Systems level investigation of cell-to-cell communication and cellular heterogeneity in neutrophil apoptosis

对免疫生物学分子和途径的详细了解带来了靶向治疗。一个突出的例子是治疗类风湿关节炎的tnf - α阻滞剂。然而，这种新疗法有严重的副作用，并经常引起意想不到的和不希望的结果。一个可能的原因是，细胞异质性和复杂、随机的细胞通讯网络所产生的特性，阻碍了开发有效疗法的努力。本研究的一个重要目标是探索一种新的策略，用于在单细胞分辨率下调查和合理化细胞通信网络的设计。新策略将分层随机数学模型与活细胞成像相结合。在这种方法中，直接使用体内测量的时间依赖分布进行建模，例如，使用细胞凋亡时间的分布而不是详细考虑细胞凋亡途径的分子步骤。因此，不需要确定大量的参数集，这是许多传统建模技术的瓶颈。主持研究的科学家（Altschuler教授，UTSW Dallas， USA）在结合实验和计算生物学方面的长期经验，以及主持研究所的高通量成像和图像分析管道，将对新战略的成功实施最有价值。我们将使用基于数据的分层随机模型来研究与医学应用高度相关的免疫细胞通讯网络：tnf - α介导的小鼠中性粒细胞的激活和凋亡。虽然中性粒细胞的及时到达和激活对于有效对抗病原体至关重要，但中性粒细胞的延迟凋亡是慢性炎症等自身免疫性疾病的主要原因。tnf - α对这一过程的复杂、随机调节是难以捉摸的，将在拟议的项目中进行研究。新方法将允许在中性粒细胞通信网络的基于数据的数学描述中考虑细胞异质性。这将为炎症组织中细胞间通讯的研究提供良好的基础。长期目标是优化靶向治疗，特别是tnf - α抑制剂的剂量、时间和辅助治疗。此外，该项目将揭示细胞通信网络的特定和一般特性，并将探索可用于研究其他细胞相互作用网络的方法。

Detailed knowledge about molecules and pathways in immune biology has brought about targeted therapies. A prominent example are TNF-alpha blockers in treatment of rheumatoid arthritis. However, such new therapies have serious adverse effects and often evoke unexpected and undesired outcomes. One likely reason is that properties emerging from cellular heterogeneity and complex, stochastic cell communication networks confound efforts to develop effective therapies. An important objective of the proposed study is to explore a new strategy for investigating and rationalizing the design of cell communication networks at single-cell resolution. The new strategy combines hierarchic stochastic mathematical models with live-cell imaging. In that approach, time dependent distributions measured in vivo are directly used for modeling, e.g. the distribution of the time to apoptosis is used instead of considering the molecular steps of the apoptosis pathway in detail. Therefore, there is no need for determination of a large set of parameters, which is a bottleneck in many conventional modeling techniques. The long-ranging experience with combined experimental and computational biology of the hosting scientist (Prof. Altschuler, UTSW Dallas, USA), and the high-throughput imaging and image analysis pipeline at the host institute will be most valuable for a successful implementation of the new strategy. We will use data based hierarchic stochastic modeling for investigation of a network of immune cell communication with high relevance to medical applications: TNF-alpha mediated activation and apoptosis of mouse neutrophils. While timely arrival and activation of neutrophils is critical to effectively fight pathogens, delayed apoptosis of neutrophils is a primary cause of autoimmune diseases like chronic inflammation. The complex, stochastic regulation by TNF-alpha of this process is elusive and will be investigated in the proposed project. The new approach will permit to consider cellular heterogeneity in a data-based mathematical description of the neutrophil communication network. It will thus provide a sound basis for investigation of cell-to-cell communication in inflamed tissue. A long-term goal is optimization of targeted therapies, in particular of dosage, timing and adjunctive therapies for TNF-alpha inhibitors. Moreover, the project will reveal both specific and general properties of cell communication networks, and will explore methods that can be used to study other cellular interaction networks in future research.