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

Regulation of plasma soluble receptors of TNF and IL-1 in patients with COVID-19 differs from that observed in sepsis

OBJECTIVES

IL-1α/β and TNF are closely linked to the pathology of severe COVID-19 and sepsis. The soluble forms of their receptors, functioning as decoy receptors, exhibit inhibitory effects. However, little is known about their regulation in severe bacterial and viral infections, which we aimed to investigate in this study.

METHODS

The circulating soluble receptors of TNF (sTNFR1 and sTNFR2) and IL-1α/β (sIL-1R1, sIL-1R2) were evaluated in the plasma of patients with COVID-19, severe bacterial infections, and sepsis and compared with healthy controls. Additionally, IL1R1, IL1R2, TNFRSF1A, and TNFRSF1B expression was evaluated at the single cell level in PBMCs derived from COVID-19 or sepsis patients.

RESULTS

Plasma concentrations of sIL-1R1, sTNFR1, and sTNFR2 were significantly higher in COVID-19 patients compared to healthy subjects. Notably, sIL-1R1 levels were particularly elevated in ICU COVID-19 patients, and transcriptome analysis indicated heightened IL1R1 expression in PBMCs from severe COVID-19 patients. In severe bacterial infections, only sTNFR1 and sTNFR2 exhibited increased levels compared to healthy controls. Sepsis patients had decreased sIL-1R1 plasma concentrations but elevated sIL-1R2, sTNFR1, and sTNFR2 levels compared to healthy individuals, reflecting the heightened expression due to the increased numbers of monocytes present in sepsis. Finally, elevated concentrations of sIL-1R2, sTNFR1, and sTNFR2 were moderately associated with reduced 28-day survival in sepsis patients.

CONCLUSION

Our study reveals distinct regulation of plasma concentrations of soluble IL-1 receptors in COVID-19 and sepsis. Moreover, soluble TNF receptors 1 and 2 consistently rise in all conditions and show a positive correlation with disease severity in sepsis.

Inhalable SPRAY nanoparticles by modular peptide assemblies reverse alveolar inflammation in lethal Gram-negative bacteria infection

Current pharmacotherapy remains futile in acute alveolar inflammation induced by Gram-negative bacteria (GNB), eliciting consequent respiratory failure. The release of lipid polysaccharides after antibiotic treatment and subsequent progress of proinflammatory cascade highlights the necessity to apply effective inflammation management simultaneously. This work describes modular self-assembling peptides for rapid anti-inflammatory programming (SPRAY) to form nanoparticles targeting macrophage specifically, having anti-inflammation and bactericidal functions synchronously. SPRAY nanoparticles accelerate the self-delivery process in macrophages via lysosomal membrane permeabilization, maintaining anti-inflammatory programming in macrophages with efficacy close to T helper 2 cytokines. By pulmonary deposition, SPRAY nanoparticles effectively suppress inflammatory infiltration and promote alveoli regeneration in murine aseptic acute lung injury. Moreover, SPRAY nanoparticles efficiently eradicate multidrug-resistant GNB in alveoli by disrupting bacterial membrane. The universal molecular design of SPRAY nanoparticles provides a robust and clinically unseen local strategy in reverse acute inflammation featured by a high accumulation of proinflammatory cellularity and drug-resistant bacteria.

GeneSPIDER2: large scale GRN simulation and benchmarking with perturbed single-cell data

Single-cell data is increasingly used for gene regulatory network (GRN) inference, and benchmarks for this have been developed based on simulated data. However, existing single-cell simulators cannot model the effects of gene perturbations. A further challenge lies in generating large-scale GRNs that often struggle with computational and stability issues. We present GeneSPIDER2, an update of the GeneSPIDER MATLAB toolbox for GRN benchmarking, inference, and analysis. Several software modules have improved capabilities and performance, and new functionalities have been added. A major improvement is the ability to generate large GRNs with biologically realistic topological properties in terms of scale-free degree distribution and modularity. Another major addition is a simulation of single-cell data, which is becoming increasingly popular as input for GRN inference. Specifically, we introduced the unique feature to generate single-cell data based on genetic perturbations. Finally, the simulated single-cell data was compared to real single-cell Perturb-seq data from two cell lines, showing that the synthetic and real data exhibit similar properties.

Immune checkpoint inhibitors and SARS-CoV2 infection

Infection with severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) triggers coronavirus disease 2019 (COVID-19), which predominantly targets the respiratory tract. SARS-CoV-2 infection, especially severe COVID-19, is associated with dysregulated immune responses against the virus, including exaggerated inflammatory responses known as the cytokine storm, together with lymphocyte and NK cell dysfunction known as immune cell exhaustion. Overexpression of negative immune checkpoints such as PD-1 and CTLA-4 plays a considerable role in the dysfunction of immune cells upon SARS-CoV-2 infection. Blockade of these checkpoints has been suggested to improve the clinical outcome of COVID-19 patients by promoting potent immune responses against the virus. In the current review, we provide an overview of the potential of checkpoint inhibitors to induce potent immune responses against SARS-CoV-2 and improving the clinical outcome of severe COVID-19 patients.

Evolution of a bispecific G-quadruplex-forming circular aptamer to block IL-6/sIL-6R interaction for inflammation inhibition

IL-6 (interleukin-6) is an essential cytokine that participates in many inflammatory and immune responses, and disrupting the interaction between IL-6 and its receptor sIL-6R (soluble form of IL-6 receptor) represents a promising treatment strategy for inflammation and related diseases. Herein we report the first-ever effort of evolving a bispecific circular aptamer, named CIL-6A6-1, that is capable of binding both IL-6 and sIL-6R with nanomolar affinities and is stable in serum for more than 48 hours. CIL-6A6-1 can effectively block the IL-6/sIL-6R interaction and significantly inhibit cell inflammation. Most importantly, this bispecific aptamer is much more effective than aptamers that bind IL-6 and sIL-6R alone as well as tocilizumab, a commercially available humanized monoclonal antibody against sIL-6R, highlighting the advantage of selecting bispecific circular aptamers as molecular tools for anti-inflammation therapy. Interestingly, CIL-6A6-1 is predicted to adopt a unique structural fold with two G-quadruplex motifs capped by a long single-stranded region, which differs from all known DNA aptamers. This unique structural fold may also contribute to its excellent functionality and high stability in biological complex media. We anticipate that our study will represent a significant step forward towards demonstrating the practical utility of bispecific DNA aptamers for therapeutic applications.

Cell-cell communication: new insights and clinical implications

Multicellular organisms are composed of diverse cell types that must coordinate their behaviors through communication. Cell-cell communication (CCC) is essential for growth, development, differentiation, tissue and organ formation, maintenance, and physiological regulation. Cells communicate through direct contact or at a distance using ligand-receptor interactions. So cellular communication encompasses two essential processes: cell signal conduction for generation and intercellular transmission of signals, and cell signal transduction for reception and procession of signals. Deciphering intercellular communication networks is critical for understanding cell differentiation, development, and metabolism. First, we comprehensively review the historical milestones in CCC studies, followed by a detailed description of the mechanisms of signal molecule transmission and the importance of the main signaling pathways they mediate in maintaining biological functions. Then we systematically introduce a series of human diseases caused by abnormalities in cell communication and their progress in clinical applications. Finally, we summarize various methods for monitoring cell interactions, including cell imaging, proximity-based chemical labeling, mechanical force analysis, downstream analysis strategies, and single-cell technologies. These methods aim to illustrate how biological functions depend on these interactions and the complexity of their regulatory signaling pathways to regulate crucial physiological processes, including tissue homeostasis, cell development, and immune responses in diseases. In addition, this review enhances our understanding of the biological processes that occur after cell-cell binding, highlighting its application in discovering new therapeutic targets and biomarkers related to precision medicine. This collective understanding provides a foundation for developing new targeted drugs and personalized treatments.

Vitamin D supplementation and calcium: Many-faced gods or nobody in fighting against Corona Virus Disease 2019

In December 2019, Corona Virus Disease 2019 (COVID-19) was first identified and designated as a pandemic in March 2020 due to rapid spread of the virus globally. At the beginning of the pandemic, only a few treatment options, mainly focused on supportive care and repurposing medications, were available. Due to its effects on immune system, vitamin D was a topic of interest during the pandemic, and researchers investigated its potential impact on COVID-19 outcomes. However, the results of studies about the impact of vitamin D on the disease are inconclusive. In the present narrative review, different roles of vitamin D regarding the COVID-19 have been discussed to show that vitamin D supplementation should be recommended carefully.

Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles

Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy (ACT), have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA sequencing (RNA-seq) datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.

Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles

Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy, have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA-seq datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.

Decreased progenitor TCF1 + T-cells correlate with COVID-19 disease severity

COVID-19, caused by SARS-CoV-2, can lead to a severe inflammatory disease characterized by significant lymphopenia. However, the underlying cause for the depletion of T-cells in COVID-19 patients remains incompletely understood. In this study, we assessed the presence of different T-cell subsets in the progression of COVID-19 from mild to severe disease, with a focus on TCF1 expressing progenitor T-cells that are needed to replenish peripheral T-cells during infection. Our results showed a preferential decline in TCF1+ progenitor CD4 and CD8+ T-cells with disease severity. This decline was seen in various TCF1+ subsets including naive, memory and effector-memory cells, and surprisingly, was accompanied by a loss in cell division as seen by a marked decline in Ki67 expression. In addition, TCF1+ T-cells showed a reduction in pro-survival regulator, BcL2, and the appearance of a new population of TCF1 negative caspase-3 expressing cells in peripheral blood from patients with severe disease. The decline in TCF1+ T-cells was also seen in a subgroup of severe patients with vitamin D deficiency. Lastly, we found that sera from severe patients inhibited TCF1 transcription ex vivo which was attenuated by a blocking antibody against the cytokine, interleukin-12 (IL12). Collectively, our findings underscore the potential significance of TCF1+ progenitor T-cells in accounting for the loss of immunity in severe COVID-19 and outline an array of markers that could be used to identify disease progression.

Cellular nucleus image-based smarter microscope system for single cell analysis

Cell imaging technology is undoubtedly a powerful tool for studying single-cell heterogeneity due to its non-invasive and visual advantages. It covers microscope hardware, software, and image analysis techniques, which are hindered by low throughput owing to abundant hands-on time and expertise. Herein, a cellular nucleus image-based smarter microscope system for single-cell analysis is reported to achieve high-throughput analysis and high-content detection of cells. By combining the hardware of an automatic fluorescence microscope and multi-object recognition/acquisition software, we have achieved more advanced process automation with the assistance of Robotic Process Automation (RPA), which realizes a high-throughput collection of single-cell images. Automated acquisition of single-cell images has benefits beyond ease and throughout and can lead to uniform standard and higher quality images. We further constructed a single-cell image database-based convolutional neural network (Efficient Convolutional Neural Network, E-CNN) exceeding 20618 single-cell nucleus images. Computational analysis of large and complex data sets enhances the content and efficiency of single-cell analysis with the assistance of Artificial Intelligence (AI), which breaks through the super-resolution microscope's hardware limitation, such as specialized light sources with specific wavelengths, advanced optical components, and high-performance graphics cards. Our system can identify single-cell nucleus images that cannot be artificially distinguished with an accuracy of 95.3%. Overall, we build an ordinary microscope into a high-throughput analysis and high-content smarter microscope system, making it a candidate tool for Imaging cytology.

Meta-analysis of Transcriptomic Data from Lung Autopsy and Cellular Models of SARS-CoV-2 Infection

Severe COVID-19 is a systemic disorder involving excessive inflammatory response, metabolic dysfunction, multi-organ damage, and several clinical features. Here, we performed a transcriptome meta-analysis investigating genes and molecular mechanisms related to COVID-19 severity and outcomes. First, transcriptomic data of cellular models of SARS-CoV-2 infection were compiled to understand the first response to the infection. Then, transcriptomic data from lung autopsies of patients deceased due to COVID-19 were compiled to analyze altered genes of damaged lung tissue. These analyses were followed by functional enrichment analyses and gene-phenotype association. A biological network was constructed using the disturbed genes in the lung autopsy meta-analysis. Central genes were defined considering closeness and betweenness centrality degrees. A sub-network phenotype-gene interaction analysis was performed. The meta-analysis of cellular models found genes mainly associated with cytokine signaling and other pathogen response pathways. The meta-analysis of lung autopsy tissue found genes associated with coagulopathy, lung fibrosis, multi-organ damage, and long COVID-19. Only genes DNAH9 and FAM216B were found perturbed in both meta-analyses. BLNK, FABP4, GRIA1, ATF3, TREM2, TPPP, TPPP3, FOS, ALB, JUNB, LMNA, ADRB2, PPARG, TNNC1, and EGR1 were identified as central elements among perturbed genes in lung autopsy and were found associated with several clinical features of severe COVID-19. Central elements were suggested as interesting targets to investigate the relation with features of COVID-19 severity, such as coagulopathy, lung fibrosis, and organ damage.

Comparative single-cell RNA sequencing analysis of immune response to inactivated vaccine and natural SARS-CoV-2 infection

Uncovering the immune response to an inactivated SARS-CoV-2 vaccine (In-Vac) and natural infection is crucial for comprehending COVID-19 immunology. Here we conducted an integrated analysis of single-cell RNA sequencing (scRNA-seq) data from serial peripheral blood mononuclear cell (PBMC) samples derived from 12 individuals receiving In-Vac compared with those from COVID-19 patients. Our study reveals that In-Vac induces subtle immunological changes in PBMC, including cell proportions and transcriptomes, compared with profound changes for natural infection. In-Vac modestly upregulates IFN-α but downregulates NF-κB pathways, while natural infection triggers hyperactive IFN-α and NF-κB pathways. Both In-Vac and natural infection alter T/B cell receptor repertoires, but COVID-19 has more significant change in preferential VJ gene, indicating a vigorous immune response. Our study reveals distinct patterns of cellular communications, including a selective activation of IL-15RA/IL-15 receptor pathway after In-Vac boost, suggesting its potential role in enhancing In-Vac-induced immunity. Collectively, our study illuminates multifaceted immune responses to In-Vac and natural infection, providing insights for optimizing SARS-CoV-2 vaccine efficacy.

scNovel: a scalable deep learning-based network for novel rare cell discovery in single-cell transcriptomics

Single-cell RNA sequencing has achieved massive success in biological research fields. Discovering novel cell types from single-cell transcriptomics has been demonstrated to be essential in the field of biomedicine, yet is time-consuming and needs prior knowledge. With the unprecedented boom in cell atlases, auto-annotation tools have become more prevalent due to their speed, accuracy and user-friendly features. However, existing tools have mostly focused on general cell-type annotation and have not adequately addressed the challenge of discovering novel rare cell types. In this work, we introduce scNovel, a powerful deep learning-based neural network that specifically focuses on novel rare cell discovery. By testing our model on diverse datasets with different scales, protocols and degrees of imbalance, we demonstrate that scNovel significantly outperforms previous state-of-the-art novel cell detection models, reaching the most AUROC performance(the only one method whose averaged AUROC results are above 94%, up to 16.26% more comparing to the second-best method). We validate scNovel's performance on a million-scale dataset to illustrate the scalability of scNovel further. Applying scNovel on a clinical COVID-19 dataset, three potential novel subtypes of Macrophages are identified, where the COVID-related differential genes are also detected to have consistent expression patterns through deeper analysis. We believe that our proposed pipeline will be an important tool for high-throughput clinical data in a wide range of applications.

Expansion of memory Vδ2 T cells following SARS-CoV-2 vaccination revealed by temporal single-cell transcriptomics

γδ T cells provide rapid cellular immunity against pathogens. Here, we conducted matched single-cell RNA-sequencing and γδ-TCR-sequencing to delineate the molecular changes in γδ T cells during a longitudinal study following mRNA SARS-CoV-2 vaccination. While the first dose of vaccine primes Vδ2 T cells, it is the second administration that significantly boosts their immune response. Specifically, the second vaccination uncovers memory features of Vδ2 T cells, shaped by the induction of AP-1 family transcription factors and characterized by a convergent central memory signature, clonal expansion, and an enhanced effector potential. This temporally distinct effector response of Vδ2 T cells was also confirmed in vitro upon stimulation with SARS-CoV-2 spike-peptides. Indeed, the second challenge triggers a significantly higher production of IFNγ by Vδ2 T cells. Collectively, our findings suggest that mRNA SARS-CoV-2 vaccination might benefit from the establishment of long-lasting central memory Vδ2 T cells to confer protection against SARS-CoV-2 infection.

Reduced monocyte proportions and responsiveness in convalescent COVID-19 patients

Introduction

The clinical manifestations of acute severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection and coronavirus disease 2019 (COVID-19) suggest a dysregulation of the host immune response that leads to inflammation, thrombosis, and organ dysfunction. It is less clear whether these dysregulated processes persist during the convalescent phase of disease or during long COVID. We sought to examine the effects of SARS-CoV-2 infection on the proportions of classical, intermediate, and nonclassical monocytes, their activation status, and their functional properties in convalescent COVID-19 patients.

Methods

Peripheral blood mononuclear cells (PBMCs) from convalescent COVID-19 patients and uninfected controls were analyzed by multiparameter flow cytometry to determine relative percentages of total monocytes and monocyte subsets. The expression of activation markers and proinflammatory cytokines in response to LPS treatment were measured by flow cytometry and ELISA, respectively.

Results

We found that the percentage of total monocytes was decreased in convalescent COVID-19 patients compared to uninfected controls. This was due to decreased intermediate and non-classical monocytes. Classical monocytes from convalescent COVID-19 patients demonstrated a decrease in activation markers, such as CD56, in response to stimulation with bacterial lipopolysaccharide (LPS). In addition, classical monocytes from convalescent COVID-19 patients showed decreased expression of CD142 (tissue factor), which can initiate the extrinsic coagulation cascade, in response to LPS stimulation. Finally, we found that monocytes from convalescent COVID-19 patients produced less TNF-α and IL-6 in response to LPS stimulation, than those from uninfected controls.

Conclusion

SARS-CoV-2 infection exhibits a clear effect on the relative proportions of monocyte subsets, the activation status of classical monocytes, and proinflammatory cytokine production that persists during the convalescent phase of disease.

COVID-19 Pneumonia and Cytokine Storm Syndrome

Virus-associated cytokine storm syndrome (CSS) has been recognized for a long time and the classic viruses associated are the herpes viruses EBV, CMV, and HHV-8 as described in chapters IVa,b. In addition, pandemic viruses such as influenza, SARS, and MERS can result in severe CSS that might ultimately lead to severe acute respiratory distress syndrome (ARDS) and death [1-3]. A new pandemic caused by SARS-CoV-2 that started in 2019 has defined another chapter in the virus-associated CSS. The clinical spectrum of SARS-CoV-2 infection has many faces. In most people, it will be asymptomatic, but it can also result in severe COVID-19 pneumonia, ARDS, and multiorgan failure depending on age, comorbidities, and immune status [4]. In addition, this pandemic has known many different stages and developed in a unique way in the first 2 years. It started in a setting where there was no immunity to the virus and after a year, highly effective vaccines were introduced and herd immunity built up over time. However, vaccine effectiveness was waning over time depending on multiple factors, and novel variant strains of the virus circulated across different areas in the world. Antiviral therapy was developed and introduced, and treatment changed from giving no immunomodulatory treatment, followed by the introduction of corticosteroids [5], and later the addition of more targeted strategies such as JAK inhibitors [6] and blocking IL-6 signaling [7]. Therefore, the scientific literature published on COVID-19 must be seen in the context of a highly dynamic and rapidly changing pandemic, making it difficult to compare results from early studies to more recent reports even within 2 years. Still, a lot has been learned over a very short period. It has become apparent that severe COVID-19 is predominantly a disease of immune dysregulation with components that can be defined as CSS. It has unique features and overlapping characteristics with other CSSs, and immunological treatment addressing the CSS has been extensively explored, which will be described here.

CellGO: a novel deep learning-based framework and webserver for cell-type-specific gene function interpretation

Interpreting the function of genes and gene sets identified from omics experiments remains a challenge, as current pathway analysis tools often fail to consider the critical biological context, such as tissue or cell-type specificity. To address this limitation, we introduced CellGO. CellGO tackles this challenge by leveraging the visible neural network (VNN) and single-cell gene expressions to mimic cell-type-specific signaling propagation along the Gene Ontology tree within a cell. This design enables a novel scoring system to calculate the cell-type-specific gene-pathway paired active scores, based on which, CellGO is able to identify cell-type-specific active pathways associated with single genes. In addition, by aggregating the activities of single genes, CellGO extends its capability to identify cell-type-specific active pathways for a given gene set. To enhance biological interpretation, CellGO offers additional features, including the identification of significantly active cell types and driver genes and community analysis of pathways. To validate its performance, CellGO was assessed using a gene set comprising mixed cell-type markers, confirming its ability to discern active pathways across distinct cell types. Subsequent benchmarking analyses demonstrated CellGO's superiority in effectively identifying cell types and their corresponding cell-type-specific pathways affected by gene knockouts, using either single genes or sets of genes differentially expressed between knockout and control samples. Moreover, CellGO demonstrated its ability to infer cell-type-specific pathogenesis for disease risk genes. Accessible as a Python package, CellGO also provides a user-friendly web interface, making it a versatile and accessible tool for researchers in the field.

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.

Single cell RNA-sequencing analysis reveals that N-acetylcysteine partially reverses hepatic immune dysfunction in biliary atresia

Background & Aims

Our previous study indicated that CD177+ neutrophil activation has a vital role in the pathogenesis of biliary atresia (BA), which is partially ameliorated by N-acetylcysteine (NAC) treatment. Here, we evaluated the clinical efficacy of NAC treatment and profiled liver-resident immune cells via single cell RNA-sequencing (scRNA-seq) analysis to provide a comprehensive immune landscape of NAC-derived immune regulation.

Methods

A pilot clinical study was conducted to evaluate the potential effects of intravenous NAC treatment on infants with BA, and a 3-month follow-up was carried out to assess treatment efficacy. scRNA-seq analysis of liver CD45+ immune cells in the control (n = 4), BA (n = 6), and BA + NAC (n = 6) groups was performed and the effects on innate cells, including neutrophil and monocyte-macrophage subsets, and lymphoid cells were evaluated.

Results

Intravenous NAC treatment demonstrated beneficial efficacy for infants with BA by improving bilirubin metabolism and bile acid flow. Two hepatic neutrophil subsets of innate cells were identified by scRNA-seq analysis. NAC treatment suppressed oxidative phosphorylation and reactive oxygen species production in immature neutrophils, which were transcriptionally and functionally similar to CD177+ neutrophils. We also observed the suppression of hepatic monocyte-mediated inflammation, decreased levels of oxidative phosphorylation, and M1 polarisation in Kupffer-like macrophages by NAC. In lymphoid cells, enhancement of humoral immune responses and attenuation of cellular immune responses were observed after NAC treatment. Moreover, cell-cell interaction analysis showed that innate/adaptive proinflammatory responses were downregulated by NAC.

Conclusions

Our clinical and scRNA-seq data demonstrated that intravenous NAC treatment partially reversed liver immune dysfunction, alleviated the proinflammatory responses in BA by targeting innate cells, and exhibited beneficial clinical efficacy.

Impact and implications

BA is a serious liver disease that affects newborns and has no effective drug treatment. In this study, scRNA-seq showed that NAC treatment can partially reverse the immune dysfunction of neutrophil extracellular trap-releasing CD177+ neutrophils and Kupffer cells, and lower the inflammatory responses of other innate immune cells in BA. In consequence, intravenous NAC treatment improved the clinical outcomes of patients with BA in term of bilirubin metabolism.

Precise identification of cell states altered in disease using healthy single-cell references

Joint analysis of single-cell genomics data from diseased tissues and a healthy reference can reveal altered cell states. We investigate whether integrated collections of data from healthy individuals (cell atlases) are suitable references for disease-state identification and whether matched control samples are needed to minimize false discoveries. We demonstrate that using a reference atlas for latent space learning followed by differential analysis against matched controls leads to improved identification of disease-associated cells, especially with multiple perturbed cell types. Additionally, when an atlas is available, reducing control sample numbers does not increase false discovery rates. Jointly analyzing data from a COVID-19 cohort and a blood cell atlas, we improve detection of infection-related cell states linked to distinct clinical severities. Similarly, we studied disease states in pulmonary fibrosis using a healthy lung atlas, characterizing two distinct aberrant basal states. Our analysis provides guidelines for designing disease cohort studies and optimizing cell atlas use.

Differences in leucocytes and inflammation-based indices among critically ill patients owing to SARS-CoV-2 variants during several successive waves of COVID-19 pandemic

BACKGROUND/AIM

Inflammatory indices are useful informative markers in assessing the severity of the COVID-19 disease course; however, their involvements during series waves of SARS-CoV-2 virus outbreaks in critical patients with COVID-19 remain unclear. Hence, we aimed to ascertain the changing dynamics of the combined inflammatory indices (NLR, dNLR, CLR, LMR, PLR, SII, and SIRI) and their associations with clinical outcomes in severe COVID-19 patients during serial waves of SARS-CoV-2.

PATIENTS AND METHODS

We retrospectively enrolled 163 severe COVID-19 patients admitted to the ICU during six SARS-CoV-2 waves.

RESULTS

We found that most of patients admitted to the ICU were from the fourth wave. Patients in the fourth wave were considerably younger and had the highest percentage of ARDS than other waves. The highest CRP was found in the first wave, while the lowest in patients admitted in the sixth wave. Although most of the COVID-19 waves were marked with leukocytosis, neutrophilia, and lymphocytopenia, the lowest of both NLR and dNLR were found in the fourth wave "Delta wave" and the lowest of both CLR and SII were observed in "Omicron wave". Interestingly, during most of the COVID-19 waves, the derived combined inflammatory ratio NLR, dNLR, CLR, SII and SIRI were sustained at high levels in fatal cases at the last day of hospitalization, while these indices declined in the alive group at the end of ICU hospitalization. No major difference was identified in lymphocyte count between admission and the last day of hospitalization in both deceased and recovered COVID-19 patients during Delta and Omicron waves. Moreover, patients admitted in the Omicron wave had less severe disease compared to those admitted in the Delta wave. The Kaplan-Meier analysis revealed no significant difference in survival rates or the probability of respiratory failure between six successive COVID-19 waves.

CONCLUSION

Taken together, our results showed marked differences in the alteration of nonspecific inflammation and damage in the adaptive immune response during the six serial SARS-CoV-2 waves. Considering the inflammatory response of infectious diseases, embedding inflammatory indices informative markers into routine clinical testing offers the potential to mitigate the impact of future pandemics of COVID-19 and other infectious diseases.

Population-level integration of single-cell datasets enables multi-scale analysis across samples

The increasing generation of population-level single-cell atlases has the potential to link sample metadata with cellular data. Constructing such references requires integration of heterogeneous cohorts with varying metadata. Here we present single-cell population level integration (scPoli), an open-world learner that incorporates generative models to learn sample and cell representations for data integration, label transfer and reference mapping. We applied scPoli on population-level atlases of lung and peripheral blood mononuclear cells, the latter consisting of 7.8 million cells across 2,375 samples. We demonstrate that scPoli can explain sample-level biological and technical variations using sample embeddings revealing genes associated with batch effects and biological effects. scPoli is further applicable to single-cell sequencing assay for transposase-accessible chromatin and cross-species datasets, offering insights into chromatin accessibility and comparative genomics. We envision scPoli becoming an important tool for population-level single-cell data integration facilitating atlas use but also interpretation by means of multi-scale analyses.

Reduced Monocyte Proportions and Responsiveness in Convalescent COVID-19 Patients

The clinical manifestations of acute severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection and COVID-19 suggest a dysregulation of the host immune response that leads to inflammation, thrombosis, and organ dysfunction. It is less clear whether these dysregulated processes persist during the convalescent phase of disease or during long COVID. We investigated the effects of SARS-CoV-2 infection on the proportions of classical, intermediate, and non-classical monocytes, their activation status, and their functional properties in convalescent COVID-19 patients and uninfected control subjects. We found that the percentage of total monocytes was decreased in convalescent COVID-19 patients compared to uninfected controls. This was due to decreased intermediate and non-classical monocytes. Classical monocytes from convalescent COVID-19 patients demonstrated a decrease in activation markers, such as CD56, in response to stimulation with bacterial lipopolysaccharide (LPS). In addition, classical monocytes from convalescent COVID-19 patients showed decreased expression of CD142 (tissue factor), which can initiate the extrinsic coagulation cascade, in response to LPS stimulation. Finally, we found that monocytes from convalescent COVID-19 patients produced less TNF-α and IL-6 in response to LPS stimulation, than those from uninfected controls. In conclusion, SARS-CoV-2 infection exhibits a clear effect on the relative proportions of monocyte subsets, the activation status of classical monocytes, and proinflammatory cytokine production that persists during the convalescent phase of disease.

Potential network markers and signaling pathways for B cells of COVID-19 based on single-cell condition-specific networks

To explore the potential network markers and related signaling pathways of human B cells infected by COVID-19, we performed standardized integration and analysis of single-cell sequencing data to construct conditional cell-specific networks (CCSN) for each cell. Then the peripheral blood cells were clustered and annotated based on the conditional network degree matrix (CNDM) and gene expression matrix (GEM), respectively, and B cells were selected for further analysis. Besides, based on the CNDM of B cells, the hub genes and 'dark' genes (a gene has a significant difference between case and control samples not in a gene expression level but in a conditional network degree level) closely related to COVID-19 were revealed. Interestingly, some of the 'dark' genes and differential degree genes (DDGs) encoded key proteins in the JAK-STAT pathway, which had antiviral effects. The protein p21 encoded by the 'dark' gene CDKN1A was a key regulator for the COVID-19 infection-related signaling pathway. Elevated levels of proteins encoded by some DDGs were directly related to disease severity of patients with COVID-19. In short, the proteins encoded by 'dark' genes complement some missing links in COVID-19 and these signaling pathways played an important role in the growth and activation of B cells.

SARS-CoV-2 niches in human placenta revealed by spatial transcriptomics

BACKGROUND

Functional placental niches are presumed to spatially separate maternal-fetal antigens and restrict the vertical transmission of pathogens. We hypothesized a high-resolution map of placental transcription could provide direct evidence for niche microenvironments with unique functions and transcription profiles.

METHODS

We utilized Visium Spatial Transcriptomics paired with H&E staining to generate 17,927 spatial transcriptomes. By integrating these spatial transcriptomes with 273,944 placental single-cell and single-nuclei transcriptomes, we generated an atlas composed of at least 22 subpopulations in the maternal decidua, fetal chorionic villi, and chorioamniotic membranes.

FINDINGS

Comparisons of placentae from uninfected healthy controls (n = 4) with COVID-19 asymptomatic (n = 4) and symptomatic (n = 5) infected participants demonstrated that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection in syncytiotrophoblasts occurred in both the presence and the absence of maternal clinical disease. With spatial transcriptomics, we found that the limit of detection for SARS-CoV-2 was 1/7,000 cells, and placental niches without detectable viral transcripts were unperturbed. In contrast, niches with high SARS-CoV-2 transcript levels were associated with significant upregulation in pro-inflammatory cytokines and interferon-stimulated genes, altered metallopeptidase signaling (TIMP1), with coordinated shifts in macrophage polarization, histiocytic intervillositis, and perivillous fibrin deposition. Fetal sex differences in gene expression responses to SARS-CoV-2 were limited, with confirmed mapping limited to the maternal decidua in males.

CONCLUSIONS

High-resolution placental transcriptomics with spatial resolution revealed dynamic responses to SARS-CoV-2 in coordinate microenvironments in the absence and presence of clinically evident disease.

FUNDING

This work was supported by the NIH (R01HD091731 and T32-HD098069), NSF (2208903), the Burroughs Welcome Fund and the March of Dimes Preterm Birth Research Initiatives, and a Career Development Award from the American Society of Gene and Cell Therapy.

T cell receptor β repertoires in patients with COVID-19 reveal disease severity signatures

Background

The immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are crucial in maintaining a delicate balance between protective effects and harmful pathological reactions that drive the progression of coronavirus disease 2019 (COVID-19). T cells play a significant role in adaptive antiviral immune responses, making it valuable to investigate the heterogeneity and diversity of SARS-CoV-2-specific T cell responses in COVID-19 patients with varying disease severity.

Methods

In this study, we employed high-throughput T cell receptor (TCR) β repertoire sequencing to analyze TCR profiles in the peripheral blood of 192 patients with COVID-19, including those with moderate, severe, or critical symptoms, and compared them with 81 healthy controls. We specifically focused on SARS-CoV-2-associated TCR clonotypes.

Results

We observed a decrease in the diversity of TCR clonotypes in COVID-19 patients compared to healthy controls. However, the overall abundance of dominant clones increased with disease severity. Additionally, we identified significant differences in the genomic rearrangement of variable (V), joining (J), and VJ pairings between the patient groups. Furthermore, the SARS-CoV-2-associated TCRs we identified enabled accurate differentiation between COVID-19 patients and healthy controls (AUC > 0.98) and distinguished those with moderate symptoms from those with more severe forms of the disease (AUC > 0.8). These findings suggest that TCR repertoires can serve as informative biomarkers for monitoring COVID-19 progression.

Conclusions

Our study provides valuable insights into TCR repertoire signatures that can be utilized to assess host immunity to COVID-19. These findings have important implications for the use of TCR β repertoires in monitoring disease development and indicating disease severity.

Constructing a full, multiple-layer interactome for SARS-CoV-2 in the context of lung disease: Linking the virus with human genes and microbes

The COVID-19 pandemic caused by the SARS-CoV-2 virus has resulted in millions of deaths worldwide. The disease presents with various manifestations that can vary in severity and long-term outcomes. Previous efforts have contributed to the development of effective strategies for treatment and prevention by uncovering the mechanism of viral infection. We now know all the direct protein-protein interactions that occur during the lifecycle of SARS-CoV-2 infection, but it is critical to move beyond these known interactions to a comprehensive understanding of the "full interactome" of SARS-CoV-2 infection, which incorporates human microRNAs (miRNAs), additional human protein-coding genes, and exogenous microbes. Potentially, this will help in developing new drugs to treat COVID-19, differentiating the nuances of long COVID, and identifying histopathological signatures in SARS-CoV-2-infected organs. To construct the full interactome, we developed a statistical modeling approach called MLCrosstalk (multiple-layer crosstalk) based on latent Dirichlet allocation. MLCrosstalk integrates data from multiple sources, including microbes, human protein-coding genes, miRNAs, and human protein-protein interactions. It constructs "topics" that group SARS-CoV-2 with genes and microbes based on similar patterns of co-occurrence across patient samples. We use these topics to infer linkages between SARS-CoV-2 and protein-coding genes, miRNAs, and microbes. We then refine these initial linkages using network propagation to contextualize them within a larger framework of network and pathway structures. Using MLCrosstalk, we identified genes in the IL1-processing and VEGFA-VEGFR2 pathways that are linked to SARS-CoV-2. We also found that Rothia mucilaginosa and Prevotella melaninogenica are positively and negatively correlated with SARS-CoV-2 abundance, a finding corroborated by analysis of single-cell sequencing data.

A LY6E-PHB1-TRIM21 assembly degrades CD14 protein to mitigate LPS-induced inflammatory response

A major theme of host against invading pathogens lies in multiple regulatory nodes that ensure sufficient signals for protection while avoiding excessive signals toward over-inflammation. The TLR4/MD-2/CD14 complex receptor-mediated response to bacterial lipopolysaccharide (LPS) represents a paradigm for understanding the proper control of anti-pathogen innate immunity. In this study, we studied the mechanism by which the glycosylphosphatidylinositol (GPI)-linked LY6E protein constrains LPS response via downregulating CD14. We first showed that LY6E downregulated CD14 via ubiquitin-dependent proteasomal degradation. The subsequent profiling of LY6E protein interactome led to the revelation that the degradation of CD14 by LY6E requires PHB1, which interacts with CD14 in a LY6E-dependent manner. Finally, we identified the PHB1-interacting TRIM21 as the major ubiquitin E3 ligase for the LY6E-mediated ubiquitination of CD14. Together, our study elucidated the molecular basis of LY6E-mediated governance of LPS response, alongside providing new insights to regulatory mechanisms controlling the homeostasis of membrane proteins.

Monocyte Distribution Width (MDW) in Patients with COVID-19: An Indicator of Disease Severity

Identifying patients with Coronavirus disease-2019 (COVID-19) who may have a severe illness is essential for timely intervention and decreasing the fatality rate. In the present study, we evaluated the performance of Monocyte Distribution Width (MDW) as a prognostic marker for identifying disease severity in COVID-19 patients. We included 145 patients with PCR-confirmed COVID-19 infection in the study. The performance of MDW was evaluated by calculating the area under the receiver operating characteristic curve (AUC), specificity, sensitivity, negative predictive value, and positive predictive value. Further analysis was conducted for the disease outcome, comparing COVID-19 patients discharged (n = 135) to deceased COVID-19 patients (n = 10). As a marker of disease severity, MDW demonstrated an AUC of 0.702 (95% CI 0.620-0.775) in ROC analysis. If MDW is considered a marker of patient outcome, AUC was 0.916 (95% CI 0.862-0.953), comparing deceased COVID-19 patients vs. those who survived. At a cut-off of > 25.4 on admission, MDW correlates well with poor disease outcomes in COVID-19 patients. MDW can be considered a helpful parameter in predicting the severity of COVID-19 disease and patient outcomes. Its role and incorporation in the standard diagnostic algorithm and management of COVID-19 patients need further validation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12288-023-01665-y.

A scalable sparse neural network framework for rare cell type annotation of single-cell transcriptome data

Automatic cell type annotation methods are increasingly used in single-cell RNA sequencing (scRNA-seq) analysis due to their fast and precise advantages. However, current methods often fail to account for the imbalance of scRNA-seq datasets and ignore information from smaller populations, leading to significant biological analysis errors. Here, we introduce scBalance, an integrated sparse neural network framework that incorporates adaptive weight sampling and dropout techniques for auto-annotation tasks. Using 20 scRNA-seq datasets with varying scales and degrees of imbalance, we demonstrate that scBalance outperforms current methods in both intra- and inter-dataset annotation tasks. Additionally, scBalance displays impressive scalability in identifying rare cell types in million-level datasets, as shown in the bronchoalveolar cell landscape. scBalance is also significantly faster than commonly used tools and comes in a user-friendly format, making it a superior tool for scRNA-seq analysis on the Python-based platform.

CD16+ monocytes are involved in the hyper-inflammatory state of Prader-Willi Syndrome by single-cell transcriptomic analysis

Background

Patients with Prader-Willi syndrome (PWS) have a reduced life expectancy due to inflammation-related disease including cardiovascular disease and diabetes. Abnormal activation of peripheral immune system is postulated as a contributor. However, detailed features of the peripheral immune cells in PWS have not been fully elucidated.

Methods

Serum inflammatory cytokines were measured in healthy controls (n=13) and PWS patients (n=10) using a 65- multiplex cytokine assays. Changes of the peripheral immune cells in PWS was assessed by single-cell RNA sequencing (scRNA-seq) and high-dimensional mass cytometry (CyTOF) using peripheral blood mononuclear cells (PBMCs) from PWS patients (n=6) and healthy controls (n=12).

Results

PWS patients exhibited hyper-inflammatory signatures in PBMCs and monocytes were the most pronounced. Most inflammatory serum cytokines were increased in PWS, including IL-1β, IL-2R, IL-12p70, and TNF-α. The characteristics of monocytes evaluated by scRNA-seq and CyTOF showed that CD16⁺ monocytes were significantly increased in PWS patients. Functional pathway analysis revealed that CD16⁺ monocytes upregulated pathways in PWS were closely associated with TNF/IL-1β- driven inflammation signaling. The CellChat analysis identified CD16⁺ monocytes transmitted chemokine and cytokine signaling to drive inflammatory process in other cell types. Finally, we explored the PWS deletion region 15q11-q13 might be responsible for elevated levels of inflammation in the peripheral immune system.

Conclusion

The study highlights that CD16⁺ monocytes contributor to the hyper-inflammatory state of PWS which provides potential targets for immunotherapy in the future and expands our knowledge of peripheral immune cells in PWS at the single cell level for the first time.

Prognostic immune markers identifying patients with severe COVID-19 who respond to tocilizumab

Introduction

A growing number of evidences suggest that the combination of hyperinflammation, dysregulated T and B cell response and cytokine storm play a major role in the immunopathogenesis of severe COVID-19. IL-6 is one of the main pro-inflammatory cytokines and its levels are increased during SARS-CoV-2 infection. Several observational and randomized studies demonstrated that tocilizumab, an IL-6R blocker, improves survival in critically ill patients both in infectious disease and intensive care units. However, despite transforming the treatment options for COVID-19, IL-6R inhibition is still ineffective in a fraction of patients.

Methods

In the present study, we investigated the impact of two doses of tocilizumab in patients with severe COVID-19 who responded or not to the treatment by analyzing a panel of cytokines, chemokines and other soluble factors, along with the composition of peripheral immune cells, paying a particular attention to T and B lymphocytes.

Results

We observed that, in comparison with non-responders, those who responded to tocilizumab had different levels of several cytokines and different T and B cells proportions before starting therapy. Moreover, in these patients, tocilizumab was further able to modify the landscape of the aforementioned soluble molecules and cellular markers.

Conclusions

We found that tocilizumab has pleiotropic effects and that clinical response to this drug remain heterogenous. Our data suggest that it is possible to identify patients who will respond to treatment and that the administration of tocilizumab is able to restore the immune balance through the re-establishment of different cell populations affected by SARS-COV-2 infection, highlighting the importance of temporal examination of the pathological features from the diagnosis.

Accurate integration of multiple heterogeneous single-cell RNA-seq data sets by learning contrastive biological variation

For most biological and medical applications of single-cell transcriptomics, an integrative study of multiple heterogeneous single-cell RNA sequencing (scRNA-seq) data sets is crucial. However, present approaches are unable to integrate diverse data sets from various biological conditions effectively because of the confounding effects of biological and technical differences. We introduce single-cell integration (scInt), an integration method based on accurate, robust cell-cell similarity construction and unified contrastive biological variation learning from multiple scRNA-seq data sets. scInt provides a flexible and effective approach to transfer knowledge from the already integrated reference to the query. We show that scInt outperforms 10 other cutting-edge approaches using both simulated and real data sets, particularly in the case of complex experimental designs. Application of scInt to mouse developing tracheal epithelial data shows its ability to integrate development trajectories from different developmental stages. Furthermore, scInt successfully identifies functionally distinct condition-specific cell subpopulations in single-cell heterogeneous samples from a variety of biological conditions.

Retrospective study on the efficacy of monocyte distribution width (MDW) as a screening test for COVID-19

BACKGROUND

Pathogenic genetic testing for coronavirus disease 2019 (COVID-19) can detect viruses with high sensitivity; however, there are several challenges. In the prevention, testing, and treatment of COVID-19, more effective, safer, and convenient methods are desired. We evaluated the possibility of monocyte distribution width (MDW) as an infection biomarker in COVID-19 testing.

METHODS

The efficacy of MDW as a screening test for COVID-19 was retrospectively assessed in 80 patients in the COVID-19 group and 232 patients in the non-COVID-19 group (141 patients with acute respiratory infection, 19 patients with nonrespiratory infection, one patient with a viral infection, 11 patients who had received treatment for COVID-19, one patient in contact with COVID-19 patients, and 59 patients with noninfectious disease).

RESULTS

The median MDW in 80 patients in the COVID-19 group was 23.3 (17.2-33.6), and the median MDW in 232 patients in the non-COVID-19 group was 19.0 (13.6-30.2) (P < 0.001). When the COVID-19 group was identified using the MDW cut-off value of 21.3 from the non-COVID-19 group, the area under the curve (AUC) was 0.844, and the sensitivity and specificity were 81.3% and 78.2%, respectively. Comparison of MDW by severity between the COVID-19 group and patients with acute respiratory infection in the non-COVID-19 group showed that MDW was significantly higher in the COVID-19 group for all mild, moderate I, and moderate II disease.

CONCLUSIONS

MDW (cut-off value: 21.3) may be used as a screening test for COVID-19 in fever outpatients. Trial registration This study was conducted after being approved by the ethics committee of National Hospital Organization Omuta National Hospital (Approval No. 3-19). This study can be accessed via https://omuta.hosp.go.jp/files/000179721.pdf .

Tocilizumab-coated solid lipid nanoparticles loaded with cannabidiol as a novel drug delivery strategy for treating COVID-19: A review

Commonly used clinical strategies against coronavirus disease 19 (COVID-19), including the potential role of monoclonal antibodies for site-specific targeted drug delivery, are discussed here. Solid lipid nanoparticles (SLN) tailored with tocilizumab (TCZ) and loading cannabidiol (CBD) are proposed for the treatment of COVID-19 by oral route. TCZ, as a humanized IgG1 monoclonal antibody and an interleukin-6 (IL-6) receptor agonist, can attenuate cytokine storm in patients infected with SARS-CoV-2. CBD (an anti-inflammatory cannabinoid and TCZ agonist) alleviates anxiety, schizophrenia, and depression. CBD, obtained from Cannabis sativa L., is known to modulate gene expression and inflammation and also shows anti-cancer and anti-inflammatory properties. It has also been recognized to modulate angiotensin-converting enzyme II (ACE2) expression in SARS-CoV-2 target tissues. It has already been proven that immunosuppressive drugs targeting the IL-6 receptor may ameliorate lethal inflammatory responses in COVID-19 patients. TCZ, as an immunosuppressive drug, is mainly used to treat rheumatoid arthritis, although several attempts have been made to use it in the active hyperinflammatory phase of COVID-19, with promising outcomes. TCZ is currently administered intravenously. It this review, we discuss the potential advances on the use of SLN for oral administration of TCZ-tailored CBD-loaded SLN, as an innovative platform for managing SARS-CoV-2 and related infections.

Tocilizumab-treated convalescent COVID-19 patients retain the cross-neutralization potential against SARS-CoV-2 variants

Although tocilizumab treatment in severe and critical coronavirus disease 2019 (COVID-19) patients has proven its efficacy at the clinical level, there is little evidence supporting the effect of short-term use of interleukin-6 receptor blocking therapy on the B cell sub-populations and the cross-neutralization of SARS-CoV-2 variants in convalescent COVID-19 patients. We performed immunological profiling of 69 tocilizumab-treated and non-treated convalescent COVID-19 patients in total. We observed that SARS-CoV-2-specific IgG1 titers depended on disease severity but not on tocilizumab treatment. The plasma of both treated and non-treated patients infected with the ancestral variant exhibit strong neutralizing activity against the ancestral virus and the Alpha, Beta, and Delta variants of SARS-CoV-2, whereas the Gamma and Omicron viruses were less sensitive to seroneutralization. Overall, we observed that, despite the clinical benefits of short-term tocilizumab therapy in modifying the cytokine storm associated with COVID-19 infections, there were no modifications in the robustness of B cell and IgG responses to Spike antigens.

DiSiR: fast and robust method to identify ligand-receptor interactions at subunit level from single-cell RNA-sequencing data

Most cell-cell interactions and crosstalks are mediated by ligand-receptor interactions. The advent of single-cell RNA-sequencing (scRNA-seq) techniques has enabled characterizing tissue heterogeneity at single-cell level. In the past few years, several methods have been developed to study ligand-receptor interactions at cell type level using scRNA-seq data. However, there is still no easy way to query the activity of a specific user-defined signaling pathway in a targeted way or to map the interactions of the same subunit with different ligands as part of different receptor complexes. Here, we present DiSiR, a fast and easy-to-use permutation-based software framework to investigate how individual cells are interacting with each other by analyzing signaling pathways of multi-subunit ligand-activated receptors from scRNA-seq data, not only for available curated databases of ligand-receptor interactions, but also for interactions that are not listed in these databases. We show that, when utilized to infer ligand-receptor interactions from both simulated and real datasets, DiSiR outperforms other well-known permutation-based methods, e.g. CellPhoneDB and ICELLNET. Finally, to demonstrate DiSiR's utility in exploring data and generating biologically relevant hypotheses, we apply it to COVID lung and rheumatoid arthritis (RA) synovium scRNA-seq datasets and highlight potential differences between inflammatory pathways at cell type level for control versus disease samples.

Multi-omics and immune cells' profiling of COVID-19 patients for ICU admission prediction: in silico analysis and an integrated machine learning-based approach in the framework of Predictive, Preventive, and Personalized Medicine

Background

Intensive care unit admission (ICUA) triage has been urgent need for solving the shortage of ICU beds, during the coronavirus disease 2019 (COVID-19) surge. In silico analysis and integrated machine learning (ML) approach, based on multi-omics and immune cells (ICs) profiling, might provide solutions for this issue in the framework of predictive, preventive, and personalized medicine (PPPM).

Methods

Multi-omics was used to screen the synchronous differentially expressed protein-coding genes (SDEpcGs), and an integrated ML approach to develop and validate a nomogram for prediction of ICUA. Finally, the independent risk factor (IRF) with ICs profiling of the ICUA was identified.

Results

Colony-stimulating factor 1 receptor (CSF1R) and peptidase inhibitor 16 (PI16) were identified as SDEpcGs, and each fold change (FC_ij) of CSF1R and PI16 was selected to develop and validate a nomogram to predict ICUA. The area under curve (AUC) of the nomogram was 0.872 (95% confidence interval (CI): 0.707 to 0.950) on the training set, and 0.822 (95% CI: 0.659 to 0.917) on the testing set. CSF1R was identified as an IRF of ICUA, expressed in and positively correlated with monocytes which had a lower fraction in COVID-19 ICU patients.

Conclusion

The nomogram and monocytes could provide added value to ICUA prediction and targeted prevention, which are cost-effective platform for personalized medicine of COVID-19 patients. The log₂fold change (log₂FC) of the fraction of monocytes could be monitored simply and economically in primary care, and the nomogram offered an accurate prediction for secondary care in the framework of PPPM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00317-5.

Lung T cell response in COVID-19

The COVID-19 pandemic has shown the potentially devastating impact of novel respiratory infections worldwide. Insightful data obtained in the last years have shed light on the pathophysiology of SARS-CoV-2 infection and the role of the inflammatory response in driving both the resolution of the disease and uncontrolled deleterious inflammatory status in severe cases. In this mini-review, we cover some important aspects of the role of T cells in COVID-19 with a special focus on the local response in the lung. We focus on the reported T cell phenotypes in mild, moderate, and severe COVID-19, focusing on lung inflammation and on both the protective and damaging roles of the T cell response, also highlighting the open questions in the field.

C1Q labels a highly aggressive macrophage-like leukemia population indicating extramedullary infiltration and relapse

Extramedullary infiltration (EMI) is a concomitant manifestation that may indicate poor outcome of acute myeloid leukemia (AML). The underlying mechanism remains poorly understood and therapeutic options are limited. Here, we employed single-cell RNA sequencing on bone marrow (BM) and EMI samples from a patient with AML presenting pervasive leukemia cutis. A complement C1Q+ macrophage-like leukemia subset, which was enriched within cutis and existed in BM before EMI manifestations, was identified and further verified in multiple patients with AML. Genomic and transcriptional profiling disclosed mutation and gene expression signatures of patients with EMI that expressed high levels of C1Q. RNA sequencing and quantitative proteomic analysis revealed expression dynamics of C1Q from primary to relapse. Univariate and multivariate analysis demonstrated adverse prognosis significance of C1Q expression. Mechanistically, C1Q expression, which was modulated by transcription factor MAF BZIP transcription factor B, endowed leukemia cells with tissue infiltration ability, which could establish prominent cutaneous or gastrointestinal EMI nodules in patient-derived xenograft and cell line-derived xenograft models. Fibroblasts attracted migration of the C1Q+ leukemia cells through C1Q-globular C1Q receptor recognition and subsequent stimulation of transforming growth factor β1. This cell-to-cell communication also contributed to survival of C1Q+ leukemia cells under chemotherapy stress. Thus, C1Q served as a marker for AML with adverse prognosis, orchestrating cancer infiltration pathways through communicating with fibroblasts and represents a compelling therapeutic target for EMI.

Biologically informed deep learning to query gene programs in single-cell atlases

The increasing availability of large-scale single-cell atlases has enabled the detailed description of cell states. In parallel, advances in deep learning allow rapid analysis of newly generated query datasets by mapping them into reference atlases. However, existing data transformations learned to map query data are not easily explainable using biologically known concepts such as genes or pathways. Here we propose expiMap, a biologically informed deep-learning architecture that enables single-cell reference mapping. ExpiMap learns to map cells into biologically understandable components representing known 'gene programs'. The activity of each cell for a gene program is learned while simultaneously refining them and learning de novo programs. We show that expiMap compares favourably to existing methods while bringing an additional layer of interpretability to integrative single-cell analysis. Furthermore, we demonstrate its applicability to analyse single-cell perturbation responses in different tissues and species and resolve responses of patients who have coronavirus disease 2019 to different treatments across cell types.

Potential long-term effects of SARS-CoV-2 infection on the pulmonary vasculature: Multilayered cross-talks in the setting of coinfections and comorbidities

The Coronavirus Disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and its sublineages pose a new challenge to healthcare systems worldwide due to its ability to efficiently spread in immunized populations and its resistance to currently available therapies. COVID-19, although targeting primarily the respiratory system, is also now well established that later affects every organ in the body. Most importantly, despite the available therapy and vaccine-elicited protection, the long-term consequences of viral infection in breakthrough and asymptomatic individuals are areas of concern. In the past two years, investigators accumulated evidence on how the virus triggers our immune system and the molecular signals involved in the cross-talk between immune cells and structural cells in the pulmonary vasculature to drive pathological lung complications such as endothelial dysfunction and thrombosis. In the review, we emphasize recent updates on the pathophysiological inflammatory and immune responses associated with SARS-CoV-2 infection and their potential long-term consequences that may consequently lead to the development of pulmonary vascular diseases.

Increased protease-activated receptor 1 autoantibodies are associated with severe COVID-19

In patients with severe #COVID19, increased levels of autoantibodies against PAR1 were found. These might serve as allosteric agonists of PAR1 on endothelial cells and platelets, and thus might contribute to the pathogenesis of microthrombosis in COVID-19. https://bit.ly/3pqM9Vv.

Immunotherapy strategies and prospects for acute lung injury: Focus on immune cells and cytokines

Acute lung injury/acute respiratory distress syndrome (ALI/ARDS) is a disastrous condition, which can be caused by a wide range of diseases, such as pneumonia, sepsis, traumas, and the most recent, COVID-19. Even though we have gained an improved understanding of acute lung injury/acute respiratory distress syndrome pathogenesis and treatment mechanism, there is still no effective treatment for acute lung injury/acute respiratory distress syndrome, which is partly responsible for the unacceptable mortality rate. In the pathogenesis of acute lung injury, the inflammatory storm is the main pathological feature. More and more evidences show that immune cells and cytokines secreted by immune cells play an irreplaceable role in the pathogenesis of acute lung injury. Therefore, here we mainly reviewed the role of various immune cells in acute lung injury from the perspective of immunotherapy, and elaborated the crosstalk of immune cells and cytokines, aiming to provide novel ideas and targets for the treatment of acute lung injury.

Global Effect of COVID-19 Pandemic on Cancer Patients and its Treatment: A Systematic Review

Background

At a global level, the COVID-19 disease outbreak has had a major impact on health services and has induced disruption in routine care of health institutions, exposing cancer patients to severe risks. To provide uninterrupted tumor treatment throughout a pandemic lockdown is a major obstacle. Coronavirus disease (COVID-19) and its causative virus, SARS-CoV-2, stance considerable challenges for the management of oncology patients. COVID-19 presents particularly severe respiratory and systemic infection in aging and immunosuppressed individuals, including patients with cancer.

Objective

In the present review, we focused on emergent evidence from cancer sufferers that have been contaminated with COVID-19 and cancer patients who were at higher risk of severe COVID-19, and indicates that anticancer treatment may either rise COVID-19 susceptibility or have a duple therapeutic impact on cancer as well as COVID-19; moreover, how SARS-CoV-2 infection impacts cancer cells. Also, to assess the global effect of the COVID-19 disease outbreak on cancer and its treatment.

Methods

A literature survey was conducted using PubMed, Web of Science (WOS), Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), and VIral Protein domain DataBase (VIP DB) between Dec 1, 2019 and Sep 23, 2021, for studies on anticancer treatments in patients with COVID-19. The characteristics of the patients, treatment types, mortality, and other additional outcomes were extracted and pooled for synthesis.

Results

This disease has a huge effect on sufferers who have cancer(s). Sufferers of COVID-19 have a greater percentage of tumor diagnoses than the rest of the population. Likewise, cancer and highest proportion is lung cancer sufferers are more susceptible to COVID-19 constriction than the rest of the population.

Conclusion

Sufferers who have both COVID-19 and tumor have a considerably elevated death risk than single COVID-19 positive patients overall. During the COVID-19 pandemic, there was a reduction in the screening of cancer and detection, and also deferral of routine therapies, which may contribute to an increase in cancer mortality there in future.

Epigenetic and transcriptomic reprogramming in monocytes of severe COVID-19 patients reflects alterations in myeloid differentiation and the influence of inflammatory cytokines

BACKGROUND

COVID-19 manifests with a wide spectrum of clinical phenotypes, ranging from asymptomatic and mild to severe and critical. Severe and critical COVID-19 patients are characterized by marked changes in the myeloid compartment, especially monocytes. However, little is known about the epigenetic alterations that occur in these cells during hyperinflammatory responses in severe COVID-19 patients.

METHODS

In this study, we obtained the DNA methylome and transcriptome of peripheral blood monocytes from severe COVID-19 patients. DNA samples extracted from CD14 + CD15- monocytes of 48 severe COVID-19 patients and 11 healthy controls were hybridized on MethylationEPIC BeadChip arrays. In parallel, single-cell transcriptomics of 10 severe COVID-19 patients were generated. CellPhoneDB was used to infer changes in the crosstalk between monocytes and other immune cell types.

RESULTS

We observed DNA methylation changes in CpG sites associated with interferon-related genes and genes associated with antigen presentation, concordant with gene expression changes. These changes significantly overlapped with those occurring in bacterial sepsis, although specific DNA methylation alterations in genes specific to viral infection were also identified. We also found these alterations to comprise some of the DNA methylation changes occurring during myeloid differentiation and under the influence of inflammatory cytokines. A progression of DNA methylation alterations in relation to the Sequential Organ Failure Assessment (SOFA) score was found to be related to interferon-related genes and T-helper 1 cell cytokine production. CellPhoneDB analysis of the single-cell transcriptomes of other immune cell types suggested the existence of altered crosstalk between monocytes and other cell types like NK cells and regulatory T cells.

CONCLUSION

Our findings show the occurrence of an epigenetic and transcriptional reprogramming of peripheral blood monocytes, which could be associated with the release of aberrant immature monocytes, increased systemic levels of pro-inflammatory cytokines, and changes in immune cell crosstalk in these patients.

Innate immunity, cytokine storm, and inflammatory cell death in COVID-19

The innate immune system serves as the first line of defense against invading pathogens; however, dysregulated innate immune responses can induce aberrant inflammation that is detrimental to the host. Therefore, careful innate immune regulation is critical during infections. The coronavirus disease 2019 (COVID-19) pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has resulted in global morbidity and mortality as well as socio-economic stresses. Innate immune sensing of SARS-CoV-2 by multiple host cell pattern recognition receptors leads to the production of various pro-inflammatory cytokines and the induction of inflammatory cell death. These processes can contribute to cytokine storm, tissue damage, and acute respiratory distress syndrome. Here, we discuss the sensing of SARS-CoV-2 to induce innate immune activation and the contribution of this innate immune signaling in the development and severity of COVID-19. In addition, we provide a conceptual framework for innate immunity driving cytokine storm and organ damage in patients with severe COVID-19. A better understanding of the molecular mechanisms regulated by innate immunity is needed for the development of targeted modalities that can improve patient outcomes by mitigating severe disease.

The deciphering of the immune cells and marker signature in COVID-19 pathogenesis: An update

The precise interaction between the immune system and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is critical in deciphering the pathogenesis of coronavirus disease 2019 (COVID-19) and is also vital for developing novel therapeutic tools, including monoclonal antibodies, antivirals drugs, and vaccines. Viral infections need innate and adaptive immune reactions since the various immune components, such as neutrophils, macrophages, CD4⁺ T, CD8⁺ T, and B lymphocytes, play different roles in various infections. Consequently, the characterization of innate and adaptive immune reactions toward SARS-CoV-2 is crucial for defining the pathogenicity of COVID-19. In this study, we explain what is currently understood concerning the conventional immune reactions to SARS-CoV-2 infection to shed light on the protective and pathogenic role of immune response in this case. Also, in particular, we investigate the in-depth roles of other immune mediators, including neutrophil elastase, serum amyloid A, and syndecan, in the immunopathogenesis of COVID-19.

Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space

Computational tools for integrative analyses of diverse single-cell experiments are facing formidable new challenges including dramatic increases in data scale, sample heterogeneity, and the need to informatively cross-reference new data with foundational datasets. Here, we present SCALEX, a deep-learning method that integrates single-cell data by projecting cells into a batch-invariant, common cell-embedding space in a truly online manner (i.e., without retraining the model). SCALEX substantially outperforms online iNMF and other state-of-the-art non-online integration methods on benchmark single-cell datasets of diverse modalities, (e.g., single-cell RNA sequencing, scRNA-seq, single-cell assay for transposase-accessible chromatin use sequencing, scATAC-seq), especially for datasets with partial overlaps, accurately aligning similar cell populations while retaining true biological differences. We showcase SCALEX's advantages by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19 patients, each assembled from diverse data sources and growing with every new data. The online data integration capacity and superior performance makes SCALEX particularly appropriate for large-scale single-cell applications to build upon previous scientific insights.