A blood atlas of COVID-19 defines hallmarks of disease severity and specificity

Reading of human acute immune dynamics in omicron SARS-CoV-2 breakthrough infection

The dynamics of the immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) breakthrough infections remain unclear, particularly when compared to responses in naive individuals. In this longitudinal prospective cohort study, 13 participants were recruited. Peripheral blood samples were collected every other day until day 7 after symptom onset. Transcriptome sequencing, single-cell sequencing, T-cell receptor (TCR) sequencing, B-cell receptor (BCR) sequencing, Olink proteomics, and antigen-antibody binding experiments were then performed. During the incubation periods of breakthrough infections, peripheral blood exhibited type 2 cytokine response, which shifted to type 1 cytokine response upon symptom onset. Plasma cytokine levels of C-X-C motif chemokine ligand 10, monocyte chemoattractant protein-1, interferon-γ, and interleukin-6 show larger changes in breakthrough infections than naïve infections. The inflammatory response in breakthrough infections rapidly subsided, returning to homeostasis by day 5 after symptom onset. Notably, the levels of monocyte-derived S100A8/A9, previously considered a marker of severe disease, physiologically significantly increased in the early stages of mild cases and persisted until day 7, suggesting a specific biological function. Longitudinal tracking also revealed that antibodies anti-Receptor Binding Domain (anti-RBD) in breakthrough infections significantly increased by day 7 after symptom onset, whereas cytotoxic T lymphocytes appeared by day 5. This study presents a reference for interpreting the immunological response to breakthrough infectious disease in humans.

Sepsis in patients who are immunocompromised: diagnostic challenges and future therapies

Sepsis is a life-threatening, dysregulated host response to infection. Immunosuppression is a risk factor for infections and sepsis. However, the specific immune derangements elevating the risk for infections and sepsis remain unclear in the individual patient, raising the question of whether a general state of immunosuppression exists. In this Review, we explore the relationship between immunosuppression and sepsis, detailing the definitions, causes, and clinical implications. We address the effect of primary immunodeficiencies, acquired conditions, and drugs on the risk of infection and the development of sepsis. Patients with sepsis who are immunocompromised often present with atypical symptoms and diagnostic test results can differ, making early recognition difficult. Future perspectives entail novel biomarkers to improve early sepsis detection and tailored treatments to modulate immune function. Including patients who are immunocompromised in clinical trials is crucial to enhance the relevance of research findings and improve treatment strategies for this vulnerable population.

Monitoring Macrophage Polarization in Infectious Disease, Lesson From SARS-CoV-2 Infection

The concept of macrophage polarization has been largely used in human diseases to define a typology of activation of myeloid cells reminiscent of lymphocyte functional subsets. In COVID-19, several studies have investigated myeloid compartment dysregulation and macrophage polarization as an indicator of disease prognosis and monitoring. SARS-CoV-2 induces an in vitro activation state in monocytes and macrophages that does not match the polarization categories in most studies. In COVID-19 patients, monocytes and macrophages are activated but they do not show a polarization profile. Therefore, the investigation of polarization under basic conditions was not relevant to assess monocyte and macrophage activation. The analysis of monocytes and macrophages with high-throughput methods has allowed the identification of new functional subsets in the context of COVID-19. This approach proposes an innovative stratification of myeloid cell activation. These new functional subsets of myeloid cells would be better biomarkers to assess the risk of complications in COVID-19, reserving the concept of polarization for pharmacological programme evaluation. This review reappraises the polarization of monocytes and macrophages in viral infections, particularly in COVID-19.

scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery

Single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) produces vast amounts of individual cell profiling data. Its analysis presents a significant challenge in accurately annotating cell types and their associated biomarkers. Different pipelines based on deep neural network (DNN) methods have been employed to tackle these issues. These pipelines have arisen as a promising resource and can extract meaningful and concise features from noisy, diverse, and high-dimensional data to enhance annotations and subsequent analysis. Existing tools require high computational resources to execute large sample datasets. We have developed a cutting-edge platform known as scaLR (Single-cell analysis using low resource) that efficiently processes data into feature subsets, samples in batches to reduce the required memory for processing large datasets, and running DNN models in multiple central processing units. scaLR is equipped with data processing, feature extraction, training, evaluation, and downstream analysis. Its novel feature extraction algorithm first trains the model on a feature subset and stores the importance of the features for all the features in that subset. At the end of the training of all subsets, the top-K features are selected based on their importance. The final model is trained on top-K features; its performance evaluation and associated downstream analysis provide significant biomarkers for different cell types and diseases/traits. Our findings indicate that scaLR offers comparable prediction accuracy and requires less model training time and computational resources than existing Python-based pipelines. We present scaLR, a Python-based platform, engineered to utilize minimal computational resources while maintaining comparable execution times and analysis costs to existing frameworks.

Exploratory analyses of leukocyte responses in hospitalized patients treated with ozanimod following a severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infection

Sphingosine-1-phosphate receptor 1 (S1P1) ligands effectively reduce immunopathological damage in viral pneumonia models. Specifically, S1P1 ligands inhibit cytokine storm and help preserve lung endothelial barrier integrity. We recently showed that the S1P receptor ligand ozanimod can be safely administered to hospitalized patients with coronavirus disease 2019 (COVID-19) exhibiting severe symptoms of viral pneumonia, with potential clinical benefits. Here, we extend on this study and investigate the impact of ozanimod on key features of the immune response in patients with severe COVID-19. We quantified circulating cytokine levels, peripheral immune cell numbers, proportions and activation status; we also monitored the quality of the humoral response by assessing anti-severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) antibodies. Our findings reveal that patients receiving ozanimod during acute SARS-CoV-2 infection exhibit significantly reduced numbers of circulating monocytes compared with those receiving standard care. Correspondingly, in the ozanimod-treated group, circulating levels of C-C motif ligand 2 (CCL2) were decreased. While treatment with ozanimod negatively impacted the humoral response to COVID-19 in unvaccinated patients, it did not impair the development of a robust anti-SARS-CoV-2 antibody response in vaccinated patients. These findings suggest that ozanimod influences key immune mechanisms during the acute phase of SARS-CoV-2 infection.

The application of machine learning in clinical microbiology and infectious diseases

With the development of artificial intelligence(AI) in computer science and statistics, it has been further applied to the medical field. These applications include the management of infectious diseases, in which machine learning has created inroads in clinical microbiology, radiology, genomics, and the analysis of electronic health record data. Especially, the role of machine learning in microbiology has gradually become prominent, and it is used in etiological diagnosis, prediction of antibiotic resistance, association between human microbiome characteristics and complex host diseases, prognosis judgment, and prevention and control of infectious diseases. Machine learning in the field of microbiology mainly adopts supervised learning and unsupervised learning, involving algorithms from classification and regression to clustering and dimensionality reduction. This Review explains crucial concepts in machine learning for unfamiliar readers, describes machine learning's current applications in clinical microbiology and infectious diseases, and summarizes important approaches clinicians must be aware of when evaluating research using machine learning.

Systemic inflammation impairs myelopoiesis and interferon type I responses in humans

Systemic inflammatory conditions are classically characterized by an acute hyperinflammatory phase, followed by a late immunosuppressive phase that elevates the susceptibility to secondary infections. Comprehensive mechanistic understanding of these phases is largely lacking. To address this gap, we leveraged a controlled, human in vivo model of lipopolysaccharide (LPS)-induced systemic inflammation encompassing both phases. Single-cell RNA sequencing during the acute hyperinflammatory phase identified an inflammatory CD163+SLC39A8+CALR+ monocyte-like subset (infMono) at 4 h post-LPS administration. The late immunosuppressive phase was characterized by diminished expression of type I interferon (IFN)-responsive genes in monocytes, impaired myelopoiesis and a pronounced attenuation of the immune response on a secondary LPS challenge 1 week after the first. The infMono gene program and impaired myelopoiesis were also detected in patient cohorts with bacterial sepsis and coronavirus disease. IFNβ treatment restored type-I IFN responses and proinflammatory cytokine production and induced monocyte maturation, suggesting a potential treatment option for immunosuppression.

Disease-specific B cell clones are shared between patients with Crohn's disease

B cells have important functions in gut homeostasis, and dysregulated B cell populations are frequently observed in patients with inflammatory bowel diseases, including both ulcerative colitis (UC) and Crohn's disease (CD). How these B cell perturbations contribute to disease remains largely unknown. Here, we perform deep sequencing of the B cell receptor (BCR) repertoire in four cohorts of patients with CD, together with healthy controls and patients with UC. We identify BCR clones that are shared between patients with CD but not found in healthy individuals nor in patients with UC, indicating CD-associated B cell immune responses. Shared clones are present in the inflamed gut mucosa, draining intestinal lymph nodes and blood, suggesting the presence of common CD-associated antigens that drive B cell responses in CD patients.

Different patterns of leukocyte immune responses to infection of ancestral SARS-CoV-2 and its variants

Background

Contributions of leukocytes to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) defense have been reported extensively. However, it remains unclear whether there are different leukocyte responses to ancestral SARS-CoV-2 and its variants.

Methods

We analyzed peripheral blood leukocyte and subtype concentrations from 575 COVID-19 patients and 950 non-COVID-19 subjects registered at the University of Connecticut John Dempsey Hospital between 2020 and 2022, which covers the ancestral strain, Delta, and Omicron variants.

Results

We found that neutrophils, immature granulocytes, and monocytes were elevated, and lymphocytes were reduced after infection. These hyperactive neutrophils/immature granulocytes and suppressed lymphocytes/monocytes were associated with poorer prognosis in ancestral strain infection. Different from the ancestral strain, hyperactive immature granulocytes were not shown in the decedents of Delta infection, and immature granulocyte concentration was not observed to be associated with mortality. In Omicron infection, suppressed lymphocytes and monocytes were not shown in the decedents, and lymphocyte/monocyte concentrations were not associated with mortality.

Conclusions

Our findings provided insights into different leukocyte immune responses to ancestral SARS-CoV-2, Delta, and Omicron variants.

Plasma metabolomic signatures for copy number variants and COVID-19 risk loci in Northern Finland populations

Copy number variants (CNVs) are an important class of genomic variation known to be important for human physiology and diseases. Here we present genome-wide metabolomic signatures for CNVs in two Finnish cohorts-The Northern Finland Birth Cohort 1966 (NFBC 1966) and NFBC 1986. We have analysed and reported CNVs in over 9,300 individuals and characterised their dosage effect (CNV-metabolomic QTL) on 228 plasma lipoproteins and metabolites. We have reported reference (normal physiology) metabolomic signatures for up to ~ 2.6 million COVID-19 GWAS results from the National Institutes of Health (NIH) GRASP database, including for outcomes related to COVID-19 death, severity, and hospitalisation. Furthermore, by analysing two exemplar genes for COVID-19 severity namely LZTFL1 and OAS1, we have reported here two additional candidate genes for COVID-19 severity biology, (1) NFIX, a gene related to viral (adenovirus) replication and hematopoietic stem cells and (2) ACSL1, a known candidate gene for sepsis and bacterial inflammation. Based on our results and current literature we hypothesise that (1) charge imbalance across the cellular membrane between cations (Fe2+, Mg2+ etc.) and anions (e.g. ROS, hydroxide ion from cellular Fenton reactions, superoxide etc.), (2) iron trafficking within and between different cell types e.g., macrophages and (3) systemic oxidative stress response (e.g. lipid peroxidation mediated inflammation), together could be of relevance in severe COVID-19 cases. To conclude, our unique atlas of univariate and multivariate metabolomic signatures for CNVs (~ 7.2 million signatures) with deep annotations of various multi-omics data sets provide an important reference knowledge base for human metabolism and diseases.

Proteomic Analysis of 442 Clinical Plasma Samples From Individuals With Symptom Records Revealed Subtypes of Convalescent Patients Who Had COVID-19

After the coronavirus disease 2019 (COVID-19) pandemic, the postacute effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have gradually attracted attention. To precisely evaluate the health status of convalescent patients with COVID-19, we analyzed symptom and proteome data of 442 plasma samples from healthy controls, hospitalized patients, and convalescent patients 6 or 12 months after SARS-CoV-2 infection. Symptoms analysis revealed distinct relationships in convalescent patients. Results of plasma protein expression levels showed that C1QA, C1QB, C2, CFH, CFHR1, and F10, which regulate the complement system and coagulation, remained highly expressed even at the 12-month follow-up compared with their levels in healthy individuals. By combining symptom and proteome data, 442 plasma samples were categorized into three subtypes: S1 (metabolism-healthy), S2 (COVID-19 retention), and S3 (long COVID). We speculated that convalescent patients reporting hair loss could have a better health status than those experiencing headaches and dyspnea. Compared to other convalescent patients, those reporting sleep disorders, appetite decrease, and muscle weakness may need more attention because they were classified into the S2 subtype, which had the most samples from hospitalized patients with COVID-19. Subtyping convalescent patients with COVID-19 may enable personalized treatments tailored to individual needs. This study provides valuable plasma proteomic datasets for further studies associated with long COVID.

Myeloid-Driven Immune Suppression Subverts Neutralizing Antibodies and T Cell Immunity in Severe COVID-19

The objective of this study was to better understand immune failure mechanisms during severe acute respiratory syndrome coronavirus 2, SARS-CoV-2 infection, which are critical for developing targeted vaccines and effective treatments. We collected 34 cases representing different disease severities and performed high-quality single-cell TCR/BCR sequencing to analyze the peripheral immune cell profiles. Additionally, we assessed antibody-neutralizing activity through in vitro experiments. Our integrated multiomics analysis uncovers a profound immune paradox in severe COVID-19: hyperinflammation coexists with immunosuppression, driven by distinct yet interconnected dysregulatory mechanisms. Severe patients develop robust humoral immunity, evidenced by clonally expanded plasma cells producing neutralizing antibodies (e.g., IGHG1-dominated responses) and antigen-specific T cell activation. However, these protective responses are counteracted by myeloid-driven immunosuppression, particularly CD14+ HMGB2+ monocytes exhibiting metabolic reprogramming and HLA-DR downregulation, coupled with progressive T cell exhaustion characterized by IFN-γ/TNF-α hyperactivation and impaired antigen presentation. Importantly, prolonged viral persistence in severe cases arises from a failure to coordinate humoral and cellular immunity-antibody-mediated neutralization cannot compensate for defective cytotoxic T cell function and monocyte-mediated immune suppression. These findings highlight the necessity for therapeutic strategies that simultaneously enhance antibody effector functions (e.g., Fc optimization), restore exhausted T cells, and reverse myeloid suppression. They also highlight the importance of vaccines designed to elicit balanced B cell memory and durable T cell responses, which are critical to preventing severe disease progression. By addressing the dual challenges of hyperinflammation and immunosuppression, such approaches could restore immune coordination and improve outcomes in severe COVID-19.

Development and Functions of MAIT Cells

Mucosal-associated invariant T (MAIT) cells are evolutionarily conserved T cells that recognize microbial metabolites. They are abundant in humans and conserved during mammalian evolution, which suggests that they have important nonredundant functions. In this article, we discuss the evolutionary conservation of MAIT cells and describe their original developmental process. MAIT cells exert a wide variety of effector functions, from killing infected cells and promoting inflammation to repairing tissues. We provide insights into these functions and discuss how they result from the context of stimulation encountered by MAIT cells in different tissues and pathological settings. We describe how MAIT cell numbers and features are modified in disease states, focusing mainly on in vivo models. Lastly, we discuss emerging strategies to manipulate MAIT cells for therapeutic purposes.

scPANDA: PAN-Blood Data Annotator with a 10-Million Single-Cell Atlas

OBJECTIVES

Recent advancements in single-cell RNA sequencing (scRNA-seq) have revolutionized the study of cellular heterogeneity, particularly within the hematological system. However, accurately annotating cell types remains challenging due to the complexity of immune cells. To address this challenge, we develop a PAN-blood single-cell Data Annotator (scPANDA), which leverages a comprehensive 10-million-cell atlas to provide precise cell type annotation.

METHODS

The atlas, constructed from data collected in 16 studies, incorporated rigorous quality control, preprocessing, and integration steps to ensure a high-quality reference for annotation. scPANDA utilizes a three-layer inference approach, progressively refining cell types from broad compartments to specific clusters. Iterative clustering and harmonization processes were employed to maintain cell type purity throughout the analysis. Furthermore, the performance of scPANDA was evaluated in three external datasets.

RESULTS

The atlas was structured hierarchically, consisting of 16 compartments, 54 classes, 4,‍460 low-level clusters (pd_cc_cl_tfs), and 611 high-level clusters (pmid_cts). Robust performance of the tool was demonstrated in annotating diverse immune scRNA-seq datasets, analyzing immune-tumor coexisting clusters in renal cell carcinoma, and identifying conserved cell clusters across species.

CONCLUSIONS

scPANDA exemplifies effective reference mapping with a large-scale atlas, enhancing the accuracy and reliability of blood cell type identification.

PD-L1+ plasma cells suppress T lymphocyte responses in patients with sepsis and mouse sepsis models

Sepsis, a leading cause of death in intensive care units, is associated with immune alterations that increase the patients' risk of secondary infections and mortality, so better understandings of the pathophysiology of sepsis-induced immunosuppression is essential for the development of therapeutic strategies. In a murine model of sepsis that recapitulates immune alterations observed in patients, here we demonstrate that PD-L1+CD44+B220LowCD138+IgM+ regulatory plasma cells are induced in spleen and regulate ex vivo proliferation and IFNɣ secretion induced by stimulation of T splenocytes. This effect is mediated both by cell-cell contact through increased PD-L1 expression on plasma cells and by production of a soluble factor. These observations are recapitulated in three cohorts of critically ill patients with bacterial and viral sepsis in association with increased mortality. Our findings thus reveal the function of regulatory plasma cells in the pathophysiology of sepsis-induced immune alterations, and present a potential therapeutic target for improving immune cell function impaired by sepsis.

APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19

MOTIVATION

Computational analyses of bulk and single-cell omics provide translational insights into complex diseases, such as COVID-19, by revealing molecules, cellular phenotypes, and signalling patterns that contribute to unfavourable clinical outcomes. Current in silico approaches dovetail differential abundance, biostatistics, and machine learning, but often overlook nonlinear proteomic dynamics, like post-translational modifications, and provide limited biological interpretability beyond feature ranking.

RESULTS

We introduce APNet, a novel computational pipeline that combines differential activity analysis based on SJARACNe co-expression networks with PASNet, a biologically informed sparse deep learning model, to perform explainable predictions for COVID-19 severity. The APNet driver-pathway network ingests SJARACNe co-regulation and classification weights to aid result interpretation and hypothesis generation. APNet outperforms alternative models in patient classification across three COVID-19 proteomic datasets, identifying predictive drivers and pathways, including some confirmed in single-cell omics and highlighting under-explored biomarker circuitries in COVID-19.

AVAILABILITY AND IMPLEMENTATION

APNet's R, Python scripts, and Cytoscape methodologies are available at https://github.com/BiodataAnalysisGroup/APNet.

Single-cell transcriptome-wide Mendelian randomization and colocalization reveals immune-mediated regulatory mechanisms and drug targets for COVID-19

BACKGROUND

COVID-19 continues to show long-term impacts on our health. Limited effective immune-mediated antiviral drugs have been launched.

METHODS

We conducted a Mendelian randomization (MR) and colocalization analysis using 26,597 single-cell expression quantitative trait loci (sc-eQTL) to proxy effects of expressions of 16,597 genes in 14 peripheral blood immune cells and tested them against four COVID-19 outcomes from COVID-19 Genetic Housing Initiative GWAS meta-analysis Round 7. We also carried out additional validations including colocalization, linkage disequilibrium check and host-pathogen interactome predictions. We integrated MR findings with clinical trial evidence from several drug gene related databases to identify drugs with repurposing potential. Finally, we developed a tier system and identified immune-cell-based prioritized drug targets for COVID-19.

FINDINGS

We identified 132 putative causal genes in 14 immune cells (343 MR associations) for COVID-19, with 58 genes that were not reported previously. 145 (73%) gene-COVID-19 pairs showed effects on COVID-19 in only one immune cell type, which implied widespread immune-cell specific effects. For pathway analyses, we found the putative causal genes were enriched in natural killer (NK) recruiting cells but de-enriched in NK cells. Using a deep learning model, we found 107 (81%) of the putative causal genes (41 novel genes) were predicted to interact with SARS-COV-2 proteins. Integrating the above evidence with drug trial information, we developed a tier system and prioritized 37 drug targets for COVID-19.

INTERPRETATION

Our study showcased the central role of immune-mediated regulatory mechanisms for COVID-19 and prioritized drug targets that might inform interventions for viral infectious diseases.

FUNDING

This work was supported by grants from the National Key Research and Development Program of China (2022YFC2505203).

Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens

Recent advancements in functional genomics have provided an unprecedented ability to measure diverse molecular modalities, but predicting causal regulatory relationships from observational data remains challenging. Here, we leverage pooled genetic screens and single-cell sequencing (Perturb-seq) to systematically identify the targets of signalling regulators in diverse biological contexts. We demonstrate how Perturb-seq is compatible with recent and commercially available advances in combinatorial indexing and next-generation sequencing, and perform more than 1,500 perturbations split across six cell lines and five biological signalling contexts. We introduce an improved computational framework (Mixscale) to address cellular variation in perturbation efficiency, alongside optimized statistical methods to learn differentially expressed gene lists and conserved molecular signatures. Finally, we demonstrate how our Perturb-seq derived gene lists can be used to precisely infer changes in signalling pathway activation for in vivo and in situ samples. Our work enhances our understanding of signalling regulators and their targets, and lays a computational framework towards the data-driven inference of an 'atlas' of perturbation signatures.

Fast, flexible analysis of differences in cellular composition with crumblr

Changes in cell type composition play an important role in human health and disease. Recent advances in single-cell technology have enabled the measurement of cell type composition at increasing cell lineage resolution across large cohorts of individuals. Yet this raises new challenges for statistical analysis of these compositional data to identify changes in cell type frequency. We introduce crumblr (DiseaseNeurogenomics.github.io/crumblr), a scalable statistical method for analyzing count ratio data using precision-weighted linear mixed models incorporating random effects for complex study designs. Uniquely, crumblr performs statistical testing at multiple levels of the cell lineage hierarchy using a multivariate approach to increase power over tests of one cell type. In simulations, crumblr increases power compared to existing methods while controlling the false positive rate. We demonstrate the application of crumblr to published single-cell RNA-seq datasets for aging, tuberculosis infection in T cells, bone metastases from prostate cancer, and SARS-CoV-2 infection.

Distinct immune cell infiltration patterns in pancreatic ductal adenocarcinoma (PDAC) exhibit divergent immune cell selection and immunosuppressive mechanisms

Pancreatic ductal adenocarcinoma has a dismal prognosis. A comprehensive analysis of single-cell multi-omic data from matched tumour-infiltrated CD45+ cells and peripheral blood in 12 patients, and two published datasets, reveals a complex immune infiltrate. Patients have either a myeloid-enriched or adaptive-enriched tumour microenvironment. Adaptive immune cell-enriched is intrinsically linked with highly distinct B and T cell clonal selection, diversification, and differentiation. Using TCR data, we see the largest clonal expansions in CD8 effector memory, senescent cells, and highly activated regulatory T cells which are induced within the tumour from naïve cells. We identify pathways that potentially lead to a suppressive microenvironment, including investigational targets TIGIT/PVR and SIRPA/CD47. Analysis of patients from the APACT clinical trial shows that myeloid enrichment had a shorter overall survival compared to those with adaptive cell enrichment. Strategies for rationale therapeutic development in this disease include boosting of B cell responses, targeting immunosuppressive macrophages, and specific Treg cell depletion approaches.

Temporal profiling of human lymphoid tissues reveals coordinated defense against viral challenge

Adaptive immunity is generated in lymphoid organs, but how these structures defend themselves during infection in humans is unknown. The nasal epithelium is a major site of viral entry, with adenoid nasal-associated lymphoid tissue (NALT) generating early adaptive responses. In the present study, using a nasopharyngeal biopsy technique, we investigated longitudinal immune responses in NALT after a viral challenge, using severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection as a natural experimental model. In acute infection, infiltrating monocytes formed a subepithelial and perifollicular shield, recruiting neutrophil extracellular trap-forming neutrophils, whereas tissue macrophages expressed pro-repair molecules during convalescence to promote the restoration of tissue integrity. Germinal center B cells expressed antiviral transcripts that inversely correlated with fate-defining transcription factors. Among T cells, tissue-resident memory CD8 T cells alone showed clonal expansion and maintained cytotoxic transcriptional programs into convalescence. Together, our study provides unique insights into how human nasal adaptive immune responses are generated and sustained in the face of viral challenge.

The T cell receptor sequence influences the likelihood of T cell memory formation

The amino acid sequence of the T cell receptor (TCR) varies between T cells of an individual's immune system. Particular TCR residues nearly guarantee mucosal-associated invariant T (MAIT) and natural killer T (NKT) cell transcriptional fates. To define how the TCR sequence affects T cell fates, we analyze the paired αβTCR sequence and transcriptome of 961,531 single cells. We find that hydrophobic complementarity-determining region (CDR)3 residues promote regulatory T cell fates in both the CD8 and CD4 lineages. Most strikingly, we find a set of TCR sequence features that promote the T cell transition from naive to memory. We quantify the extent of these features through our TCR scoring function "TCR-mem." Using TCR transduction experiments, we demonstrate that increased TCR-mem promotes T cell activation, even among T cells that recognize the same antigen. Our results reveal a common set of TCR sequence features that enable T cell activation and immunological memory.

Integrative Mapping of Pre-existing Immune Landscapes for Vaccine Response Prediction

Predicting individual vaccine responses remains a significant challenge due to the complexity and variability of immune processes. To address this gap, we developed immunaut, an open-source, data-driven framework implemented as an R package specifically designed for all systems vaccinologists seeking to analyze and predict immunological outcomes across diverse vaccination settings. Leveraging one of the most comprehensive live attenuated influenza vaccine (LAIV) datasets to date - 244 Gambian children enrolled in a phase 4 immunogenicity study - immunaut integrates humoral, mucosal, cellular, transcriptomic, and microbiological parameters collected before and after vaccination, providing an unprecedentedly holistic view of LAIV-induced immunity. Through advanced dimensionality reduction, clustering, and predictive modeling, immunaut identifies distinct immunophenotypic responder profiles and their underlying baseline determinants. In this study, immunaut delineated three immunophenotypes: (1) CD8 T-cell responders, marked by strong baseline mucosal immunity and extensive prior influenza virus exposure that boosts memory CD8 T-cell responses, without generating influenza virus-specific antibody responses; (2) Mucosal responders, characterized by pre-existing systemic influenza A virus immunity (specifically to H3N2) and stable epithelial integrity, leading to potent mucosal IgA expansions and subsequent seroconversion to influenza B virus; and (3) Systemic, broad influenza A virus responders, who start with relatively naive immunity and leverage greater initial viral replication to drive broad systemic antibody responses against multiple influenza A virus variants beyond those included in the LAIV vaccine. By integrating pathway-level analysis, model-derived contribution scores, and hierarchical decision rules, immunaut elucidates how distinct immunological landscapes shape each response trajectory and how key baseline features, including pre-existing immunity, mucosal preparedness, and cellular support, dictate vaccine outcomes. Collectively, these findings emphasize the power of integrative, predictive frameworks to advance precision vaccinology, and highlight immunaut as a versatile, community-available resource for optimizing immunization strategies across diverse populations and vaccine platforms.

Identification of an immunological signature of long COVID syndrome

Introduction

Acute COVID-19 infection causes significant alterations in the innate and adaptive immune systems. While most individuals recover naturally, some develop long COVID (LC) syndrome, marked by persistent or new symptoms weeks to months after SARS-CoV-2 infection. Despite its prevalence, there are no clinical tests to distinguish LC patients from those fully recovered. Understanding the immunological basis of LC is essential for improving diagnostic and treatment approaches.

Methods

We performed deep immunophenotyping and functional assays to examine the immunological profiles of LC patients, individuals with active COVID-19, recovered patients, and healthy donors. This analysis assessed both innate and adaptive immune features, identifying potential biomarkers for LC syndrome. A Binomial Generalized Linear Model (BGLM) was used to pinpoint immune features characterizing LC.

Results

COVID-19 patients exhibited depletion of innate immune cell subsets, including plasmacytoid and conventional dendritic cells, classical, non-classical, and intermediate monocytes, and monocyte-derived inflammatory dendritic cells. Elevated basal inflammation was observed in COVID-19 patients compared to LC patients, whose immune profiles were closer to those of healthy donors and recovered individuals. However, LC patients displayed persistent immune alterations, including reduced T cell subsets (CD4, CD8, Tregs) and switched memory B cells, similar to COVID-19 patients. Through BGLM, a unique adaptive immune signature for LC was identified, featuring memory CD8 and gd T cells with low proliferative capacity and diminished expression of activation and homing receptors.

Discussion

The findings highlight a unique immunological signature associated with LC syndrome, characterized by persistent adaptive immune dysregulation. While LC patients displayed recovery in innate immune profiles comparable to healthy and Recovered individuals, deficits in T cell and memory B cell populations were evident, differentiating LC from full recovery. These findings provide insights into LC pathogenesis and may support the development of diagnostic tools and targeted therapies.

Investigating the relationship between the immune response and the severity of COVID-19: a large-cohort retrospective study

The COVID-19 pandemic has left an indelible mark globally, presenting numerous challenges to public health. This crisis, while disruptive and impactful, has provided a unique opportunity to gather precious clinical data extensively. In this observational, case-control study, we utilized data collected at the Azienda Sanitaria Universitaria Friuli Centrale, Italy, to comprehensively characterize the immuno-inflammatory features in COVID-19 patients. Specifically, we employed multicolor flow cytometry, cytokine assays, and inflammatory biomarkers to elucidate the interplay between the infectious agent and the host's immune status. We characterized immuno-inflammatory profiles within the first 72 hours of hospital admission, stratified by age, disease severity, and time elapsed since symptom onset. Our findings indicate that patients admitted to the hospital shortly after symptom onset exhibit a distinct pattern compared to those who arrive later, characterized by a more active immune response and heightened cytokine activity, but lower markers of tissue damage. We used univariate and multivariate logistic regression models to identify informative markers for outcome severity. Predictors incorporating the immuno-inflammatory features significantly outperformed standard baselines, identifying up to 59% of patients with positive outcomes while maintaining a false omission rate as low as 4%. Overall, our study sheds light on the immuno-inflammatory aspects observed in COVID-19 patients prior to vaccination, providing insights for guiding the clinical management of first-time infections by a novel virus.

Type I Interferon Drives a Cellular State Inert to TCR-Stimulation and Could Impede Effective T-Cell Differentiation in Cancer

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is linked to human papillomavirus (HPV) infection. HPV-positive and HPV-negative HNSCC exhibit distinct molecular and clinical characteristics. Although checkpoint inhibitors have shown efficiency in recurrent/metastatic HNSCC, response variability persists regardless of HPV status. This study aimed to explore the CD8+ T-cell landscape in HPV-negative HNSCC.

METHODS

We performed simultaneous single-cell RNA and TCR sequencing of CD8+ tumor-infiltrating lymphocytes (TILs) from treatment-naïve HPV-negative HNSCC patients. Additionally, cells were stimulated ex vivo, which allowed for the tracking of clonal transcriptomic responses.

RESULTS

Our analysis identified a subset of CD8+ TILs highly enriched for interferon-stimulated genes (ISG). TCR analysis revealed ISG cells are clonally related to a population of granzyme K (GZMK)-expressing cells. However, unlike GZMK cells, which exhibited rapid effector-like phenotypes following stimulation, ISG cells were transcriptionally inert. Additionally, ISG cells showed specific enrichment within tumor and were found across multiple tumor entities.

CONCLUSIONS

ISG-enriched CD8+ TILs are a consistent feature of various tumor entities. These cells are poorly understood but possess characteristics that may impact antitumor immunity. Understanding the unique properties and functionality of ISG cells could offer innovative treatment approaches to improve patient outcomes in HPV-negative HNSCC and other cancer types.

Considerations for building and using integrated single-cell atlases

The rapid adoption of single-cell technologies has created an opportunity to build single-cell 'atlases' integrating diverse datasets across many laboratories. Such atlases can serve as a reference for analyzing and interpreting current and future data. However, it has become apparent that atlasing approaches differ, and the impact of these differences are often unclear. Here we review the current atlasing literature and present considerations for building and using atlases. Importantly, we find that no one-size-fits-all protocol for atlas building exists, but rather we discuss context-specific considerations and workflows, including atlas conceptualization, data collection, curation and integration, atlas evaluation and atlas sharing. We further highlight the benefits of integrated atlases for analyses of new datasets and deriving biological insights beyond what is possible from individual datasets. Our overview of current practices and associated recommendations will improve the quality of atlases to come, facilitating the shift to a unified, reference-based understanding of single-cell biology.

The characterization of CD8+ T-cell responses in COVID-19

This review gives an overview of the protective role of CD8+ T cells in SARS-CoV-2 infection. The cross-reactive responses intermediated by CD8+ T cells in unexposed cohorts are described. Additionally, the relevance of resident CD8+ T cells in the upper and lower airway during infection and CD8+ T-cell responses following vaccination are discussed, including recent worrisome breakthrough infections and variants of concerns (VOCs). Lastly, we explain the correlation between CD8+ T cells and COVID-19 severity. This review aids in a deeper comprehension of the association between CD8+ T cells and SARS-CoV-2 and broadens a vision for future exploration.

SARS-CoV-2 human challenge reveals biomarkers that discriminate early and late phases of respiratory viral infections

Blood transcriptional biomarkers of acute viral infections typically reflect type 1 interferon (IFN) signalling, but it is not known whether there are biological differences in their regulation that can be leveraged for distinct translational applications. We use high frequency sampling in the SARS-CoV-2 human challenge model to show induction of IFN-stimulated gene (ISG) expression with different temporal and cellular profiles. MX1 gene expression correlates with a rapid and transient wave of ISG expression across all cell types, which may precede PCR detection of replicative infection. Another ISG, IFI27, shows a delayed but sustained response restricted to myeloid cells, attributable to gene and cell-specific epigenetic regulation. These findings are reproducible in experimental and naturally acquired infections with influenza, respiratory syncytial virus and rhinovirus. Blood MX1 expression is superior to IFI27 expression for diagnosis of early infection, as a correlate of viral load and for discrimination of virus culture positivity. Therefore, MX1 expression offers potential to stratify patients for antiviral therapy or infection control interventions. Blood IFI27 expression is superior to MX1 expression for diagnostic accuracy across the time course of symptomatic infection and thereby, offers higher diagnostic yield for respiratory virus infections that incur a delay between transmission and testing.

Tensor decomposition reveals trans-regulated gene modules in maize drought response

When plants respond to drought stress, dynamic cellular changes occur, accompanied by alterations in gene expression, which often act through trans-regulation. However, the detection of trans-acting genetic variants and networks of genes is challenged by the large number of genes and markers. Using a tensor decomposition method, we identify trans-acting expression quantitative trait loci (trans-eQTLs) linked to gene modules, rather than individual genes, which were associated with maize drought response. Module-to-trait association analysis demonstrates that half of the modules are relevant to drought-related traits. Genome-wide association studies of the expression patterns of each module identify 286 trans-eQTLs linked to drought-responsive modules, the majority of which cannot be detected based on individual gene expression. Notably, the trans-eQTLs located in the regions selected during maize improvement tend towards relatively strong selection. We further prioritize the genes that affect the transcriptional regulation of multiple genes in trans, as exemplified by two transcription factor genes. Our analyses highlight that multidimensional reduction could facilitate the identification of trans-acting variations in gene expression in response to dynamic environments and serve as a promising technique for high-order data processing in future crop breeding.

A longitudinal single-cell atlas of anti-tumour necrosis factor treatment in inflammatory bowel disease

Precision medicine in immune-mediated inflammatory diseases (IMIDs) requires a cellular understanding of treatment response. We describe a therapeutic atlas for Crohn's disease (CD) and ulcerative colitis (UC) following adalimumab, an anti-tumour necrosis factor (anti-TNF) treatment. We generated ~1 million single-cell transcriptomes, organised into 109 cell states, from 216 gut biopsies (41 subjects), revealing disease-specific differences. A systems biology-spatial analysis identified granuloma signatures in CD and interferon (IFN)-response signatures localising to T cell aggregates and epithelial damage in CD and UC. Pretreatment differences in epithelial and myeloid compartments were associated with remission outcomes in both diseases. Longitudinal comparisons demonstrated disease progression in nonremission: myeloid and T cell perturbations in CD and increased multi-cellular IFN signalling in UC. IFN signalling was also observed in rheumatoid arthritis (RA) synovium with a lymphoid pathotype. Our therapeutic atlas represents the largest cellular census of perturbation with the most common biologic treatment, anti-TNF, across multiple inflammatory diseases.

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.

Potential association between COVID-19 and neurological disorders: analysis of common genes and therapeutics

As the COVID-19 pandemic persists, the increasing evidences suggest that the patients with COVID-19 may face the risks of the neurological complications and sequelae. To address this issue, we conducted a comprehensive study aimed at exploring the relationship between COVID-19 and various neurological disorders, with a particular focus on the shared dysregulated genes and the potential therapeutic targets. We selected six neurological disorders for investigation, including Alzheimer's disease, epilepsy, stroke, Parkinson's disease, and the sleep disorders. Through the bioinformatics analysis of the association between these disorders and COVID-19, we aimed to uncover the common molecular mechanisms and the potential treatment pathways. In this study, we utilized the publicly available RNA-Seq and microarray datasets, and employed tools such as Limma and DESeq2 for the differential gene analysis. Through the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, we explored the common biological features and pathways. Additionally, we focused on analyzing the regulatory roles of miRNA and transcription factors on the shared differentially expressed genes, and predicted the potential drugs interacting with these genes. These analyses contribute to a better understanding of the relationship between COVID-19 and the neurological disorders, and provide a theoretical basis for the future treatment strategies. Through this research, we aim to offer the deeper insights to the scientific community and present the new perspectives for the clinical practice in addressing the challenges of the neurological complications and sequelae faced by the COVID-19 patients.

Dynamic Characteristics of Lymphocyte Subsets and Their Predictive Value for Disease Progression and Prognosis in Primary Infection and Unvaccinated COVID-19 Patients

AIM

Our cohort study aimed to investigate the dynamic changes of lymphocyte subsets and their abilities to predict disease severity and prognosis in primary infection and unvaccinated COVID-19 patients.

METHODS

A total of 773 cases, including 718 primary infection and unvaccinated COVID-19 patients and 55 controls. COVID-19 patients were assigned to severe and nonsevere groups according to disease severity, as well as survival and death groups according to prognosis. Serum samples were collected to measure the numbers of total lymphocytes and lymphocyte subsets. The differences among different severity groups were analyzed. Spearman correlation was performed to assess associations between lymphocyte subsets and disease severity and prognosis. Meanwhile, receiver operating characteristic (ROC) curves were also analyzed to find optimal cutoff points.

RESULTS

At admission, the severe group demonstrated significantly lower total lymphocyte counts and percentages, CD3+ and CD3+CD4+ T cell counts and percentages, CD3+CD8+ T cell counts, CD19+ B cell counts and CD56+ NK cell counts and percentages than the nonsevere group. Meanwhile, compared with the survival group, the death group also had lower total lymphocyte counts and percentages, CD3+, CD3+CD4+ and CD3+CD8+ T cell counts. Additionally, differences in these parameters were also noticed within four weeks after admission. Furthermore, Spearman analysis reported that disease severity was negatively correlated with lymphocyte counts and percentages, CD3+, CD3+CD4+ and CD3+CD8+ T cell counts, CD3+ and CD3+CD4+ T cell percentages (r=-0.166, -0.179, -0.173, -0.186, -0.127, -0.117, -0.149, respectively)(all P<0.05). The prognosis of death was also negatively correlated with total lymphocyte counts and percentages, CD3+, CD3+CD4+ and CD3+CD8+ T cell counts (r=-0.125, -0.121, -0.123, -0.123, -0.091, respectively)(all P<0.05).

CONCLUSION

In primary infection and unvaccinated COVID-19 patients total lymphocytes and T cell, B cell and NK cell subsets at COVID-19 onset play valuable roles in predicting disease severity and prognosis.

CLINICAL TRIAL REGISTRY

Chinese Clinical Trial Register ChiCTR2000034563.

Utilizing press needle acupuncture to treat mild-to-moderate COVID-19: A single-blind, randomized controlled trial

BACKGROUND

In China, acupuncture has been employed as an adjunctive therapy for coronavirus disease 2019 (COVID-19). Press needle acupuncture is a special type of acupuncture that provides prolonged stimulation to acupuncture points and simultaneously reduces the pain associated with traditional acupuncture. This study assessed the effectiveness of integrating press needles alongside pharmacologic treatment in patients with mild-to-moderate COVID-19.

METHODS

Patients hospitalized with mild-to-moderate COVID-19 symptoms between December 2022 and January 2023 were included in the study. The enrolled patients were randomly assigned to receive pharmacologic treatment alone (control group) or both pharmacologic treatment and press needle acupuncture (intervention group). Patients were evaluated for clinical outcomes, including symptom scores, deterioration rates, fever durations, and nucleic acid test results. The patients' complete blood count and C-reactive protein levels were also analyzed using venous blood samples both before and after treatment.

RESULTS

Both groups exhibited a reduction in clinical symptom scores, but symptoms regressed faster in the intervention group. Nucleic acid test negativity was achieved faster in the intervention group than in the control group. The intervention group also had a lower deterioration rate. Furthermore, the increase in the lymphocyte count and decrease in C-reactive protein levels following treatment were more pronounced in the intervention group than in the control group.

CONCLUSION

This study suggests that utilizing press needle acupuncture as an adjunct to pharmacologic treatment can be effective in patients with mild-to-moderate COVID-19 symptoms.

Integrative Analysis by Mendelian Randomization and Large-Scale Single-Cell Transcriptomics Reveals Causal Links between B Cell Subtypes and Diabetic Kidney Disease

Introduction

The increasing incidence of diabetic kidney disease (DKD) and the challenges in its management highlight the necessity for a deeper understanding of its pathogenesis. While recent studies have underscored the substantial impact of circulating immunity on the development of diabetic microvascular complications such as retinopathy and neuropathy, research on circulating immunity in DKD remains limited.

Methods

This study utilized Mendelian randomization analysis to explore the potential independent causal relationships between circulating immune cells and DKD pathogenesis. Additionally, a combination of single-cell disease relevance score (scDRS) and immune cell infiltration analysis was employed to map the circulating immunity landscape in DKD patients.

Results

Ten immune traits, including 5 of B cells, 2 of T cells, 2 of granulocytes, and one of monocytes, were defined to be associated with the pathogenesis of DKD. Notably, IgD - CD27 - B cell Absolute Count (IVW: OR, 1.102 [1.023-1.189], p = 0.011) and IgD - CD24 - B cell Absolute Count (IVW: OR, 1.106 [1.030-1.188], p = 0.005) were associated with promoting DKD pathogenesis, while CD24 + CD27 + B cell %B cell (IVW: OR, 0.943 [0.898-0.989], p = 0.016) demonstrated a protective effect against DKD onset. The presence of B cell-activating factor receptor (BAFF-R) on CD20 - CD38 - B cell (IVW: OR, 0.946 [0.904-0.989], p = 0.015) and BAFF-R on IgD - CD38 + B cell (IVW: OR, 0.902 [0.834-0.975], p = 0.009) also indicated a potential role in preventing DKD. scDRS analysis revealed that two main subsets of B cells, naïve B and memory B cells, had a higher proportion of DKD-related cells or a higher scDRS score of DKD phenotype, indicating their strong association with DKD. Furthermore, immune infiltrate deconvolution analysis showed a notable decrease in the circulating memory B cells and class-switched memory B cells in DKD patients compared to those of DM patients without DKD.

Conclusion

Our study revealed the causal relations between circulating immunity and DKD susceptibility, particularly highlighted the potential roles of B cell subtypes in DKD development. Further studies addressing the related mechanisms would broaden the current understanding of DKD pathogenesis.

Transcriptional control of interferon-stimulated genes

Interferon-induced genes are among the best-studied groups of coregulated genes. Nevertheless, intense research into their regulation, supported by new technologies, is continuing to provide insights into their many layers of transcriptional regulation and to reveal how cellular transcriptomes change with pathogen-induced innate and adaptive immunity. This article gives an overview of recent findings on interferon-induced gene regulation, paying attention to contributions beyond the canonical JAK-STAT pathways.

Discovery of sparse, reliable omic biomarkers with Stabl

Adoption of high-content omic technologies in clinical studies, coupled with computational methods, has yielded an abundance of candidate biomarkers. However, translating such findings into bona fide clinical biomarkers remains challenging. To facilitate this process, we introduce Stabl, a general machine learning method that identifies a sparse, reliable set of biomarkers by integrating noise injection and a data-driven signal-to-noise threshold into multivariable predictive modeling. Evaluation of Stabl on synthetic datasets and five independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used sparsity-promoting regularization methods while maintaining predictive performance; it distills datasets containing 1,400-35,000 features down to 4-34 candidate biomarkers. Stabl extends to multi-omic integration tasks, enabling biological interpretation of complex predictive models, as it hones in on a shortlist of proteomic, metabolomic and cytometric events predicting labor onset, microbial biomarkers of pre-term birth and a pre-operative immune signature of post-surgical infections. Stabl is available at https://github.com/gregbellan/Stabl .

A spatially resolved single-cell lung atlas integrated with clinical and blood signatures distinguishes COVID-19 disease trajectories

COVID-19 is characterized by a broad range of symptoms and disease trajectories. Understanding the correlation between clinical biomarkers and lung pathology during acute COVID-19 is necessary to understand its diverse pathogenesis and inform more effective treatments. Here, we present an integrated analysis of longitudinal clinical parameters, peripheral blood markers, and lung pathology in 142 Brazilian patients hospitalized with COVID-19. We identified core clinical and peripheral blood signatures differentiating disease progression between patients who recovered from severe disease compared with those who succumbed to the disease. Signatures were heterogeneous among fatal cases yet clustered into two patient groups: "early death" (<15 days until death) and "late death" (>15 days). Progression to early death was characterized systemically and in lung histopathological samples by rapid endothelial and myeloid activation and the presence of thrombi associated with SARS-CoV-2+ macrophages. In contrast, progression to late death was associated with fibrosis, apoptosis, and SARS-CoV-2+ epithelial cells in postmortem lung tissue. In late death cases, cytotoxicity, interferon, and T helper 17 (TH17) signatures were only detectable in the peripheral blood after 2 weeks of hospitalization. Progression to recovery was associated with higher lymphocyte counts, TH2 responses, and anti-inflammatory-mediated responses. By integrating antemortem longitudinal blood signatures and spatial single-cell lung signatures from postmortem lung samples, we defined clinical parameters that could be used to help predict COVID-19 outcomes.

scConfluence: single-cell diagonal integration with regularized Inverse Optimal Transport on weakly connected features

The abundance of unpaired multimodal single-cell data has motivated a growing body of research into the development of diagonal integration methods. However, the state-of-the-art suffers from the loss of biological information due to feature conversion and struggles with modality-specific populations. To overcome these crucial limitations, we here introduce scConfluence, a method for single-cell diagonal integration. scConfluence combines uncoupled autoencoders on the complete set of features with regularized Inverse Optimal Transport on weakly connected features. We extensively benchmark scConfluence in several single-cell integration scenarios proving that it outperforms the state-of-the-art. We then demonstrate the biological relevance of scConfluence in three applications. We predict spatial patterns for Scgn, Synpr and Olah in scRNA-smFISH integration. We improve the classification of B cells and Monocytes in highly heterogeneous scRNA-scATAC-CyTOF integration. Finally, we reveal the joint contribution of Fezf2 and apical dendrite morphology in Intra Telencephalic neurons, based on morphological images and scRNA.

Immune system adaptation during gender-affirming testosterone treatment

Infectious, inflammatory and autoimmune conditions present differently in males and females. SARS-CoV-2 infection in naive males is associated with increased risk of death, whereas females are at increased risk of long COVID1, similar to observations in other infections2. Females respond more strongly to vaccines, and adverse reactions are more frequent3, like most autoimmune diseases4. Immunological sex differences stem from genetic, hormonal and behavioural factors5 but their relative importance is only partially understood6-8. In individuals assigned female sex at birth and undergoing gender-affirming testosterone therapy (trans men), hormone concentrations change markedly but the immunological consequences are poorly understood. Here we performed longitudinal systems-level analyses in 23 trans men and found that testosterone modulates a cross-regulated axis between type-I interferon and tumour necrosis factor. This is mediated by functional attenuation of type-I interferon responses in both plasmacytoid dendritic cells and monocytes. Conversely, testosterone potentiates monocyte responses leading to increased tumour necrosis factor, interleukin-6 and interleukin-15 production and downstream activation of nuclear factor kappa B-regulated genes and potentiation of interferon-γ responses, primarily in natural killer cells. These findings in trans men are corroborated by sex-divergent responses in public datasets and illustrate the dynamic regulation of human immunity by sex hormones, with implications for the health of individuals undergoing hormone therapy and our understanding of sex-divergent immune responses in cisgender individuals.

scDAPP: a comprehensive single-cell transcriptomics analysis pipeline optimized for cross-group comparison

Single-cell transcriptomics profiling has increasingly been used to evaluate cross-group (or condition) differences in cell population and cell-type gene expression. This often leads to large datasets with complex experimental designs that need advanced comparative analysis. Concurrently, bioinformatics software and analytic approaches also become more diverse and constantly undergo improvement. Thus, there is an increased need for automated and standardized data processing and analysis pipelines, which should be efficient and flexible too. To address these, we develop the single-cell Differential Analysis and Processing Pipeline (scDAPP), a R-based workflow for comparative analysis of single cell (or nucleus) transcriptomic data between two or more groups and at the levels of single cells or 'pseudobulking' samples. The pipeline automates many steps of pre-processing using data-learnt parameters, uses previously benchmarked software, and generates comprehensive intermediate data and final results that are valuable for both beginners and experts of scRNA-seq analysis. Moreover, the analytic reports, augmented by extensive data visualization, increase the transparency of computational analysis and parameter choices, while facilitate users to go seamlessly from raw data to biological interpretation. scDAPP is freely available under the MIT license, with source code, documentation and sample data at the GitHub (https://github.com/bioinfoDZ/scDAPP).

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

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

SARS-CoV2 infection in whole lung primarily targets macrophages that display subset-specific responses

Deciphering the initial steps of SARS-CoV-2 infection, that influence COVID-19 outcomes, is challenging because animal models do not always reproduce human biological processes and in vitro systems do not recapitulate the histoarchitecture and cellular composition of respiratory tissues. To address this, we developed an innovative ex vivo model of whole human lung infection with SARS-CoV-2, leveraging a lung transplantation technique. Through single-cell RNA-seq, we identified that alveolar and monocyte-derived macrophages (AMs and MoMacs) were initial targets of the virus. Exposure of isolated lung AMs, MoMacs, classical monocytes and non-classical monocytes (ncMos) to SARS-CoV-2 variants revealed that while all subsets responded, MoMacs produced higher levels of inflammatory cytokines than AMs, and ncMos contributed the least. A Wuhan lineage appeared to be more potent than a D614G virus, in a dose-dependent manner. Amidst the ambiguity in the literature regarding the initial SARS-CoV-2 cell target, our study reveals that AMs and MoMacs are dominant primary entry points for the virus, and suggests that their responses may conduct subsequent injury, depending on their abundance, the viral strain and dose. Interfering on virus interaction with lung macrophages should be considered in prophylactic strategies.

Potential use of sodium glucose co-transporter 2 inhibitors during acute illness: a systematic review based on COVID-19

OBJECTIVE

SGLT-2i are increasingly recognized for their benefits in patients with cardiometabolic risk factors. Additionally, emerging evidence suggests potential applications in acute illnesses, including COVID-19. This systematic review aims to evaluate the effects of SGLT-2i in patients facing acute illness, particularly focusing on SARS-CoV-2 infection.

METHODS

Following PRISMA guidelines, a systematic search of PubMed, Scopus, medRxiv, Research Square, and Google Scholar identified 22 studies meeting inclusion criteria, including randomized controlled trials and observational studies. Data extraction and quality assessment were conducted independently.

RESULTS

Out of the 22 studies included in the review, six reported reduced mortality in DM-2 patients taking SGLT-2i, while two found a decreased risk of hospitalization. Moreover, one study demonstrated a lower in-hospital mortality rate in DM-2 patients under combined therapy of metformin plus SGLT-2i. However, three studies showed a neutral effect on the risk of hospitalization. No increased risk of developing COVID-19 was associated with SGLT-2i use in DM-2 patients. Prior use of SGLT-2i was not associated with ICU admission and need for MV. The risk of acute kidney injury showed variability, with inconsistent evidence regarding diabetic ketoacidosis.

CONCLUSION

Our systematic review reveals mixed findings on the efficacy of SGLT-2i use in COVID-19 patients with cardiometabolic risk factors. While some studies suggest potential benefits in reducing mortality and hospitalizations, others report inconclusive results. Further research is needed to clarify optimal usage and mitigate associated risks, emphasizing caution in clinical interpretation.

Novel immunomodulatory properties of adenosine analogs promote their antiviral activity against SARS-CoV-2

The COVID-19 pandemic reminded us of the urgent need for new antivirals to control emerging infectious diseases and potential future pandemics. Immunotherapy has revolutionized oncology and could complement the use of antivirals, but its application to infectious diseases remains largely unexplored. Nucleoside analogs are a class of agents widely used as antiviral and anti-neoplastic drugs. Their antiviral activity is generally based on interference with viral nucleic acid replication or transcription. Based on our previous work and computer modeling, we hypothesize that antiviral adenosine analogs, like remdesivir, have previously unrecognized immunomodulatory properties which contribute to their therapeutic activity. In the case of remdesivir, we here show that these properties are due to its metabolite, GS-441524, acting as an Adenosine A2A Receptor antagonist. Our findings support a new rationale for the design of next-generation antiviral agents with dual - immunomodulatory and intrinsic - antiviral properties. These compounds could represent game-changing therapies to control emerging viral diseases and future pandemics.

Peripheral Blood Mononuclear Cells and Serum Cytokines in Patients with Lupus Nephritis after COVID-19

Systemic lupus erythematosus (SLE) patients have an increased risk of infections and infection-related mortality. Therefore, during the global SARS-CoV-2 pandemic, SLE patients were particularly vulnerable to SARS-CoV-2 infections. Also, compared to other patients, SLE patients seem to develop more severe manifestations of coronavirus disease 2019 (COVID-19), with higher rates of hospitalization, invasive ventilation requirements, or death. This study evaluated the immune parameters after SARS-CoV-2 infection in SLE patients. We analyzed subpopulations of peripheral blood cells collected from patients with renal manifestation of SLE (lupus nephritis, LN). LN patients were divided into two subgroups: those unexposed to SARS-CoV-2 (LN CoV-2(-)) and those who had confirmed COVID-19 (LN-CoV-2(+)) six months earlier. We analyzed basic subpopulations of T cells, B cells, monocytes, dendritic cells (DCs), and serum cytokines using flow cytometry. All collected data were compared to a healthy control group without SARS-CoV-2 infection in medical history. LN patients were characterized by a decreased percentage of helper T (Th) cells and an increased percentage of cytotoxic T (Tc) cells regardless of SARS-CoV-2 infection. LN CoV-2(+) patients had a higher percentage of regulatory T cells (Tregs) and plasmablasts (PBs) and a lower percentage of non-switched memory (NSM) B cells compared to LN CoV-2(-) patients or healthy controls (HC CoV-2(-)). LN patients had a higher percentage of total monocytes compared with HC CoV-2(-). LN CoV-2(+) patients had a higher percentage of classical and intermediate monocytes than LN CoV-2(-) patients and HC CoV-2(-). LN CoV-2(+) patients had higher serum IL-6 levels than HC CoV-2(-), while LN CoV-2(-) patients had higher levels of serum IL-10. LN patients are characterized by disturbances in the blood's basic immunological parameters. However, SARS-CoV-2 infection influences B-cell and monocyte compartments.

Higher levels of IL-1ra, IL-6, IL-8, MCP-1, MIP-3α, MIP-3β, and fractalkine are associated with 90-day mortality in 132 non-immunomodulated hospitalized patients with COVID-19

INTRODUCTION

Immune dysregulation with an excessive release of cytokines has been identified as a key driver in the development of severe COVID-19. The aim of this study was to evaluate the initial cytokine profile associated with 90-day mortality and respiratory failure in a cohort of patients hospitalized with COVID 19 that did not receive immunomodulatory therapy.

METHODS

Levels of 45 cytokines were measured in blood samples obtained at admission from patients with confirmed COVID-19. Logistic regression analysis was utilized to determine the association between cytokine levels and outcomes. The primary outcome was death within 90 days from admission and the secondary outcome was need for mechanical ventilation.

RESULTS

A total of 132 patients were included during the spring of 2020. We found that one anti-inflammatory cytokine, one pro-inflammatory cytokine, and five chemokines were associated with the odds of 90-day mortality, specifically: interleukin-1 receptor antagonist, interleukin-6, interleukin-8, monocyte chemoattractant protein-1, macrophage inflammatory protein-3α, macrophage inflammatory protein-3β, and fractalkine. All but fractalkine were also associated with the odds of respiratory failure during admission. Monocyte chemoattractant protein-1 showed the strongest estimate of association with both outcomes.

CONCLUSION

We showed that one anti-inflammatory cytokine, one pro-inflammatory cytokine, and five chemokines were associated with 90-day mortality in patients hospitalized with COVID-19 that did not receive immunomodulatory therapy.

Panpipes: a pipeline for multiomic single-cell and spatial transcriptomic data analysis

Single-cell multiomic analysis of the epigenome, transcriptome, and proteome allows for comprehensive characterization of the molecular circuitry that underpins cell identity and state. However, the holistic interpretation of such datasets presents a challenge given a paucity of approaches for systematic, joint evaluation of different modalities. Here, we present Panpipes, a set of computational workflows designed to automate multimodal single-cell and spatial transcriptomic analyses by incorporating widely-used Python-based tools to perform quality control, preprocessing, integration, clustering, and reference mapping at scale. Panpipes allows reliable and customizable analysis and evaluation of individual and integrated modalities, thereby empowering decision-making before downstream investigations.