Zhao Chen, Medeiros Thalyta X, Sové Richard J, Annex Brian H, Popel Aleksander S
Department of Biomedical Engineering, Johns Hopkins University School of Medicine, 720 Rutland Avenue, 613 Traylor Bldg, Baltimore, MD 21205, USA.
Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA 30912, USA.
iScience. 2021 Jan 29;24(2):102112. doi: 10.1016/j.isci.2021.102112. eCollection 2021 Feb 19.
Macrophages are highly plastic immune cells that dynamically integrate microenvironmental signals to shape their own functional phenotypes, a process known as polarization. Here we develop a large-scale mechanistic computational model that for the first time enables a systems-level characterization, from quantitative, temporal, dose-dependent, and single-cell perspectives, of macrophage polarization driven by a complex multi-pathway signaling network. The model was extensively calibrated and validated against literature and focused on in-house experimental data. Using the model, we generated dynamic phenotype maps in response to numerous combinations of polarizing signals; we also probed into an population of model-based macrophages to examine the impact of polarization continuum at the single-cell level. Additionally, we analyzed the model under an condition of peripheral arterial disease to evaluate strategies that can potentially induce therapeutic macrophage repolarization. Our model is a key step toward the future development of a network-centric, comprehensive "virtual macrophage" simulation platform.
巨噬细胞是高度可塑性的免疫细胞，能够动态整合微环境信号以塑造自身的功能表型，这一过程称为极化。在此，我们开发了一个大规模的机制计算模型，首次能够从定量、时间、剂量依赖性和单细胞角度，对由复杂多通路信号网络驱动的巨噬细胞极化进行系统水平的表征。该模型针对文献进行了广泛校准，并以内置实验数据为重点进行了验证。利用该模型，我们生成了响应多种极化信号组合的动态表型图谱；我们还深入研究了基于模型的巨噬细胞群体，以在单细胞水平上检查极化连续体的影响。此外，我们在周围动脉疾病的条件下分析了该模型，以评估可能诱导治疗性巨噬细胞重极化的策略。我们的模型是朝着以网络为中心的综合“虚拟巨噬细胞”模拟平台的未来发展迈出的关键一步。
