BIOPHYSICAL CUES SHAPING MACROPHAGE AND T-CELL FUNCTIONS

塑造巨噬细胞和 T 细胞功能的生物物理线索

• 批准号: 10723460
• 负责人: Omokolade Adebowale
• 金额: $ 12.5万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10723460
• 关键词: 3-Dimensional; Adopted; Adoptive Cell Transfers; Alginates; Alzheimer' s Disease; Arthritis; Atherosclerosis; Basement membrane; Behavior; Biochemical; Biological Assay; Biophysics; CD8-Positive T-Lymphocytes; Cell Communication; Cell Shape; Cell physiology; Cells; Cessation of life; Characteristics; Cholesterol; Clinical; Computer Analysis; Cues; Data; Dependence; Diabetes Mellitus; Disease; Disease Progression; Elasticity; Encapsulated; Environment; Epigenetic Process; Evolution; Exhibits; Extracellular Matrix; Flow Cytometry; Genetic; Genetic Transcription; Harvest; Heterogeneity; Homing; Hydrogels; Image; Immune; Immune response; Immune signaling; Immunofluorescence Immunologic; Immunology; Immunophenotyping; Immunotherapy; Inflammation; Inflammation Mediators; Inflammatory; Liquid substance; Macrophage; Malignant Neoplasms; Mammary Neoplasms; Measurement; Mechanics; Membrane Lipids; Modification; Monitor; Morphology; Mus; Myeloid Cells; Nuclear Translocation; Pathology; Pharmaceutical Preparations; Phenotype; Property; Research; Resolution; Role; Series; Shapes; Signaling Molecule; Solid; Surface; System; T-Lymphocyte; Time; Tissues; Tumor-associated macrophages; Viscosity; advanced disease; biophysical properties; cancer cell; cancer immunotherapy; cancer infiltrating T cells; cell behavior; cell killing; computerized tools; confocal imaging; cytokine; exhaustion; fighting; global health; human disease; in vivo; inflammatory modulation; insight; machine learning algorithm; nanobodies; novel; novel therapeutics; physical property; programmed cell death protein 1; programs; receptor; reconstitution; response; restraint; single cell analysis; single-cell RNA sequencing; small molecule inhibitor; spatiotemporal; tool; trafficking; transcription factor; transcriptome sequencing; translational impact; tumor; tumor microenvironment; tumor progression; two-dimensional; unsupervised learning; viscoelasticity

Project summary. Inflammation underlies majority of human diseases including diabetes, atherosclerosis, and cancer. These diseases are responsible for majority of deaths and represent substantial global health burden. Macrophages and T-cells, subsets of immune cells, have emerged as key mediators of inflammation. The role of biochemical cues in shaping the transcriptional response of these cells have been investigated. However, accumulating evidence has shown that physical factors also tune their phenotype and effector functions. Recent two-dimensional studies have shown that mechanical confinement directs the nuclear translocation of transcription factors in macrophages. Another study found enhanced T-cell killing of cancer cells stiffened through cholesterol depletion. These studies have contributed to the field of mechano-immunology that seeks to understand how physical factors direct immune cell fate. Recent mechano-immunology findings have laid the groundwork for my proposal aimed at determining how biophysical cues shape macrophage and T-cell cell behavior. We have developed a three-dimensional culture that allows us to interrogate how biophysical cues regulate immune cell trafficking and macrophage-T- cell interaction in the tumor microenvironment. We have already identified that naïve macrophages are more efficient at trafficking to tumors than polarized macrophages. Furthermore, macrophages adopt different shapes depending on their activation state and their local microenvironment. Our preliminary results show that T-cells have longer-lived interactions with rounded macrophages, compared to elongated ones. This implicates macrophage shape, a biophysical property, in regulating its interaction with T-cells. We will extend these findings by elucidating the role of matrix viscoelasticity on immune cells behavior and performing a rigorous immunophenotyping of these cells. In addition, the proposal will implement machine learning algorithms to high resolution spatiotemporal information obtained from live confocal imaging. This will unlock the potential to identify heterogenous phenotypic states and quantify their evolution over time. Further, the proposal will integrate confocal live imaging with the single-cell RNA sequencing data. Such detailed, single cell analysis will identify genetic programs that are responsible for heterogenous morphometric states. The proposed research will be significant because it is expected to yield mechanistic insights that have broad translational impact for a myriad of diseases where inflammation is the underlying cause. These include Alzheimer’s, atherosclerosis, arthritis, diabetes, and cancer, which represent a growing global burden. The pathology of these diseases is orchestrated by macrophages and T-cells. Insight into the mechanobiology of macrophages, T-cells, and associated intracellular, transcriptional, and epigenetic modifications will deliver novel therapeutic options. Analysis of morphological heterogeneity using machine learning algorithms will provide a useful clinical and research tool to monitor disease progression.

项目总结。炎症是大多数人类疾病的基础，包括糖尿病、动脉粥样硬化和 癌症。这些疾病造成了大多数死亡，并构成了巨大的全球健康负担。 巨噬细胞和 T 细胞是免疫细胞的亚群，已成为炎症的关键介质。角色 已经研究了塑造这些细胞转录反应的生化线索。然而， 越来越多的证据表明，身体因素也会调节其表型和效应功能。最近的 二维研究表明机械约束引导核易位 巨噬细胞中的转录因子。另一项研究发现 T 细胞对癌细胞的杀伤能力增强 通过胆固醇消耗。这些研究对机械免疫学领域做出了贡献，旨在 了解物理因素如何影响免疫细胞的命运。 最近的机械免疫学研究结果为我的提议奠定了基础，旨在确定 生物物理线索如何塑造巨噬细胞和 T 细胞的行为。我们开发了三维 培养物使我们能够探究生物物理线索如何调节免疫细胞运输和巨噬细胞-T- 肿瘤微环境中的细胞相互作用。我们已经发现，幼稚的巨噬细胞更容易 比极化巨噬细胞更有效地运输到肿瘤。此外，巨噬细胞具有不同的形状 取决于它们的激活状态和局部微环境。我们的初步结果表明，T 细胞 与细长巨噬细胞相比，它们与圆形巨噬细胞的相互作用寿命更长。这意味着 巨噬细胞形状是一种生物物理特性，可调节其与 T 细胞的相互作用。我们将扩展这些发现 通过阐明基质粘弹性对免疫细胞行为的作用并执行严格的 这些细胞的免疫表型。此外，该提案还将机器学习算法实施到高 从实时共焦成像获得的分辨率时空信息。这将释放识别的潜力 异质表型状态并量化它们随时间的演变。此外，该提案将整合 使用单细胞 RNA 测序数据进行共焦实时成像。这种详细的单细胞分析将确定 负责异质形态测量状态的遗传程序。 拟议的研究将具有重要意义，因为预计它将产生机械见解 对炎症是根本原因的多种疾病具有广泛的转化影响。这些包括 阿尔茨海默病、动脉粥样硬化、关节炎、糖尿病和癌症，这些都是日益严重的全球负担。这 这些疾病的病理学是由巨噬细胞和 T 细胞精心策划的。深入了解机械生物学 巨噬细胞、T 细胞以及相关的细胞内、转录和表观遗传修饰将带来新的 治疗选择。使用机器学习算法分析形态异质性将提供 监测疾病进展的有用的临床和研究工具。