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November 26, 2020 Immunity
Longitudinal multi-omics analyses identify responses of megakaryocytes, erythroid cells and plasmablasts as hallmarks of severe COVID-19 trajectories
Temporal resolution of cellular features associated with a severe COVID-19 disease trajectory is required for understanding skewed immune responses and finding outcome predictors. Here, we performed a longitudinal multi-omics study using a two-centre cohort of 14 patients. 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. Validation was performed in two independent cohorts of COVID-19 patients. Severe COVID-19 was characterized by an increase of proliferating, metabolically hyperactive plasmablasts. Coinciding with critical illness, we also identified an expansion of IFN-activated circulating megakaryocytes and increased erythropoiesis with features of hypoxic signalling. Megakaryocyte- and erythroid cell-derived co-expression modules were predictive of fatal disease outcome. The study demonstrates broad cellular effects of SARS-CoV-2 infection beyond classical immune cells and may serve as an entry point to develop biomarkers and targeted treatments of patients with COVID-19.
All data and analyses available on FASTGenomics via https://beta.fastgenomics.org/p/565003
Bernardes et al. (2020). Longitudinal multi-omics analyses identify responses of megakaryocytes, erythroid cells and plasmablasts as hallmarks of severe COVID-19 trajectories. Immunity. doi: 10.1016/j.immuni.2020.11.017
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August 5, 2020 Cell
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 progresses 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, remains 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 vs. 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 it reveals profound alterations in the myeloid cell compartment associated with severe COVID-19.
All data and analyses are available on FASTGenomics via the Schulte-Schrepping et al. Project page.
Schulte-Schrepping et al. (2020). Severe COVID-19 Is Marked by a Dysregulated Myeloid Cell Compartment. Cell. doi: 10.1016/j.cell.2020.08.001
May 27, 2020 Cell
SARS-CoV-2 Receptor ACE2 is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Enriched in Specific Cell Subsets Across Tissues
There is pressing urgency to better understand the pathogenesis of the severe acute respiratory syndrome (SARS) coronavirus (CoV) clade SARS-CoV-2. SARS-CoV-2, like SARS-CoV, utilizes ACE2 to bind host cells. While initial SARS-CoV-2 cell entry and infection depend on ACE2 in concert with the protease TMPRSS2 for spike (S) protein activation, the specific cell subsets targeted by SARS-CoV-2 in host tissues, and the factors that regulate ACE2 expression, remain unknown. Here, we leverage human and non-human primate (NHP) single-cell RNA-sequencing (scRNA-seq) datasets to uncover the cell subsets that may serve as cellular targets of SARS-CoV-2. We identify ACE2/TMPRSS2 co-expressing cells within type II pneumocytes, absorptive enterocytes, and nasal goblet secretory cells. Strikingly, we discover that ACE2 is an interferon-stimulated gene (ISG) in human barrier tissue epithelial cells. Thus, SARS-CoV-2 may exploit IFN-driven upregulation of ACE2, a key tissue-protective mediator during lung injury, to enhance infection.
Ziegler et al. (2020) SARS-CoV-2 Receptor ACE2 is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Enriched in Specific Cell Subsets Across Tissues. Cell. doi: 10.2139/ssrn.3555145
April 23, 2020
SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes
We investigated SARS-CoV-2 potential tropism by surveying expression of viral entry-associated genes in single-cell RNA-sequencing data from multiple tissues from healthy human donors. We co-detected these transcripts in specific respiratory, corneal and intestinal epithelial cells, potentially explaining the high efficiency of SARS-CoV-2 transmission. These genes are co-expressed in nasal epithelial cells with genes involved in innate immunity, highlighting the cells’ potential role in initial viral infection, spread and clearance. The study offers a useful resource for further lines of inquiry with valuable clinical samples from COVID-19 patients and we provide our data in a comprehensive, open and user-friendly fashion at www.covid19cellatlas.org.
Sungnak et al. (2020) SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes. Nature Medicine. doi: 10.1038/s41591-020-0868-6
September 18, 2019 Vascular Surgery (Springer)
Artificial intelligence in single cell genomics
The individual cell represents the fundamental unit of life. Its manner of functioning has been the focus of biomedical research for centuries. In recent years, advances in high throughput so-called single cell sequencing techniques have made it possible to study individual cells and their genetic profile. This enables revolutionary new insights into tissue composition, cell–cell interactions and dynamic processes in health and disease. The resulting profile data, e.g. from single cell transcriptomics, however, provide analysts with new challenges: data sets are typically very large, noisy and highly interconnected with other annotation data, making them unsuitable for established procedures. The setting calls for the application of novel algorithms originating from the field of artificial intelligence, which are adapted to deal with this type of challenge.
Dickten, Kratsch & Reiz (2019). Die künstliche Intelligenz in der Einzelzellgenomik. Gefässchirurgie. doi: 10.1007/s00772-019-00572-9
September 1, 2018 Laborjournal
FASTGenomics – Single-Cell IT aus Bonn (2018)
Abstract (publication in german): FASTGenomics offers tools and workflows for the analysis of single-cell transcriptomics data. The FASTGenomics project is led by Comma Soft AG in collaboration with the LIMES Institute in Bonn, Germany. Modern single-cell RNA-seq technologies generate vast amounts of data that cannot be analyzed with conventional pipelines. FASTGenomics offers easy-to-use workflows and AI-based analyses and allows sharing of data and results. In this endeavour, FASTGenomics was granted support by the Federal Ministry for Economic Affairs and Energy on the basis of a decision of the German Bundestag. The flagship project FASTGenomics is part of the Smart Data grant and develops Big Data technology for LifeScience use-cases.
Rembold 2018, FASTGenomics – Single-Cell IT aus Bonn, in: Laborjournal, 09, p52-53
March 22, 2018 BiorXiv
FASTGenomics: An analytical ecosystem for single-cell RNA sequencing data
Recent technological advances enable genomics of individual cells, the building blocks of all living organisms. Single cell data characteristics differ from those of bulk data, which led to a plethora of new analytical strategies. However, solutions are only useful for experts and currently, there are no widely accepted gold standards for single cell data analysis. To meet the requirements of analytical flexibility, ease of use and data security, we developed FASTGenomics (https://fastgenomics.org) as a powerful, efficient, versatile, robust, safe and intuitive analytical ecosystem for single-cell transcriptomics.
July 13, 2017 BigData Insider
Datenflut bei der Genomsequenzierung (2017)
Die Einzelzellsequenzierung des menschlichen Genoms stellt große Herausforderungen an deutsche Forschungseinrichtungen. Problem sind die gigantischen Datenmengen.
Mit einer speziellen Plattform zur Analyse will das Projekt FASTGenomics die Analyse der Daten nun vereinfachen. Die Plattform, die im Rahmen des vom Bundesministerium für Wirtschaft und Energie (BMWi) geförderten Programms „Smart Data – Innovation aus Daten“ entwickelt wird, soll einerseits nutzerfreundlich sein, andererseits hohen datenschutzrechtlichen Standards entsprechen.
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