Aortic Cellular Heterogeneity in Health and Disease: Novel Insights Into Aortic Diseases From Single-Cell RNA Transcriptomic Data Sets

Aortic diseases such as atherosclerosis, aortic aneurysms, and aortic stiffening are significant complications that can have significant impact on end-stage cardiovascular disease. With limited pharmacological therapeutic strategies that target the structural changes in the aorta, surgical intervention remains the only option for some patients with these diseases. Although there have been significant contributions to our understanding of the cellular architecture of the diseased aorta, particularly in the context of atherosclerosis, furthering our insight into the cellular drivers of disease is required. The major cell types of the aorta are well defined; however, the advent of single-cell RNA sequencing provides unrivaled insights into the cellular heterogeneity of each aortic cell type and the inferred biological processes associated with each cell in health and disease. This review discusses previous concepts that have now been enhanced with recent advances made by single-cell RNA sequencing with a focus on aortic cellular heterogeneity.

Multi-omic analysis of the cardiac cellulome defines a vascular contribution to cardiac diastolic dysfunction in obese female mice

Coronary microvascular dysfunction (CMD) is associated with cardiac dysfunction and predictive of cardiac mortality in obesity, especially in females. Clinical data further support that CMD associates with development of heart failure with preserved ejection fraction and that mineralocorticoid receptor (MR) antagonism may be more efficacious in obese female, versus male, HFpEF patients. Accordingly, we examined the impact of smooth muscle cell (SMC)-specific MR deletion on obesity-associated coronary and cardiac diastolic dysfunction in female mice. Obesity was induced in female mice via western diet (WD) feeding alongside littermates fed standard diet. Global MR blockade with spironolactone prevented coronary and cardiac dysfunction in obese females and specific deletion of SMC-MR was sufficient to prevent obesity-associated coronary and cardiac diastolic dysfunction. Cardiac gene expression profiling suggested reduced cardiac inflammation in WD-fed mice with SMC-MR deletion independent of blood pressure, aortic stiffening, and cardiac hypertrophy. Further mechanistic studies utilizing single-cell RNA sequencing of non-cardiomyocyte cell populations revealed novel impacts of SMC-MR deletion on the cardiac cellulome in obese mice. Specifically, WD feeding induced inflammatory gene signatures in non-myocyte populations including B/T cells, macrophages, and endothelium as well as increased coronary VCAM-1 protein expression, independent of cardiac fibrosis, that was prevented by SMC-MR deletion. Further, SMC-MR deletion induced a basal reduction in cardiac mast cells and prevented WD-induced cardiac pro-inflammatory chemokine expression and leukocyte recruitment. These data reveal a central role for SMC-MR signaling in obesity-associated coronary and cardiac dysfunction, thus supporting the emerging paradigm of a vascular origin of cardiac dysfunction in obesity.

Deficiency of MicroRNA-181a Results in Transcriptome-Wide Cell-Specific Changes in the Kidney and Increases Blood Pressure

[Figure: see text].

High-Resolution Transcriptomic Profiling of the Heart During Chronic Stress Reveals Cellular Drivers of Cardiac Fibrosis and Hypertrophy

Supplemental Digital Content is available in the text.

Background:

Cardiac fibrosis is a key antecedent to many types of cardiac dysfunction including heart failure. Physiological factors leading to cardiac fibrosis have been recognized for decades. However, the specific cellular and molecular mediators that drive cardiac fibrosis, and the relative effect of disparate cell populations on cardiac fibrosis, remain unclear.

Methods:

We developed a novel cardiac single-cell transcriptomic strategy to characterize the cardiac cellulome, the network of cells that forms the heart. This method was used to profile the cardiac cellular ecosystem in response to 2 weeks of continuous administration of angiotensin II, a profibrotic stimulus that drives pathological cardiac remodeling.

Results:

Our analysis provides a comprehensive map of the cardiac cellular landscape uncovering multiple cell populations that contribute to pathological remodeling of the extracellular matrix of the heart. Two phenotypically distinct fibroblast populations, Fibroblast-Cilp and Fibroblast-Thbs4, emerged after induction of tissue stress to promote fibrosis in the absence of smooth muscle actin–expressing myofibroblasts, a key profibrotic cell population. After angiotensin II treatment, Fibroblast-Cilp develops as the most abundant fibroblast subpopulation and the predominant fibrogenic cell type. Mapping intercellular communication networks within the heart, we identified key intercellular trophic relationships and shifts in cellular communication after angiotensin II treatment that promote the development of a profibrotic cellular microenvironment. Furthermore, the cellular responses to angiotensin II and the relative abundance of fibrogenic cells were sexually dimorphic.

Conclusions:

These results offer a valuable resource for exploring the cardiac cellular landscape in health and after chronic cardiovascular stress. These data provide insights into the cellular and molecular mechanisms that promote pathological remodeling of the mammalian heart, highlighting early transcriptional changes that precede chronic cardiac fibrosis.

Powerful differential expression analysis incorporating network topology for next-generation sequencing data

Motivation

RNA-seq has become the technology of choice for interrogating the transcriptome. However, most methods for RNA-seq differential expression (DE) analysis do not utilize prior knowledge of biological networks to detect DE genes. With the increased availability and quality of biological network databases, methods that can utilize this prior knowledge are needed and will offer biologists with a viable, more powerful alternative when analyzing RNA-seq data.

Results

We propose a three-state Markov Random Field (MRF) method that utilizes known biological pathways and interaction to improve sensitivity and specificity and therefore reducing false discovery rates (FDRs) when detecting differentially expressed genes from RNA-seq data. The method requires normalized count data (e.g. in Fragments or Reads Per Kilobase of transcript per Million mapped reads (FPKM/RPKM) format) as its input and it is implemented in an R package pathDESeq available from Github. Simulation studies demonstrate that our method outperforms the two-state MRF model for various sample sizes. Furthermore, for a comparable FDR, it has better sensitivity than DESeq, EBSeq, edgeR and NOISeq. The proposed method also picks more top Gene Ontology terms and KEGG pathways terms when applied to real dataset from colorectal cancer and hepatocellular carcinoma studies, respectively. Overall, these findings clearly highlight the power of our method relative to the existing methods that do not utilize prior knowledge of biological network.

Availability and Implementation

As an R package at https://github.com/MalathiSIDona/pathDESeq.

To install the package type

install_github("MalathiSIDona/pathDESeq",build_vignettes = TRUE). After installation, type vignette("pathDESeq") to access the vignette.

Contact

a.salim@latrobe.edu.au.

Supplementary information

Supplementary data are available at Bioinformatics online.