Vascular smooth muscle cell phenotype switching in carotid atherosclerosis

Hemodynamics regulate spatiotemporal artery muscularization in the developing circle of Willis

Vascular smooth muscle cells (VSMCs) envelop vertebrate brain arteries, playing a crucial role in regulating cerebral blood flow and neurovascular coupling. The dedifferentiation of VSMCs is implicated in cerebrovascular diseases and neurodegeneration. Despite its importance, the process of VSMC differentiation on brain arteries during development remains inadequately characterized. Understanding this process could aid in reprogramming and regenerating differentiated VSMCs in cerebrovascular diseases. In this study, we investigated VSMC differentiation on the zebrafish circle of Willis (CoW), comprising major arteries that supply blood to the vertebrate brain. We observed that the arterial expression of CoW endothelial cells (ECs) occurs after their migration from the cranial venous plexus to form CoW arteries. Subsequently, acta2+ VSMCs differentiate from pdgfrb+ mural cell progenitors upon recruitment to CoW arteries. The progression of VSMC differentiation exhibits a spatiotemporal pattern, advancing from anterior to posterior CoW arteries. Analysis of blood flow suggests that earlier VSMC differentiation in anterior CoW arteries correlates with higher red blood cell velocity wall shear stress. Furthermore, pulsatile blood flow is required for differentiation of human brain pdgfrb+ mural cells into VSMCs as well as VSMC differentiation on zebrafish CoW arteries. Consistently, the flow-responsive transcription factor klf2a is activated in ECs of CoW arteries prior to VSMC differentiation, and klf2a knockdown delays VSMC differentiation on anterior CoW arteries. In summary, our findings highlight the role of blood flow activation of endothelial klf2a as a mechanism regulating the initial VSMC differentiation on vertebrate brain arteries.

Canonical and non-canonical roles of complement in atherosclerosis

Cardiovascular diseases are the leading cause of death globally, and atherosclerosis is the major contributor to the development and progression of cardiovascular diseases. Immune responses have a central role in the pathogenesis of atherosclerosis, with the complement system being an acknowledged contributor. Chronic activation of liver-derived and serum-circulating canonical complement sustains endothelial inflammation and innate immune cell activation, and deposition of complement activation fragments on inflamed endothelial cells is a hallmark of atherosclerotic plaques. However, increasing evidence indicates that liver-independent, cell-autonomous and non-canonical complement activities are underappreciated contributors to atherosclerosis. Furthermore, complement activation can also have atheroprotective properties. These specific detrimental or beneficial contributions of the complement system to the pathogenesis of atherosclerosis are dictated by the location of complement activation and engagement of its canonical versus non-canonical functions in a temporal fashion during atherosclerosis progression. In this Review, we summarize the classical and the emerging non-classical roles of the complement system in the pathogenesis of atherosclerosis and discuss potential strategies for therapeutic modulation of complement for the prevention and treatment of atherosclerotic cardiovascular disease.

Roles of Skeletal Muscle in Development: A Bioinformatics and Systems Biology Overview

The ability to assess various cellular events consequent to perturbations, such as genetic mutations, disease states and therapies, has been recently revolutionized by technological advances in multiple "omics" fields. The resulting deluge of information has enabled and necessitated the development of tools required to both process and interpret the data. While of tremendous value to basic researchers, the amount and complexity of the data has made it extremely difficult to manually draw inference and identify factors key to the study objectives. The challenges of data reduction and interpretation are being met by the development of increasingly complex tools that integrate disparate knowledge bases and synthesize coherent models based on current biological understanding. This chapter presents an example of how genomics data can be integrated with biological network analyses to gain further insight into the developmental consequences of genetic perturbations. State of the art methods for conducting similar studies are discussed along with modern methods used to analyze and interpret the data.

Aortic Cellular Diversity and Quantitative Genome-Wide Association Study Trait Prioritization Through Single-Nuclear RNA Sequencing of the Aneurysmal Human Aorta

BACKGROUND

Mural cells in ascending aortic aneurysms undergo phenotypic changes that promote extracellular matrix destruction and structural weakening. To explore this biology, we analyzed the transcriptional features of thoracic aortic tissue.

METHODS

Single-nuclear RNA sequencing was performed on 13 samples from human donors, 6 with thoracic aortic aneurysm, and 7 without aneurysm. Individual transcriptomes were then clustered based on transcriptional profiles. Clusters were used for between-disease differential gene expression analyses, subcluster analysis, and analyzed for intersection with genetic aortic trait data.

RESULTS

We sequenced 71 689 nuclei from human thoracic aortas and identified 14 clusters, aligning with 11 cell types, predominantly vascular smooth muscle cells (VSMCs) consistent with aortic histology. With unbiased methodology, we found 7 vascular smooth muscle cell and 6 fibroblast subclusters. Differentially expressed genes analysis revealed a vascular smooth muscle cell group accounting for the majority of differential gene expression. Fibroblast populations in aneurysm exhibit distinct behavior with almost complete disappearance of quiescent fibroblasts. Differentially expressed genes were used to prioritize genes at aortic diameter and distensibility genome-wide association study loci highlighting the genes JUN, LTBP4 (latent transforming growth factor beta-binding protein 1), and IL34 (interleukin 34) in fibroblasts, ENTPD1, PDLIM5 (PDZ and LIM domain 5), ACTN4 (alpha-actinin-4), and GLRX in vascular smooth muscle cells, as well as LRP1 in macrophage populations.

CONCLUSIONS

Using nuclear RNA sequencing, we describe the cellular diversity of healthy and aneurysmal human ascending aorta. Sporadic aortic aneurysm is characterized by differential gene expression within known cellular classes rather than by the appearance of novel cellular forms. Single-nuclear RNA sequencing of aortic tissue can be used to prioritize genes at aortic trait loci.

New Insights into Cerebral Vessel Disease Landscapes at Single-Cell Resolution: Pathogenetic and Therapeutic Perspectives

Cerebrovascular diseases are a leading cause of death and disability globally. The development of new therapeutic targets for cerebrovascular diseases (e.g., ischemic, and hemorrhagic stroke, vascular dementia) is limited by a lack of knowledge of the cellular and molecular biology of health and disease conditions and the factors that cause injury to cerebrovascular structures. Here, we describe the role of advances in omics technology, particularly RNA sequencing, in studying high-dimensional, multifaceted profiles of thousands of individual blood and vessel cells at single-cell resolution. This analysis enables the dissection of the heterogeneity of diseased cerebral vessels and their atherosclerotic plaques, including the microenvironment, cell evolutionary trajectory, and immune response pathway. In animal models, RNA sequencing permits the tracking of individual cells (including immunological, endothelial, and vascular smooth muscle cells) that compose atherosclerotic plaques and their alteration under experimental settings such as phenotypic transition. We describe how single-cell RNA transcriptomics in humans allows mapping to the molecular and cellular levels of atherosclerotic plaques in cerebral arteries, tracking individual lymphocytes and macrophages, and how these data can aid in identifying novel immune mechanisms that could be exploited as therapeutic targets for cerebrovascular diseases. Single-cell multi-omics approaches will likely provide the unprecedented resolution and depth of data needed to generate clinically relevant cellular and molecular signatures for the precise treatment of cerebrovascular diseases.