COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas

Systematical identification of regulatory GPCRs by single-cell trajectory inference reveals the role of ADGRD1 and GPR39 in adipogenesis

Adipogenesis is the healthy expansion of white adipose tissue (WAT), serving as a compensatory response to maintain metabolic homeostasis in the presence of excess energy in the body. Therefore, the identification of novel regulatory molecules in adipogenesis, specifically membrane receptors such as G protein-coupled receptors (GPCRs), holds significant clinical promise. These receptors can serve as viable targets for pharmaceuticals, offering potential for restoring metabolic homeostasis in individuals with obesity. We utilized trajectory inference methods to analyze three distinct single-nucleus sequencing (sNuc-seq) datasets of adipose tissue and systematically identified GPCRs with the potential to regulate adipogenesis. Through verification in primary adipose progenitor cells (APCs) of mice, we discovered that ADGRD1 promoted the differentiation of APCs, while GPR39 inhibits this process. In the obese mouse model induced by a high-fat diet (HFD), both gain-of-function and loss-of-function studies validated that ADGRD1 promoted adipogenesis, thereby improving metabolic homeostasis, while GPR39 inhibited adipogenesis, leading to metabolic dysfunction. Additionally, through the analysis of 2,400 ChIP-seq data and 1,204 bulk RNA-seq data, we found that the transcription factors (TFs) MEF2D and TCF12 regulated the expression of ADGRD1 and GPR39, respectively. Our study revealed the regulatory role of GPCRs in adipogenesis, providing novel targets for clinical intervention of metabolic dysfunction in obese patients.

Single-cell transcriptome analysis reveals cellular reprogramming and changes of immune cell subsets following tetramethylpyrazine treatment in LPS-induced acute lung injury

Background

Acute lung injury (ALI) is a disordered pulmonary disease characterized by acute respiratory insufficiency with tachypnea, cyanosis refractory to oxygen and diffuse alveolar infiltrates. Despite increased research into ALI, current clinical treatments lack effectiveness. Tetramethylpyrazine (TMP) has shown potential in ALI treatment, and understanding its effects on the pulmonary microenvironment and its underlying mechanisms is imperative.

Methods

We established a mouse model of lipopolysaccharide (LPS)-induced ALI and performed single cell RNA sequencing (scRNA-seq). Bioinformatic analyses of the immune, epithelial and endothelial cells were then performed to explore the dynamic changes of the lung tissue microenvironment. We also analyzed the effects of TMP on the cell subtypes, differential gene expression and potential regulation of transcriptional factors involved. Immunohistochemistry and enzyme-linked immunosorbent assay were performed to identify the effects of TMP on immune inflammatory response.

Results

We found that TMP efficiently protected against LPS-induced acute lung injury. Results of scRNA-seq showed that the cells were divided into seven major cell clusters, including immune cells, fibroblasts, endothelial cells and epithelial cells. Neither dexamethasone (Dex) nor TMP treatment showed any significant protective effects in these clusters. However, TMP treatment in the LPS-induced ALI model significantly increased follicular helper T cells and reduced CD8+ naive T cells, Vcan-positive monocytes and Siva-positive NK cells. In addition, TMP treatment increased the number of basal epithelial cells and lymphatic endothelial cells (LECs), indicating its protective effects on these cell types. Scenic analysis suggested that TMP likely mitigates LPS-induced injury in epithelial and endothelial cells by promoting FOSL1 in basal epithelial cells and JunB in LECs.

Conclusions

Our findings suggest that TMP appears to alleviate LPS-induced lung injury by regulating the immune response, promoting epithelial cell survival and boosting the antioxidant potential of endothelial cells. This study highlights the potential therapeutic use of TMP in the management of ALI.

Immune-cell signatures of persistent inflammation, immunosuppression, and catabolism syndrome after sepsis

BACKGROUND

Management of persistent inflammation, immunosuppression, and catabolism syndrome (PICS) after sepsis remains challenging for patients in the intensive care unit, experiencing poor quality of life and death. However, immune-cell signatures in patients with PICS after sepsis remain unclear.

METHODS

We determined immune-cell signatures of PICS after sepsis at single-cell resolution. Murine cecal ligation and puncture models of PICS were applied for validation.

FINDINGS

Immune functions of two enriched monocyte subpopulations, Mono1 and Mono4, were suppressed substantially in patients with sepsis and were partially restored in patients with PICS after sepsis and exhibited immunosuppressive and pro-apoptotic effects on B and CD8T cells. Patients with PICS and sepsis had reduced naive and memory B cells and proliferated plasma cells. Besides, naive and memory B cells in patients with PICS showed an active antigen processing and presentation gene signature compared to those with sepsis. PICS patients with better prognoses exhibited more active memory B cells and IGHA1-plasma cells. CD8TEMRA displayed signs of proliferation and immune dysfunction in the PICS-death group in contrast with the PICS-alive group. Megakaryocytes proliferation was more pronounced in patients with PICS and sepsis than in healthy controls, with notable changes in the anti-inflammatory and immunomodulatory effects observed in patients with PICS and verified in mice models.

CONCLUSIONS

Our study evaluated PICS after sepsis at the single-cell level, identifying the heterogeneity present within immune-cell subsets, facilitating the prediction of disease progression and the development of effective intervention.

FUNDING

This work was supported by the National Natural Science Foundation of China, Shanghai Municipal Health Commission "Yiyuan New Star" Youth Medical Talent Cultivating Program, and Shanghai Clinical Research Center for Anesthesiology.

scImmOmics: a manually curated resource of single-cell multi-omics immune data

Single-cell sequencing technology has enabled the discovery and characterization of subpopulations of immune cells with unique functions, which is critical for revealing immune responses under healthy or disease conditions. Efforts have been made to collect and curate single-cell RNA sequencing (scRNA-seq) data, yet an immune-specific single-cell multi-omics atlas with harmonized metadata is still lacking. Here, we present scImmOmics (https://bio.liclab.net/scImmOmics/home), a manually curated single-cell multi-omics immune database constructed based on high-quality immune cells with known immune cell labels. Currently, scImmOmics documents >2.9 million cell-type labeled immune cells derived from seven single-cell sequencing technologies, involving 131 immune cell types, 47 tissues and 4 species. To ensure data consistency, we standardized the nomenclature of immune cell types and presented them in a hierarchical tree structure to clearly describe the lineage relationships within the immune system. scImmOmics also provides comprehensive immune regulatory information, including T-cell/B-cell receptor sequencing clonotype information, cell-specific regulatory information (e.g. gene/chromatin accessibility/protein/transcription factor states within known cell types, cell-to-cell communication and co-expression networks) and immune cell responses to cytokines. Collectively, scImmOmics is a comprehensive and valuable platform for unraveling the heterogeneity and diversity of immune cells and elucidating the specific regulatory mechanisms at the single-cell level.

Unveiling patterns in spatial transcriptomics data: a novel approach utilizing graph attention autoencoder and multiscale deep subspace clustering network

BACKGROUND

The accurate deciphering of spatial domains, along with the identification of differentially expressed genes and the inference of cellular trajectory based on spatial transcriptomic (ST) data, holds significant potential for enhancing our understanding of tissue organization and biological functions. However, most of spatial clustering methods can neither decipher complex structures in ST data nor entirely employ features embedded in different layers.

RESULTS

This article introduces STMSGAL, a novel framework for analyzing ST data by incorporating graph attention autoencoder and multiscale deep subspace clustering. First, STMSGAL constructs ctaSNN, a cell type-aware shared nearest neighbor graph, using Louvian clustering exclusively based on gene expression profiles. Subsequently, it integrates expression profiles and ctaSNN to generate spot latent representations using a graph attention autoencoder and multiscale deep subspace clustering. Lastly, STMSGAL implements spatial clustering, differential expression analysis, and trajectory inference, providing comprehensive capabilities for thorough data exploration and interpretation. STMSGAL was evaluated against 7 methods, including SCANPY, SEDR, CCST, DeepST, GraphST, STAGATE, and SiGra, using four 10x Genomics Visium datasets, 1 mouse visual cortex STARmap dataset, and 2 Stereo-seq mouse embryo datasets. The comparison showcased STMSGAL's remarkable performance across Davies-Bouldin, Calinski-Harabasz, S_Dbw, and ARI values. STMSGAL significantly enhanced the identification of layer structures across ST data with different spatial resolutions and accurately delineated spatial domains in 2 breast cancer tissues, adult mouse brain (FFPE), and mouse embryos.

CONCLUSIONS

STMSGAL can serve as an essential tool for bridging the analysis of cellular spatial organization and disease pathology, offering valuable insights for researchers in the field.

A pan-immune panorama of bacterial pneumonia revealed by a large-scale single-cell transcriptome atlas

Bacterial pneumonia is a significant public health burden, contributing to substantial morbidity, mortality, and healthcare costs. Current therapeutic strategies beyond antibiotics and adjuvant therapies are limited, highlighting the need for a deeper understanding of the disease pathogenesis. Here, we employed single-cell RNA sequencing of 444,146 bronchoalveolar lavage fluid cells (BALFs) from a large cohort of 74 individuals, including 58 patients with mild (n = 22) and severe (n = 36) diseases as well as 16 healthy donors. Enzyme-linked immunosorbent and histological assays were applied for validation within this cohort. The heterogeneity of immune responses in bacterial pneumonia was observed, with distinct immune cell profiles related to disease severity. Severe bacterial pneumonia was marked by an inflammatory cytokine storm resulting from systemic upregulation of S100A8/A9 and CXCL8, primarily due to specific macrophage and neutrophil subsets. In contrast, mild bacterial pneumonia exhibits an effective humoral immune response characterized by the expansion of T follicular helper and T helper 2 cells, facilitating B cell activation and antibody production. Although both disease groups display T cell exhaustion, mild cases maintained robust cytotoxic CD8+T cell function, potentially reflecting a compensatory mechanism. Dysregulated neutrophil and macrophage responses contributed significantly to the pathogenesis of severe disease. Immature neutrophils promote excessive inflammation and suppress T cell activation, while a specific macrophage subset (Macro_03_M1) displaying features akin to myeloid-derived suppressor cells (M-MDSCs) suppress T cells and promote inflammation. Together, these findings highlight potential therapeutic targets for modulating immune responses and improving clinical outcomes in bacterial pneumonia.

Single-cell transcriptome atlas of peripheral immune features to Omicron breakthrough infection under booster vaccination strategies

Introduction

The high percentage of Omicron breakthrough infection in vaccinees is an emerging problem, of which we have a limited understanding of the phenomenon.

Methods

We performed single-cell transcriptome coupled with T-cell/B-cell receptor (TCR/BCR) sequencing in 15 peripheral blood mononuclear cell (PBMC) samples from Omicron infection and naïve with booster vaccination.

Results

We found that after breakthrough infection, multiple cell clusters showed activation of the type I IFN pathway and widespread expression of Interferon-stimulated genes (ISGs); T and B lymphocytes exhibited antiviral and proinflammatory-related differentiation features with pseudo-time trajectories; and large TCR clonal expansions were concentrated in effector CD8 T cells, and clonal expansions of BCRs showed a preference for IGHV3. In addition, myeloid cells in the BA.5.2 breakthrough infection with the fourth dose of aerosolized Ad5-nCoV were characterized by enhanced proliferation, chemotactic migration, and antigen presentation.

Discussion

Collectively, our study informs the comprehensive understandings of immune characterization for Omicron breakthrough infection, revealing the positive antiviral potential induced by booster doses of vaccine and the possible "trained immunity" phenomenon in the fourth dose of aerosolized Ad5-nCoV, providing a basis for the selection of vaccination strategies.

Expansion of peripheral cytotoxic CD4+ T cells in Alzheimer's disease: New insights from multi-omics evidence

The significance of the adaptive immune response in Alzheimer's disease (AD) is increasingly recognized. We analyzed scRNA-Seq data from AD patients, revealing a notable rise in CD4 cytotoxic T cells (CD4-CTLs) in peripheral blood mononuclear cells (PBMCs), validated in vivo and in vitro. This rise correlates with cognitive decline in AD patients. We also identified transcription factors TBX21 and MYBL1 as key drivers of CD4-CTL expansion. Further analyses indicate these cells are terminally differentiated, showing clonal expansion, metabolic changes, and unique communication patterns. Mendelian randomization identified risk genes SRGN and ITGB1, suggesting their genetic regulation in CD4-CTLs may contribute to AD. To summarize, our findings characterize the expansion of CD4-CTLs in the PBMCs of AD patients, providing valuable understanding into the possible mechanisms involved in the expansion of CD4-CTLs in AD.

Sustained Vascular Inflammatory Effects of SARS-CoV-2 Spike Protein on Human Endothelial Cells

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been associated with systemic inflammation and vascular injury, which contribute to the development of acute respiratory syndrome (ARDS) and the mortality of COVID-19 infection. Moreover, multiorgan complications due to persistent endothelial dysfunction have been suspected as the cause of post-acute sequelae of SARS-CoV-2 infection. Therefore, elucidation of the vascular inflammatory effect of SARS-CoV-2 will increase our understanding of how endothelial cells (ECs) contribute to the short- and long-term consequences of SARS-CoV-2 infection. Here, we investigated the interaction of SARS-CoV-2 spike protein with human ECs from aortic (HAoEC) and pulmonary microvascular (HPMC) origins, cultured under physiological flow conditions. We showed that the SARS-CoV-2 spike protein triggers prolonged expression of cell adhesion markers in both ECs, similar to the effect of TNF-α. SARS-CoV-2 spike treatment also led to the release of various cytokines and chemokines observed in severe COVID-19 patients. Moreover, increased binding of leucocytes to the endothelial surface and a procoagulant state of the endothelium were observed. Transcriptomic profiles of SARS-CoV-2 spike-activated HPMC and HAoEC showed prolonged upregulation of genes and pathways associated with responses to virus, cytokine-mediated signaling, pattern recognition, as well as complement and coagulation pathways. Our findings support experimental and clinical observations of the vascular consequences of SARS-CoV-2 infection and highlight the importance of EC protection as one of the strategies to mitigate the severe effects as well as the possible post-acute complications of COVID-19 disease.

Antibodies to the RBD of SARS-CoV-2 spike mediate productive infection of primary human macrophages

The role of myeloid cells in the pathogenesis of SARS-CoV-2 is well established, in particular as drivers of cytokine production and systemic inflammation characteristic of severe COVID-19. However, the potential for myeloid cells to act as bona fide targets of productive SARS-CoV-2 infection, and the specifics of entry, remain unclear. Using a panel of anti-SARS-CoV-2 monoclonal antibodies (mAbs) we performed a detailed assessment of antibody-mediated infection of monocytes/macrophages. mAbs with the most consistent potential to mediate infection were those targeting a conserved region of the receptor binding domain (RBD; group 1/class 4). Infection was closely related to the neutralising concentration of the mAbs, with peak infection occurring below the IC50, while pre-treating cells with remdesivir or FcγRI-blocking antibodies inhibited infection. Studies performed in primary macrophages demonstrated high-level and productive infection, with infected macrophages appearing multinucleated and syncytial. Infection was not seen in the absence of antibody with the same quantity of virus. Addition of ruxolitinib significantly increased infection, indicating restraint of infection through innate immune mechanisms rather than entry. High-level production of pro-inflammatory cytokines directly correlated with macrophage infection levels. We hypothesise that infection via antibody-FcR interactions could contribute to pathogenesis in primary infection, systemic virus spread or persistent infection.

Single-cell sequencing reveals the heterogeneity of immune landscape in drug users with HIV infection

BACKGROUND

Injection drug use (IDU) leads to immune system dysfunction, thereby increasing the risk of opportunistic infection. There is a critical need to reveal the role of IDU in the immunopathogenesis of HIV infection.

METHODS

We performed single-cell RNA sequencing (scRNA-seq) on peripheral blood mononuclear cells (PBMCs) derived from healthy control (HC) individuals, HIV-infected patients with IDU (HIV-IDU) and without IDU (HIV-nIDU). In addition, the Gene Set Enrichment Analysis (GSEA) was used to analyze the immunomodulatory effects of differential immune cells.

RESULTS

Seven types of cells were identified with specific expressions of maker genes. Specific subsets such as CD14+ monocytes, plasmacytoid dendritic cells (pDCs), plasma cells, and CD8+ T cells displayed a high degree of heterogeneity among HC, HIV-nIDU, and HIV-IDU. We identified signature genes for each subset in distinct groups, including CFP+ CD14+ monocytes, PTPRCAP+ pDCs, IGHD+ plasma cells, and IFITM1+ CD8+T cells from HIV-IDU, whereas these genes were not expressed in such cells from HIV-nIDU. Moreover, considerable heterogeneity in the function of these immune cells was observed across different groups, especially the elevated IFN-α/β signaling for CD14+ monocytes, histone H2A/2B and H3/4 pathway for pDCs, the creation of C4 and C2 activators for plasma cells, and drug metabolism cytochrome p450 for CD8+ T cells in HIV-IDU individuals.

CONCLUSION

Our comprehensive analyses clarify the heterogeneous characteristics of the immune landscape between HIV-IDU and HIV-nIDU. These insights provide a deeper understanding of the IDU-mediated immunopathogenesis in HIV infection.

Immune Signatures of SARS-CoV-2 Infection Resolution in Human Lung Tissues

While human autopsy samples have provided insights into pulmonary immune mechanisms associated with severe viral respiratory diseases, the mechanisms that contribute to a clinically favorable resolution of viral respiratory infections remain unclear due to the lack of proper experimental systems. Using mice co-engrafted with a genetically matched human immune system and fetal lung xenograft (fLX), we mapped the immunological events defining successful resolution of SARS-CoV-2 infection in human lung tissues. Viral infection is rapidly cleared from fLX following a peak of viral replication, histopathological manifestations of lung disease and loss of AT2 program, as reported in human COVID-19 patients. Infection resolution is associated with the activation of a limited number of hematopoietic subsets, including inflammatory monocytes and non-canonical double-negative T-cells with cytotoxic functions, which are highly enriched in viral RNA and dissipate upon infection resolution. Activation of specific human fibroblast and endothelial subsets also elicit robust antiviral and monocyte chemotaxis signatures, respectively. Notably, systemic depletion of human CD4+ cells, but not CD3+ cells, abrogates infection resolution in fLX and induces persistent infection, supporting evidence that peripheral CD4+ monocytes are important contributors to SARS-CoV-2 infection resolution in lung tissues. Collectively, our findings unravel a comprehensive picture of the immunological events defining effective resolution of SARS-CoV-2 infection in human lung tissues, revealing markedly divergent immunological trajectories between resolving and fatal COVID-19 cases.

Advancements in the Application of scRNA-Seq in Breast Research: A Review

Single-cell sequencing technology provides apparent advantages in cell population heterogeneity, allowing individuals to better comprehend tissues and organs. Sequencing technology is currently moving beyond the standard transcriptome to the single-cell level, which is likely to bring new insights into the function of breast cells. In this study, we examine the primary cell types involved in breast development, as well as achievements in the study of scRNA-seq in the microenvironment, stressing the finding of novel cell subsets using single-cell approaches and analyzing the problems and solutions to scRNA-seq. Furthermore, we are excited about the field's promising future.

An insight into COVID-19 host immunity at single-cell resolution

Host immunity helps the body to fight against COVID-19. Single-cell transcriptomics has provided the scope of investigating cellular and molecular underpinnings of host immune response against SARS-CoV-2 infection at high resolution. In this review, we have systematically described the virus-induced dysregulation of relative abundance as well as molecular behavior of each innate and adaptive immune cell type and cell state during COVID-19 infection and for different vaccinations, based on single-cell studies published in last three-four years. Identification and characterization of these disease-associated specific cell populations might help to design better, efficient, and targeted therapeutic avenues.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

Remodeling of the chromatin landscape in peripheral blood cells in patients with severe Delta COVID-19

COVID-19 is characterized by systemic pro-inflammatory shifts with the development of serious alterations in the functioning of the immune system. Investigations of the gene expression changes accompanying the infection state provide insight into the molecular and cellular processes depending on the sickness severity and virus variants. Severe Delta COVID-19 has been characterized by the appearance of a monocyte subset enriched for proinflammatory gene expression signatures and a shift in ligand-receptor interactions. We profiled the chromatin accessibility landscape of 140,000 nuclei in PBMC samples from healthy individuals or individuals with COVID-19. We investigated cis-regulatory elements and identified the core transcription factors governing gene expression in immune cells during COVID-19 infection. In severe cases, we discovered that regulome and chromatin co-accessibility modules were significantly altered across many cell types. Moreover, cases with the Delta variant were accompanied by a specific monocyte subtype discovered using scATAC-seq data. Our analysis showed that immune cells of individuals with severe Delta COVID-19 underwent significant remodeling of the chromatin accessibility landscape and development of the proinflammatory expression pattern. Using a gene regulatory network modeling approach, we investigated the core transcription factors governing the cell state and identified the most pronounced chromatin changes in CD14+ monocytes from individuals with severe Delta COVID-19. Together, our results provide novel insights into cis-regulatory module organization and its impact on gene activity in immune cells during SARS-CoV-2 infection.

Lethal COVID-19 associates with RAAS-induced inflammation for multiple organ damage including mediastinal lymph nodes

Lethal COVID-19 outcomes are attributed to classic cytokine storm. We revisit this using RNA sequencing of nasopharyngeal and 40 autopsy samples from patients dying of SARS-CoV-2. Subsets of the 100 top-upregulated genes in nasal swabs are upregulated in the heart, lung, kidney, and liver, but not mediastinal lymph nodes. Twenty-two of these are "noncanonical" immune genes, which we link to components of the renin-angiotensin-activation-system that manifest as increased fibrin deposition, leaky vessels, thrombotic tendency, PANoptosis, and mitochondrial dysfunction. Immunohistochemistry of mediastinal lymph nodes reveals altered architecture, excess collagen deposition, and pathogenic fibroblast infiltration. Many of the above findings are paralleled in animal models of SARS-CoV-2 infection and human peripheral blood mononuclear and whole blood samples from individuals with early and later SARS-CoV-2 variants. We then redefine cytokine storm in lethal COVID-19 as driven by upstream immune gene and mitochondrial signaling producing downstream RAAS (renin-angiotensin-aldosterone system) overactivation and organ damage, including compromised mediastinal lymph node function.

Detecting global and local hierarchical structures in cell-cell communication using CrossChat

Cell-cell communication (CCC) occurs across different biological scales, ranging from interactions between large groups of cells to interactions between individual cells, forming a hierarchical structure. Globally, CCC may exist between clusters or only subgroups of a cluster with varying size, while locally, a group of cells as sender or receiver may exhibit distinct signaling properties. Current existing methods infer CCC from single-cell RNA-seq or Spatial Transcriptomics only between predefined cell groups, neglecting the existing hierarchical structure within CCC that are determined by signaling molecules, in particular, ligands and receptors. Here, we develop CrossChat, a novel computational framework designed to infer and analyze the hierarchical cell-cell communication structures using two complementary approaches: a global hierarchical structure using a multi-resolution clustering method, and multiple local hierarchical structures using a tree detection method. This framework provides a comprehensive approach to understand the hierarchical relationships within CCC that govern complex tissue functions. By applying our method to two nonspatial scRNA-seq datasets sampled from COVID-19 patients and mouse embryonic skin, and two spatial transcriptomics datasets generated from Stereo-seq of mouse embryo and 10x Visium of mouse wounded skin, we showcase CrossChat's functionalities for analyzing both global and local hierarchical structures within cell-cell communication.

Hyperglycemia-triggered lipid peroxidation destabilizes STAT4 and impairs anti-viral Th1 responses in type 2 diabetes

Patients with type 2 diabetes (T2D) are more susceptible to severe respiratory viral infections, but the underlying mechanisms remain elusive. Here, we show that patients with T2D and coronavirus disease 2019 (COVID-19) infections, and influenza-infected T2D mice, exhibit defective T helper 1 (Th1) responses, which are an essential component of anti-viral immunity. This defect stems from intrinsic metabolic perturbations in CD4+ T cells driven by hyperglycemia. Mechanistically, hyperglycemia triggers mitochondrial dysfunction and excessive fatty acid synthesis, leading to elevated oxidative stress and aberrant lipid accumulation within CD4+ T cells. These abnormalities promote lipid peroxidation (LPO), which drives carbonylation of signal transducer and activator of transcription 4 (STAT4), a crucial Th1-lineage-determining factor. Carbonylated STAT4 undergoes rapid degradation, causing reduced T-bet induction and diminished Th1 differentiation. LPO scavenger ameliorates Th1 defects in patients with T2D who have poor glycemic control and restores viral control in T2D mice. Thus, this hyperglycemia-LPO-STAT4 axis underpins reduced Th1 activity in T2D hosts, with important implications for managing T2D-related viral complications.

Cross-species single-cell analysis reveals divergence and conservation of peripheral blood mononuclear cells

BACKGROUND

Single-cell transcriptome sequencing (scRNA-seq) has revolutionized the study of immune cells by overcoming the limitations of traditional antibody-based identification and isolation methods. This advancement allows us to obtain comprehensive gene expression profiles from a diverse array of vertebrate species, facilitating the identification of various cell types. Comparative immunology across vertebrates presents a promising approach to understanding the evolution of immune cell types. In this study, we conducted a comparative transcriptome analysis of peripheral blood mononuclear cells (PBMCs) at the single-cell level across 12 species.

RESULTS

Our findings shed light on the cellular compositional features of PBMCs, spanning from fish to mammals. Notably, we identified genes that exhibit vertebrate universality in characterizing immune cells. Moreover, our investigation revealed that monocytes have maintained a conserved transcriptional regulatory program throughout evolution, emphasizing their pivotal role in orchestrating immune cells to execute immune programs.

CONCLUSIONS

This comprehensive analysis provides valuable insights into the evolution of immune cells across vertebrates.

Investigating TCR-pMHC interactions for TCRs without identified epitopes by constructing a computational pipeline

The molecular mechanisms underlying epitope recognition by T cell receptors (TCRs) are critical for activating T cell immune responses and rationally designing TCR-based therapeutics. Single-cell sequencing techniques vastly boost the accumulation of TCR sequences, while the limitation of available TCR-pMHC structures hampers further investigations. In this study, we proposed a computational pipeline that incorporates structural information and single-cell sequencing data to investigate the epitope-recognition mechanisms for TCRs without identified epitopes. By antigen specificity clustering, we mapped the epitope sequences between epitope-known and epitope-unknown TCRs from COVID-19 patients. One reported SARS-CoV-2 epitope, NQKLIANQF (S919-927), was identified for a TCR expressed by 614 T cells (TCR-614). Epitope screening also identified a potential cross-reactive epitope, KLKTLVATA (NSP31790-1798), for a TCR expressed by 204 T cells (TCR-204). By molecular dynamics (MD) simulations, we revealed the detailed epitope-recognition mechanisms for both TCRs. The structural motifs responsible for epitope recognition revealed by the MD simulations are consistent with the sequential features recognized by the sequence-based clustering method. We hope that this strategy could facilitate the discovery and optimization of TCR-based therapeutics.

Multi-omic characteristics of longitudinal immune profiling after breakthrough infections caused by Omicron BA.5 sublineages

BACKGROUND

Omicron sub-variants breakthrough infections (BTIs) have led to millions of coronavirus disease 2019 (COVID-19) cases worldwide. The acute-phase immune status is critical for prognosis, however, the dynamic immune profiling of COVID-19 during the first month after BTIs remains unclear.

METHODS

In this study, we monitored the immune dynamics at various timepoints in a longitudinal cohort during the first month post-BTIs through clinical evaluation, single-cell RNA sequencing (scRNA-seq), T cell receptor (TCR)/B cell receptor (BCR) sequencing, and antibody mass spectrometry.

FINDINGS

Serological analysis revealed limited impairment to functions of major organs, active cellular and humoral immunity at 2 weeks post-BTI, with significant increases in cytokines (CKs) and neutralizing antibody levels. However, 1 month post-BTI, organ function parameters and CK levels reverted to pre-infection levels, whereas neutralizing antibody levels remained high. Notably, scRNA-seq showed that lymphocytes maintained strong antiviral activity and cell depletion at 2 weeks and 1 month post-BTI, with genes CD81, ABHD17A, CXCR4, DUSP1, etc. upregulated, and genes PFDN5, DYNLRB1, CD52, etc. downregulated, indicating that lymphocytes status take longer to recover to normal levels than that routine blood tests revealed. Additionally, T cell-exhaustion associated genes, including LAG3, TIGIT, PDCD1, CTLA4, HAVCR2, and TOX, were upregulated after BTI. TCRs and BCRs exhibited higher clonotypes, mainly in CD8Tem or plasmablast cells, at 2 weeks post-BTI comparing 1 month. More IgG and IgA-type BCRs were found in the groups of 1 month post-BTI, with higher somatic hypermutation, indicating greater maturity. Verification of monoclonal antibodies corresponding to amplified BCRs highlighted the antigen-specific and broad-spectrum characteristics.

INTERPRETATION

Our study elucidated the dynamic immune profiling of individuals after Omicron BA.5 sublineages BTI. Strong immune activation, antiviral response, antibody maturation and class transition at 2 weeks and 1 month after BTI may provide essential insights into pathogenicity, sequential immune status, recovery mechanisms of Omicron sublineage BTI.

FUNDING

This study was supported by the National Key R&D Program of China, the China Postdoctoral Science Foundation, Guangdong Basic and Applied Basic Research Foundation, the National Natural Science Foundation of China, CAS Project for Young Scientists in Basic Research, and the Air Force Special Medical Center Science and Technology Booster Program.

Is there a rationale for hyperbaric oxygen therapy in the patients with Post COVID syndrome? : A critical review

The SARS-CoV-2 pandemic has resulted in 762 million infections worldwide from 2020 to date, of which approximately ten percent are suffering from the effects after infection in 2019 (COVID-19) [1, 40]. In Germany, it is now assumed that at least one million people suffer from post-COVID condition with long-term consequences. These have been previously reported in diseases like Myalgic Encephalomyelitis (ME) and Chronic Fatigue Syndrome (CFS). Symptoms show a changing variability and recent surveys in the COVID context indicate that 10-30 % of outpatients, 50 to 70% of hospitalised patients suffer from sequelae. Recent data suggest that only 13% of all ill people were completely free of symptoms after recovery [3, 9]. Current hypotheses consider chronic inflammation, mitochondrial dysfunction, latent viral persistence, autoimmunity, changes of the human microbiome or multilocular sequelae in various organ system after infection. Hyperbaric oxygen therapy (HBOT) is applied since 1957 for heart surgery, scuba dive accidents, CO intoxication, air embolisms and infections with anaerobic pathogens. Under hyperbaric pressure, oxygen is physically dissolved in the blood in higher concentrations and reaches levels four times higher than under normobaric oxygen application. Moreover, the alternation of hyperoxia and normoxia induces a variety of processes at the cellular level, which improves oxygen supply in areas of locoregional hypoxia. Numerous target gene effects on new vessel formation, anti-inflammatory and anti-oedematous effects have been demonstrated [74]. The provision of intermittently high, local oxygen concentrations increases repair and regeneration processes and normalises the predominance of hyperinflammation. At present time only one prospective, randomized and placebo-controlled study exists with positive effects on global cognitive function, attention and executive function, psychiatric symptoms and pain interference. In conclusion, up to this date HBO is the only scientifically proven treatment in a prospective randomized controlled trial to be effective for cognitive improvement, regeneration of brain network and improvement of cardiac function. HBOT may have not only theoretical but also potential impact on targets of current pathophysiology of Post COVID condition, which warrants further scientific studies in patients.

Single-Cell Transcriptomics Reveals a Pivotal Role of DOCK2 in Sjögren Disease

OBJECTIVE

Sjögren disease (SjD) is an autoimmune condition characterized by the dysfunction of the salivary and lacrimal glands. The study aimed to decipher the pathogenic cell populations and their immunologic pathways in the salivary glands. We further determined the therapeutic effect of inhibiting dedicator of cytokinesis 2 (DOCK2) shared by novel clusters of CD8+ T cells in an SjD mouse model.

METHODS

This study employed single-cell RNA sequencing to examine the composition and dynamics of immune cells in the salivary glands of SjD mice. By analyzing the transcriptomic data and employing clustering analysis, a specific target was identified, leading to the treatment of mice with a targeted inhibitor.

RESULTS

The results showed diverse immune cell types, including B cells, CD4+ T cells, CD8+ T cells, macrophages, and natural killer cells. We identified specific clusters possessing phenotypic characteristics of immune cell subpopulations, thereby showing specific genes/pathways associated with the disease. The most striking finding was the elevated expression of DOCK2 in CD8+ T cells in the SjD model. This discovery is significant because subsequent treatment with a DOCK2 inhibitor 4-[3-(2-Chlorophenyl)-2-propen-1-ylidene]-1-phenyl-3,5-pyrazolidinedione (CPYPP) led to a marked amelioration of SjD signs.

CONCLUSION

The effectiveness of DOCK2 inhibition in alleviating SjD signs highlights the potential of DOCK2 as a therapeutic target, opening new avenues for treatment strategies that could modulate the immune response more effectively in SjD.

Single-cell analysis of human peripheral blood reveals high immune response activity in successful ageing individuals

Beneficial remodeling of the immune system in successful ageing individuals (centenarians and supercentenarians) is critical for healthy ageing. However, mechanisms for dynamic regulation of immunity during ageing remain unclear. We use single-cell RNA sequencing (scRNA-seq) as an analytical strategy to study the dynamic regulation of immunity during aging and its molecular mechanisms at the single-cell level. We performed an integrative analysis of 87,215 peripheral blood mononuclear cells, from seven supercentenarians, three centenarians, and four elderly controls, generated by single-cell transcriptomics complemented with fluorescence-activated cell sorting. Animals experiments were also conducted to validate the makers of healthy aging found by our bioinformatic analysis and further explore the dynamic of immune changes during aging process. We found that CD8+ effector memory T cells and terminally differentiated B cells were enriched in the longevity group (centenarians and supercentenarians), whereas naïve T cells and Tregs were enriched in elderly controls. CD56dim NK cells in the longevity group activated Fc-γ receptor signaling. The higher antigen-presenting ability of CD14+ monocytes in the longevity group and the CellChat analysis indicated that CD14+ monocytes might assist active T and B cells. Here, we revealed the adaptive immune remodeling geromarkers of immunosenescence in centenarians and supercentenarians, which could be considered as biomarkers of healthy aging, and might help sustain immune responses and achieve exceptional longevity.

COVID-19 progression and convalescence in common variable immunodeficiency patients show dysregulated adaptive immune responses and persistent type I interferon and inflammasome activation

Common variable immunodeficiency (CVID) is the most prevalent primary immunodeficiency, marked by hypogammaglobulinemia, poor antibody responses, and increased infection susceptibility. The COVID-19 pandemic provided a unique opportunity to study the effects of prolonged viral infections on the immune responses of CVID patients. Here we use single-cell RNA-seq and spectral flow cytometry of peripheral blood samples before, during, and after SARS-CoV-2 infection showing that COVID-19 CVID patients display a persistent type I interferon signature at convalescence across immune compartments. Alterations in adaptive immunity include sustained activation of naïve B cells, increased CD21low B cells, impaired Th1 polarization, CD4+ T central memory exhaustion, and increased CD8+ T cell cytotoxicity. NK cell differentiation is defective, although cytotoxicity remains intact. Monocytes show persistent activation of inflammasome-related genes. These findings suggest the involvement of intact humoral immunity in regulating these processes and might indicate the need for early intervention to manage viral infections in CVID patients.

Explainable deep neural networks for predicting sample phenotypes from single-cell transcriptomics

Recent advances in single-cell RNA-Sequencing (scRNA-Seq) technologies have revolutionized our ability to gather molecular insights into different phenotypes at the level of individual cells. The analysis of the resulting data poses significant challenges, and proper statistical methods are required to analyze and extract information from scRNA-Seq datasets. Sample classification based on gene expression data has proven effective and valuable for precision medicine applications. However, standard classification schemas are often not suitable for scRNA-Seq due to their unique characteristics, and new algorithms are required to effectively analyze and classify samples at the single-cell level. Furthermore, existing methods for this purpose have limitations in their usability. Those reasons motivated us to develop singleDeep, an end-to-end pipeline that streamlines the analysis of scRNA-Seq data training deep neural networks, enabling robust prediction and characterization of sample phenotypes. We used singleDeep to make predictions on scRNA-Seq datasets from different conditions, including systemic lupus erythematosus, Alzheimer's disease and coronavirus disease 2019. Our results demonstrate strong diagnostic performance, validated both internally and externally. Moreover, singleDeep outperformed traditional machine learning methods and alternative single-cell approaches. In addition to prediction accuracy, singleDeep provides valuable insights into cell types and gene importance estimation for phenotypic characterization. This functionality provided additional and valuable information in our use cases. For instance, we corroborated that some interferon signature genes are consistently relevant for autoimmunity across all immune cell types in lupus. On the other hand, we discovered that genes linked to dementia have relevant roles in specific brain cell populations, such as APOE in astrocytes.

scGO: interpretable deep neural network for cell status annotation and disease diagnosis

Machine learning has emerged as a transformative tool for elucidating cellular heterogeneity in single-cell RNA sequencing. However, a significant challenge lies in the "black box" nature of deep learning models, which obscures the decision-making process and limits interpretability in cell status annotation. In this study, we introduced scGO, a Gene Ontology (GO)-inspired deep learning framework designed to provide interpretable cell status annotation for scRNA-seq data. scGO employs sparse neural networks to leverage the intrinsic biological relationships among genes, transcription factors, and GO terms, significantly augmenting interpretability and reducing computational cost. scGO outperforms state-of-the-art methods in the precise characterization of cell subtypes across diverse datasets. Our extensive experimentation across a spectrum of scRNA-seq datasets underscored the remarkable efficacy of scGO in disease diagnosis, prediction of developmental stages, and evaluation of disease severity and cellular senescence status. Furthermore, we incorporated in silico individual gene manipulations into the scGO model, introducing an additional layer for discovering therapeutic targets. Our results provide an interpretable model for accurately annotating cell status, capturing latent biological knowledge, and informing clinical practice.

QOT: Quantized Optimal Transport for sample-level distance matrix in single-cell omics

Single-cell technologies have enabled the high-dimensional characterization of cell populations at an unprecedented scale. The innate complexity and increasing volume of data pose significant computational and analytical challenges, especially in comparative studies delineating cellular architectures across various biological conditions (i.e. generation of sample-level distance matrices). Optimal Transport is a mathematical tool that captures the intrinsic structure of data geometrically and has been applied to many bioinformatics tasks. In this paper, we propose QOT (Quantized Optimal Transport), a new method enabling efficient computation of sample-level distance matrix from large-scale single-cell omics data through a quantization step. We apply our algorithm to real-world single-cell genomics and pathomics datasets, aiming to extrapolate cell-level insights to inform sample-level categorizations. Our empirical study shows that QOT outperforms existing two OT-based algorithms in accuracy and robustness when obtaining a distance matrix from high throughput single-cell measures at the sample level. Moreover, the sample level distance matrix could be used in the downstream analysis (i.e. uncover the trajectory of disease progression), highlighting its usage in biomedical informatics and data science.

Uncovering disease-related multicellular pathway modules on large-scale single-cell transcriptomes with scPAFA

Pathway analysis is a crucial analytical phase in disease research on single-cell RNA sequencing (scRNA-seq) data, offering biological interpretations based on prior knowledge. However, currently available tools for generating cell-level pathway activity scores (PAS) exhibit computational inefficacy in large-scale scRNA-seq datasets. Additionally, disease-related pathways are often identified through cross-condition comparisons within specific cell types, overlooking potential patterns that involve multiple cell types. Here, we present single-cell pathway activity factor analysis (scPAFA), a Python library designed for large-scale single-cell datasets allowing rapid PAS computation and uncovering biologically interpretable disease-related multicellular pathway modules, which are low-dimensional representations of disease-related PAS alterations in multiple cell types. Application on colorectal cancer (CRC) datasets and large-scale lupus atlas over 1.2 million cells demonstrated that scPAFA can achieve over 40-fold reductions in the runtime of PAS computation and further identified reliable and interpretable multicellular pathway modules that capture the heterogeneity of CRC and transcriptional abnormalities in lupus patients, respectively. Overall, scPAFA presents a valuable addition to existing research tools in disease research, with the potential to reveal complex disease mechanisms and support biomarker discovery at the pathway level.

Serum mitochondrial-encoded NADH dehydrogenase 6 and Annexin A1 as novel biomarkers for mortality prediction in critically ill patients with sepsis

Objectives

Formyl peptide receptor 1 (FPR1) is a member of G protein-coupled receptor (GPCR) family that detects potentially danger signals characterized by the appearance of N-formylated peptides which originate from either bacteria or host mitochondria during organ injury, including sepsis. Mitochondrial-encoded NADH dehydrogenase 6 (MT-ND6) and Annexin A1 (ANXA1), as mitochondrial damage-associated molecular patterns (mtDAMPs) agonist and endogenous agonist of FPR1 respectively, interact with FPR1 regulating polymorphonuclear leukocytes (PMNs) function and inflammatory response during sepsis. However, there is no direct evidence of MT-ND6 or ANXA1 in the circulation of patients with sepsis and their potential role in clinical significance, including diagnosis and mortality prediction during sepsis.

Methods

A prospective cohort study was conducted in ICU within a large academic hospital. We measured serum MT-ND6 or ANXA1 in a cohort of patients with sepsis in ICU (n=180) and patients with non-sepsis in ICU (n=60) by Enzyme-linked immunosorbent assays (ELISA). The ROC curve and Kaplan Meier analysis was used to evaluate the diagnostic and prognostic ability of two biomarkers for patients with sepsis.

Results

The concentration of MT-ND6 and ANXA1 were significantly elevated in the patients with sepsis, and the diagnostic values of MT-ND6 (0.789) for sepsis patients was second only to SOFA scores (AUC = 0.870). Higher serum concentrations of MT-ND6 (>1.41 ng/ml) and lower concentrations of ANXA1 (< 8.09 ng/mL) were closely related to the higher mortality in patients with sepsis, with the predictive values were 0.705 and 0.694, respectively. When patients with sepsis classified based on four pro-inflammation and two anti-inflammation cytokines, it was shown that combination of MT-ND6 and ANXA1 obviously improved the predictive values in the septic patients with mixed hyperinflammation or immunosuppression phenotypes.

Conclusion

Our findings provide valuable models testing patient risk prediction and strengthen the evidence for agonists of FPR1, MT-ND6 and ANXA1, as novel biomarker for patient selection for novel therapeutic agents to target mtDAMPs and regulator of GPCRs in sepsis.

scGraphformer: unveiling cellular heterogeneity and interactions in scRNA-seq data using a scalable graph transformer network

The precise classification of cell types from single-cell RNA sequencing (scRNA-seq) data is pivotal for dissecting cellular heterogeneity in biological research. Traditional graph neural network (GNN) models are constrained by reliance on predefined graphs, limiting the exploration of complex cell-to-cell relationships. We introduce scGraphformer, a transformer-based GNN that transcends these limitations by learning an all-encompassing cell-cell relational network directly from scRNA-seq data. Through an iterative refinement process, scGraphformer constructs a dense graph structure that captures the full spectrum of cellular interactions. This comprehensive approach enables the identification of subtle and previously obscured cellular patterns and relationships. Evaluated on multiple datasets, scGraphformer demonstrates superior performance in cell type identification compared to existing methods and showcases its scalability with large-scale datasets. Our method not only provides enhanced cell type classification ability but also reveals the underlying cell interactions, offering deeper insights into functional cellular relationships. The scGraphformer thus holds the potential to significantly advance the field of single-cell analysis and contribute to a more nuanced understanding of cellular behavior.

Immunopathological markers and cell types linked to COVID-19 symptom manifestation

BACKGROUND

Numerous studies have investigated the molecular properties that contribute to the symptoms of COVID-19, such as the virus's genetic makeup, its replication mechanisms, and how it interacts with host cells. However, identifying the immunopathological properties, such as the immune system's response, cytokine levels, and the presence of specific biomarkers, that are associated with the severity of the infection remains crucial for developing effective treatments and preventions.

METHODS

We analyzed blood protein factor profiles from 420 individuals to identify features differentiating between test-negative healthy, asymptomatic, and symptomatic individuals using statistical comparison and the least absolute shrinkage and selection operator (i.e., LASSO) algorithm. Additionally, we examined single-cell RNA sequencing data from 141 individuals to identify specific cell types associated with the COVID-19 symptoms.

RESULTS

Healthy individuals who tested negative had distinct blood protein factor levels compared to asymptomatic individuals. We identified two key protein factors, Serpin A10 and Complement C9, that differentiate between asymptomatic and symptomatic patients. Symptomatic patients showed lower levels of CD4+ T naïve, CD4+ T effector & memory, and CD8+ T naïve cells, along with higher levels of CD14+ classical monocytes compared to asymptomatic patients. Additionally, CD16+ non-classical monocytes, major producers of C1QA/B/C, appeared to contribute to the observed Complement C9 levels.

CONCLUSIONS

These findings advance our understanding of the immunopathological mechanisms underlying COVID-19 and may inform the development of targeted therapies and preventative measures. Future research should focus on further elucidating these mechanisms and exploring their potential clinical applications in managing COVID-19 severity.

Single-cell sequencing: Current applications in various tuberculosis specimen types

Tuberculosis (TB) is a chronic disease caused by Mycobacterium tuberculosis (M.tb) and responsible for millions of deaths worldwide each year. It has a complex pathogenesis that primarily affects the lungs but can also impact systemic organs. In recent years, single-cell sequencing technology has been utilized to characterize the composition and proportion of immune cell subpopulations associated with the pathogenesis of TB disease since it has a high resolution that surpasses conventional techniques. This paper reviews the current use of single-cell sequencing technologies in TB research and their application in analysing specimens from various sources of TB, primarily peripheral blood and lung specimens. The focus is on how these technologies can reveal dynamic changes in immune cell subpopulations, genes and proteins during disease progression after M.tb infection. Based on the current findings, single-cell sequencing has significant potential clinical value in the field of TB research. Next, we will focus on the real-world applications of the potential targets identified through single-cell sequencing for diagnostics, therapeutics and the development of effective vaccines.

Longitudinal transcriptional changes reveal genes from the natural killer cell-mediated cytotoxicity pathway as critical players underlying COVID-19 progression

Patients present a wide range of clinical severities in response severe acute respiratory syndrome coronavirus 2 infection, but the underlying molecular and cellular reasons why clinical outcomes vary so greatly within the population remains unknown. Here, we report that negative clinical outcomes in severely ill patients were associated with divergent RNA transcriptome profiles in peripheral immune cells compared with mild cases during the first weeks after disease onset. Protein-protein interaction analysis indicated that early-responding cytotoxic natural killer cells were associated with an effective clearance of the virus and a less severe outcome. This innate immune response was associated with the activation of select cytokine-cytokine receptor pathways and robust Th1/Th2 cell differentiation profiles. In contrast, severely ill patients exhibited a dysregulation between innate and adaptive responses affiliated with divergent Th1/Th2 profiles and negative outcomes. This knowledge forms the basis of clinical triage that may be used to preemptively detect high-risk patients before life-threatening outcomes ensue.

BCR, not TCR, repertoire diversity is associated with favorable COVID-19 prognosis

Introduction

The SARS-CoV-2 pandemic has had a widespread and severe impact on society, yet there have also been instances of remarkable recovery, even in critically ill patients.

Materials and methods

In this study, we used single-cell RNA sequencing to analyze the immune responses in recovered and deceased COVID-19 patients during moderate and critical stages.

Results

Expanded T cell receptor (TCR) clones were predominantly SARS-CoV-2-specific, but represented only a small fraction of the total repertoire in all patients. In contrast, while deceased patients exhibited monoclonal B cell receptor (BCR) expansions without COVID-19 specificity, survivors demonstrated diverse and specific BCR clones. These findings suggest that neither TCR diversity nor BCR monoclonal expansions are sufficient for viral clearance and subsequent recovery. Differential gene expression analysis revealed that protein biosynthetic processes were enriched in survivors, but that potentially damaging mitochondrial ATP metabolism was activated in the deceased.

Conclusion

This study underscores that BCR repertoire diversity, but not TCR diversity, correlates with favorable outcomes in COVID-19.

Single-cell RNA sequencing uncovers molecular mechanisms of intravenous immunoglobulin plus methylprednisolone in Kawasaki disease: attenuated monocyte-driven inflammation and improved NK cell cytotoxicity

Introduction

Intravenous immunoglobulin (IVIG) plus methylprednisolone as initial intensive therapy or additional therapy in Kawasaki disease (KD) has been used in clinical practice. However, its molecular and cellular mechanism is unclear.

Methods

We performed single-cell analysis on 14 peripheral blood mononuclear cell (PBMC) samples obtained from 7 KD patients who received either IVIG monotherapy or IVIG plus methylprednisolone therapy. This encompassed 4 samples from KD patients collected before and after IVIG treatment, as well as 3 samples from KD patients before and after IVIG plus methylprednisolone therapy.

Results

Both IVIG monotherapy and IVIG plus methylprednisolone therapy can increase lymphocyte counts (e.g. CD4+T, CD8+T, and gdT cells) to address lymphopenia. They can also decrease monocyte counts and repress the expression of S100A12, NLRP3, and genes associated with immune-cell migration in monocytes. IVIG combined with methylprednisolone downregulates more monocyte-driven inflammatory pathways than IVIG alone. Additionally, this combination uniquely enhances NK cell cytotoxicity by modulating receptor homeostasis, while significantly upregulating interferon-related genes in CD4+ T cells, CD8+ T cells, and B cells, particularly type I interferons.

Conclusion

The combination of IVIG with methylprednisolone attenuated monocyte-driven inflammation and improved NK cell cytotoxicity which might provide clues for pediatricians to consider treatment options for children with KD. Whether the monocyte-driven hyperinflammatory state and NK cell function can be indicators for the clinical choice of IVIG with methylprednisolone therapy in KD needs further investigation.

Immunoglobin attenuates fulminant myocarditis by inhibiting overactivated innate immune response

BACKGROUND AND PURPOSE

Fulminant myocarditis (FM) is a myocardial inflammatory disease that can result from either viral diseases or autoimmune diseases. In this study, we have determined the treatment effects of immunomodulatory drugs on FM.

EXPERIMENTAL APPROACH

FM was induced in A/JGpt mice by intraperitoneal administration of coxsackievirus B3, after which immunoglobins were administered daily by intraperitoneal injection. On the seventh day, the cardiac structure and function were determined using echocardiography and cardiac catheterisation. Single-cell RNA sequencing (scRNA-seq) was performed to evaluate CD45+ cells in the heart.

KEY RESULTS

Immunoglobin, a typical immunomodulatory drug, dramatically reduced mortality and significantly improved cardiac function in mice with FM. ScRNA-seq revealed that immunoglobin treatment effectively modulated cardiac immune homeostasis, particularly by attenuating overactivated innate immune responses. At the cellular level, immunoglobin predominantly targeted Plac8+ monocytes and S100a8+ neutrophils, suppressing their proinflammatory activities, and enhancing antigen processing and presentation capabilities, thereby amplifying the efficiency and potency of the immune response against the virus. Immunoglobin benefits are mediated by the modulation of multiple signalling pathways, including relevant receptors on immune cells, direction of inflammatory cell chemotaxis, antigen presentation and anti-viral effects. Subsequently, Bst2-ILT7 ligand-receptor-mediated cellular interactions manipulated by immunoglobin were further confirmed in vivo.

CONCLUSIONS AND IMPLICATIONS

Immunoglobin treatment significantly attenuated FM-induced cardiac inflammation and improved cardiac function by inhibiting overactivated innate immune responses.

SARS-CoV-2 infection induces hyaluronan production in vitro and hyaluronan levels in COVID-19 patients relate to morbidity and long-term lung impairment: a prospective cohort study

We previously demonstrated that the lungs of deceased COVID-19 patients were filled with a clear hydrogel consisting of hyaluronan (HA). In this translational study, we investigated the role of HA at all stages of COVID-19 disease to map the consequences of elevated HA on morbidity and identify the mechanism of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-induced HA production. A reduced alveolar surface area was observed in the lungs of deceased COVID-19 patients compared to healthy controls, as visualized by a 3D rendering of lung morphology using light-sheet fluorescence microscopy. We confirmed the presence of HA in lung biopsies and found large quantities of proinflammatory fragmented HA. The association of systemic HA in blood plasma and disease severity was assessed in patients with mild (WHO Clinical Progression Scale, WHO-CPS, 1-5) and severe COVID-19 (WHO-CPS, 6-9) during the acute and convalescent phases and related to lung function. We found that systemic levels of HA were high during acute COVID-19 disease, remained elevated during convalescence, and were associated with a reduced diffusion capacity. In vitro 3D-lung models, differentiated from primary human bronchial epithelial cells, were used to study the effects of SARS-CoV-2 infection on HA metabolism, and transcriptomic analyses revealed a dysregulation of HA synthases and hyaluronidases, both contributing to increased HA in apical secretions. Furthermore, corticosteroid treatment reduced the inflammation and downregulated HA synthases. Our findings demonstrate that HA plays a role in COVID-19 morbidity and that sustained elevated HA concentrations may contribute to long-term respiratory impairment.IMPORTANCEThis study provides insights into the role of hyaluronan (HA) in the severity and long-term impact of COVID-19 on lung function. Through extensive morphological examination of lung tissues and a multicenter study, we identified that HA levels are significantly elevated in COVID-19 patients, correlating with a reduced lung diffusion capacity during convalescence. Using a 3D-lung model, we further uncovered how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 infection causes a dysregulated HA metabolism, leading to increased HA production. Our findings provide valuable insights into the pathogenesis of SARS-CoV-2 and suggest that targeting HA metabolism could offer new therapeutic avenues for managing COVID-19, particularly to prevent long-term lung impairment. Additionally, HA holds potential as a biomarker for predicting disease severity, which could guide personalized treatment strategies.

Single-cell spatiotemporal analysis of the lungs reveals Slamf9+ macrophages involved in viral clearance and inflammation resolution

How the lung achieves immune homeostasis after a pulmonary infection is not fully understood. Here, we analyzed the spatiotemporal changes in the lungs over a 2-week natural recovery from severe pneumonia in a Syrian hamster model of SARS-CoV-2 infection. We find that SARS-CoV-2 infects multiple cell types and causes massive cell death at the early stage, including alveolar macrophages. We identify a group of monocyte-derived Slamf9+ macrophages, which are induced after SARS-CoV-2 infection and resistant to impairment caused by SARS-CoV-2. Slamf9+ macrophages contain SARS-CoV-2, recruit and interact with Isg12+Cst7+ neutrophils to clear the viruses. After viral clearance, Slamf9+ macrophages differentiate into Trem2+ and Fbp1+ macrophages, contributing to inflammation resolution at the late stage, and finally replenish alveolar macrophages. These findings are validated in a SARS-CoV-2-infected hACE2 mouse model and confirmed with publicly available human autopsy single-cell RNA-seq data, demonstrating the potential role of Slamf9+ macrophages and their coordination with neutrophils in post-injury tissue repair and inflammation resolution.

Scm6A: A Fast and Low-cost Method for Quantifying m6A Modifications at the Single-cell Level

It is widely accepted that N6-methyladenosine (m6A) exhibits significant intercellular specificity, which poses challenges for its detection using existing m6A quantitative methods. In this study, we introduced Single-cell m6A Analysis (Scm6A), a machine learning-based approach for single-cell m6A quantification. Scm6A leverages input features derived from the expression levels of m6A trans regulators and cis sequence features, and offers remarkable prediction efficiency and reliability. To further validate the robustness and precision of Scm6A, we first applied Scm6A to single-cell RNA sequencing (scRNA-seq) data from peripheral blood mononuclear cells (PBMCs) and calculated the m6A levels in CD4+ and CD8+ T cells. We also applied a winscore-based m6A calculation method to conduct N6-methyladenosine sequencing (m6A-seq) analysis on CD4+ and CD8+ T cells isolated through magnetic-activated cell sorting (MACS) from the same samples. Notably, the m6A levels calculated by Scm6A exhibited a significant positive correlation with those quantified through m6A-seq in different cells isolated by MACS, providing compelling evidence for Scm6A's reliability. Additionally, we performed single-cell-level m6A analysis on lung cancer tissues as well as blood samples from patients with coronavirus disease 2019 (COVID-19), and demonstrated the landscape and regulatory mechanisms of m6A in different T cell subtypes from these diseases. In summary, Scm6A is a novel, dependable, and accurate method for single-cell m6A detection and has broad applications in the realm of m6A-related research.

Identification and evaluation of candidate COVID-19 critical genes and medicinal drugs related to plasma cells

The ongoing COVID-19 pandemic, caused by the SARS-CoV-2 virus, represents one of the most significant global health crises in recent history. Despite extensive research into the immune mechanisms and therapeutic options for COVID-19, there remains a paucity of studies focusing on plasma cells. In this study, we utilized the DESeq2 package to identify differentially expressed genes (DEGs) between COVID-19 patients and controls using datasets GSE157103 and GSE152641. We employed the xCell algorithm to perform immune infiltration analyses, revealing notably elevated levels of plasma cells in COVID-19 patients compared to healthy individuals. Subsequently, we applied the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm to identify COVID-19 related plasma cell module genes. Further, positive cluster biomarker genes for plasma cells were extracted from single-cell RNA sequencing data (GSE171524), leading to the identification of 122 shared genes implicated in critical biological processes such as cell cycle regulation and viral infection pathways. We constructed a robust protein-protein interaction (PPI) network comprising 89 genes using Cytoscape, and identified 20 hub genes through cytoHubba. These genes were validated in external datasets (GSE152418 and GSE179627). Additionally, we identified three potential small molecules (GSK-1070916, BRD-K89997465, and idarubicin) that target key hub genes in the network, suggesting a novel therapeutic approach. These compounds were characterized by their ability to down-regulate AURKB, KIF11, and TOP2A effectively, as evidenced by their low free binding energies determined through computational analyses using cMAP and AutoDock. This study marks the first comprehensive exploration of plasma cells' role in COVID-19, offering new insights and potential therapeutic targets. It underscores the importance of a systematic approach to understanding and treating COVID-19, expanding the current body of knowledge and providing a foundation for future research.

Harnessing high-throughput OMICS in emerging zoonotic virus preparedness and response activities

Emerging and re-emerging viruses pose unpredictable and significant challenges to global health. Emerging zoonotic infectious diseases, which are transmitted between humans and non-human animals, have been estimated to be responsible for nearly two-thirds of emerging infectious disease events and emergence events attributed to these pathogens have been increasing in frequency with the potential for high global health and economic burdens. In this review we will focus on the application of highthroughput OMICS approaches to emerging zoonotic virus investigtations. We highlight the key contributions of transcriptome and proteome investigations to emerging zoonotic virus preparedness and response activities with a focus on SARS-CoV-2, avian influenza virus subtype H5N1, and Orthoebolavirus investigations.

Age-associated clonal B cells drive B cell lymphoma in mice

Although cancer is an age-related disease, how the processes of aging contribute to cancer progression is not well understood. In this study, we uncovered how mouse B cell lymphoma develops as a consequence of a naturally aged system. We show here that this malignancy is associated with an age-associated clonal B cell (ACBC) population that likely originates from age-associated B cells. Driven by c-Myc activation, promoter hypermethylation and somatic mutations, IgM+ ACBCs clonally expand independently of germinal centers and show increased biological age. ACBCs become self-sufficient and support malignancy when transferred into young recipients. Inhibition of mTOR or c-Myc in old mice attenuates pre-malignant changes in B cells during aging. Although the etiology of mouse and human B cell lymphomas is considered distinct, epigenetic changes in transformed mouse B cells are enriched for changes observed in human B cell lymphomas. Together, our findings characterize the spontaneous progression of cancer during aging through both cell-intrinsic and microenvironmental changes and suggest interventions for its prevention.

Identification of a Novel ECM Remodeling Macrophage Subset in AKI to CKD Transition by Integrative Spatial and Single-Cell Analysis

The transition from acute kidney injury (AKI) to chronic kidney disease (CKD) is a critical clinical issue. Although previous studies have suggested macrophages as a key player in promoting inflammation and fibrosis during this transition, the heterogeneity and dynamic characterization of macrophages are still poorly understood. Here, we used integrated single-cell RNA sequencing and spatial transcriptomic to characterize the spatiotemporal heterogeneity of macrophages in murine AKI-to-CKD model of unilateral ischemia-reperfusion injury. A marked increase in macrophage infiltration at day 1 was followed by a second peak at day 14 post AKI. Spatiotemporal profiling revealed that injured tubules and macrophages co-localized early after AKI, whereas in late chronic stages had spatial proximity to fibroblasts. Further pseudotime analysis revealed two distinct lineages of macrophages in this transition: renal resident macrophages differentiated into the pro-repair subsets, whereas infiltrating monocyte-derived macrophages contributed to chronic inflammation and fibrosis. A novel macrophage subset, extracellular matrix remodeling-associated macrophages (EAMs) originating from monocytes, linked to renal fibrogenesis and communicated with fibroblasts via insulin-like growth factors (IGF) signalling. In sum, our study identified the spatiotemporal dynamics of macrophage heterogeneity with a unique subset of EAMs in AKI-to-CKD transition, which could be a potential therapeutic target for preventing CKD development.

Altered immune cell in human severe acute pancreatitis revealed by single-cell RNA sequencing

Background

Severe acute pancreatitis (SAP) is characterized by inflammation, with inflammatory immune cells playing a pivotal role in disease progression. This study aims to understand variations in specific immune cell subtypes in SAP, uncover their mechanisms of action, and identify potential biological markers for predicting Acute Pancreatitis (AP) severity.

Methods

We collected peripheral blood from 7 untreated SAP patients and employed single-cell RNA sequencing for the first time to construct a transcriptome atlas of peripheral blood mononuclear cells (PBMCs) in SAP. Integrating SAP transcriptomic data with 6 healthy controls from the GEO database facilitated the analysis of immune cell roles in SAP. We obtained comprehensive transcriptomic datasets from AP samples in the GEO database and identified potential biomarkers associated with AP severity using the "Scissor" tool in single-cell transcriptomic data.

Results

This study presents the inaugural construction of a peripheral blood single-cell atlas for SAP patients, identifying 20 cell subtypes. Notably, there was a significant decrease in effector T cell subsets and a noteworthy increase in monocytes compared to healthy controls. Moreover, we identified a novel monocyte subpopulation expressing high levels of PPBP and PF4 which was significantly elevated in SAP. The proportion of monocyte subpopulations with high CCL3 expression was also markedly increased compared to healthy controls, as verified by flow cytometry. Additionally, cell communication analysis revealed insights into immune and inflammation-related signaling pathways in SAP patient monocytes. Finally, our findings suggest that the subpopulation with high CCL3 expression, along with upregulated pro-inflammatory genes such as S100A12, IL1B, and CCL3, holds promise as biomarkers for predicting AP severity.

Conclusion

This study reveals monocytes' crucial role in SAP initiation and progression, characterized by distinct pro-inflammatory features intricately linked to AP severity. A monocyte subpopulation with elevated PPBP and CCL3 levels emerges as a potential biomarker and therapeutic target.

Deciphering breast cancer dynamics: insights from single-cell and spatial profiling in the multi-omics era

As one of the most common tumors in women, the pathogenesis and tumor heterogeneity of breast cancer have long been the focal point of research, with the emergence of tumor metastasis and drug resistance posing persistent clinical challenges. The emergence of single-cell sequencing (SCS) technology has introduced novel approaches for gaining comprehensive insights into the biological behavior of malignant tumors. SCS is a high-throughput technology that has rapidly developed in the past decade, providing high-throughput molecular insights at the individual cell level. Furthermore, the advent of multitemporal point sampling and spatial omics also greatly enhances our understanding of cellular dynamics at both temporal and spatial levels. The paper provides a comprehensive overview of the historical development of SCS, and highlights the most recent advancements in utilizing SCS and spatial omics for breast cancer research. The findings from these studies will serve as valuable references for future advancements in basic research, clinical diagnosis, and treatment of breast cancer.

Unraveling the spatial organization and development of human thymocytes through integration of spatial transcriptomics and single-cell multi-omics profiling

The structural components of the thymus are essential for guiding T cell development, but a thorough spatial view is still absent. Here we develop the TSO-his tool, designed to integrate multimodal data from single-cell and spatial transcriptomics to decipher the intricate structure of human thymus. Specifically, we characterize dynamic changes in cell types and critical markers, identifying ELOVL4 as a mediator of CD4+ T cell positive selection in the cortex. Utilizing the mapping function of TSO-his, we reconstruct thymic spatial architecture at single-cell resolution and recapitulates classical cell types and their essential co-localization for T cell development; additionally, previously unknown co-localization relationships such as that of CD8αα with memory B cells and monocytes are identified. Incorporating VDJ sequencing data, we also delineate distinct intermediate thymocyte states during αβ T cell development. Overall, these insights enhance our understanding of thymic biology and may inform therapeutic interventions targeting T cell-mediated immune responses.