Harnessing antigen-specific T cell immunity in time and space

在时间和空间上利用抗原特异性 T 细胞免疫

• 批准号: MC_UU_00036/5
• 负责人: Hashem Koohy
• 金额: $ 191.64万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Intramural
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MC_UU_00036%2F5
• 关键词: Harnessing; antigen; specific; cell; immunity

The research undertaken by Prof. Koohy’s group delves into the intricate world of the immune system to understand how it fights off diseases. We focus on a type of immune cell called T cells, which play a key role in protecting our bodies. These T cells recognize harmful invaders by identifying specific molecules, known as antigens, on the surface of infected cells. Despite the importance of this process, many details remain unclear. To unravel these mysteries, the team uses advanced techniques like machine learning and innovative experiments with single cells.Our goal is to create a detailed map of the molecules that T cells use to detect antigens in different contexts. We're also working on understanding how T cells respond to different situations, during infections or following administration of vaccines. This involves studying the changing patterns of molecules expressed by T cells over time and in different parts of the body. We're also exploring T cells that can recognize invaders without using the usual markers. To crack these puzzles, my team combines computer-based methods with deep knowledge of how the immune system works. We're collaborating with experts from around the world to piece together these intricate rules of immune defence. Ultimately, this research could lead to breakthroughs in how we understand and treat diseases.

Koohy教授的团队开展的这项研究深入研究了免疫系统的复杂世界，以了解它是如何抵御疾病的。我们专注于一种名为T细胞的免疫细胞，它在保护我们的身体方面发挥着关键作用。这些T细胞通过识别感染细胞表面的特定分子，即所谓的抗原，来识别有害的入侵者。尽管这一过程很重要，但许多细节仍不清楚。为了揭开这些谜团，该团队使用了机器学习和单细胞创新实验等先进技术。我们的目标是创建T细胞在不同环境中用来检测抗原的分子的详细图谱。我们还在努力了解T细胞在感染期间或接种疫苗后对不同情况的反应。这涉及到研究T细胞表达的分子随时间和身体不同部位的变化模式。我们还在探索可以识别入侵者的T细胞，而不需要使用常见的标记。为了破解这些谜题，我的团队将基于计算机的方法与对免疫系统如何工作的深入了解结合在一起。我们正在与世界各地的专家合作，拼凑出这些错综复杂的免疫防御规则。最终，这项研究可能会在我们如何理解和治疗疾病方面取得突破。

项目成果

Can we predict T cell specificity with digital biology and machine learning?

• DOI: 10.1038/s41577-023-00835-3
• 发表时间: 2023-08
• 期刊: Nature reviews. Immunology

Group A Streptococcus induces CD1a-autoreactive T cells and promotes psoriatic inflammation

• DOI: 10.1126/sciimmunol.add9232
• 发表时间: 2023-06-01
• 期刊: SCIENCE IMMUNOLOGY
• 影响因子: 24.8
• 作者: Chen，Yi-Ling;Ng，Jessica Soo Weei;Ogg，Graham S.
• 通讯作者: Ogg，Graham S.

A comparison of clustering models for inference of T cell receptor antigen specificity

用于推断 T 细胞受体抗原特异性的聚类模型的比较

• DOI: 10.1016/j.immuno.2024.100033
• 发表时间: 2024
• 期刊: ImmunoInformatics
• 作者: Hudson D