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.

项目摘要。炎症是大多数人类疾病的基础，包括糖尿病、动脉粥样硬化和糖尿病。 癌这些疾病是造成大多数死亡的原因，并构成巨大的全球健康负担。 巨噬细胞和T细胞，免疫细胞的子集，已经成为炎症的关键介质。的作用 的生化线索在塑造这些细胞的转录反应进行了调查。然而，在这方面， 越来越多的证据表明，物理因素也调节它们的表型和效应器功能。最近 二维研究表明，机械约束指导核转位， 巨噬细胞中的转录因子。另一项研究发现，增强T细胞杀死癌细胞的能力， 通过胆固醇消耗。这些研究为机械免疫学领域做出了贡献， 了解物理因素如何指导免疫细胞的命运。 最近的机械免疫学发现为我的建议奠定了基础， 生物物理线索如何塑造巨噬细胞和T细胞行为。我们开发了一种三维的 文化，使我们能够询问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，幼稚巨噬细胞 比极化的巨噬细胞更有效地运输到肿瘤。此外，巨噬细胞采用不同的形状， 这取决于它们的激活状态和本地微环境。我们的初步结果表明，T细胞 与细长的巨噬细胞相比，与圆形巨噬细胞的相互作用寿命更长。这意味着 巨噬细胞的形状，生物物理特性，在调节其与T细胞的相互作用。我们将把这些发现 通过阐明基质粘弹性对免疫细胞行为的作用， 这些细胞的免疫表型。此外，该提案将机器学习算法实现到高 分辨率的时空信息从现场共焦成像获得。这将释放出识别 异质性表型状态，并量化它们随时间的演变。此外，该提案将整合 用单细胞RNA测序数据进行共聚焦实时成像。如此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。 拟议的研究将是重要的，因为它预计将产生机械的见解， 广泛的翻译影响的无数疾病的炎症是根本原因。这些包括 阿尔茨海默氏症、动脉粥样硬化、关节炎、糖尿病和癌症，这些疾病是日益增长的全球负担。的 这些疾病的病理学是由巨噬细胞和T细胞协调的。深入了解机械生物学 巨噬细胞、T细胞和相关的细胞内、转录和表观遗传修饰将提供新的 治疗选择使用机器学习算法的形态异质性分析将提供 有用的临床和研究工具，以监测疾病进展。