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生殖免疫领域的单细胞技术。

Single-cell technologies in reproductive immunology.

机构信息

Division of Reproductive Sciences, Department of Obstetrics and Gynecology, University of Wisconsin-Madison, Madison, Wisconsin.

Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin.

出版信息

Am J Reprod Immunol. 2019 Sep;82(3):e13157. doi: 10.1111/aji.13157. Epub 2019 Jun 26.

Abstract

The maternal-fetal interface represents a unique immune privileged site that maintains the ability to defend against pathogens while orchestrating the necessary tissue remodeling required for placentation. The recent discovery of novel cellular families (innate lymphoid cells, tissue-resident NK cells) suggests that our understanding of the decidual immunome is incomplete. To understand this complex milieu, new technological developments allow reproductive immunologists to collect increasingly complex data at a cellular resolution. Polychromatic flow cytometry allows for greater resolution in the identification of novel cell types by surface and intracellular protein. Single-cell RNA-seq coupled with microfluidics allows for efficient cellular transcriptomics. The extreme dimensionality and size of data sets generated, however, requires the application of novel computational approaches for unbiased analysis. There are now multiple dimensionality reduction (tSNE, SPADE) and visualization tools (SPICE) that allow researchers to efficiently analyze flow cytometry data. Development of computational tools has also been extended to RNA-seq data (including scRNA-seq), which requires specific analytical tools. Here, we provide an overview and a brief primer for the reproductive immunology community on data acquisition and computational tools for the analysis of complex flow cytometry and RNA-seq data.

摘要

母胎界面代表了一个独特的免疫特权部位,它既能抵御病原体,又能协调胎盘形成所需的必要组织重塑。最近发现的新型细胞家族(固有淋巴样细胞、组织驻留 NK 细胞)表明,我们对蜕膜免疫组的理解并不完整。为了理解这个复杂的环境,新技术的发展使生殖免疫学家能够以细胞分辨率收集越来越复杂的数据。多色流式细胞术允许通过表面和细胞内蛋白更精确地识别新型细胞类型。单细胞 RNA-seq 与微流控技术相结合可实现高效的细胞转录组学。然而,生成的数据集具有极高的维度和规模,这就需要应用新的计算方法进行无偏分析。现在有多种降维(tSNE、SPADE)和可视化工具(SPICE)可帮助研究人员高效地分析流式细胞术数据。计算工具的开发也已扩展到 RNA-seq 数据(包括 scRNA-seq),这需要特定的分析工具。在这里,我们为生殖免疫学领域提供了一个概述,并简要介绍了用于分析复杂流式细胞术和 RNA-seq 数据的获取和计算工具。

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