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1.
Cell Rep Med ; : 100652, 2022 May 17.
Article in English | MEDLINE | ID: covidwho-1852245

ABSTRACT

Disease recovery dynamics are often difficult to assess, as patients display heterogeneous recovery courses. To model recovery dynamics, exemplified by severe COVID-19, we apply a computational scheme on longitudinally sampled blood transcriptomes, generating recovery states, which we then link to cellular and molecular mechanisms, presenting a framework for studying the kinetics of recovery compared with non-recovery over time and long-term effects of the disease. Specifically, a decrease in mature neutrophils is the strongest cellular effect during recovery, with direct implications on disease outcome. Furthermore, we present strong indications for global regulatory changes in gene programs, decoupled from cell compositional changes, including an early rise in T cell activation and differentiation, resulting in immune rebalancing between interferon and NF-κB activity and restoration of cell homeostasis. Overall, we present a clinically relevant computational framework for modeling disease recovery, paving the way for future studies of the recovery dynamics in other diseases and tissues.

3.
Front Public Health ; 9: 583377, 2021.
Article in English | MEDLINE | ID: covidwho-1399182

ABSTRACT

Background: Due 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. Methods: We 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 several realistic scenarios and propose fast and reliable strategies for massive testing procedures. Key Findings: We 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 1.47 million inhabitants of Munich, Germany, could be tested using only around 141 thousand tests if the infection rate is below 0.4% is assumed. Using 1 million tests, the 6.69 million inhabitants from the city of Rio de Janeiro, Brazil, could be tested as long as the infection rate does not exceed 1%. Moreover, we provide an interactive web application, available at www.grouptexting.com, for visualizing the different strategies and designing pooling schemes according to specific prevalence scenarios and test configurations. Interpretation: Altogether, this work may help provide a basis for an efficient upscaling of current testing procedures, which takes the population heterogeneity into account and is fine-grained towards the desired study populations, e.g., mild/asymptomatic individuals vs. symptomatic ones but also mixtures thereof. Funding: German Science Foundation (DFG), German Federal Ministry of Education and Research (BMBF), Chan Zuckerberg Initiative DAF, and Austrian Science Fund (FWF).


Subject(s)
COVID-19 , SARS-CoV-2 , Brazil , COVID-19 Testing , Humans , Pandemics
4.
Nat Biotechnol ; 40(1): 121-130, 2022 01.
Article in English | MEDLINE | ID: covidwho-1379318

ABSTRACT

Large single-cell atlases are now routinely generated to serve as references for analysis of smaller-scale studies. Yet learning from reference data is complicated by batch effects between datasets, limited availability of computational resources and sharing restrictions on raw data. Here we introduce a deep learning strategy for mapping query datasets on top of a reference called single-cell architectural surgery (scArches). scArches uses transfer learning and parameter optimization to enable efficient, decentralized, iterative reference building and contextualization of new datasets with existing references without sharing raw data. Using examples from mouse brain, pancreas, immune and whole-organism atlases, we show that scArches preserves biological state information while removing batch effects, despite using four orders of magnitude fewer parameters than de novo integration. scArches generalizes to multimodal reference mapping, allowing imputation of missing modalities. Finally, scArches retains coronavirus disease 2019 (COVID-19) disease variation when mapping to a healthy reference, enabling the discovery of disease-specific cell states. scArches will facilitate collaborative projects by enabling iterative construction, updating, sharing and efficient use of reference atlases.


Subject(s)
Datasets as Topic/standards , Deep Learning , Organ Specificity , Single-Cell Analysis/standards , Animals , COVID-19/pathology , Humans , Mice , Reference Standards , SARS-CoV-2/pathogenicity
5.
Nat Commun ; 12(1): 4515, 2021 07 26.
Article in English | MEDLINE | ID: covidwho-1327196

ABSTRACT

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 use 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 induce transcriptional shifts by antigenic stimulation in vitro and take advantage of natural T cell receptor (TCR) sequences of clonally expanded T cells as barcodes for 'reverse phenotyping'. This allows identification of SARS-CoV-2-reactive TCRs and reveals phenotypic effects introduced by antigen-specific stimulation. We characterize transcriptional signatures of currently and previously activated SARS-CoV-2-reactive T cells, and show 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.


Subject(s)
COVID-19/immunology , Gene Expression Profiling/methods , Sequence Analysis, RNA/methods , Single-Cell Analysis/methods , T-Lymphocytes/metabolism , Aged , Aged, 80 and over , CD4-Positive T-Lymphocytes/metabolism , CD4-Positive T-Lymphocytes/virology , COVID-19/epidemiology , COVID-19/virology , Cells, Cultured , Cohort Studies , Female , Humans , Male , Middle Aged , Pandemics , Receptors, Antigen, T-Cell/genetics , Receptors, Antigen, T-Cell/immunology , Receptors, Antigen, T-Cell/metabolism , SARS-CoV-2/physiology , T-Lymphocytes/virology
6.
Cell ; 181(5): 1016-1035.e19, 2020 05 28.
Article in English | MEDLINE | ID: covidwho-100497

ABSTRACT

There is pressing urgency to understand the pathogenesis of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2), which causes the disease COVID-19. SARS-CoV-2 spike (S) protein binds angiotensin-converting enzyme 2 (ACE2), and in concert with host proteases, principally transmembrane serine protease 2 (TMPRSS2), promotes cellular entry. The cell subsets targeted by SARS-CoV-2 in host tissues and the factors that regulate ACE2 expression remain unknown. Here, we leverage human, non-human primate, and mouse single-cell RNA-sequencing (scRNA-seq) datasets across health and disease to uncover putative targets of SARS-CoV-2 among tissue-resident cell subsets. We identify ACE2 and TMPRSS2 co-expressing cells within lung type II pneumocytes, ileal absorptive enterocytes, and nasal goblet secretory cells. Strikingly, we discovered that ACE2 is a human interferon-stimulated gene (ISG) in vitro using airway epithelial cells and extend our findings to in vivo viral infections. Our data suggest that SARS-CoV-2 could exploit species-specific interferon-driven upregulation of ACE2, a tissue-protective mediator during lung injury, to enhance infection.


Subject(s)
Alveolar Epithelial Cells/metabolism , Enterocytes/metabolism , Goblet Cells/metabolism , Interferon Type I/metabolism , Nasal Mucosa/cytology , Peptidyl-Dipeptidase A/genetics , Adolescent , Alveolar Epithelial Cells/immunology , Angiotensin-Converting Enzyme 2 , Animals , Betacoronavirus/physiology , COVID-19 , Cell Line , Cells, Cultured , Child , Coronavirus Infections/virology , Enterocytes/immunology , Goblet Cells/immunology , HIV Infections/immunology , Humans , Influenza, Human/immunology , Interferon Type I/immunology , Lung/cytology , Lung/pathology , Macaca mulatta , Mice , Mycobacterium tuberculosis , Nasal Mucosa/immunology , Pandemics , Peptidyl-Dipeptidase A/metabolism , Pneumonia, Viral/virology , Receptors, Virus/genetics , SARS-CoV-2 , Serine Endopeptidases/metabolism , Single-Cell Analysis , Tuberculosis/immunology , Up-Regulation
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