Single-cell RNA sequencing reveals in vivo signatures of SARS-CoV-2-reactive T cells through 'reverse phenotyping'

The in vivo phenotypic profile of T cells reactive to severe acute respiratory syndrome (SARS)-CoV-2 antigens remains poorly understood. Conventional methods to detect antigen-reactive T cells require in vitro antigenic re-stimulation or highly individualized peptide-human leukocyte antigen (pHLA) multimers. Here, we used single-cell RNA sequencing to identify and profile SARS-CoV-2-reactive T cells from Coronavirus Disease 2019 (COVID-19) patients. To do so, we induced transcriptional shifts by antigenic stimulation in vitro and took advantage of natural T cell receptor (TCR) sequences of clonally expanded T cells as barcodes for reverse phenotyping. This allowed identification of SARS-CoV-2-reactive TCRs and revealed phenotypic effects introduced by antigen-specific stimulation. We characterized transcriptional signatures of currently and previously activated SARS-CoV-2-reactive T cells, and showed correspondence with phenotypes of T cells from the respiratory tract of patients with severe disease in the presence or absence of virus in independent cohorts. Reverse phenotyping is a powerful tool to provide an integrated insight into cellular states of SARS-CoV-2-reactive T cells across tissues and activation states.

Determinants of SARS-CoV-2 receptor gene expression in upper and lower airways

BackgroundThe recent outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has led to a worldwide pandemic. A subset of COVID-19 patients progresses to severe disease, with high mortality and limited treatment options. Detailed knowledge of the expression regulation of genes required for viral entry into respiratory epithelial cells is urgently needed. MethodsHere we assess the expression patterns of genes required for SARS-CoV-2 entry into cells, and their regulation by genetic, epigenetic and environmental factors, throughout the respiratory tract using samples collected from the upper (nasal) and lower airways (bronchi). FindingsGenes encoding viral receptors and activating protease are increased in the nose compared to the bronchi in matched samples and associated with the proportion of secretory epithelial cells in cellular deconvolution analyses. Current or ex-smoking was found to increase expression of these genes only in lower airways, which was associated with a significant increase in the predicted proportion of goblet cells. Both acute and second hand smoke exposure were found to increase ACE2 expression while inhaled corticosteroids decrease ACE2 expression in the lower airways. A strong association of DNA- methylation with ACE2 and TMPRSS2- mRNA expression was identified. InterpretationGenes associated with SARS-CoV-2 viral entry into cells are high in upper airways, but strongly increased in lower airways by smoke exposure. In contrast, ICS decreases ACE2 expression, indicating that inhaled corticosteroids are unlikely to increase the risk for more severe COVID-19 disease. FundingThis work was supported by a Seed Network grant from the Chan Zuckerberg Initiative to M.C.N. and by the European Unions H2020 Research and Innovation Program under grant agreement no. 874656 (discovAIR) to M.C.N. U BIOPRED was supported by an Innovative Medicines Initiative Joint Undertaking (No. 115010), resources from the European Unions Seventh Framework Programme (FP7/2007-2013) and EFPIA companies in kind contribution (www.imi.europa.eu). Longfonds Junior Fellowship. We acknowledge the contribution of the whole U-BIOPRED team as listed in the Appendix S1. SDB, FM and RFS would like to thank the Helmholtz Association, Germany, for support." NIH K08HL146943; Parker B. Francis Fellowship; ATS Foundation/Boehringer Ingelheim Pharmaceuticals Inc. Research Fellowship in IPF. RCR is part funded by Cancer Research UK Cambridge Centre and the Cambridge NIHR Biomedical Research Centre. BAP was funded by programme support from Cancer Research UK. The CRUKPAP Study was supported by the CRUK Cambridge Cancer Centre, by the NIHR Cambridge Biomedical Research Centre and by the Cambridge Bioresource. PIAMA was supported by The Netherlands Organization for Health Research and Development; The Netherlands Organization for Scientific Research; The Netherlands Lung Foundation (with methylation studies supported by AF 4.1.14.001); The Netherlands Ministry of Spatial Planning, Housing, and the Environment; and The Netherlands Ministry of Health, Welfare, and Sport. Dr. Qi is supported by a grant from the China Scholarship Council.

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.

Group testing for SARS-CoV-2 allows for up to 10-fold efficiency increase across realistic scenarios and testing strategies

BackgroundDue to the ongoing COVID-19 pandemic, demand for diagnostic testing has increased drastically, resulting in shortages of necessary materials to conduct the tests and overwhelming the capacity of testing laboratories. The supply scarcity and capacity limits affect test administration: priority must be given to hospitalized patients and symptomatic individuals, which can prevent the identification of asymptomatic and presymptomatic individuals and hence effective tracking and tracing policies. We describe optimized group testing strategies applicable to SARS-CoV-2 tests in scenarios tailored to the current COVID-19 pandemic and assess significant gains compared to individual testing. MethodsWe account for biochemically realistic scenarios in the context of dilution effects on SARS-CoV-2 samples and consider evidence on specificity and sensitivity of PCR-based tests for the novel coronavirus. Because of the current uncertainty and the temporal and spatial changes in the prevalence regime, we provide analysis for a number of realistic scenarios and propose fast and reliable strategies for massive testing procedures. FindingsWe find significant efficiency gaps between different group testing strategies in realistic scenarios for SARS-CoV-2 testing, highlighting the need for an informed decision of the pooling protocol depending on estimated prevalence, target specificity, and high-vs. low-risk population. For example, using one of the presented methods, all 1{middle dot}47 million inhabitants of Munich, Germany, could be tested using only around 141 thousand tests if the infection rate is below 0{middle dot}4% is assumed. Using 1 million tests, the 6{middle dot}69 million inhabitants from the city of Rio de Janeiro, Brazil, could be tested as long as the infection rate does not exceed 1%. InterpretationAltogether this work may help provide a basis for efficient upscaling of current testing procedures, taking the population heterogeneity into account and fine grained towards the desired study populations, e.g. cross-sectional versus health-care workers and adapted mixtures thereof. FundingGerman Science Foundation (DFG), German Federal Ministry of Education and Research (BMBF), Chan Zuckerberg Initiative DAF and Austrian Science Fund (FWF). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSThe concept of group testing goes back to mathematical ideas developed in the 1940s and has already been successfully implemented for various infectious diseases but also in non-medical settings such as testing for failures of electronic components. The issue of group testing for SARS-CoV-2 has been addressed in a number of very recent papers and preprints including feasibility studies of different laboratories and a few methodological overview papers. Nevertheless, to the best of our knowledge, no study provided a comprehensive comparison contrasting hierarchical testing, array testing, and informative testing strategies based on combined groups for stratified populations and relying on up-to-date data about the accuracy of PCR-based test - all of them feasible to be implemented in the current pandemic. Moreover, a discussion of massive informative testing strategies for pandemic scenarios, employing combined pools consisting of high-risk and low-risk individuals, was still missing in the public health literature. Added value of this studyWe analyse sensitivity, specificity, and throughput of group testing schemes in a series of scenarios tailored to realistic prevalence rates for SARS-CoV-2 in stratified populations and to the characteristics of the qRT-PCR tests used to diagnose COVID-19. Our analysis yields a comprehensive characterisation of a wide range of pooling schemes, broken down by population characteristics, that can serve as a guideline to be queried by testing entities to meet their settings. In particular, our findings demonstrate that a promising strategy to test asymptomatic or presymptomatic individuals in conjunction with high-risk individuals such as healthcare workers is to amalgamate them together in a suitable way, and we provide adequate pool configurations. Such strategies had not been explored for SARS-CoV-2 testing as of yet. We also provide insights on group testing under constraints, i.e. when the number of stages, maximum group size and false negative rate of the whole method are restricted to a certain range of realistic values. We introduce efficient paralleliz-able non-adaptive test procedures for simplified and fast large-scale test design in case of severe shortage of test components. We develop an intuitive web application that can be used by any researcher working on the front line of testing procedures to visualize all the different strategies and to design pooling schemes in an flexible manner according to their specific prevalence scenario and test configuration. Implications of all the available evidenceTesting for SARS-CoV-2 presents new challenges to authorities such as rapidly changing prevalence estimates and bottlenecks in testing capacity. In this context, applying an appropriately chosen group testing procedure can allow for up to 10-fold increase of the feasible throughput, such that the existing testing capacities can be used to test a significantly increased number of individuals. As a consequence, adequate tracing and quarantine strategies to reduce community transmission can be established and valuable epidemiological studies relying on accurate prevalence rates can be performed for the ongoing pandemic situation.

Protocol of a population-based prospective COVID-19 cohort study Munich, Germany (KoCo19)

BackgroundThe SARS-CoV-2 pandemic is leading to the global introduction of public health interventions to prevent the spread of the virus and avoid the overload of health care systems, especially for the most severely affected patients. Scientific studies to date have focused primarily on describing the clinical course of patients, identifying treatment options and developing vaccines. In Germany, as in many other regions, current tests for SARS-CoV2 are not being conducted on a representative basis and in a longitudinal design. Furthermore, knowledge about the immune status of the population is lacking. Yet these data are needed to understand the dynamics of the pandemic and to thus appropriately design and evaluate interventions. For this purpose, we recently started a prospective population-based cohort in Munich, Germany, with the aim to better understand the state and dynamics of the pandemic. MethodsIn 100, randomly selected constituencies out of 755, 3,000 Munich households are identified via random route and offered enrollment into the study. All household members are asked to complete a baseline questionnaire and subjects [≥]14 years of age are asked to provide a venous blood sample of [≤]3 ml for the determination of SARS-CoV-2 IgG/IgA status. The residual plasma and the blood pellet are preserved for later genetic and molecular biological investigations. For twelve months, each household member is asked to keep a diary of daily symptoms, whereabouts and contacts via WebApp. If symptoms suggestive for COVID-19 are reported, family members, including children <14 years, are offered a pharyngeal swab taken at the Division of Infectious Diseases and Tropical Medicine, LMU University Hospital Munich, for molecular testing for SARS-CoV-2. In case of severe symptoms, participants will be transferred to a Munich hospital. For one year, the study teams re-visits the households for blood sampling every six weeks. DiscussionWith the planned study we will establish a reliable epidemiological tool to improve the understanding of the spread of SARS-CoV-2 and to better assess the effectiveness of public health measures as well as their socio-economic effects. This will support policy makers in managing the epidemic based on scientific evidence.

Whither systems medicine?

New technologies to generate, store and retrieve medical and research data are inducing a rapid change in clinical and translational research and health care. Systems medicine is the interdisciplinary approach wherein physicians and clinical investigators team up with experts from biology, biostatistics, informatics, mathematics and computational modeling to develop methods to use new and stored data to the benefit of the patient. We here provide a critical assessment of the opportunities and challenges arising out of systems approaches in medicine and from this provide a definition of what systems medicine entails. Based on our analysis of current developments in medicine and healthcare and associated research needs, we emphasize the role of systems medicine as a multilevel and multidisciplinary methodological framework for informed data acquisition and interdisciplinary data analysis to extract previously inaccessible knowledge for the benefit of patients.