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联合瞬时消融和单细胞 RNA 测序揭示了骨髓胸腺上皮细胞的发育。

Combined transient ablation and single-cell RNA-sequencing reveals the development of medullary thymic epithelial cells.

机构信息

Department of Genetics, Stanford University School of Medicine, Stanford, United States.

Diabetes Center, University of California, San Francisco, San Francisco, United States.

出版信息

Elife. 2020 Nov 23;9:e60188. doi: 10.7554/eLife.60188.

Abstract

Medullary thymic epithelial cells (mTECs) play a critical role in central immune tolerance by mediating negative selection of autoreactive T cells through the collective expression of the peripheral self-antigen compartment, including tissue-specific antigens (TSAs). Recent work has shown that gene-expression patterns within the mTEC compartment are heterogenous and include multiple differentiated cell states. To further define mTEC development and medullary epithelial lineage relationships, we combined lineage tracing and recovery from transient in vivo mTEC ablation with single-cell RNA-sequencing in . The combination of bioinformatic and experimental approaches revealed a non-stem transit-amplifying population of cycling mTECs that preceded expression. We propose a branching model of mTEC development wherein a heterogeneous pool of transit-amplifying cells gives rise to - and -expressing mTEC subsets. We further use experimental techniques to show that within the expressing developmental branch, TSA expression peaked as expression decreased, implying expression must be established before TSA expression can occur. Collectively, these data provide a roadmap of mTEC development and demonstrate the power of combinatorial approaches leveraging both in vivo models and high-dimensional datasets.

摘要

髓质胸腺上皮细胞 (mTEC) 通过集体表达外周自身抗原区室(包括组织特异性抗原 [TSA]),对自身反应性 T 细胞的阴性选择发挥关键作用。最近的研究表明,mTEC 区室中的基因表达模式具有异质性,并包含多个分化的细胞状态。为了进一步定义 mTEC 的发育和 medullary 上皮谱系关系,我们结合谱系追踪和体内瞬时 mTEC 消融后的恢复,以及单细胞 RNA 测序在. 中进行。生物信息学和实验方法的结合揭示了一个具有周期性 mTEC 的非干细胞过渡扩增群体,该群体先于 表达。我们提出了一个 mTEC 发育的分支模型,其中异质的过渡扩增细胞池产生 和 表达的 mTEC 亚群。我们进一步使用实验技术表明,在表达发育分支内,随着 表达的减少,TSA 表达达到峰值,这意味着 TSA 表达必须在发生之前建立 表达。总之,这些数据提供了 mTEC 发育的路线图,并证明了结合体内模型和高维数据集的组合方法的强大功能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a330/7771965/fcde46c8c7d6/elife-60188-fig1.jpg

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