生物
仿形(计算机编程)
多样性(政治)
细胞
表达式(计算机科学)
细胞生物学
基因表达谱
免疫学
计算生物学
病毒学
遗传学
基因表达
基因
程序设计语言
人类学
计算机科学
社会学
操作系统
作者
Maximilien Evrard,Étienne Becht,Raíssa Fonseca,Andreas Obers,Simone L. Park,Nagela Ghabdan-Zanluqui,Jan Schroeder,Susan N. Christo,Dominik Schienstock,Junyun Lai,Thomas N. Burn,Allison Clatch,Imran G. House,Paul A. Beavis,Axel Kallies,Florent Ginhoux,Scott N. Mueller,Raphaël Gottardo,Evan W. Newell,Laura K. Mackay
出处
期刊:Immunity
[Cell Press]
日期:2023-06-30
卷期号:56 (7): 1664-1680.e9
被引量:47
标识
DOI:10.1016/j.immuni.2023.06.005
摘要
Memory CD8+ T cells can be broadly divided into circulating (TCIRCM) and tissue-resident memory T (TRM) populations. Despite well-defined migratory and transcriptional differences, the phenotypic and functional delineation of TCIRCM and TRM cells, particularly across tissues, remains elusive. Here, we utilized an antibody screening platform and machine learning prediction pipeline (InfinityFlow) to profile >200 proteins in TCIRCM and TRM cells in solid organs and barrier locations. High-dimensional analyses revealed unappreciated heterogeneity within TCIRCM and TRM 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 TCIRCM or TRM 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI