The Myeloid Cell Compartment-Cell by Cell.

Myeloid cells are a major cellular compartment of the immune system comprising monocytes, dendritic cells, tissue macrophages, and granulocytes. Models of cellular ontogeny, activation, differentiation, and tissue-specific functions of myeloid cells have been revisited during the last years with surprising results; for example, most tissue macrophages are yolk sac derived, monocytes and macrophages follow a multidimensional model of activation, and tissue signals have a significant impact on the functionality of all these cells. While these exciting results have brought these cells back to center stage, their enormous plasticity and heterogeneity, during both homeostasis and disease, are far from understood. At the same time, the ongoing revolution in single-cell genomics, with single-cell RNA sequencing (scRNA-seq) leading the way, promises to change this. Prevailing models of hematopoiesis with distinct intermediates are challenged by scRNA-seq data suggesting more continuous developmental trajectories in the myeloid cell compartment. Cell subset structures previously defined by protein marker expression need to be revised based on unbiased analyses of scRNA-seq data. Particularly in inflammatory conditions, myeloid cells exhibit substantially vaster heterogeneity than previously anticipated, and work performed within large international projects, such as the Human Cell Atlas, has already revealed novel tissue macrophage subsets. Based on these exciting developments, we propose the next steps to a full understanding of the myeloid cell compartment in health and diseases.

CRELD1 modulates homeostasis of the immune system in mice and humans.

CRELD1 is a pivotal factor for heart development, the function of which is unknown in adult life. We here provide evidence that CRELD1 is an important gatekeeper of immune system homeostasis. Exploiting expression variance in large human cohorts contrasting individuals with the lowest and highest CRELD1 expression levels revealed strong phenotypic, functional and transcriptional differences, including reduced CD4+ T cell numbers. These findings were validated in T cell-specific Creld1-deficient mice. Loss of Creld1 was associated with simultaneous overactivation and increased apoptosis, resulting in a net loss of T cells with age. Creld1 was transcriptionally and functionally linked to Wnt signaling. Collectively, gene expression variance in large human cohorts combined with murine genetic models, transcriptomics and functional testing defines CRELD1 as an important modulator of immune homeostasis.

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.

Machine learning models for outcome prediction in thrombectomy for large anterior vessel occlusion.

OBJECTIVE: Predicting long-term functional outcomes shortly after a stroke is challenging, even for experienced neurologists. Therefore, we aimed to evaluate multiple machine learning models and the importance of clinical/radiological parameters to develop a model that balances minimal input data with reliable predictions of long-term functional independency. METHODS: Our study utilized data from the German Stroke Registry on patients with large anterior vessel occlusion who underwent endovascular treatment. We trained seven machine learning models using 30 parameters from the first day postadmission to predict a modified Ranking Scale of 0-2 at 90 days poststroke. Model performance was assessed using a 20-fold cross-validation and one-sided Wilcoxon rank-sum tests. Key features were identified through backward feature selection. RESULTS: We included 7485 individuals with a median age of 75 years and a median NIHSS score at admission of 14 in our analysis. Our Deep Neural Network model demonstrated the best performance among all models including data from 24 h postadmission. Backward feature selection identified the seven most important features to be NIHSS after 24 h, age, modified Ranking Scale after 24 h, premorbid modified Ranking Scale, intracranial hemorrhage within 24 h, intravenous thrombolysis, and NIHSS at admission. Narrowing the Deep Neural Network model's input data to these features preserved the high performance with an AUC of 0.9 (CI: 0.89-0.91). INTERPRETATION: Our Deep Neural Network model, trained on over 7000 patients, predicts 90-day functional independence using only seven clinical/radiological features from the first day postadmission, demonstrating both high accuracy and practicality for clinical implementation on stroke units.

Identification of drug candidates targeting monocyte reprogramming in people living with HIV.

Introduction: People living with HIV (PLHIV) are characterized by functional reprogramming of innate immune cells even after long-term antiretroviral therapy (ART). In order to assess technical feasibility of omics technologies for application to larger cohorts, we compared multiple omics data layers. Methods: Bulk and single-cell transcriptomics, flow cytometry, proteomics, chromatin landscape analysis by ATAC-seq as well as ex vivo drug stimulation were performed in a small number of blood samples derived from PLHIV and healthy controls from the 200-HIV cohort study. Results: Single-cell RNA-seq analysis revealed that most immune cells in peripheral blood of PLHIV are altered in their transcriptomes and that a specific functional monocyte state previously described in acute HIV infection is still existing in PLHIV while other monocyte cell states are only occurring acute infection. Further, a reverse transcriptome approach on a rather small number of PLHIV was sufficient to identify drug candidates for reversing the transcriptional phenotype of monocytes in PLHIV. Discussion: These scientific findings and technological advancements for clinical application of single-cell transcriptomics form the basis for the larger 2000-HIV multicenter cohort study on PLHIV, for which a combination of bulk and single-cell transcriptomics will be included as the leading technology to determine disease endotypes in PLHIV and to predict disease trajectories and outcomes.

Decoding mechanism of action and sensitivity to drug candidates from integrated transcriptome and chromatin state.

Omics-based technologies are driving major advances in precision medicine, but efforts are still required to consolidate their use in drug discovery. In this work, we exemplify the use of multi-omics to support the development of 3-chloropiperidines, a new class of candidate anticancer agents. Combined analyses of transcriptome and chromatin accessibility elucidated the mechanisms underlying sensitivity to test agents. Furthermore, we implemented a new versatile strategy for the integration of RNA- and ATAC-seq (Assay for Transposase-Accessible Chromatin) data, able to accelerate and extend the standalone analyses of distinct omic layers. This platform guided the construction of a perturbation-informed basal signature predicting cancer cell lines' sensitivity and to further direct compound development against specific tumor types. Overall, this approach offers a scalable pipeline to support the early phases of drug discovery, understanding of mechanisms, and potentially inform the positioning of therapeutics in the clinic.

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

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.

Alveolar macrophages in early stage COPD show functional deviations with properties of impaired immune activation.

Despite its high prevalence, the cellular and molecular mechanisms of chronic obstructive pulmonary disease (COPD) are far from being understood. Here, we determine disease-related changes in cellular and molecular compositions within the alveolar space and peripheral blood of a cohort of COPD patients and controls. Myeloid cells were the largest cellular compartment in the alveolar space with invading monocytes and proliferating macrophages elevated in COPD. Modeling cell-to-cell communication, signaling pathway usage, and transcription factor binding predicts TGF-ß1 to be a major upstream regulator of transcriptional changes in alveolar macrophages of COPD patients. Functionally, macrophages in COPD showed reduced antigen presentation capacity, accumulation of cholesteryl ester, reduced cellular chemotaxis, and mitochondrial dysfunction, reminiscent of impaired immune activation.

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+Lin-HLA-DR+CD66b-Autofluorescencehi 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.

Scalable Prediction of Acute Myeloid Leukemia Using High-Dimensional Machine Learning and Blood Transcriptomics.

Acute myeloid leukemia (AML) is a severe, mostly fatal hematopoietic malignancy. We were interested in whether transcriptomic-based machine learning could predict AML status without requiring expert input. Using 12,029 samples from 105 different studies, we present a large-scale study of machine learning-based prediction of AML in which we address key questions relating to the combination of machine learning and transcriptomics and their practical use. We find data-driven, high-dimensional approaches-in which multivariate signatures are learned directly from genome-wide data with no prior knowledge-to be accurate and robust. Importantly, these approaches are highly scalable with low marginal cost, essentially matching human expert annotation in a near-automated workflow. Our results support the notion that transcriptomics combined with machine learning could be used as part of an integrated -omics approach wherein risk prediction, differential diagnosis, and subclassification of AML are achieved by genomics while diagnosis could be assisted by transcriptomic-based machine learning.