Multi-layered transcriptomic analyses reveal an immunological overlap between COVID-19 and hemophagocytic lymphohistiocytosis associated with disease severity

Severe COVID-19 patients present a clinical and laboratory overlap with other hyperinflammatory conditions such as hemophagocytic lymphohistiocytosis (HLH). However, the underlying mechanisms of these conditions remain to be explored. Here, we investigated the transcriptome of 1596 individuals, including patients with COVID-19 in comparison to healthy controls, other acute inflammatory states (HLH, multisystem inflammatory syndrome in children [MIS-C], Kawasaky disease [KD]), and different respiratory infections (seasonal coronavirus, influenza, bacterial pneumonia). We observed that COVID-19 and HLH share immunological pathways (cytokine/chemokine signaling and neutrophil-mediated immune responses), including gene signatures that stratify COVID-19 patients admitted to the intensive care unit (ICU) and COVID-19_nonICU patients. Of note, among the common differentially expressed genes (DEG), there is a cluster of neutrophil-associated genes that reflects a generalized hyperinflamatory state since it is also dysregulated in patients with KD and bacterial pneumonia. These genes are dysregulated at protein level across several COVID-19 studies and form an interconnected network with differentially expressed plasma proteins that point to neutrophil hyperactivation in COVID-19 patients admitted to the intensive care unit. scRNAseq analysis indicated that these genes are specifically upregulated across different leukocyte populations, including lymphocyte subsets and immature neutrophils. Artificial intelligence modeling confirmed the strong association of these genes with COVID-19 severity. Thus, our work indicates putative therapeutic pathways for intervention.

Longitudinal multi-omics analysis identifies responses of megakaryocytes, erythroid cells and plasmablasts as hallmarks of severe COVID-19 trajectories

The pandemic spread of the potentially life-threatening disease COVID-19 requires a thorough understanding of the longitudinal dynamics of host responses. Temporal resolution of cellular features associated with a severe disease trajectory will be a pre-requisite for finding disease outcome predictors. Here, we performed a longitudinal multi-omics study using a two-centre German cohort of 13 patients (from Cologne and Kiel, cohort 1). We analysed the bulk transcriptome, bulk DNA methylome, and single-cell transcriptome (>358,000 cells, including BCR profiles) of peripheral blood samples harvested from up to 5 time points. The results from single-cell and bulk transcriptome analyses were validated in two independent cohorts of COVID-19 patients from Bonn (18 patients, cohort 2) and Nijmegen (40 patients, cohort 3), respectively. We observed an increase of proliferating, activated plasmablasts in severe COVID-19, and show a distinct expression pattern related to a hyperactive cellular metabolism of these cells. We further identified a notable expansion of type I IFN-activated circulating megakaryocytes and their progenitors, indicative of emergency megakaryopoiesis, which was confirmed in cohort 2. These changes were accompanied by increased erythropoiesis in the critical phase of the disease with features of hypoxic signalling. Finally, projecting megakaryocyte- and erythroid cell-derived co-expression modules to longitudinal blood transcriptome samples from cohort 3 confirmed an association of early temporal changes of these features with fatal COVID-19 disease outcome. In sum, our longitudinal multi-omics study demonstrates distinct cellular and gene expression dynamics upon SARS-CoV-2 infection, which point to metabolic shifts of circulating immune cells, and reveals changes in megakaryocytes and increased erythropoiesis as important outcome indicators in severe COVID-19 patients.

Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients

The SARS-CoV-2 pandemic is currently leading to increasing numbers of COVID-19 patients all over the world. Clinical presentations range from asymptomatic, mild respiratory tract infection, to severe cases with acute respiratory distress syndrome, respiratory failure, and death. Reports on a dysregulated immune system in the severe cases calls for a better characterization and understanding of the changes in the immune system. Here, we profiled whole blood transcriptomes of 39 COVID-19 patients and 10 control donors enabling a data-driven stratification based on molecular phenotype. Neutrophil activation-associated signatures were prominently enriched in severe patient groups, which was corroborated in whole blood transcriptomes from an independent second cohort of 30 as well as in granulocyte samples from a third cohort of 11 COVID-19 patients. Comparison of COVID-19 blood transcriptomes with those of a collection of over 2,800 samples derived from 11 different viral infections, inflammatory diseases and independent control samples revealed highly specific transcriptome signatures for COVID-19. Further, stratified transcriptomes predicted patient subgroup-specific drug candidates targeting the dysregulated systemic immune response of the host.

Suppressive myeloid cells are a hallmark of severe COVID-19

Severe Acute Respiratory Syndrome - Coronavirus-2 (SARS-CoV-2) infection causes Coronavirus Disease 2019 (COVID-19), a mild to moderate respiratory tract infection in the majority of patients. A subset of patients, however, progresses to severe disease and respiratory failure with acute respiratory distress syndrome (ARDS). Severe COVID-19 has been associated with increased neutrophil counts and dysregulated immune responses. The mechanisms of protective immunity in mild forms and the pathogenesis of dysregulated inflammation in severe courses of COVID-19 remain largely unclear. Here, we combined two single-cell RNA-sequencing technologies and single-cell proteomics in whole blood and peripheral blood mononuclear cells (PBMC) to determine changes in immune cell composition and activation in two independent dual-center patient cohorts (n=46+n=54 COVID-19 samples), each with mild and severe cases of COVID-19. We observed a specific increase of HLA-DRhiCD11chi inflammatory monocytes that displayed a strong interferon (IFN)-stimulated gene signature in patients with mild COVID-19, which was absent in severe disease. Instead, we found evidence of emergency myelopoiesis, marked by the occurrence of immunosuppressive pre-neutrophils and immature neutrophils and populations of dysfunctional and suppressive mature neutrophils, as well as suppressive HLA-DRto monocytes in severe COVID-19. Our study provides detailed insights into systemic immune response to SARS-CoV-2 infection and it reveals profound alterations in the peripheral myeloid cell compartment associated with severe courses of COVID-19.

Swarm Learning as a privacy-preserving machine learning approach for disease classification

Identification of patients with life-threatening diseases including leukemias or infections such as tuberculosis and COVID-19 is an important goal of precision medicine. We recently illustrated that leukemia patients are identified by machine learning (ML) based on their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed because of privacy legislation. To facilitate integration of any omics data from any data owner world-wide without violating privacy laws, we here introduce Swarm Learning (SL), a decentralized machine learning approach uniting edge computing, blockchain-based peer-to-peer networking and coordination as well as privacy protection without the need for a central coordinator thereby going beyond federated learning. Using more than 14,000 blood transcriptomes derived from over 100 individual studies with non-uniform distribution of cases and controls and significant study biases, we illustrate the feasibility of SL to develop disease classifiers based on distributed data for COVID-19, tuberculosis or leukemias that outperform those developed at individual sites. Still, SL completely protects local privacy regulations by design. We propose this approach to noticeably accelerate the introduction of precision medicine.