Dissecting the effects of inhibitory signalling on T cell activation using optogenetics

课程概述：这个由MRC资助的博士培训伙伴关系(DTP)将尖端分子和分析科学与数据分析中的创新计算方法结合在一起，使学生能够解决假设引导的生物医学研究问题。这是一个为期4年的课程，第一年包括一系列的教学模块和两个基于实验室的研究项目，这些项目导致了跨学科生物医学研究的硕士学位。前两个学期包括精选的教学模块，使学生能够在多学科的科学中获得坚实的基础。学生们还参加了由学术和行业专家主持的一系列大师课程，这些专家涉及分子、细胞和组织动力学、微生物学和传染病、应用生物医学技术以及人工智能和数据科学等领域。在第三学期和夏季学期，学生们在他们选择的实验室进行了两个为期11周的研究项目。项目概述：T细胞是我们免疫系统的重要组成部分，它可以消除感染，使我们保持健康，尽管我们经常接触病原体。T细胞包含一个复杂的信号网络，它决定我们身体的细胞是否受到感染，但有时这种决策过程无效，并导致疾病。目前的许多药物都是为了操纵这些信号通路，以提高对感染或癌症的免疫反应。虽然在识别这些网络的部分方面已经取得了很大的进展，但我们也必须了解它们之间的动态联系，以了解这些疗法是如何起作用的，并有望改进它们。然而，对于大多数信令网络来说，这种知识仍然非常有限。为了解决这个问题，我们开发了新的分子工具来研究T细胞信号网络的动力学，通过设计对光响应的输入，使我们能够在空间和时间上精确地控制信号。在这个项目中，我们将探索T细胞表达的抑制性受体对下游输出反应的影响。有人提出，这些抑制性受体只控制信号网络的一部分，但这一结果很难得到证实。我们将开发和实施新的工具来在机械层面上研究这一假说。这一结果具有重要的后果，因为目前用于癌症免疫治疗的许多“检查点”抑制剂针对的是PD-1等抑制性受体，因此需要阐明它们的工作原理，以改善其临床功能。承担这一项目的博士生将掌握一系列生化和细胞生物学技术，这些技术需要使用定量方法来调查与医学研究相关的问题。学生还将从第二导师那里获得良好的基础，使用计算和数学方法进行数据集建模的原理和应用，这将提供强大的跨学科技能培训。

Programme overview:This MRC-funded doctoral training partnership (DTP) brings together cutting-edge molecular and analytical sciences with innovative computational approaches in data analysis to enable students to address hypothesis-led biomedical research questions. This is a 4-year programme whose first year involves a series of taught modules and two laboratory-based research projects that lead to an MSc in Interdisciplinary Biomedical Research. The first two terms consist of a selection of taught modules that allow students to gain a solid grounding in multidisciplinary science. Students also attend a series of masterclasses led by academic and industry experts in areas of molecular, cellular and tissue dynamics, microbiology and infection, applied biomedical technologies and artificial intelligence and data science. During the third and summer terms students conduct two eleven-week research projects in labs of their choice.Project overview:T cells are an essential part of our immune system that eliminate infections to keep us healthy despite constant exposure to pathogens. T cells contain an intricate signalling network that decides whether the cells of our body have become infected, but sometimes this decision-making process is ineffective and leads to disease. Many current drugs are designed to manipulate these signalling pathways to improve the immune response to infection or cancer. Whilst great progress has been made in identifying the parts of these networks, we must also understand the dynamic connections between them to know how these therapies work and hopefully improve them. However, for most signalling networks this knowledge remains very limited. To address this, we develop new molecular tools to investigate the dynamics of the T cell signalling network by engineering inputs that are light-responsive, giving us precise control over signalling in space and time.In this project, we will explore the effect inhibitory receptors expressed by T cells have on the downstream output response. It has been suggested that these inhibitory receptors only control one part of the signalling network, but this result has been hard to verify. We will develop and implement new tools to investigate this hypothesis at the mechanistic level. The result has important consequences, as many of the 'checkpoint' inhibitors currently used for cancer immunotherapy target inhibitory receptors such as PD 1, so elucidating how they work is required to improve their clinical function.The PhD student undertaking this project will master an array of biochemical and cell-biological techniques required to investigate questions relevant to medical research using quantitative approaches. The student will also get an excellent grounding in the principles and application of dataset modelling using computational and mathematical approaches from the second supervisor, which will provide strong interdisciplinary skills-training.