Integrated computational and in vivo models reveal Key Insights into macrophage behavior during bone healing.
Integrated computational and in vivo models reveal Key Insights into macrophage behavior during bone healing.
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DOI：
10.1371/journal.pcbi.1009839
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发表时间：
2022-05
影响因子：
4.3
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中科院分区：
文献类型：
--
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骨髓来源的单核细胞和巨噬细胞是骨中有助于重塑和损伤修复的关键细胞。然而，它们的时间极化状态和控制骨吸收破骨细胞和骨形成成骨细胞的反应在很大程度上是未知的。在这项研究中，我们重点关注单核细胞/巨噬细胞动力学和极化状态随时间的两个方面：1）损伤触发的促炎和抗炎单核细胞/巨噬细胞的时间分布，2）促炎与抗炎单核细胞/巨噬细胞在协调愈合反应中的贡献。骨愈合是一个复杂的多细胞动态过程。虽然传统的体外和体内实验可以以高分辨率捕获选定群体的行为，但它们不能同时跟踪多个群体的行为。为了解决这个问题，我们使用了一个集成的耦合常微分方程（ODE）为基础的框架，描述了多个细胞物种在体内骨损伤数据，以确定和测试各种假设骨细胞群动力学。我们的方法使我们能够推断出几种生物学见解，包括但不限于：1）抗炎巨噬细胞是早期破骨细胞抑制和促炎巨噬细胞抑制的关键，2）促炎巨噬细胞参与破骨细胞骨吸收活性，而成骨细胞促进破骨细胞分化，3）促炎单核细胞/巨噬细胞在两个扩张波期间上升，这可以通过两个波之间的抗炎巨噬细胞介导的抑制相来解释。此外，我们通过将模拟结果与独立的实验数据集进行比较，进一步测试了数学模型的鲁棒性。总之，这种新的综合数学框架使我们能够确定最能概括骨损伤数据的生物学机制，并解释该过程中涉及的耦合细胞群体动力学。此外，我们的假设检验方法可用于其他背景下来破译复杂多细胞过程中的机制。骨髓来源的单核细胞/巨噬细胞是骨重建和损伤修复的关键细胞。然而，它们的时间极化状态和控制骨吸收破骨细胞和骨形成成骨细胞的反应在很大程度上是未知的。在这项研究中，我们集中在两个方面的单核细胞/巨噬细胞群体动力学：1）损伤触发的促炎和抗炎单核细胞/巨噬细胞的时间分布，2）促炎与抗炎单核细胞/巨噬细胞在协调愈合反应中的贡献。为了测试各种假设骨细胞群的动力学，我们已经集成了一个耦合的常微分方程为基础的框架，描述多个细胞物种在体内骨损伤数据。我们的方法使我们能够推断出几种生物学见解，包括：1）抗炎巨噬细胞是早期破骨细胞抑制和促炎巨噬细胞抑制的关键，2）促炎巨噬细胞参与破骨细胞骨吸收活性，而成骨细胞促进破骨细胞分化，3）促炎单核细胞/巨噬细胞在两个扩张波期间上升，这可以通过两个波之间的抗炎巨噬细胞介导的抑制相来解释。总之，这个数学框架使我们能够确定概括骨损伤数据的生物学机制，并解释该过程中涉及的耦合细胞群体动力学。
Myeloid-derived monocyte and macrophages are key cells in the bone that contribute to remodeling and injury repair. However, their temporal polarization status and control of bone-resorbing osteoclasts and bone-forming osteoblasts responses is largely unknown. In this study, we focused on two aspects of monocyte/macrophage dynamics and polarization states over time: 1) the injury-triggered pro- and anti-inflammatory monocytes/macrophages temporal profiles, 2) the contributions of pro- versus anti-inflammatory monocytes/macrophages in coordinating healing response. Bone healing is a complex multicellular dynamic process. While traditional in vitro and in vivo experimentation may capture the behavior of select populations with high resolution, they cannot simultaneously track the behavior of multiple populations. To address this, we have used an integrated coupled ordinary differential equations (ODEs)-based framework describing multiple cellular species to in vivo bone injury data in order to identify and test various hypotheses regarding bone cell populations dynamics. Our approach allowed us to infer several biological insights including, but not limited to,: 1) anti-inflammatory macrophages are key for early osteoclast inhibition and pro-inflammatory macrophage suppression, 2) pro-inflammatory macrophages are involved in osteoclast bone resorptive activity, whereas osteoblasts promote osteoclast differentiation, 3) Pro-inflammatory monocytes/macrophages rise during two expansion waves, which can be explained by the anti-inflammatory macrophages-mediated inhibition phase between the two waves. In addition, we further tested the robustness of the mathematical model by comparing simulation results to an independent experimental dataset. Taken together, this novel comprehensive mathematical framework allowed us to identify biological mechanisms that best recapitulate bone injury data and that explain the coupled cellular population dynamics involved in the process. Furthermore, our hypothesis testing methodology could be used in other contexts to decipher mechanisms in complex multicellular processes. Myeloid-derived monocytes/macrophages are key cells for bone remodeling and injury repair. However, their temporal polarization status and control of bone-resorbing osteoclasts and bone-forming osteoblasts responses is largely unknown. In this study, we focused on two aspects of monocyte/macrophage population dynamics: 1) the injury-triggered pro- and anti-inflammatory monocytes/macrophages temporal profiles, 2) the contributions of pro- versus anti-inflammatory monocytes/macrophages in coordinating healing response. In order to test various hypotheses regarding bone cell populations dynamics, we have integrated a coupled ordinary differential equations-based framework describing multiple cellular species to in vivo bone injury data. Our approach allowed us to infer several biological insights including: 1) anti-inflammatory macrophages are key for early osteoclast inhibition and pro-inflammatory macrophage suppression, 2) pro-inflammatory macrophages are involved in osteoclast bone resorptive activity, whereas osteoblasts promote osteoclast differentiation, 3) Pro-inflammatory monocytes/macrophages rise during two expansion waves, which can be explained by the anti-inflammatory macrophages-mediated inhibition phase between the two waves. Taken together, this mathematical framework allowed us to identify biological mechanisms that recapitulate bone injury data and that explain the coupled cellular population dynamics involved in the process.
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影响因子：
3.7
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