资源环境科技发展态势分析平台

Macrophages are the first cells of the nascent immune system to emerge during embryonic development. In mice, embryonic macrophages infiltrate developing organs, where they differentiate symbiotically into tissue-resident macrophages (TRMs)(1). However, our understanding of the origins and specialization of macrophages in human embryos is limited. Here we isolated CD45(+) haematopoietic cells from human embryos at Carnegie stages 11 to 23 and subjected them to transcriptomic profiling by single-cell RNA sequencing, followed by functional characterization of a population of CD45(+)CD34(+)CD44(+) yolk sac-derived myeloid-biased progenitors (YSMPs) by single-cell culture. We also mapped macrophage heterogeneity across multiple anatomical sites and identified diverse subsets, including various types of embryonic TRM (in the head, liver, lung and skin). We further traced the specification trajectories of TRMs from either yolk sac-derived primitive macrophages or YSMP-derived embryonic liver monocytes using both transcriptomic and developmental staging information, with a focus on microglia. Finally, we evaluated the molecular similarities between embryonic TRMs and their adult counterparts. Our data represent a comprehensive characterization of the spatiotemporal dynamics of early macrophage development during human embryogenesis, providing a reference for future studies of the development and function of human TRMs.

Single-cell RNA sequencing of haematopoietic cells from human embryos at different developmental stages sheds light on the development and specification of macrophages in different tissues.

A video-based deep learning algorithm-EchoNet-Dynamic-accurately identifies subtle changes in ejection fraction and classifies heart failure with reduced ejection fraction using information from multiple cardiac cycles.

Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease(1), screening for cardiotoxicity(2) and decisions regarding the clinical management of patients with a critical illness(3). However, human assessment of cardiac function focuses on a limited sampling of cardiac cycles and has considerable inter-observer variability despite years of training(4,5). Here, to overcome this challenge, we present a video-based deep learning algorithm-EchoNet-Dynamic-that surpasses the performance of human experts in the critical tasks of segmenting the left ventricle, estimating ejection fraction and assessing cardiomyopathy. Trained on echocardiogram videos, our model accurately segments the left ventricle with a Dice similarity coefficient of 0.92, predicts ejection fraction with a mean absolute error of 4.1% and reliably classifies heart failure with reduced ejection fraction (area under the curve of 0.97). In an external dataset from another healthcare system, EchoNet-Dynamic predicts the ejection fraction with a mean absolute error of 6.0% and classifies heart failure with reduced ejection fraction with an area under the curve of 0.96. Prospective evaluation with repeated human measurements confirms that the model has variance that is comparable to or less than that of human experts. By leveraging information across multiple cardiac cycles, our model can rapidly identify subtle changes in ejection fraction, is more reproducible than human evaluation and lays the foundation for precise diagnosis of cardiovascular disease in real time. As a resource to promote further innovation, we also make publicly available a large dataset of 10,030 annotated echocardiogram videos.

Inflammatory bowel disease (IBD) is a complex genetic disease that is instigated and amplified by the confluence of multiple genetic and environmental variables that perturb the immune-microbiome axis. The challenge of dissecting pathological mechanisms underlying IBD has led to the development of transformative approaches in human genetics and functional genomics. Here we describe IBD as a model disease in the context of leveraging human genetics to dissect interactions in cellular and molecular pathways that regulate homeostasis of the mucosal immune system. Finally, we synthesize emerging insights from multiple experimental approaches into pathway paradigms and discuss future prospects for disease-subtype classification and therapeutic intervention.

This Review examines inflammatory bowel disease in the context of human genetics studies that help to identify pathways that regulate homeostasis of the mucosal immune system and discusses future prospects for disease-subtype classification and therapeutic intervention.