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单细胞蛋白质表达谱分析解析了循环和驻留记忆 T 细胞在不同组织和感染环境下的多样性。

Single-cell protein expression profiling resolves circulating and resident memory T cell diversity across tissues and infection contexts.

机构信息

Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3010, Australia.

Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.

出版信息

Immunity. 2023 Jul 11;56(7):1664-1680.e9. doi: 10.1016/j.immuni.2023.06.005. Epub 2023 Jun 30.

DOI:10.1016/j.immuni.2023.06.005
PMID:37392736
Abstract

Memory CD8 T cells can be broadly divided into circulating (T) and tissue-resident memory T (T) populations. Despite well-defined migratory and transcriptional differences, the phenotypic and functional delineation of T and T cells, particularly across tissues, remains elusive. Here, we utilized an antibody screening platform and machine learning prediction pipeline (InfinityFlow) to profile >200 proteins in T and T cells in solid organs and barrier locations. High-dimensional analyses revealed unappreciated heterogeneity within T and T cell lineages across nine different organs after either local or systemic murine infection models. Additionally, we demonstrated the relative effectiveness of strategies allowing for the selective ablation of T or T populations across organs and identified CD55, KLRG1, CXCR6, and CD38 as stable markers for characterizing memory T cell function during inflammation. Together, these data and analytical framework provide an in-depth resource for memory T cell classification in both steady-state and inflammatory conditions.

摘要

记忆性 CD8 T 细胞可广泛分为循环(T)和组织驻留记忆 T(T)细胞群体。尽管在迁移和转录方面存在明确的差异,但 T 和 T 细胞的表型和功能划分,特别是在不同组织中,仍然难以捉摸。在这里,我们利用抗体筛选平台和机器学习预测管道(InfinityFlow)来对实体器官和屏障位置的 T 和 T 细胞中的>200 种蛋白进行分析。高维分析揭示了在局部或全身小鼠感染模型后,九个不同器官中的 T 和 T 细胞谱系内存在以前未被重视的异质性。此外,我们还证明了允许在器官之间选择性清除 T 或 T 群体的策略的相对有效性,并确定 CD55、KLRG1、CXCR6 和 CD38 是在炎症期间表征记忆性 T 细胞功能的稳定标志物。总的来说,这些数据和分析框架为稳态和炎症条件下记忆性 T 细胞的分类提供了深入的资源。

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