Exploiting an artificial APC to induce different T cell subsets

 描述（由申请人提供）：人T细胞区室含有不同的T细胞亚群，这些亚群在离体扩增和过继转移后的持续和应答能力方面非常不同。T细胞编程为不同的T细胞亚群在很大程度上取决于专职抗原传递给T细胞的信号的质量和数量 提呈细胞（APC）。这刺激了开发人工抗原呈递细胞（aAPC）的努力，所述人工抗原呈递细胞允许对提供给T细胞的信号进行最佳控制。为了更接近地模拟天然系统，脂质双层表面已被用作aAPC，从而证明膜流动性对T细胞活化的显著影响。然而，迄今为止已经开发的这种和其他膜模拟物不能用于将膜蛋白组织成微结构域，在微结构域内细胞表面受体聚集。在抗原呈递细胞（APC）表面呈递抗原肽的主要组织相容性复合体（MHC）蛋白彼此形成簇并与其他细胞表面蛋白形成簇。MHC聚集和共聚集的变化可能是调节T细胞反应性的敏感机制。该应用程序的目标是开发模型膜系统，该系统可以以受控的方式重现免疫受体的聚集。我们建议利用可生物降解的纳米脂蛋白颗粒（NLP）作为一个通用的平台来模拟分子膜簇。NLP在溶液中自组装以形成盘状纳米结构，所述盘状纳米结构含有通过载脂蛋白分子在周边稳定的脂质双层。NLP的大小范围为8至30 nm，允许捕获多达50个可溶性配体分子，并使我们能够实现接近生理学的模型簇大小和密度。我们将使用NLP将pMHC和其他膜配体组装成模型膜贴片。我们将研究这些补丁的配体密度和组成的变化如何影响模型膜补丁与活T细胞的结合以及TCR介导的信号传导的动力学和幅度。这将提供一个基础的工程aAPC轴承模型膜补丁纳入脂质双层覆盖的玻璃珠表面。这样的aAPC将允许我们校准T细胞刺激的强度。我们将利用这些新的aAPC来改变 刺激来自OT-1 TCR转基因小鼠的幼稚CD 8 + T细胞的强度，以诱导具有相同特异性的不同活化T细胞亚群。建造装甲运兵车 有望使我们能够用指导性程序扩增T细胞，使T细胞在过继转移后以所需的方式持续存在、发挥功能和迁移。实验数据还将为构建数学模型提供基础，以表征APC表面上配体的聚集如何决定活化和幼稚CD 8 T细胞对pMHC I配体的速度、灵敏度和辨别力。

 DESCRIPTION (provided by applicant): The human T cell compartment contains distinct T cell subsets that are very different in their capacity to persist and to respond after ex vivo expansion and adoptive transfer. T cell programming into distinct T cell subsets depends in a large extent on the quality and quantity of the signal delivered to T cells by professional antigen presenting cells (APC). This has stimulated efforts to develop artificial antigen presenting cells (aAPC) that allow for optimal control over the signals provided to T cells. To more closely mimic natural systems, lipid bilayer surfaces have been used as aAPCs, thereby demonstrating a significant effect of membrane fluidity on T cell activation. However, this and other membrane mimics, which have been exploited thus far, cannot be used to organize membrane proteins into microdomains, within which cell surface receptors are clustered. Major histocompatibility complex (MHC) proteins that present antigenic peptides on the surface of antigen- presenting cells (APC) form clusters with each other and with other cell surface proteins. The changes in MHC clustering and co-clustering could serve as a sensitive mechanism to modulate T cell responsiveness. The goal of the application is to develop the model membrane systems that can recapitulate clustering of immune receptors in a controlled manner. We propose to utilize biodegradable nanolipoprotein particles (NLPs) as a universal platform to mimic molecular membrane clustering. NLPs are self-assembled in solution to form discoidal nanostructures containing lipid bilayers stabilized at the perimeter by apolipoprotein molecules. The size of the NLP ranges from 8 to 30 nm that allow capturing up to 50 molecules of soluble ligands and enable us to achieve model cluster size and density close to physiological. We will use the NLPs to assemble pMHC and other membrane ligands into model membrane patches. We will study how changes in the ligands density and composition of these patches affect binding of the model membrane patches to live T cells and the kinetics and magnitude of TCR-mediated signaling. This will provide a basis for the engineering of aAPC bearing the model membrane patches incorporated into lipid bilayers covering the surface of glass beads. Such aAPCs will allow us to calibrate the strength of T cell stimulation. We will utilize these novel aAPCs to vary the strength of stimulation of naïve CD8+ T cells derived from OT-1 TCR transgenic mice in order to induce different subsets of activated T cells with the same specificity. Building of aAPCs is expected to enable us to expand T cells with instructional programs that allow T cells to persist, function, and migrate in a desired fashion after adoptive transfer. The experimental data will also provide the basis for building a mathematical model to characterize how clustering of ligands on an APC surface determines speed, sensitivity and discrimination of the pMHC I ligand by activated and naïve CD8 T cells.