Stanford Cardiovascular Institute, Stanford, CA, USA.
Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Nat Rev Cardiol. 2020 Aug;17(8):457-473. doi: 10.1038/s41569-020-0359-y. Epub 2020 Mar 30.
Advances in single-cell RNA sequencing (scRNA-seq) technologies in the past 10 years have had a transformative effect on biomedical research, enabling the profiling and analysis of the transcriptomes of single cells at unprecedented resolution and throughput. Specifically, scRNA-seq has facilitated the identification of novel or rare cell types, the analysis of single-cell trajectory construction and stem or progenitor cell differentiation, and the comparison of healthy and disease-related tissues at single-cell resolution. These applications have been critical in advances in cardiovascular research in the past decade as evidenced by the generation of cell atlases of mammalian heart and blood vessels and the elucidation of mechanisms involved in cardiovascular development and stem or progenitor cell differentiation. In this Review, we summarize the currently available scRNA-seq technologies and analytical tools and discuss the latest findings using scRNA-seq that have substantially improved our knowledge on the development of the cardiovascular system and the mechanisms underlying cardiovascular diseases. Furthermore, we examine emerging strategies that integrate multimodal single-cell platforms, focusing on future applications in cardiovascular precision medicine that use single-cell omics approaches to characterize cell-specific responses to drugs or environmental stimuli and to develop effective patient-specific therapeutics.
在过去的 10 年中,单细胞 RNA 测序(scRNA-seq)技术的进步对生物医学研究产生了变革性的影响,使单细胞转录组的分析和研究能够以前所未有的分辨率和通量进行。具体而言,scRNA-seq 促进了新型或稀有细胞类型的鉴定、单细胞轨迹构建和干细胞或祖细胞分化的分析,以及单细胞分辨率下健康组织和疾病相关组织的比较。这些应用在过去十年的心血管研究中至关重要,这一点可以从哺乳动物心脏和血管的细胞图谱的生成以及心血管发育和干细胞或祖细胞分化所涉及的机制的阐明中得到证明。在这篇综述中,我们总结了目前可用的 scRNA-seq 技术和分析工具,并讨论了使用 scRNA-seq 取得的最新发现,这些发现大大提高了我们对心血管系统发育和心血管疾病相关机制的认识。此外,我们还研究了新兴的整合多模态单细胞平台的策略,重点关注利用单细胞组学方法来描述药物或环境刺激对特定细胞的反应,并开发有效的针对患者的治疗方法的心血管精准医学的未来应用。