cost-efficient-transcriptomic-based-drug-screening

Cost-Efficient Transcriptomic-Based Drug Screening

Transcriptomics allows&#160;to obtain comprehensive insights into cellular programs and their responses to perturbations. Despite a significant decrease ...

German Center for Neurodegenerative Diseases (DZNE) eV.

University of Bonn

DZNE and University of Bonn

A novel computational architecture for large-scale genomics.

The introduction of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into the human population represents a tremendous medical and economic crisis. Innate immunity-as the first line of defense of our immune system-plays a central role in combating this novel virus. Here, we provide a conceptual framework for the interaction of the human innate immune system with SARS-CoV-2 to link the clinical observations with experimental findings that have been made during the first year of the pandemic. We review evidence that variability in innate immune system components among humans is a main contributor to the heterogeneous disease courses observed for coronavirus disease 2019 (COVID-19), the disease spectrum induced by SARS-CoV-2. A better understanding of the pathophysiological mechanisms observed for cells and soluble mediators involved in innate immunity is a prerequisite for the development of diagnostic markers and therapeutic strategies targeting COVID-19. However, this will also require additional studies addressing causality of events, which so far are lagging behind.

COVID-19 and the human innate immune system.

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. 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.

Swarm Learning for decentralized and confidential clinical machine learning.

Time for a voluntary crisis research service.

Human immunology may soon benefit from the use of artificial intelligence and blockchain technologies. Here, we discuss how Swarm Learning could foster collaborative worldwide immunology studies that fully respect local data privacy regulations by sharing insights, not data.

Swarm immunology: harnessing blockchain technology and artificial intelligence in human immunology.

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.

Mature neutrophils and a NF-κB-to-IFN transition determine the unifying disease recovery dynamics in COVID-19.

The immune system is highly complex and distributed throughout an organism, with hundreds to thousands of cell states existing in parallel with diverse molecular pathways interacting in a highly dynamic and coordinated fashion. Although the characterization of individual genes and molecules is of the utmost importance for understanding immune-system function, high-throughput, high-resolution omics technologies combined with sophisticated computational modeling and machine-learning approaches are creating opportunities to complement standard immunological methods with new insights into immune-system dynamics. Like systems immunology itself, immunology researchers must take advantage of these technologies and form their own diverse networks, connecting with researchers from other disciplines. This Review is an introduction and 'how-to guide' for immunologists with no particular experience in the field of omics but with the intention to learn about and apply these systems-level approaches, and for immunologists who want to make the most of interdisciplinary networks.

A guide to systems-level immunomics.

Recent developments in sequencing technologies, the computer and data sciences, as well as increasingly high-throughput immunological measurements have made it possible to derive holistic views on pathophysiological processes of disease and treatment effects directly in humans. We and others have illustrated that incredibly predictive data for immune cell function can be generated by single cell multi-omics (SCMO) technologies and that these technologies are perfectly suited to dissect pathophysiological processes in a new disease such as COVID-19, triggered by SARS-CoV-2 infection. Systems level interrogation not only revealed the different disease endotypes, highlighted the differential dynamics in context of disease severity, and pointed towards global immune deviation across the different arms of the immune system, but was already instrumental to better define long COVID phenotypes, suggest promising biomarkers for disease and therapy outcome predictions and explains treatment responses for the widely used corticosteroids. As we identified SCMO to be the most informative technologies in the vest to better understand COVID-19, we propose to routinely include such single cell level analysis in all future clinical trials and cohorts addressing diseases with an immunological component.

Article	Year
A novel computational architecture for large-scale genomics. Nature biotechnology\| PubMed ID: 32999473	2020
COVID-19 and the human innate immune system. Cell\| PubMed ID: 33743212	2021
Swarm Learning for decentralized and confidential clinical machine learning. Nature\| PubMed ID: 34040261	2021
Time for a voluntary crisis research service. Cell death and differentiation\| PubMed ID: 35314771	2022
Swarm immunology: harnessing blockchain technology and artificial intelligence in human immunology. Nature reviews. Immunology\| PubMed ID: 35624333	2022
Mature neutrophils and a NF-κB-to-IFN transition determine the unifying disease recovery dynamics in COVID-19. Cell reports. Medicine\| PubMed ID: 35675822	2022
A guide to systems-level immunomics. Nature immunology\| PubMed ID: 36138185	2022
Systems analysis of human innate immunity in COVID-19. Seminars in immunology\| PubMed ID: 37267758	2023

Joachim Schultze

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