The life-saving benefit of dexamethasone in severe COVID-19 is linked to a reversal of monocyte dysregulation.

Dexamethasone is a life-saving treatment for severe COVID-19, yet its mechanism of action is unknown, and many patients deteriorate or die despite timely treatment initiation. Here, we identify dexamethasone treatment-induced cellular and molecular changes associated with improved survival in COVID-19 patients. We observed a reversal of transcriptional hallmark signatures in monocytes associated with severe COVID-19 and the induction of a monocyte substate characterized by the expression of glucocorticoid-response genes. These molecular responses to dexamethasone were detected in circulating and pulmonary monocytes, and they were directly linked to survival. Monocyte single-cell RNA sequencing (scRNA-seq)-derived signatures were enriched in whole blood transcriptomes of patients with fatal outcome in two independent cohorts, highlighting the potential for identifying non-responders refractory to dexamethasone. Our findings link the effects of dexamethasone to specific immunomodulation and reversal of monocyte dysregulation, and they highlight the potential of single-cell omics for monitoring in vivo target engagement of immunomodulatory drugs and for patient stratification for precision medicine approaches.

Complement activation induces excessive T cell cytotoxicity in severe COVID-19.

Severe COVID-19 is linked to both dysfunctional immune response and unrestrained immunopathology, and it remains unclear whether T cells contribute to disease pathology. Here, we combined single-cell transcriptomics and single-cell proteomics with mechanistic studies to assess pathogenic T cell functions and inducing signals. We identified highly activated CD16+ T cells with increased cytotoxic functions in severe COVID-19. CD16 expression enabled immune-complex-mediated, T cell receptor-independent degranulation and cytotoxicity not found in other diseases. CD16+ T cells from COVID-19 patients promoted microvascular endothelial cell injury and release of neutrophil and monocyte chemoattractants. CD16+ T cell clones persisted beyond acute disease maintaining their cytotoxic phenotype. Increased generation of C3a in severe COVID-19 induced activated CD16+ cytotoxic T cells. Proportions of activated CD16+ T cells and plasma levels of complement proteins upstream of C3a were associated with fatal outcome of COVID-19, supporting a pathological role of exacerbated cytotoxicity and complement activation in COVID-19.

Severe COVID-19 Is Marked by a Dysregulated Myeloid Cell Compartment.

Coronavirus disease 2019 (COVID-19) is a mild to moderate respiratory tract infection, however, a subset of patients progress to severe disease and respiratory failure. The mechanism of protective immunity in mild forms and the pathogenesis of severe COVID-19 associated with increased neutrophil counts and dysregulated immune responses remain unclear. In a dual-center, two-cohort study, we combined single-cell RNA-sequencing and single-cell proteomics of whole-blood and peripheral-blood mononuclear cells to determine changes in immune cell composition and activation in mild versus severe COVID-19 (242 samples from 109 individuals) over time. HLA-DRhiCD11chi inflammatory monocytes with an interferon-stimulated gene signature were elevated in mild COVID-19. Severe COVID-19 was marked by occurrence of neutrophil precursors, as evidence of emergency myelopoiesis, dysfunctional mature neutrophils, and HLA-DRlo monocytes. Our study provides detailed insights into the systemic immune response to SARS-CoV-2 infection and reveals profound alterations in the myeloid cell compartment associated with severe COVID-19.

A guide to systems-level immunomics.

The immune system is highly complex and distributed throughout an organism, with hundreds to thousands of cell states existing in parallel with diverse molecular pathways interacting in a highly dynamic and coordinated fashion. Although the characterization of individual genes and molecules is of the utmost importance for understanding immune-system function, high-throughput, high-resolution omics technologies combined with sophisticated computational modeling and machine-learning approaches are creating opportunities to complement standard immunological methods with new insights into immune-system dynamics. Like systems immunology itself, immunology researchers must take advantage of these technologies and form their own diverse networks, connecting with researchers from other disciplines. This Review is an introduction and 'how-to guide' for immunologists with no particular experience in the field of omics but with the intention to learn about and apply these systems-level approaches, and for immunologists who want to make the most of interdisciplinary networks.

CRELD1 modulates homeostasis of the immune system in mice and humans.

CRELD1 is a pivotal factor for heart development, the function of which is unknown in adult life. We here provide evidence that CRELD1 is an important gatekeeper of immune system homeostasis. Exploiting expression variance in large human cohorts contrasting individuals with the lowest and highest CRELD1 expression levels revealed strong phenotypic, functional and transcriptional differences, including reduced CD4+ T cell numbers. These findings were validated in T cell-specific Creld1-deficient mice. Loss of Creld1 was associated with simultaneous overactivation and increased apoptosis, resulting in a net loss of T cells with age. Creld1 was transcriptionally and functionally linked to Wnt signaling. Collectively, gene expression variance in large human cohorts combined with murine genetic models, transcriptomics and functional testing defines CRELD1 as an important modulator of immune homeostasis.

Early IFN-α signatures and persistent dysfunction are distinguishing features of NK cells in severe COVID-19.

Longitudinal analyses of the innate immune system, including the earliest time points, are essential to understand the immunopathogenesis and clinical course of coronavirus disease (COVID-19). Here, we performed a detailed characterization of natural killer (NK) cells in 205 patients (403 samples; days 2 to 41 after symptom onset) from four independent cohorts using single-cell transcriptomics and proteomics together with functional studies. We found elevated interferon (IFN)-α plasma levels in early severe COVD-19 alongside increased NK cell expression of IFN-stimulated genes (ISGs) and genes involved in IFN-α signaling, while upregulation of tumor necrosis factor (TNF)-induced genes was observed in moderate diseases. NK cells exert anti-SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) activity but are functionally impaired in severe COVID-19. Further, NK cell dysfunction may be relevant for the development of fibrotic lung disease in severe COVID-19, as NK cells exhibited impaired anti-fibrotic activity. Our study indicates preferential IFN-α and TNF responses in severe and moderate COVID-19, respectively, and associates a prolonged IFN-α-induced NK cell response with poorer disease outcome.

Swarm Learning for decentralized and confidential clinical machine learning.

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning-a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.

Faecal microbial transfer and complex carbohydrates mediate protection against COPD.

OBJECTIVE: Chronic obstructive pulmonary disease (COPD) is a major cause of global illness and death, most commonly caused by cigarette smoke. The mechanisms of pathogenesis remain poorly understood, limiting the development of effective therapies. The gastrointestinal microbiome has been implicated in chronic lung diseases via the gut-lung axis, but its role is unclear. DESIGN: Using an in vivo mouse model of cigarette smoke (CS)-induced COPD and faecal microbial transfer (FMT), we characterised the faecal microbiota using metagenomics, proteomics and metabolomics. Findings were correlated with airway and systemic inflammation, lung and gut histopathology and lung function. Complex carbohydrates were assessed in mice using a high resistant starch diet, and in 16 patients with COPD using a randomised, double-blind, placebo-controlled pilot study of inulin supplementation. RESULTS: FMT alleviated hallmark features of COPD (inflammation, alveolar destruction, impaired lung function), gastrointestinal pathology and systemic immune changes. Protective effects were additive to smoking cessation, and transfer of CS-associated microbiota after antibiotic-induced microbiome depletion was sufficient to increase lung inflammation while suppressing colonic immunity in the absence of CS exposure. Disease features correlated with the relative abundance of Muribaculaceae, Desulfovibrionaceae and Lachnospiraceae family members. Proteomics and metabolomics identified downregulation of glucose and starch metabolism in CS-associated microbiota, and supplementation of mice or human patients with complex carbohydrates improved disease outcomes. CONCLUSION: The gut microbiome contributes to COPD pathogenesis and can be targeted therapeutically.

Creld2 function during unfolded protein response is essential for liver metabolism homeostasis.

The unfolded protein response (UPR) is associated with hepatic metabolic function, yet it is not well understood how endoplasmic reticulum (ER) disturbance might influence metabolic homeostasis. Here, we describe the physiological function of Cysteine-rich with EGF-like domains 2 (Creld2), previously characterized as a downstream target of the ER-stress signal transducer Atf6. To this end, we generated Creld2-deficient mice and induced UPR by injection of tunicamycin. Creld2 augments protein folding and creates an interlink between the UPR axes through its interaction with proteins involved in the cellular stress response. Thereby, Creld2 promotes tolerance to ER stress and recovery from acute stress. Creld2-deficiency leads to a dysregulated UPR and causes the development of hepatic steatosis during ER stress conditions. Moreover, Creld2-dependent enhancement of the UPR assists in the regulation of energy expenditure. Furthermore, we observed a sex dimorphism in human and mouse livers with only male patients showing an accumulation of CRELD2 protein during the progression from non-alcoholic fatty liver disease to non-alcoholic steatohepatitis and only male Creld2-deficient mice developing hepatic steatosis upon aging. These results reveal a Creld2 function at the intersection between UPR and metabolic homeostasis and suggest a mechanism in which chronic ER stress underlies fatty liver disease in males.

Creld1 regulates myocardial development and function.

CRELD1 (Cysteine-Rich with EGF-Like Domains 1) is a risk gene for non-syndromic atrioventricular septal defects in human patients. In a mouse model, Creld1 has been shown to be essential for heart development, particularly in septum and valve formation. However, due to the embryonic lethality of global Creld1 knockout (KO) mice, its cell type-specific function during peri- and postnatal stages remains unknown. Here, we generated conditional Creld1 KO mice lacking Creld1 either in the endocardium (KOTie2) or the myocardium (KOMyHC). Using a combination of cardiac phenotyping, histology, immunohistochemistry, RNA-sequencing, and flow cytometry, we demonstrate that Creld1 function in the endocardium is dispensable for heart development. Lack of myocardial Creld1 causes extracellular matrix remodeling and trabeculation defects by modulation of the Notch1 signaling pathway. Hence, KOMyHC mice die early postnatally due to myocardial hypoplasia. Our results reveal that Creld1 not only controls the formation of septa and valves at an early stage during heart development, but also cardiac maturation and function at a later stage. These findings underline the central role of Creld1 in mammalian heart development and function.

Cost-Efficient Transcriptomic-Based Drug Screening.

Transcriptomics allows to obtain comprehensive insights into cellular programs and their responses to perturbations. Despite a significant decrease in the costs of library production and sequencing in the last decade, applying these technologies at the scale necessary for drug screening remains prohibitively expensive, obstructing the immense potential of these methods. Our study presents a cost-effective system for transcriptome-based drug screening, combining miniaturized perturbation cultures with mini-bulk transcriptomics. The optimized mini-bulk protocol provides informative biological signals at cost-effective sequencing depth, enabling extensive screening of known drugs and new molecules. Depending on the chosen treatment and incubation time, this protocol will result in sequencing libraries within approximately 2 days. Due to several stopping points within this protocol, the library preparation, as well as the sequencing, can be performed time-independently. Processing simultaneously a high number of samples is possible; measurement of up to 384 samples was tested without loss of data quality. There are also no known limitations to the number of conditions and/or drugs, despite considering variability in optimal drug incubation times.

Isogenic patient-derived organoids reveal early neurodevelopmental defects in spinal muscular atrophy initiation.

Whether neurodevelopmental defects underlie postnatal neuronal death in neurodegeneration is an intriguing hypothesis only recently explored. Here, we focus on spinal muscular atrophy (SMA), a neuromuscular disorder caused by reduced survival of motor neuron (SMN) protein levels leading to spinal motor neuron (MN) loss and muscle wasting. Using the first isogenic patient-derived induced pluripotent stem cell (iPSC) model and a spinal cord organoid (SCO) system, we show that SMA SCOs exhibit abnormal morphological development, reduced expression of early neural progenitor markers, and accelerated expression of MN progenitor and MN markers. Longitudinal single-cell RNA sequencing reveals marked defects in neural stem cell specification and fewer MNs, favoring mesodermal progenitors and muscle cells, a bias also seen in early SMA mouse embryos. Surprisingly, SMN2-to-SMN1 conversion does not fully reverse these developmental abnormalities. These suggest that early neurodevelopmental defects may underlie later MN degeneration, indicating that postnatal SMN-increasing interventions might not completely amend SMA pathology in all patients.

huva: A human variation analysis framework to predict gene perturbation from population-scale multi-omics data.

Variance of gene expression is intrinsic to any given natural population. Here, we present a protocol to analyze this variance using a conditional quasi loss- and gain-of-function approach. The huva (human variation) package takes advantage of population-scale multi-omics data to infer gene function and the relationship between phenotype and gene expression. We describe the steps for setting up the huva workspace, formatting datasets, performing huva experiments, and exporting data. For complete details on the use and execution of this protocol, please refer to Bonaguro et al. (2022).1.

Lung T cell response in COVID-19.

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

Identification of the novel FOXP3-dependent Treg cell transcription factor MEOX1 by high-dimensional analysis of human CD4+ T cells.

CD4+ T cells play a central role in the adaptive immune response through their capacity to activate, support and control other immune cells. Although these cells have become the focus of intense research, a comprehensive understanding of the underlying regulatory networks that orchestrate CD4+ T cell function and activation is still incomplete. Here, we analyzed a large transcriptomic dataset consisting of 48 different human CD4+ T cell conditions. By performing reverse network engineering, we identified six common denominators of CD4+ T cell functionality (CREB1, E2F3, AHR, STAT1, NFAT5 and NFATC3). Moreover, we also analyzed condition-specific genes which led us to the identification of the transcription factor MEOX1 in Treg cells. Expression of MEOX1 was comparable to FOXP3 in Treg cells and can be upregulated by IL-2. Epigenetic analyses revealed a permissive epigenetic landscape for MEOX1 solely in Treg cells. Knockdown of MEOX1 in Treg cells revealed a profound impact on downstream gene expression programs and Treg cell suppressive capacity. These findings in the context of CD4+ T cells contribute to a better understanding of the transcriptional networks and biological mechanisms controlling CD4+ T cell functionality, which opens new avenues for future therapeutic strategies.

Chromatin accessibility profiling of targeted cell populations with laser capture microdissection coupled to ATAC-seq.

Spatially resolved omics technologies reveal context-dependent cellular regulatory networks in tissues of interest. Beyond transcriptome analysis, information on epigenetic traits and chromatin accessibility can provide further insights on gene regulation in health and disease. Nevertheless, compared to the enormous advancements in spatial transcriptomics technologies, the field of spatial epigenomics is much younger and still underexplored. In this study, we report laser capture microdissection coupled to ATAC-seq (LCM-ATAC-seq) applied to fresh frozen samples for the spatial characterization of chromatin accessibility. We first demonstrate the efficient use of LCM coupled to in situ tagmentation and evaluate its performance as a function of cell number, microdissected areas, and tissue type. Further, we demonstrate its use for the targeted chromatin accessibility analysis of discrete contiguous or scattered cell populations in tissues via single-nuclei capture based on immunostaining for specific cellular markers.

Identification of drug candidates targeting monocyte reprogramming in people living with HIV.

Introduction: People living with HIV (PLHIV) are characterized by functional reprogramming of innate immune cells even after long-term antiretroviral therapy (ART). In order to assess technical feasibility of omics technologies for application to larger cohorts, we compared multiple omics data layers. Methods: Bulk and single-cell transcriptomics, flow cytometry, proteomics, chromatin landscape analysis by ATAC-seq as well as ex vivo drug stimulation were performed in a small number of blood samples derived from PLHIV and healthy controls from the 200-HIV cohort study. Results: Single-cell RNA-seq analysis revealed that most immune cells in peripheral blood of PLHIV are altered in their transcriptomes and that a specific functional monocyte state previously described in acute HIV infection is still existing in PLHIV while other monocyte cell states are only occurring acute infection. Further, a reverse transcriptome approach on a rather small number of PLHIV was sufficient to identify drug candidates for reversing the transcriptional phenotype of monocytes in PLHIV. Discussion: These scientific findings and technological advancements for clinical application of single-cell transcriptomics form the basis for the larger 2000-HIV multicenter cohort study on PLHIV, for which a combination of bulk and single-cell transcriptomics will be included as the leading technology to determine disease endotypes in PLHIV and to predict disease trajectories and outcomes.

Systemic alterations in neutrophils and their precursors in early-stage chronic obstructive pulmonary disease.

Systemic inflammation is established as part of late-stage severe lung disease, but molecular, functional, and phenotypic changes in peripheral immune cells in early disease stages remain ill defined. Chronic obstructive pulmonary disease (COPD) is a major respiratory disease characterized by small-airway inflammation, emphysema, and severe breathing difficulties. Using single-cell analyses we demonstrate that blood neutrophils are already increased in early-stage COPD, and changes in molecular and functional neutrophil states correlate with lung function decline. Assessing neutrophils and their bone marrow precursors in a murine cigarette smoke exposure model identified similar molecular changes in blood neutrophils and precursor populations that also occur in the blood and lung. Our study shows that systemic molecular alterations in neutrophils and their precursors are part of early-stage COPD, a finding to be further explored for potential therapeutic targets and biomarkers for early diagnosis and patient stratification.

Decoding mechanism of action and sensitivity to drug candidates from integrated transcriptome and chromatin state.

Omics-based technologies are driving major advances in precision medicine, but efforts are still required to consolidate their use in drug discovery. In this work, we exemplify the use of multi-omics to support the development of 3-chloropiperidines, a new class of candidate anticancer agents. Combined analyses of transcriptome and chromatin accessibility elucidated the mechanisms underlying sensitivity to test agents. Furthermore, we implemented a new versatile strategy for the integration of RNA- and ATAC-seq (Assay for Transposase-Accessible Chromatin) data, able to accelerate and extend the standalone analyses of distinct omic layers. This platform guided the construction of a perturbation-informed basal signature predicting cancer cell lines' sensitivity and to further direct compound development against specific tumor types. Overall, this approach offers a scalable pipeline to support the early phases of drug discovery, understanding of mechanisms, and potentially inform the positioning of therapeutics in the clinic.

Human variation in population-wide gene expression data predicts gene perturbation phenotype.

Population-scale datasets of healthy individuals capture genetic and environmental factors influencing gene expression. The expression variance of a gene of interest (GOI) can be exploited to set up a quasi loss- or gain-of-function "in population" experiment. We describe here an approach, huva (human variation), taking advantage of population-scale multi-layered data to infer gene function and relationships between phenotypes and expression. Within a reference dataset, huva derives two experimental groups with LOW or HIGH expression of the GOI, enabling the subsequent comparison of their transcriptional profile and functional parameters. We demonstrate that this approach robustly identifies the phenotypic relevance of a GOI allowing the stratification of genes according to biological functions, and we generalize this concept to almost 16,000 genes in the human transcriptome. Additionally, we describe how huva predicts monocytes to be the major cell type in the pathophysiology of STAT1 mutations, evidence validated in a clinical cohort.