Single-Cell RNA Sequencing of the Cardiovascular System: New Looks for Old Diseases

Abstract

Cardiovascular disease encompasses a wide range of conditions, resulting in the highest number of deaths worldwide. The underlying pathologies surrounding cardiovascular disease include a vast and complicated network of both cellular and molecular mechanisms. Unique phenotypic alterations in specific cell types, visualized as varying RNA expression-levels (both coding and non-coding), have been identified as crucial factors in the pathology underlying conditions such as heart failure and atherosclerosis. Recent advances in single-cell RNA sequencing (scRNA-seq) have elucidated a new realm of cell subpopulations and transcriptional variations that are associated with normal and pathological physiology in a wide variety of diseases. This breakthrough in the phenotypical understanding of our cells has brought novel insight into cardiovascular basic science. scRNA-seq allows for separation of widely distinct cell subpopulations which were, until recently, simply averaged together with bulk-tissue RNA-seq. scRNA-seq has been used to identify novel cell types in the heart and vasculature that could be implicated in a variety of disease pathologies. Furthermore, scRNA-seq has been able to identify significant heterogeneity of phenotypes within individual cell subtype populations. The ability to characterize single cells based on transcriptional phenotypes allows researchers the ability to map development of cells and identify changes in specific subpopulations due to diseases at a very high throughput. This review looks at recent scRNA-seq studies of various aspects of the cardiovascular system and discusses their potential value to our understanding of the cardiovascular system and pathology.

Keywords: RNA; cardiology; epigenetics; genetics; single cell RNA sequencing.

Figure 1

scRNA-seq Data Processing and Analysis. (A) UMIs, short DNA sequences tagged to cDNA fragments before amplification, identify unique reads vs. PCR duplicates thereby normalizing the transcript counts. (B) A common analysis pipeline for scRNA-seq data includes: normalizing data to account for sources of technical and biological noise (pictured here, sequencing depth), clustering cells to identify novel and known cell types as well as subpopulations, ordering cell types and states into trajectories, and performing differential expression analysis, which can allow for identification of biomarkers, assigning function to cell cluster.