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September 3, 2021 Immunity

Early IFN-α signatures and persistent dysfunction are distinguishing features of NK cells in severe COVID-19

Longitudinal analyses of the innate immune system, including the earliest time points, are essential to understand the immunopathogenesis and clinical course of coronavirus disease (COVID-19). Here, we performed a detailed characterization of natural killer (NK) cells in 205 patients (403 samples; days 2 to 41 after symptom onset) from four independent cohorts using single-cell transcriptomics and proteomics together with functional studies. We found elevated interferon (IFN)-α plasma levels in early severe COVD-19 alongside increased NK cell expression of IFN-stimulated genes (ISGs) and genes involved in IFN-α signaling, while upregulation of tumor necrosis factor (TNF)-induced genes was observed in moderate diseases. NK cells exert anti-SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) activity but are functionally impaired in severe COVID-19. Further, NK cell dysfunction may be relevant for the development of fibrotic lung disease in severe COVID-19, as NK cells exhibited impaired anti-fibrotic activity. Our study indicates preferential IFN-α and TNF responses in severe and moderate COVID-19, respectively, and associates a prolonged IFN-α-induced NK cell response with poorer disease outcome.

All data and analyses available on FASTGenomics via

Krämer et al., 2021: Early IFN-α signatures and persistent dysfunction are distinguishing features of NK cells in severe COVID-19, Immunity, DOI:

May 26, 2021 Nature

Swarm Learning for decentralized and confidential clinical machine learning

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.

All data and analyses available on FASTGenomics via

Warnat-Herresthal, S., Schultze, H., Shastry, K.L. et al. Swarm Learning for decentralized and confidential clinical machine learning. Nature 594, 265–270 (2021).

April 24, 2021 ERJ Open Research

Alveolar macrophage transcriptomic profiling in COPD shows major lipid metabolism changes

Background Immune cells play a major role in the pathogenesis of COPD. Changes in the distribution and cellular functions of major immune cells, such as alveolar macrophages (AMs) and neutrophils are well known; however, their transcriptional reprogramming and contribution to the pathophysiology of COPD are still not fully understood.

Method To determine changes in transcriptional reprogramming and lipid metabolism in the major immune cell type within bronchoalveolar lavage fluid, we analysed whole transcriptomes and lipidomes of sorted CD45+LinHLA-DR+CD66bAutofluorescencehi AMs from controls and COPD patients.

Results We observed global transcriptional reprogramming featuring a spectrum of activation states, including pro- and anti-inflammatory signatures. We further detected significant changes between COPD patients and controls in genes involved in lipid metabolism, such as fatty acid biosynthesis in GOLD2 patients. Based on these findings, assessment of a total of 202 lipid species in sorted AMs revealed changes of cholesteryl esters, monoacylglycerols and phospholipids in a disease grade-dependent manner.

Conclusions Transcriptome and lipidome profiling of COPD AMs revealed GOLD grade-dependent changes, such as in cholesterol metabolism and interferon-α and γ responses.

All data and analyses available on FASTGenomics via

Wataru et al., 2021, Alveolar macrophage transcriptomic profiling in COPD shows major lipid metabolism changes,

February 11, 2021 Nature Immunology

Urban living in healthy Tanzanians is associated with an inflammatory status driven by dietary and metabolic changes

Sub-Saharan Africa currently experiences an unprecedented wave of urbanization, which has important consequences for health and disease patterns. This study aimed to investigate and integrate the immune and metabolic consequences of rural or urban lifestyles and the role of nutritional changes associated with urban living. In a cohort of 323 healthy Tanzanians, urban as compared to rural living was associated with a pro-inflammatory immune phenotype, both at the transcript and protein levels. We identified different food-derived and endogenous circulating metabolites accounting for these differences. Serum from urban dwellers induced reprogramming of innate immune cells with higher tumor necrosis factor production upon microbial re-stimulation in an in vitro model of trained immunity. These data demonstrate important shifts toward an inflammatory phenotype associated with an urban lifestyle and provide new insights into the underlying dietary and metabolic factors, which may affect disease epidemiology in sub-Sahara African countries.

All data and analyses available on FASTGenomics via

Temba, G.S., Kullaya, V., Pecht, T. et al. Urban living in healthy Tanzanians is associated with an inflammatory status driven by dietary and metabolic changes. Nat Immunol 22, 287–300 (2021).

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

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 ( 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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