Visualization of cardiovascular development, physiology and disease at the single-cell level: Opportunities and future challenges

Macrophage-driven cardiac inflammation and healing: insights from homeostasis and myocardial infarction

Early and prompt reperfusion therapy has markedly improved the survival rates among patients enduring myocardial infarction (MI). Nonetheless, the resulting adverse remodeling and the subsequent onset of heart failure remain formidable clinical management challenges and represent a primary cause of disability in MI patients worldwide. Macrophages play a crucial role in immune system regulation and wield a profound influence over the inflammatory repair process following MI, thereby dictating the degree of myocardial injury and the subsequent pathological remodeling. Despite numerous previous biological studies that established the classical polarization model for macrophages, classifying them as either M1 pro-inflammatory or M2 pro-reparative macrophages, this simplistic categorization falls short of meeting the precision medicine standards, hindering the translational advancement of clinical research. Recently, advances in single-cell sequencing technology have facilitated a more profound exploration of macrophage heterogeneity and plasticity, opening avenues for the development of targeted interventions to address macrophage-related factors in the aftermath of MI. In this review, we provide a summary of macrophage origins, tissue distribution, classification, and surface markers. Furthermore, we delve into the multifaceted roles of macrophages in maintaining cardiac homeostasis and regulating inflammation during the post-MI period.

Single-cell RNA-seq uncovers distinct pathways and genes in endothelial cells during atherosclerosis progression

Background: Atherosclerosis (AS) is a chronic inflammatory disease involving various cell types, cytokines, and adhesion molecules. Herein, we aimed to uncover its key molecular mechanisms by single-cell RNA-seq (scRNA-seq) analysis. Methods: ScRNA-seq data of cells from atherosclerotic human coronary arteries were analyzed using the Seurat package. Cell types were clustered, and differentially expressed genes (DEGs) were screened. GSVA (Gene Set Variation Analysis) scores of hub pathways were compared among different cell clusters. DEGs in endothelial cells between apolipoprotein-E (ApoE)^-/- mice and specific TGFbR1/2 KO ApoE^-/- mice fed with high-fat diet were overlapped with those from human AS coronary arteries. In fluid shear stress and AS, hub genes were determined based on the protein-protein interaction (PPI) network, which were verified in ApoE^-/- mice. Finally, hub genes were validated in three pairs of AS coronary arteries and normal tissues by histopathological examination. Results: ScRNA-seq identified nine cell clusters in human coronary arteries, namely, fibroblasts, endothelial cells, macrophages, B cells, adipocytes, HSCs, NK cells, CD8⁺ T cells, and monocytes. Among them, endothelial cells had the lowest fluid shear stress and AS and TGF-beta signaling pathway scores. Compared to ApoE^-/- mice fed with normal diet, fluid shear stress and AS and TGF-beta scores were both significantly lower in endothelial cells from TGFbR1/2 KO ApoE^-/- mice fed with normal or high-fat diet. Furthermore, the two hub pathways had a positive correlation. Three hub genes (ICAM1, KLF2, and VCAM1) were identified, and their expression was distinctly downregulated in endothelial cells from TGFbR1/2 KO ApoE^-/- mice fed with normal or high-fat diet than in those from ApoE^-/- mice fed with a normal diet, which were confirmed in human AS coronary artery. Conclusion: Our findings clarified the pivotal impacts of pathways (fluid shear stress and AS and TGF-beta) and genes (ICAM1, KLF2, and VCAM1) in endothelial cells on AS progression.

Heterogeneity of macrophages in atherosclerosis revealed by single-cell RNA sequencing

Technology at the single-cell level has advanced dramatically in characterizing molecular heterogeneity. These technologies have enabled cell subtype diversity to be seen in all tissues, including atherosclerotic plaques. Critical in atherosclerosis pathogenesis and progression are macrophages. Previous studies have only determined macrophage phenotypes within the plaque, mainly by bulk analysis. However, recent progress in single-cell technologies now enables the comprehensive mapping of macrophage subsets and phenotypes present in plaques. In this review, we have updated and discussed the definition and classification of macrophage subsets in mice and humans using single-cell RNA sequencing. We summarized the different classification methods and perspectives: traditional classification with an updated scoring system, inflammatory macrophages, foamy macrophages, and atherosclerotic-resident macrophages. In addition, some special types of macrophages were identified by specific markers, including IFN-inducible and cavity macrophages. Furthermore, we discussed macrophage subset-specific markers and their functions. In the future, these novel insights into the characteristics and phenotypes of these macrophage subsets within atherosclerotic plaques can provide additional therapeutic targets for cardiovascular diseases.

Complex Analysis of Single-Cell RNA Sequencing Data

Single-cell RNA sequencing (scRNA-seq) is a revolutionary tool for studying the physiology of normal and pathologically altered tissues. This approach provides information about molecular features (gene expression, mutations, chromatin accessibility, etc.) of cells, opens up the possibility to analyze the trajectories/phylogeny of cell differentiation and cell-cell interactions, and helps in discovery of new cell types and previously unexplored processes. From a clinical point of view, scRNA-seq facilitates deeper and more detailed analysis of molecular mechanisms of diseases and serves as a basis for the development of new preventive, diagnostic, and therapeutic strategies. The review describes different approaches to the analysis of scRNA-seq data, discusses the advantages and disadvantages of bioinformatics tools, provides recommendations and examples of their successful use, and suggests potential directions for improvement. We also emphasize the need for creating new protocols, including multiomics ones, for the preparation of DNA/RNA libraries of single cells with the purpose of more complete understanding of individual cells.

New insights into macrophage subsets in atherosclerosis

Macrophages in atherosclerotic patients are notably plastic and heterogeneous. Single-cell RNA sequencing (Sc RNA-seq) can provide information about all the RNAs in individual cells, and it is used to identify cell subpopulations in atherosclerosis (AS) and reveal the heterogeneity of these cells. Recently, some findings from Sc RNA-seq experiments have suggested the existence of multiple macrophage subsets in atherosclerotic plaque lesions, and these subsets exhibit significant differences in their gene expression levels and functions. These cells affect various aspects of plaque lesion development, stabilization, and regression, as well as plaque rupture. This article aims to review the content and results of current studies that used RNA-seq to explore the different types of macrophages in AS and the related molecular mechanisms as well as to identify the potential roles of these macrophage types in the pathogenesis of atherosclerotic plaques. Also, this review listed some new therapeutic targets for delaying atherosclerotic lesion progression and treatment based on the experimental results.

Applications of Single-Cell RNA Sequencing in Cardiovascular Research

In recent years, cardiovascular disease (CVD) continues to be the leading cause of global disease burden. Extensive efforts have been made across basic, translational, and clinical research domains to curb the CVD epidemic and improve the health of the population. The successful completion of the Human Genome Project catapulted sequencing technology into the mainstream and aroused the interests of clinicians and scientific researchers alike. Advances in single-cell RNA sequencing (scRNA-seq), which is based on the transcriptional phenotypes of individual cells, have enabled the investigation of cellular fate, heterogeneity, and cell–cell interactions, as well as cell lineage determination, at a single-cell resolution. In this review, we summarize recent findings on the embryological development of the cardiovascular system and the pathogenesis and treatment of cardiovascular disease, as revealed by scRNA-seq technology. In particular, we discuss how scRNA-seq can help identify potential targets for the treatment of cardiovascular diseases and conclude with future perspectives for scRNA-seq.

Enhanced single-cell RNA-seq workflow reveals coronary artery disease cellular cross-talk and candidate drug targets

BACKGROUND AND AIMS

The atherosclerotic plaque microenvironment is highly complex, and selective agents that modulate plaque stability are not yet available. We sought to develop a scRNA-seq analysis workflow to investigate this environment and uncover potential therapeutic approaches. We designed a user-friendly, reproducible workflow that will be applicable to other disease-specific scRNA-seq datasets.

METHODS

Here we incorporated automated cell labeling, pseudotemporal ordering, ligand-receptor evaluation, and drug-gene interaction analysis into a ready-to-deploy workflow. We applied this pipeline to further investigate a previously published human coronary single-cell dataset by Wirka et al. Notably, we developed an interactive web application to enable further exploration and analysis of this and other cardiovascular single-cell datasets.

RESULTS

We revealed distinct derivations of fibroblast-like cells from smooth muscle cells (SMCs), and showed the key changes in gene expression along their de-differentiation path. We highlighted several key ligand-receptor interactions within the atherosclerotic environment through functional expression profiling and revealed several avenues for future pharmacological development for precision medicine. Further, our interactive web application, PlaqView (www.plaqview.com), allows lay scientists to explore this and other datasets and compare scRNA-seq tools without prior coding knowledge.

CONCLUSIONS

This publicly available workflow and application will allow for more systematic and user-friendly analysis of scRNA datasets in other disease and developmental systems. Our analysis pipeline provides many hypothesis-generating tools to unravel the etiology of coronary artery disease. We also highlight potential mechanisms for several drugs in the atherosclerotic cellular environment. Future releases of PlaqView will feature more scRNA-seq and scATAC-seq atherosclerosis-related datasets to provide a critical resource for the field, and to promote data harmonization and biological interpretation.

Single-Cell RNA Sequencing of the Rat Carotid Arteries Uncovers Potential Cellular Targets of Neointimal Hyperplasia

Aims: In-stent restenosis (ISR) remains an Achilles heel of drug-eluting stents despite technical advances in devices and procedural techniques. Neointimal hyperplasia (NIH) is the most important pathophysiological process of ISR. The present study mapped normal arteries and stenotic arteries to uncover potential cellular targets of neointimal hyperplasia.

Methods and Results: By comparing the left (control) and right (balloon injury) carotid arteries of rats, we mapped 11 clusters in normal arteries and 11 mutual clusters in both the control and experimental groups. Different clusters were categorized into 6 cell types, including vascular smooth muscle cells (VSMCs), fibroblasts, endothelial cells (ECs), macrophages, unknown cells and others. An abnormal cell type expressing both VSMC and fibroblast markers at the same time was termed a transitional cell via pseudotime analysis. Due to the high proportion of VSMCs, we divided them into 6 clusters and analyzed their relationship with VSMC phenotype switching. Moreover, N-myristoyltransferase 1 (NMT1) was verified as a credible VSMC synthetic phenotype marker. Finally, we proposed several novel target genes by disease susceptibility gene analysis, such as Cyp7a1 and Cdk4, which should be validated in future studies.

Conclusion: Maps of the heterogeneous cellular landscape in the carotid artery were defined by single-cell RNA sequencing and revealed several cell types with their internal relations in the ISR model. This study highlights the crucial role of VSMC phenotype switching in the progression of neointimal hyperplasia and provides clues regarding the underlying mechanism of NIH.

Computational strategies for single-cell multi-omics integration

Single-cell omics technologies are currently solving biological and medical problems that earlier have remained elusive, such as discovery of new cell types, cellular differentiation trajectories and communication networks across cells and tissues. Current advances especially in single-cell multi-omics hold high potential for breakthroughs by integration of multiple different omics layers. To pair with the recent biotechnological developments, many computational approaches to process and analyze single-cell multi-omics data have been proposed. In this review, we first introduce recent developments in single-cell multi-omics in general and then focus on the available data integration strategies. The integration approaches are divided into three categories: early, intermediate, and late data integration. For each category, we describe the underlying conceptual principles and main characteristics, as well as provide examples of currently available tools and how they have been applied to analyze single-cell multi-omics data. Finally, we explore the challenges and prospective future directions of single-cell multi-omics data integration, including examples of adopting multi-view analysis approaches used in other disciplines to single-cell multi-omics.