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-DR hi CD11c hi 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-DR lo 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.

Background 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 call for a better characterization and understanding of the changes in the immune system. Methods In order to dissect COVID-19-driven immune host responses, we performed RNA-seq of whole blood cell transcriptomes and granulocyte preparations from mild and severe COVID-19 patients and analyzed the data using a combination of conventional and data-driven co-expression analysis. Additionally, publicly available data was used to show the distinction from COVID-19 to other diseases. Reverse drug target prediction was used to identify known or novel drug candidates based on finding from data-driven findings. Results 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 16 COVID-19 patients (44 samples). Comparison of COVID-19 blood transcriptomes with those of a collection of over 3100 samples derived from 12 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. Conclusions Our study provides novel insights in the distinct molecular subgroups or phenotypes that are not simply explained by clinical parameters. We show that whole blood transcriptomes are extremely informative for COVID-19 since they capture granulocytes which are major drivers of disease severity.

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.

Despite the wealth of genomic and transcriptomic data in Parkinson's disease (PD), the initial molecular events are unknown. Using LD score regression analysis, we show significant enrichment in PD heritability within regulatory sites for LPS-activated monocytes and that TLR4 expression is highest within human substantia nigra, the most affected brain region, suggesting a role for TLR4 inflammatory responses. We then performed extended incubation of cells with physiological concentrations of small alpha-synuclein oligomers observing the development of a TLR4-dependent sensitized inflammatory response with time, including TNF-α production. ROS and cell death in primary neuronal cultures were significantly reduced by TLR4 antagonists revealing that an indirect inflammatory mechanism involving cytokines produced by glial cells makes a major contribution to neuronal death. Prolonged exposure to low levels of alpha-synuclein oligomers sensitizes TLR4 responsiveness in astrocytes and microglial, explaining how they become pro-inflammatory, and may be an early causative event in PD.

'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-DR high CD11c high 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-DR low 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.

Background and PurposeTransient receptor potential melastatin 3 (TRPM3) proteins form non-selective but calcium-permeable membrane channels, rapidly activated by extracellular application of the steroid pregnenolone sulphate and the dihydropyridine nifedipine. Our aim was to characterize the steroid binding site by analysing the structural chemical requirements for TRPM3 activation.Experimental ApproachWhole-cell patch-clamp recordings and measurements of intracellular calcium concentrations were performed on HEK293 cells transfected with TRPM3 (or untransfected controls) during superfusion with pharmacological substances.Key ResultsPregnenolone sulphate and nifedipine activated TRPM3 channels supra-additively over a wide concentration range. Other dihydropyridines inhibited TRPM3 channels. The natural enantiomer of pregnenolone sulphate was more efficient in activating TRPM3 channels than its synthetic mirror image. However, both enantiomers exerted very similar inhibitory effects on proton-activated outwardly rectifying anion channels. Epiallopregnanolone sulphate activated TRPM3 almost equally as well as pregnenolone sulphate. Exchanging the sulphate for other chemical moieties showed that a negative charge at this position is required for activating TRPM3 channels.Conclusions and ImplicationsOur data demonstrate that nifedipine and pregnenolone sulphate act at different binding sites when activating TRPM3. The latter activates TRPM3 by binding to a chiral and thus proteinaceous binding site, as inferred from the differential effects of the enantiomers. The double bond between position C5 and C6 of pregnenolone sulphate is not strictly necessary for the activation of TRPM3 channels, but a negative charge at position C3 of the steroid is highly important. These results provide a solid basis for understanding mechanistically the rapid chemical activation of TRPM3 channels.

Background: TRPM3 proteins form Ca 2ϩ permeable ion channels involved in insulin secretion and pain perception. Results: A domain indispensable for TRPM3 channel function (ICF) is subject to alternative splicing. Conclusion: This domain contributes essentially to the formation of TRPM channels and removing it by splicing modulates TRPM3-mediated Ca 2ϩ signaling. Significance: Alternative splicing of the ICF domain regulates biological functions attributed to TRPM3.

Using nanopipettes to locally deliver molecules to the surface of living cells could potentially open up studies of biological processes down to the level of single molecules. However, in order to achieve precise and quantitative local delivery it is essential to be able to determine the amount and distribution of the molecules being delivered. In this work, we investigate how the size of the nanopipette, the magnitude of the applied pressure or voltage, which drives the delivery, and the distance to the underlying surface influences the number and spatial distribution of the delivered molecules. Analytical expressions describing the delivery are derived and compared with the results from finite element simulations and experiments on delivery from a 100 nm nanopipette in bulk solution and to the surface of sensory neurons. We then developed a setup for rapid and quantitative delivery to multiple subcellular areas, delivering the molecule capsaicin to stimulate opening of Transient Receptor Potential Vanilloid subfamily member 1 (TRPV1) channels, membrane receptors involved in pain sensation. Overall, precise and quantitative delivery of molecules from nanopipettes has been demonstrated, opening up many applications in biology such as locally stimulating and mapping receptors on the surface of live cells.