通过单细胞转录组学研究细胞间通讯的概况
The landscape of cell-cell communication through single-cell transcriptomics.
作者信息
Almet Axel A, Cang Zixuan, Jin Suoqin, Nie Qing
机构信息
The NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, CA 92627, USA.
Department of Mathematics, University of California Irvine, Irvine, CA 92627, USA.
出版信息
Curr Opin Syst Biol. 2021 Jun;26:12-23. doi: 10.1016/j.coisb.2021.03.007. Epub 2021 Mar 26.
Abstract
Cell-cell communication is a fundamental process that shapes biological tissue. Historically, studies of cell-cell communication have been feasible for one or two cell types and a few genes. With the emergence of single-cell transcriptomics, we are now able to examine the genetic profiles of individual cells at unprecedented scale and depth. The availability of such data presents an exciting opportunity to construct a more comprehensive description of cell-cell communication. This review discusses the recent explosion of methods that have been developed to infer cell-cell communication from non-spatial and spatial single-cell transcriptomics, two promising technologies which have complementary strengths and limitations. We propose several avenues to propel this rapidly expanding field forward in meaningful ways.
摘要
细胞间通讯是塑造生物组织的一个基本过程。从历史上看，对细胞间通讯的研究对于一两种细胞类型和少数基因来说是可行的。随着单细胞转录组学的出现，我们现在能够以前所未有的规模和深度检查单个细胞的基因图谱。这些数据的可用性为构建更全面的细胞间通讯描述提供了一个令人兴奋的机会。本综述讨论了最近为从非空间和空间单细胞转录组学推断细胞间通讯而开发的大量方法，这两种有前景的技术具有互补的优势和局限性。我们提出了几种途径，以有意义的方式推动这个迅速扩展的领域向前发展。
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Sci Adv. 2021 Apr 14;7(16). doi: 10.1126/sciadv.abf1356. Print 2021 Apr.
2
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3
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4
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5
Deciphering cell-cell interactions and communication from gene expression.从基因表达中破译细胞间的相互作用和通讯。
Nat Rev Genet. 2021 Feb;22(2):71-88. doi: 10.1038/s41576-020-00292-x. Epub 2020 Nov 9.
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