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Cell identity is governed by the complex regulation of gene expression, represented as gene-regulatory networks 1 . Here we use gene-regulatory networks inferred from single-cell multi-omics data to perform in silico transcription factor perturbations, simulating the consequent changes in cell identity using only unperturbed wild-type data. We apply this machine-learning-based approach, CellOracle, to well-established paradigms—mouse and human haematopoiesis, and zebrafish embryogenesis—and we correctly model reported changes in phenotype that occur as a result of transcription factor perturbation. Through systematic in silico transcription factor perturbation in the developing zebrafish, we simulate and experimentally validate a previously unreported phenotype that results from the loss of noto , an established notochord regulator. Furthermore, we identify an axial mesoderm regulator, lhx1a . Together, these results show that CellOracle can be used to analyse the regulation of cell identity by transcription factors, and can provide mechanistic insights into development and differentiation. A machine-learning-based strategy called CellOracle combines computational perturbation with modelling of gene-regulatory networks to analyse how cell identity is regulated by transcription factors, and correctly predicts phenotypic changes after transcription factor perturbation in the developing zebrafish.

Methylation patterns of circulating cell-free DNA (cfDNA) contain rich information about recent cell death events in the body. Here, we present an approach for unbiased determination of the tissue origins of cfDNA, using a reference methylation atlas of 25 human tissues and cell types. The method is validated using in silico simulations as well as in vitro mixes of DNA from different tissue sources at known proportions. We show that plasma cfDNA of healthy donors originates from white blood cells (55%), erythrocyte progenitors (30%), vascular endothelial cells (10%) and hepatocytes (1%). Deconvolution of cfDNA from patients reveals tissue contributions that agree with clinical findings in sepsis, islet transplantation, cancer of the colon, lung, breast and prostate, and cancer of unknown primary. We propose a procedure which can be easily adapted to study the cellular contributors to cfDNA in many settings, opening a broad window into healthy and pathologic human tissue dynamics. The methylation status of circulating cell-free DNA (cfDNA) can be informative about recent cell death events. Here the authors present an approach to determine the tissue origins of cfDNA, using a reference methylation atlas of 25 human tissues and cell types, and find that cfDNA from patients reveals tissue contributions that agree with clinical findings.

A new computational approach enables integrative analysis of disparate single-cell RNA–sequencing data sets by identifying shared patterns of variation between cell subpopulations. Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat ( http://satijalab.org/seurat/ ), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

Emerging evidence has spurred a considerable evolution of concepts relating to atherosclerosis, and has called into question many previous notions. Here I review this evidence, and discuss its implications for understanding of atherosclerosis. The risk of developing atherosclerosis is no longer concentrated in Western countries, and it is instead involved in the majority of deaths worldwide. Atherosclerosis now affects younger people, and more women and individuals from a diverse range of ethnic backgrounds, than was formerly the case. The risk factor profile has shifted as levels of low-density lipoprotein (LDL) cholesterol, blood pressure and smoking have decreased. Recent research has challenged the protective effects of high-density lipoprotein, and now focuses on triglyceride-rich lipoproteins in addition to low-density lipoprotein as causal in atherosclerosis. Non-traditional drivers of atherosclerosis—such as disturbed sleep, physical inactivity, the microbiome, air pollution and environmental stress—have also gained attention. Inflammatory pathways and leukocytes link traditional and emerging risk factors alike to the altered behaviour of arterial wall cells. Probing the pathogenesis of atherosclerosis has highlighted the role of the bone marrow: somatic mutations in stem cells can cause clonal haematopoiesis, which represents a previously unrecognized but common and potent age-related contributor to the risk of developing cardiovascular disease. Characterizations of the mechanisms that underpin thrombotic complications of atherosclerosis have evolved beyond the ‘vulnerable plaque’ concept. These advances in our understanding of the biology of atherosclerosis have opened avenues to therapeutic interventions that promise to improve the prevention and treatment of now-ubiquitous atherosclerotic diseases. This Review discusses recent research that has transformed our understanding of the biology of atherosclerosis, and examines its implications for the treatment of atherosclerotic cardiovascular disease.

Macrophages are found in tissues, body cavities, and mucosal surfaces. Most tissue macrophages are seeded in the early embryo before definitive hematopoiesis is established. Others are derived from blood monocytes. The macrophage lineage diversification and plasticity are key aspects of their functionality. Macrophages can also be generated from monocytes and undergo classical (LPS+IFN-γ) or alternative (IL-4) activation. , macrophages with different polarization and different activation markers coexist in tissues. Certain mouse strains preferentially promote T-helper-1 (Th1) responses and others Th2 responses. Their macrophages preferentially induce iNOS or arginase and have been called M1 and M2, respectively. In many publications, M1 and classically activated and M2 and alternatively activated are used interchangeably. We tested whether this is justified by comparing the gene lists positively [M1(=LPS+)] or negatively [M2(=LPS-)] correlated with the ratio of and in transcriptomes of LPS-treated peritoneal macrophages with classically (LPS, IFN-γ) vs. alternatively activated (IL-4) bone marrow derived macrophages, both from published datasets. Although there is some overlap between M1(=LPS+) and classically activated (LPS+IFN-γ) and M2(=LPS-) and alternatively activated macrophages, many more genes are regulated in opposite or unrelated ways. Thus, M1(=LPS+) macrophages are not equivalent to classically activated, and M2(=LPS-) macrophages are not equivalent to alternatively activated macrophages. This fundamental discrepancy explains why most surface markers identified on generated macrophages do not translate to the situation. Valid M1/M2 surface markers remain to be discovered.

Stress-linked psychiatric disorders, including anxiety and major depressive disorder, are associated with systemic inflammation. Recent studies have reported stress-induced alterations in haematopoiesis that result in monocytosis, neutrophilia, lymphocytopenia and, consequently, in the upregulation of pro-inflammatory processes in immunologically relevant peripheral tissues. There is now evidence that this peripheral inflammation contributes to the development of psychiatric symptoms as well as to common co-morbidities of psychiatric disorders such as metabolic syndrome and immunosuppression. Here, we review the specific brain and spinal regions, and the neuronal populations within them, that respond to stress and transmit signals to peripheral tissues via the autonomic nervous system or neuroendocrine pathways to influence immunological function. We comprehensively summarize studies that have employed retrograde tracing to define neurocircuits linking the brain to the bone marrow, spleen, gut, adipose tissue and liver. Moreover, we highlight studies that have used chemogenetic or optogenetic manipulation or intracerebroventricular administration of peptide hormones to control somatic immune responses. Collectively, this growing body of literature illustrates potential mechanisms through which stress signals are conveyed from the CNS to immune cells to regulate stress-relevant behaviours and comorbid pathophysiology.Stress modulates immune system function and systemic inflammation is linked to stress-related disorders, including depression. Russo and colleagues outline the neural circuits through which the CNS regulates immune cell function in peripheral tissues in response to stress and consider how these responses contribute to stress-related pathophysiology.

Of 30 patients with severe sickle cell disease who were treated with gene-edited autologous hematopoietic stem and progenitor cells, 29 were free from vaso-occlusive crises for at least 12 consecutive months.

Learning effective molecular feature representation to facilitate molecular property prediction is of great significance for drug discovery. Recently, there has been a surge of interest in pre-training graph neural networks (GNNs) via self-supervised learning techniques to overcome the challenge of data scarcity in molecular property prediction. However, current self-supervised learning-based methods suffer from two main obstacles: the lack of a well-defined self-supervised learning strategy and the limited capacity of GNNs. Here, we propose Knowledge-guided Pre-training of Graph Transformer (KPGT), a self-supervised learning framework to alleviate the aforementioned issues and provide generalizable and robust molecular representations. The KPGT framework integrates a graph transformer specifically designed for molecular graphs and a knowledge-guided pre-training strategy, to fully capture both structural and semantic knowledge of molecules. Through extensive computational tests on 63 datasets, KPGT exhibits superior performance in predicting molecular properties across various domains. Moreover, the practical applicability of KPGT in drug discovery has been validated by identifying potential inhibitors of two antitumor targets: hematopoietic progenitor kinase 1 (HPK1) and fibroblast growth factor receptor 1 (FGFR1). Overall, KPGT can provide a powerful and useful tool for advancing the artificial intelligence (AI)-aided drug discovery process. Accurate property prediction relies on effective molecular representation. Here, the authors introduce KPGT, a knowledge-guided self-supervised framework that improves molecular representation, leading to superior predictions of molecular properties and advancing AI-driven drug discovery.

Florent Ginhoux reflects on a 2002 paper by Merad and colleagues that challenged the dogma that adult Langerhans cells arise from blood-circulating precursors.