Vascular Health and Risk Management (Oct 2021)
Single-Cell RNA Sequencing (scRNA-seq) in Cardiac Tissue: Applications and Limitations
Abstract
Mingqiang Wang,1 Mingxia Gu,2,3 Ling Liu,4 Yu Liu,1 Lei Tian1 1Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, 94305, USA; 2Perinatal Institute, Division of Pulmonary Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, 45229, USA; 3Center for Stem Cell and Organoid Medicine, CuSTOM, Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, 45229, USA; 4Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, 94305, USACorrespondence: Mingqiang Wang; Lei TianStanford University School of Medicine, Stanford Cardiovascular Institute, 1291 Welch Road, Biomedical Innovations Building, Stanford, CA, 94305-5454, USATel +1 650 723-5386Fax +1 650 736-7925Email [email protected]; [email protected]: Cardiovascular diseases (CVDs) are a group of disorders of the blood vessels and heart, which are considered as the leading causes of death worldwide. The pathology of CVDs could be related to the functional abnormalities of multiple cell types in the heart. Single-cell RNA sequencing (scRNA-seq) technology is a powerful method for characterizing individual cells and elucidating the molecular mechanisms by providing a high resolution of transcriptomic changes at the single-cell level. Specifically, scRNA-seq has provided novel insights into CVDs by identifying rare cardiac cell types, inferring the trajectory tree, estimating RNA velocity, elucidating the cell–cell communication, and comparing healthy and pathological heart samples. In this review, we summarize the different scRNA-seq platforms and published single-cell datasets in the cardiovascular field, and describe the utilities and limitations of this technology. Lastly, we discuss the future perspective of the application of scRNA-seq technology into cardiovascular research.Keywords: cardiovascular diseases, clustering, trajectory inference, RNA velocity, cell–cell communication, spatial genomics
