, p = 0.01), and ageusia (aOR = 0.08, p = 0.01). CD4+, IFN-γ, and IL-6 were impaired in seropositive individuals, with a confounding role of socio-demographic factors or comorbidities. Although the WHO declared the end of COVID-19 as a public health emergency, the findings of this study indicate the need for continuous surveillance to adequately control the disease in Cameroon.

SARS-CoV-2: A Glance at the Innate Immune Response Elicited by Infection and Vaccination

The COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has led to almost seven million deaths worldwide. SARS-CoV-2 causes infection through respiratory transmission and can occur either without any symptoms or with clinical manifestations which can be mild, severe or, in some cases, even fatal. Innate immunity provides the initial defense against the virus by sensing pathogen-associated molecular patterns and triggering signaling pathways that activate the antiviral and inflammatory responses, which limit viral replication and help the identification and removal of infected cells. However, temporally dysregulated and excessive activation of the innate immune response is deleterious for the host and associates with severe COVID-19. In addition to its defensive role, innate immunity is pivotal in priming the adaptive immune response and polarizing its effector function. This capacity is relevant in the context of both SARS-CoV-2 natural infection and COVID-19 vaccination. Here, we provide an overview of the current knowledge of the innate immune responses to SARS-CoV-2 infection and vaccination.

Dictionary learning for integrative, multimodal and scalable single-cell analysis

Mapping single-cell sequencing profiles to comprehensive reference datasets provides a powerful alternative to unsupervised analysis. However, most reference datasets are constructed from single-cell RNA-sequencing data and cannot be used to annotate datasets that do not measure gene expression. Here we introduce 'bridge integration', a method to integrate single-cell datasets across modalities using a multiomic dataset as a molecular bridge. Each cell in the multiomic dataset constitutes an element in a 'dictionary', which is used to reconstruct unimodal datasets and transform them into a shared space. Our procedure accurately integrates transcriptomic data with independent single-cell measurements of chromatin accessibility, histone modifications, DNA methylation and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to improve computational scalability and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach, implemented in version 5 of our Seurat toolkit ( http://www.satijalab.org/seurat ), broadens the utility of single-cell reference datasets and facilitates comparisons across diverse molecular modalities.

Cell and tissue-specific glycosylation pathways informed by single-cell transcriptomics

While single cell studies have made significant impacts in various subfields of biology, they lag in the Glycosciences. To address this gap, we analyzed single-cell glycogene expressions in the Tabula Sapiens dataset of human tissues and cell types using a recent glycosylation-specific gene ontology (GlycoEnzOnto). At the median sequencing (count) depth, ~40-50 out of 400 glycogenes were detected in individual cells. Upon increasing the sequencing depth, the number of detectable glycogenes saturates at ~200 glycogenes, suggesting that the average human cell expresses about half of the glycogene repertoire. Hierarchies in glycogene and glycopathway expressions emerged from our analysis: nucleotide-sugar synthesis and transport exhibited the highest gene expressions, followed by genes for core enzymes, glycan modification and extensions, and finally terminal modifications. Interestingly, the same cell types showed variable glycopathway expressions based on their organ or tissue origin, suggesting nuanced cell- and tissue-specific glycosylation patterns. Probing deeper into the transcription factors (TFs) of glycogenes, we identified distinct groupings of TFs controlling different aspects of glycosylation: core biosynthesis, terminal modifications, etc. We present webtools to explore the interconnections across glycogenes, glycopathways, and TFs regulating glycosylation in human cell/tissue types. Overall, the study presents an overview of glycosylation across multiple human organ systems.

Vulture: cloud-enabled scalable mining of microbial reads in public scRNA-seq data

The rapidly growing collection of public single-cell sequencing data has become a valuable resource for molecular, cellular, and microbial discovery. Previous studies mostly overlooked detecting pathogens in human single-cell sequencing data. Moreover, existing bioinformatics tools lack the scalability to deal with big public data. We introduce Vulture, a scalable cloud-based pipeline that performs microbial calling for single-cell RNA sequencing (scRNA-seq) data, enabling meta-analysis of host-microbial studies from the public domain. In our benchmarking experiments, Vulture is 66% to 88% faster than local tools (PathogenTrack and Venus) and 41% faster than the state-of-the-art cloud-based tool Cumulus, while achieving comparable microbial read identification. In terms of the cost on cloud computing systems, Vulture also shows a cost reduction of 83% ($12 vs. ${\$}$70). We applied Vulture to 2 coronavirus disease 2019, 3 hepatocellular carcinoma (HCC), and 2 gastric cancer human patient cohorts with public sequencing reads data from scRNA-seq experiments and discovered cell type-specific enrichment of severe acute respiratory syndrome coronavirus 2, hepatitis B virus (HBV), and Helicobacter pylori-positive cells, respectively. In the HCC analysis, all cohorts showed hepatocyte-only enrichment of HBV, with cell subtype-associated HBV enrichment based on inferred copy number variations. In summary, Vulture presents a scalable and economical framework to mine unknown host-microbial interactions from large-scale public scRNA-seq data. Vulture is available via an open-source license at https://github.com/holab-hku/Vulture.

Single-cell multi-omics analysis of COVID-19 patients with pre-existing autoimmune diseases shows aberrant immune responses to infection

In COVID-19, hyperinflammatory and dysregulated immune responses contribute to severity. Patients with pre-existing autoimmune conditions can therefore be at increased risk of severe COVID-19 and/or associated sequelae, yet SARS-CoV-2 infection in this group has been little studied. Here, we performed single-cell analysis of peripheral blood mononuclear cells from patients with three major autoimmune diseases (rheumatoid arthritis, psoriasis, or multiple sclerosis) during SARS-CoV-2 infection. We observed compositional differences between the autoimmune disease groups coupled with altered patterns of gene expression, transcription factor activity, and cell-cell communication that substantially shape the immune response under SARS-CoV-2 infection. While enrichment of HLA-DRlow CD14+ monocytes was observed in all three autoimmune disease groups, type-I interferon signaling as well as inflammatory T cell and monocyte responses varied widely between the three groups of patients. Our results reveal disturbed immune responses to SARS-CoV-2 in patients with pre-existing autoimmunity, highlighting important considerations for disease treatment and follow-up.

Development trends of human organoid-based COVID-19 research based on bibliometric analysis

Coronavirus disease 2019 (COVID-19), a global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has posed a catastrophic threat to human health worldwide. Human stem cell-derived organoids serve as a promising platform for exploring SARS-CoV-2 infection. Several review articles have summarized the application of human organoids in COVID-19, but the research status and development trend of this field have seldom been systematically and comprehensively studied. In this review, we use bibliometric analysis method to identify the characteristics of organoid-based COVID-19 research. First, an annual trend of publications and citations, the most contributing countries or regions and organizations, co-citation analysis of references and sources and research hotspots are determined. Next, systematical summaries of organoid applications in investigating the pathology of SARS-CoV-2 infection, vaccine development and drug discovery, are provided. Lastly, the current challenges and future considerations of this field are discussed. The present study will provide an objective angle to identify the current trend and give novel insights for directing the future development of human organoid applications in SARS-CoV-2 infection.

Single-cell transcriptomic atlas of distinct early immune responses induced by SARS-CoV-2 Proto or its variants in rhesus monkey

Immune responses induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection play a critical role in the pathogenesis and outcome of coronavirus disease 2019 (COVID-19). However, the dynamic profile of immune responses postinfection by SARS-CoV-2 variants of concern (VOC) is not fully understood. In this study, peripheral blood mononuclear cells single-cell sequencing was performed to determine dynamic profiles of immune response to Prototype, Alpha, Beta, and Delta in a rhesus monkey model. Overall, all strains induced dramatic changes in both cellular subpopulations and gene expression levels at 1 day postinfection (dpi), which associated function including adaptive immune response, innate immunity, and IFN response. COVID-19-related genes revealed different gene profiles at 1 dpi among the four SARS-CoV-2 strains, including genes reported in COVID-19 patients with increased risk of autoimmune disease and rheumatic diseases. Delta-infected animal showed inhibition of translation pathway. B cells, T cells, and monocytes showed much commonality rather than specificity among the four strains. Monocytes were the major responders to SARS-CoV-2 infection, and the response lasted longer in Alpha than the other strains. Thus, this study reveals the early immune responses induced by SARS-CoV-2 Proto or its variants in nonhuman primates, which is important information for controlling rapidly evolving viruses.

High-dimensional phenotyping of the peripheral immune response in community-acquired pneumonia

Background

Community-acquired pneumonia (CAP) represents a major health burden worldwide. Dysregulation of the immune response plays an important role in adverse outcomes in patients with CAP.

Methods

We analyzed peripheral blood mononuclear cells by 36-color spectral flow cytometry in adult patients hospitalized for CAP (n=40), matched control subjects (n=31), and patients hospitalized for COVID-19 (n=35).

Results

We identified 86 immune cell metaclusters, 19 of which (22.1%) were differentially abundant in patients with CAP versus matched controls. The most notable differences involved classical monocyte metaclusters, which were more abundant in CAP and displayed phenotypic alterations reminiscent of immunosuppression, increased susceptibility to apoptosis, and enhanced expression of chemokine receptors. Expression profiles on classical monocytes, driven by CCR7 and CXCR5, divided patients with CAP into two clusters with a distinct inflammatory response and disease course. The peripheral immune response in patients with CAP was highly similar to that in patients with COVID-19, but increased CCR7 expression on classical monocytes was only present in CAP.

Conclusion

CAP is associated with profound cellular changes in blood that mainly relate to classical monocytes and largely overlap with the immune response detected in COVID-19.

RAGE engagement by SARS-CoV-2 enables monocyte infection and underlies COVID-19 severity

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has fueled the COVID-19 pandemic with its enduring medical and socioeconomic challenges because of subsequent waves and long-term consequences of great concern. Here, we chart the molecular basis of COVID-19 pathogenesis by analyzing patients' immune responses at single-cell resolution across disease course and severity. This approach confirms cell subpopulation-specific dysregulation in COVID-19 across disease course and severity and identifies a severity-associated activation of the receptor for advanced glycation endproducts (RAGE) pathway in monocytes. In vitro THP1-based experiments indicate that monocytes bind the SARS-CoV-2 S1-receptor binding domain (RBD) via RAGE, pointing to RAGE-Spike interaction enabling monocyte infection. Thus, our results demonstrate that RAGE is a functional receptor of SARS-CoV-2 contributing to COVID-19 severity.

Workability of mRNA Sequencing for Predicting Protein Abundance

Transcriptomics methods (RNA-Seq, PCR) today are more routine and reproducible than proteomics methods, i.e., both mass spectrometry and immunochemical analysis. For this reason, most scientific studies are limited to assessing the level of mRNA content. At the same time, protein content (and its post-translational status) largely determines the cell's state and behavior. Such a forced extrapolation of conclusions from the transcriptome to the proteome often seems unjustified. The ratios of "transcript-protein" pairs can vary by several orders of magnitude for different genes. As a rule, the correlation coefficient between transcriptome-proteome levels for different tissues does not exceed 0.3-0.5. Several characteristics determine the ratio between the content of mRNA and protein: among them, the rate of movement of the ribosome along the mRNA and the number of free ribosomes in the cell, the availability of tRNA, the secondary structure, and the localization of the transcript. The technical features of the experimental methods also significantly influence the levels of the transcript and protein of the corresponding gene on the outcome of the comparison. Given the above biological features and the performance of experimental and bioinformatic approaches, one may develop various models to predict proteomic profiles based on transcriptomic data. This review is devoted to the ability of RNA sequencing methods for protein abundance prediction.

Subtyping of COVID-19 samples based on cell-cell interaction in single cell transcriptomes

In single-cell transcriptome analysis, numerous biomarkers related to COVID-19 severity, including cell subtypes, genes, and pathways, have been identified. Nevertheless, most studies have focused on severity groups based on clinical features, neglecting immunological heterogeneity within the same severity level. In this study, we employed sample-level clustering using cell-cell interaction scores to investigate patient heterogeneity and uncover novel subtypes. The clustering results were validated using external datasets, demonstrating superior reproducibility and purity compared to gene expression- or gene set enrichment-based clustering. Furthermore, the cell-cell interaction score-based clusters exhibited a strong correlation with the WHO ordinal severity score based on clinical characteristics. By characterizing the identified subtypes through known COVID-19 severity-associated biomarkers, we discovered a "Severe-like moderate" subtype. This subtype displayed clinical features akin to moderate cases; however, molecular features, such as gene expression and cell-cell interactions, resembled those of severe cases. Notably, all patients who progressed from moderate to severe belonged to this subtype, underscoring the significance of cell-cell interactions in COVID-19 patient heterogeneity and severity.

Multi-omic analysis characterizes molecular susceptibility of receptors to SARS-CoV-2 spike protein

In the post COVID-19 era, new SARS-CoV-2 variant strains may continue emerging and long COVID is poised to be another public health challenge. Deciphering the molecular susceptibility of receptors to SARS-CoV-2 spike protein is critical for understanding the immune responses in COVID-19 and the rationale of multi-organ injuries. Currently, such systematic exploration remains limited. Here, we conduct multi-omic analysis of protein binding affinities, transcriptomic expressions, and single-cell atlases to characterize the molecular susceptibility of receptors to SARS-CoV-2 spike protein. Initial affinity analysis explains the domination of delta and omicron variants and demonstrates the strongest affinities between BSG (CD147) receptor and most variants. Further transcriptomic data analysis on 4100 experimental samples and single-cell atlases of 1.4 million cells suggest the potential involvement of BSG in multi-organ injuries and long COVID, and explain the high prevalence of COVID-19 in elders as well as the different risks for patients with underlying diseases. Correlation analysis validated moderate associations between BSG and viral RNA abundance in multiple cell types. Moreover, similar patterns were observed in primates and validated in proteomic expressions. Overall, our findings implicate important therapeutic targets for the development of receptor-specific vaccines and drugs for COVID-19.

Cell-intrinsic and -extrinsic effects of SARS-CoV-2 RNA on pathogenesis: single-cell meta-analysis

Single-cell RNA-seq has been used to characterize human COVID-19. To determine if preclinical models successfully mimic the cell-intrinsic and -extrinsic effects of severe disease, we conducted a meta-analysis of single-cell data across five model species. To assess whether dissemination of viral RNA in lung cells tracks pathology and results in cell-intrinsic and -extrinsic transcriptomic changes in COVID-19. We conducted a meta-analysis by analyzing six publicly available, scRNA-seq data sets. We used dual mapping (host and virus) and differential gene expression analyses to compare viral+ and viral- cell populations. We conducted a principal component analysis to identify successful models of human COVID-19. We found expression of viral RNA in many non-epithelial cell types. Fibroblasts, macrophages, and endothelial cells exhibit clear evidence of viral-intrinsic and -extrinsic effects on host gene expression. Using viral RNA expression, we found that K18-hACE2 mice most closely modeled severe human COVID-19, followed by hamsters. Ferrets and macaques are poor models of human disease due to the low presence of viral RNA. Moreover, we found that increased transcripts of certain key inflammatory genes such as IL1B, IL18, and CXCL10 are not restricted to virally infected cells, suggesting these genes are regulated in a paracrine or autocrine fashion. These data affirm widespread dissemination of viral RNA in the lung, which may be key in the pathogenesis of severe COVID-19 and demonstrate ferrets and Rhesus macaques are poor models of human COVID-19. IMPORTANCE We conducted a high-resolution meta-analysis of scRNA-seq data from humans and five animal models of COVID-19. This study reports viral RNA dissemination in several cell types in human data as well as in some of the pre-clinical models. Using this metric, the K18-hACE2 mouse model, followed by the hamster model, most closely resembled human COVID-19. We observed clear evidence of viral-intrinsic effects within cells (e.g., IRF5 expression) as well as viral-extrinsic cytokine modulation (e.g., IL1B, IL18, CXCL10). We observed proinflammatory chemokine expression in cells devoid of viral RNA expression, suggesting autocrine/paracrine interferon regulation. This report serves as a resource-synthesizing data from COVID-19 humans and animal models and suggesting improvements for relevant pre-clinical models that may aid future diagnostic and therapeutic development projects.

Intravenous BCG vaccination reduces SARS-CoV-2 severity and promotes extensive reprogramming of lung immune cells

Bacillus Calmette-Guérin (BCG) confers heterologous immune protection against viral infections and has been proposed as vaccine against SARS-CoV-2 (SCV2). Here, we tested intravenous BCG vaccination against COVID-19 using the golden Syrian hamster model. BCG vaccination conferred a modest reduction on lung SCV2 viral load, bronchopneumonia scores, and weight loss, accompanied by a reversal of SCV2-mediated T cell lymphopenia, and reduced lung granulocytes. BCG uniquely recruited immunoglobulin-producing plasma cells to the lung suggesting accelerated local antibody production. BCG vaccination also recruited elevated levels of Th1, Th17, Treg, CTLs, and Tmem cells, with a transcriptional shift away from exhaustion markers and toward antigen presentation and repair. Similarly, BCG enhanced recruitment of alveolar macrophages and reduced key interstitial macrophage subsets, that show reduced IFN-associated gene expression. Our observations indicate that BCG vaccination protects against SCV2 immunopathology by promoting early lung immunoglobulin production and immunotolerizing transcriptional patterns among key myeloid and lymphoid populations.

Dual spatially resolved transcriptomics for human host-pathogen colocalization studies in FFPE tissue sections

Technologies to study localized host-pathogen interactions are urgently needed. Here, we present a spatial transcriptomics approach to simultaneously capture host and pathogen transcriptome-wide spatial gene expression information from human formalin-fixed paraffin-embedded (FFPE) tissue sections at a near single-cell resolution. We demonstrate this methodology in lung samples from COVID-19 patients and validate our spatial detection of SARS-CoV-2 against RNAScope and in situ sequencing. Host-pathogen colocalization analysis identified putative modulators of SARS-CoV-2 infection in human lung cells. Our approach provides new insights into host response to pathogen infection through the simultaneous, unbiased detection of two transcriptomes in FFPE samples.

Single-cell analysis technologies for cancer research: from tumor-specific single cell discovery to cancer therapy

Single-cell sequencing (SCS) technology is changing our understanding of cellular components, functions, and interactions across organisms, because of its inherent advantage of avoiding noise resulting from genotypic and phenotypic heterogeneity across numerous samples. By directly and individually measuring multiple molecular characteristics of thousands to millions of single cells, SCS technology can characterize multiple cell types and uncover the mechanisms of gene regulatory networks, the dynamics of transcription, and the functional state of proteomic profiling. In this context, we conducted systematic research on SCS techniques, including the fundamental concepts, procedural steps, and applications of scDNA, scRNA, scATAC, scCITE, and scSNARE methods, focusing on the unique clinical advantages of SCS, particularly in cancer therapy. We have explored challenging but critical areas such as circulating tumor cells (CTCs), lineage tracing, tumor heterogeneity, drug resistance, and tumor immunotherapy. Despite challenges in managing and analyzing the large amounts of data that result from SCS, this technique is expected to reveal new horizons in cancer research. This review aims to emphasize the key role of SCS in cancer research and promote the application of single-cell technologies to cancer therapy.

Understanding virus-host interactions in tissues

Although virus-host interactions are usually studied in a single cell type using in vitro assays in immortalized cell lines or isolated cell populations, it is important to remember that what is happening inside one infected cell does not translate to understanding how an infected cell behaves in a tissue, organ or whole organism. Infections occur in complex tissue environments, which contain a host of factors that can alter the course of the infection, including immune cells, non-immune cells and extracellular-matrix components. These factors affect how the host responds to the virus and form the basis of the protective response. To understand virus infection, tools are needed that can profile the tissue environment. This Review highlights methods to study virus-host interactions in the infection microenvironment.

sc-ImmuCC: hierarchical annotation for immune cell types in single-cell RNA-seq

Accurately identifying immune cell types in single-cell RNA-sequencing (scRNA-Seq) data is critical to uncovering immune responses in health or disease conditions. However, the high heterogeneity and sparsity of scRNA-Seq data, as well as the similarity in gene expression among immune cell types, poses a great challenge for accurate identification of immune cell types in scRNA-Seq data. Here, we developed a tool named sc-ImmuCC for hierarchical annotation of immune cell types from scRNA-Seq data, based on the optimized gene sets and ssGSEA algorithm. sc-ImmuCC simulates the natural differentiation of immune cells, and the hierarchical annotation includes three layers, which can annotate nine major immune cell types and 29 cell subtypes. The test results showed its stable performance and strong consistency among different tissue datasets with average accuracy of 71-90%. In addition, the optimized gene sets and hierarchical annotation strategy could be applied to other methods to improve their annotation accuracy and the spectrum of annotated cell types and subtypes. We also applied sc-ImmuCC to a dataset composed of COVID-19, influenza, and healthy donors, and found that the proportion of monocytes in patients with COVID-19 and influenza was significantly higher than that in healthy people. The easy-to-use sc-ImmuCC tool provides a good way to comprehensively annotate immune cell types from scRNA-Seq data, and will also help study the immune mechanism underlying physiological and pathological conditions.

Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2

The recent emergence of multi-sample multi-condition single-cell multi-cohort studies allows researchers to investigate different cell states. The effective integration of multiple large-cohort studies promises biological insights into cells under different conditions that individual studies cannot provide. Here, we present scMerge2, a scalable algorithm that allows data integration of atlas-scale multi-sample multi-condition single-cell studies. We have generalized scMerge2 to enable the merging of millions of cells from single-cell studies generated by various single-cell technologies. Using a large COVID-19 data collection with over five million cells from 1000+ individuals, we demonstrate that scMerge2 enables multi-sample multi-condition scRNA-seq data integration from multiple cohorts and reveals signatures derived from cell-type expression that are more accurate in discriminating disease progression. Further, we demonstrate that scMerge2 can remove dataset variability in CyTOF, imaging mass cytometry and CITE-seq experiments, demonstrating its applicability to a broad spectrum of single-cell profiling technologies.

Identifying SARS-CoV-2 infected cells with scVDN

Introduction

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for understanding cellular heterogeneity and identifying cell types in virus-related research. However, direct identification of SARS-CoV-2-infected cells at the single-cell level remains challenging, hindering the understanding of viral pathogenesis and the development of effective treatments.

Methods

In this study, we propose a deep learning framework, the single-cell virus detection network (scVDN), to predict the infection status of single cells. The scVDN is trained on scRNA-seq data from multiple nasal swab samples obtained from several contributors with varying cell types. To objectively evaluate scVDN's performance, we establish a model evaluation framework suitable for real experimental data.

Results and Discussion

Our results demonstrate that scVDN outperforms four state-of-the-art machine learning models in identifying SARS-CoV-2-infected cells, even with extremely imbalanced labels in real data. Specifically, scVDN achieves a perfect AUC score of 1 in four cell types. Our findings have important implications for advancing virus research and improving public health by enabling the identification of virus-infected cells at the single-cell level, which is critical for diagnosing and treating viral infections. The scVDN framework can be applied to other single-cell virus-related studies, and we make all source code and datasets publicly available on GitHub at https://github.com/studentiz/scvdn.

Systems analysis of human innate immunity in COVID-19

Recent developments in sequencing technologies, the computer and data sciences, as well as increasingly high-throughput immunological measurements have made it possible to derive holistic views on pathophysiological processes of disease and treatment effects directly in humans. We and others have illustrated that incredibly predictive data for immune cell function can be generated by single cell multi-omics (SCMO) technologies and that these technologies are perfectly suited to dissect pathophysiological processes in a new disease such as COVID-19, triggered by SARS-CoV-2 infection. Systems level interrogation not only revealed the different disease endotypes, highlighted the differential dynamics in context of disease severity, and pointed towards global immune deviation across the different arms of the immune system, but was already instrumental to better define long COVID phenotypes, suggest promising biomarkers for disease and therapy outcome predictions and explains treatment responses for the widely used corticosteroids. As we identified SCMO to be the most informative technologies in the vest to better understand COVID-19, we propose to routinely include such single cell level analysis in all future clinical trials and cohorts addressing diseases with an immunological component.

COVID-19db linkage maps of cell surface proteins and transcription factors in immune cells

The highly contagious SARS-CoV-2 and its associated disease (COVID-19) are a threat to global public health and economies. To develop effective treatments for COVID-19, we must understand the host cell types, cell states and regulators associated with infection and pathogenesis such as dysregulated transcription factors (TFs) and surface proteins, including signaling receptors. To link cell surface proteins with TFs, we recently developed SPaRTAN (Single-cell Proteomic and RNA-based Transcription factor Activity Network) by integrating parallel single-cell proteomic and transcriptomic data based on Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) and gene cis-regulatory information. We apply SPaRTAN to CITE-seq data sets from patients with varying degrees of COVID-19 severity and healthy controls to identify the associations between surface proteins and TFs in host immune cells. Here, we present COVID-19db of Immune Cell States (https://covid19db.streamlit.app/), a web server containing cell surface protein expression, SPaRTAN-inferred TF activities, and their associations with major host immune cell types. The data include four high-quality COVID-19 CITE-seq data sets with a toolset for user-friendly data analysis and visualization. We provide interactive surface protein and TF visualizations across major immune cell types for each data set, allowing comparison between various patient severity groups for the discovery of potential therapeutic targets and diagnostic biomarkers.

Applications of single-cell RNA sequencing in drug discovery and development

Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, we illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.

Single-cell analysis reveals novel clonally expanded monocytes associated with IL1β-IL1R2 pair in acute inflammatory demyelinating polyneuropathy

Guillain-Barré syndrome (GBS) is an autoimmune disorder wherein the composition and gene expression patterns of peripheral blood immune cells change significantly. It is triggered by antigens with similar epitopes to Schwann cells that stimulate a maladaptive immune response against peripheral nerves. However, an atlas for peripheral blood immune cells in patients with GBS has not yet been constructed. This is a monocentric, prospective study. We collected 5 acute inflammatory demyelinating polyneuropathy (AIDP) patients and 3 healthy controls hospitalized in the First Affiliated Hospital of Harbin Medical University from December 2020 to May 2021, 3 AIDP patients were in the peak stage and 2 were in the convalescent stage. We performed single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) from these patients. Furthermore, we performed cell clustering, cell annotation, cell-cell communication, differentially expressed genes (DEGs) identification and pseudotime trajectory analysis. Our study identified a novel clonally expanded CD14+ CD163+ monocyte subtype in the peripheral blood of patients with AIDP, and it was enriched in cellular response to IL1 and chemokine signaling pathways. Furthermore, we observed increased IL1β-IL1R2 cell-cell communication between CD14+ and CD16+ monocytes. In short, by analyzing the single-cell landscape of the PBMCs in patients with AIDP we hope to widen our understanding of the composition of peripheral immune cells in patients with GBS and provide a theoretical basis for future studies.

Insufficient epitope-specific T cell clones are responsible for impaired cellular immunity to inactivated SARS-CoV-2 vaccine in older adults

Aging is a critical risk factor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine efficacy. The immune responses to inactivated vaccine for older adults, and the underlying mechanisms of potential differences to young adults, are still unclear. Here we show that neutralizing antibody production by older adults took a longer time to reach similar levels in young adults after inactivated SARS-CoV-2 vaccination. We screened SARS-CoV-2 variant strains for epitopes that stimulate specific CD8 T cell response, and older adults exhibited weaker CD8 T-cell-mediated responses to these epitopes. Comparison of lymphocyte transcriptomes from pre-vaccinated and post-vaccinated donors suggested that the older adults had impaired antigen processing and presentation capability. Single-cell sequencing revealed that older adults had less T cell clone expansion specific to SARS-CoV-2, likely due to inadequate immune receptor repertoire size and diversity. Our study provides mechanistic insights for weaker response to inactivated vaccine by older adults and suggests the need for further vaccination optimization for the old population.

Relevance of TMPRSS2, CD163/CD206, and CD33 in clinical severity stratification of COVID-19

Background

Approximately 13.8% and 6.1% of coronavirus disease 2019 (COVID-19) patients require hospitalization and sometimes intensive care unit (ICU) admission, respectively. There is no biomarker to predict which of these patients will develop an aggressive stage that we could improve their quality of life and healthcare management. Our main goal is to include new markers for the classification of COVID-19 patients.

Methods

Two tubes of peripheral blood were collected from a total of 66 (n = 34 mild and n = 32 severe) samples (mean age 52 years). Cytometry analysis was performed using a 15-parameter panel included in the Maxpar® Human Monocyte/Macrophage Phenotyping Panel Kit. Cytometry by time-of-flight mass spectrometry (CyTOF) panel was performed in combination with genetic analysis using TaqMan® probes for ACE2 (rs2285666), MX1 (rs469390), and TMPRSS2 (rs2070788) variants. GemStone™ and OMIQ software were used for cytometry analysis.

Results

The frequency of CD163+/CD206- population of transitional monocytes (T-Mo) was decreased in the mild group compared to that of the severe one, while T-Mo CD163-/CD206- were increased in the mild group compared to that of the severe one. In addition, we also found differences in CD11b expression in CD14dim monocytes in the severe group, with decreased levels in the female group (p = 0.0412). When comparing mild and severe disease, we also found that CD45- [p = 0.014; odds ratio (OR) = 0.286, 95% CI 0.104–0.787] and CD14dim/CD33+ (p = 0.014; OR = 0.286, 95% CI 0.104–0.787) monocytes were the best options as biomarkers to discriminate between these patient groups. CD33 was also indicated as a good biomarker for patient stratification by the analysis of GemStone™ software. Among genetic markers, we found that G carriers of TMPRSS2 (rs2070788) have an increased risk (p = 0.02; OR = 3.37, 95% CI 1.18–9.60) of severe COVID-19 compared to those with A/A genotype. This strength is further increased when combined with CD45-, T-Mo CD163+/CD206-, and C14dim/CD33+.

Conclusions

Here, we report the interesting role of TMPRSS2, CD45-, CD163/CD206, and CD33 in COVID-19 aggressiveness. This strength is reinforced for aggressiveness biomarkers when TMPRSS2 and CD45-, TMPRSS2 and CD163/CD206, and TMPRSS2 and CD14dim/CD33+ are combined.

Single-cell profiling identifies T cell subsets associated with control of tuberculosis dissemination

To identify T cell subsets associated with control of tuberculosis, single-cell transcriptome and T cell receptor sequencing were performed on total T cells from patients with tuberculosis and healthy controls. Fourteen distinct subsets of T cells were identified by unbiased UMAP clustering. A GZMK-expressing CD8⁺ cytotoxic T cell cluster and a SOX4-expressing CD4⁺ central memory T cell cluster were depleted, while a MKI67-expressing proliferating CD3⁺ T cell cluster was expanded in patients with tuberculosis compared with healthy controls. The ratio of Granzyme K-expressing CD8⁺CD161^-Ki-67^- and CD8⁺Ki-67⁺ T cell subsets was significantly reduced and inversely correlated with the extent of TB lesions in patients with TB. In contrast, ratio of Granzyme B-expressing CD8⁺Ki-67⁺ and CD4⁺CD161⁺Ki-67^- T cells and Granzyme A-expressing CD4⁺CD161⁺Ki-67^- T cells were correlated with the extent of TB lesions. It is concluded that granzyme K-expressing CD8⁺ T cell subsets might contribute to protection against tuberculosis dissemination.

Dynamic activity in cis-regulatory elements of leukocytes identifies transcription factor activation and stratifies COVID-19 severity in ICU patients

Transcription factor programs mediating the immune response to coronavirus disease 2019 (COVID-19) are not fully understood. Capturing active transcription initiation from cis-regulatory elements such as enhancers and promoters by capped small RNA sequencing (csRNA-seq), in contrast to capturing steady-state transcripts by conventional RNA-seq, allows unbiased identification of the underlying transcription factor activity and regulatory pathways. Here, we profile transcription initiation in critically ill COVID-19 patients, identifying transcription factor motifs that correlate with clinical lung injury and disease severity. Unbiased clustering reveals distinct subsets of cis-regulatory elements that delineate the cell type, pathway-specific, and combinatorial transcription factor activity. We find evidence of critical roles of regulatory networks, showing that STAT/BCL6 and E2F/MYB regulatory programs from myeloid cell populations are activated in patients with poor disease outcomes and associated with COVID-19 susceptibility genetic variants. More broadly, we demonstrate how capturing acute, disease-mediated changes in transcription initiation can provide insight into the underlying molecular mechanisms and stratify patient disease severity.

Machine learning of flow cytometry data reveals the delayed innate immune responses correlate with the severity of COVID-19

Introduction

The COVID-19 pandemic has posed a major burden on healthcare and economic systems across the globe for over 3 years. Even though vaccines are available, the pathogenesis is still unclear. Multiple studies have indicated heterogeneity of immune responses to SARS-CoV-2, and potentially distinct patient immune types that might be related to disease features. However, those conclusions are mainly inferred by comparing the differences of pathological features between moderate and severe patients, some immunological features may be subjectively overlooked.

Methods

In this study, the relevance scores(RS), reflecting which features play a more critical role in the decision-making process, between immunological features and the COVID-19 severity are objectively calculated through neural network, where the input features include the immune cell counts and the activation marker concentrations of particular cell, and these quantified characteristic data are robustly generated by processing flow cytometry data sets containing the peripheral blood information of COVID-19 patients through PhenoGraph algorithm.

Results

Specifically, the RS between immune cell counts and COVID-19 severity with time indicated that the innate immune responses in severe patients are delayed at the early stage, and the continuous decrease of classical monocytes in peripherial blood is significantly associated with the severity of disease. The RS between activation marker concentrations and COVID-19 severity suggested that the down-regulation of IFN-γ in classical monocytes, Treg, CD8 T cells, and the not down-regulation of IL_17a in classical monocytes, Tregs are highly correlated with the occurrence of severe disease. Finally, a concise dynamic model of immune responses in COVID-19 patients was generalized.

Discussion

These results suggest that the delayed innate immune responses in the early stage, and the abnormal expression of IL-17a and IFN-γ in classical monocytes, Tregs, and CD8 T cells are primarily responsible for the severity of COVID-19.

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.

Towards precision medicine: Omics approach for COVID-19

The coronavirus disease 2019 (COVID-19) pandemic had a devastating impact on human society. Beginning with genome surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the development of omics technologies brought a clearer understanding of the complex SARS-CoV-2 and COVID-19. Here, we reviewed how omics, including genomics, proteomics, single-cell multi-omics, and clinical phenomics, play roles in answering biological and clinical questions about COVID-19. Large-scale sequencing and advanced analysis methods facilitate COVID-19 discovery from virus evolution and severity risk prediction to potential treatment identification. Omics would indicate precise and globalized prevention and medicine for the COVID-19 pandemic under the utilization of big data capability and phenotypes refinement. Furthermore, decoding the evolution rule of SARS-CoV-2 by deep learning models is promising to forecast new variants and achieve more precise data to predict future pandemics and prevent them on time.

Susceptibility identification for seasonal influenza A/H3N2 based on baseline blood transcriptome

Introduction

Influenza susceptibility difference is a widely existing trait that has great practical significance for the accurate prevention and control of influenza.

Methods

Here, we focused on the human susceptibility to the seasonal influenza A/H3N2 of healthy adults at baseline level. Whole blood expression data for influenza A/H3N2 susceptibility from GEO were collected firstly (30 symptomatic and 19 asymptomatic). Then to explore the differences at baseline, a suite of systems biology approaches - the differential expression analysis, co-expression network analysis, and immune cell frequencies analysis were utilized.

Results

We found the baseline condition, especially immune condition between symptomatic and asymptomatic, was different. Co-expression module that is positively related to asymptomatic is also related to immune cell type of naïve B cell. Function enrichment analysis showed significantly correlation with "B cell receptor signaling pathway", "immune response-activating cell surface receptor signaling pathway" and so on. Also, modules that are positively related to symptomatic are also correlated to immune cell type of neutrophils, with function enrichment analysis showing significantly correlations with "response to bacterium", "inflammatory response", "cAMP-dependent protein kinase complex" and so on. Responses of symptomatic and asymptomatic hosts after virus exposure show differences on resisting the virus, with more effective frontline defense for asymptomatic hosts. A prediction model was also built based on only baseline transcription information to differentiate symptomatic and asymptomatic population with accuracy of 0.79.

Discussion

The results not only improve our understanding of the immune system and influenza susceptibility, but also provide a new direction for precise and targeted prevention and therapy of influenza.

Tracking the clonal dynamics of SARS-CoV-2-specific T cells in children and adults with mild/asymptomatic COVID-19

Children infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) develop less severe coronavirus disease 2019 (COVID-19) than adults. The mechanisms for the age-specific differences and the implications for infection-induced immunity are beginning to be uncovered. We show by longitudinal multimodal analysis that SARS-CoV-2 leaves a small footprint in the circulating T cell compartment in children with mild/asymptomatic COVID-19 compared to adult household contacts with the same disease severity who had more evidence of systemic T cell interferon activation, cytotoxicity and exhaustion. Children harbored diverse polyclonal SARS-CoV-2-specific naïve T cells whereas adults harbored clonally expanded SARS-CoV-2-specific memory T cells. A novel population of naïve interferon-activated T cells is expanded in acute COVID-19 and is recruited into the memory compartment during convalescence in adults but not children. This was associated with the development of robust CD4+ memory T cell responses in adults but not children. These data suggest that rapid clearance of SARS-CoV-2 in children may compromise their cellular immunity and ability to resist reinfection.

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.

Clustering Deviation Index (CDI): a robust and accurate internal measure for evaluating scRNA-seq data clustering

Most single-cell RNA sequencing (scRNA-seq) analyses begin with cell clustering; thus, the clustering accuracy considerably impacts the validity of downstream analyses. In contrast with the abundance of clustering methods, the tools to assess the clustering accuracy are limited. We propose a new Clustering Deviation Index (CDI) that measures the deviation of any clustering label set from the observed single-cell data. We conduct in silico and experimental scRNA-seq studies to show that CDI can select the optimal clustering label set. As a result, CDI also informs the optimal tuning parameters for any given clustering method and the correct number of cluster components.