free for academic use
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
Visit full article
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.
TRY THE PLATFORM NOW!
This site uses functional cookies and external scripts to improve your experience.
This site uses functional cookies and external scripts to improve your experience. Which cookies and scripts are used and how they impact your visit is specified on the left. You may change your settings at any time. Your choices will not impact your visit.
NOTE: These settings will only apply to the browser and device you are currently using.
Our website and newsletters use simple analysis mechanisms. In the process data is anonymised and only used for statistical purposes.
The for the controller has installed the component Google Analytics (with anonymisation function) on this website. Google Analytics is a web analysis service. Web analysis is the collection, compilation and analysis of data and behaviour of visitors to websites. Among other things, web analysis service collects data about the website which redirected the data subject to a website (so-called referrer), the subpages of the website viewed and how often a subpage was viewed and for how long. A web analysis is primarily used to optimise a website and for internet advertising cost-benefit analysis purposes.
We use the “_gat._anonymizeIp” function for web analysis through Google Analytics. This function anonymises the IP address of the data subject’s internet connection when accessing our website from a member state of the European Union or from other signatories to the Treaty on the European Economic Area.
The operating company of the Google Analytics component is Google LLC, 1600 Amphitheatre Pkwy, Mountain View, CA 94043-1351, USA.
Google Analytics installs a cookie on the IT system of the data subject. Cookies were explained above. Installing the cookie allows Google to analyse the use of our internet offers. When visiting a specific page operated by us with a Google Analytics component installed, the respective Google Analytics component automatically causes the web browser on the data subjec’s IT system to transmit data to Google for the purpose of online analysis. During this technical process, Google receives personal data such as the IP address of the data subject, which among other things allows Google to determine the origin of the visitor and track clicks and subsequently calculate payments.
The cookies collect personal information such as the access time, location where the access originated, and how often the data subject visits our website. When visiting our website this personal data including IP address of the data subject’s internet connection are transmitted to Google in the United States of America. This personal data is stored by Google in the United States of America. Google may share this personal data collected using this technical process with third parties.