Single cell RNA sequencing approaches to cardiac development and congenital heart disease

生物 细胞 心脏发育 电池类型 单细胞分析 心脏病 计算生物学 遗传学 核糖核酸 基因 疾病 转录组 胚胎干细胞 生物信息学 基因表达 病理 医学
作者
Tahmina Samad,Sean M. Wu
出处
期刊:Seminars in Cell & Developmental Biology [Elsevier]
卷期号:118: 129-135 被引量:14
标识
DOI:10.1016/j.semcdb.2021.04.023
摘要

The development of single cell RNA sequencing technologies has accelerated the ability of scientists to understand healthy and disease states of the cardiovascular system. Congenital heart defects occur in approximately 40,000 births each year and 1 out of 4 children are born with critical congenital heart disease requiring surgical interventions and a lifetime of monitoring. An understanding of how the normal heart develops and how each cell contributes to normal and pathological anatomy is an important goal in pediatric cardiovascular research. Single cell sequencing has provided the tools to increase the ability to discover rare cell types and novel genes involved in normal cardiac development. Knowledge of gene expression of single cells within cardiac tissue has contributed to the understanding of how each cell type contributes to the anatomic structures of the heart. In this review, we summarize how single cell RNA sequencing has been utilized to understand cardiac developmental processes and congenital heart disease. We discuss the advantages and disadvantages of whole cell versus single nuclei RNA sequencing and describe the approaches to analyze the interactomes, transcriptomes, and differentiation trajectory from single cell data. We summarize the currently available single cell RNA sequencing technologies and technical aspects of performing single cell analysis and how to overcome common obstacles. We also review data from the recently published human and mouse fetal heart atlases and advancements that have occurred within the field due to the application of these single cell tools. Finally we highlight the potential for single cell technologies to uncover novel mechanisms of disease pathogenesis by leveraging findings from genome wide association studies.
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