University of Virginia, Charlottesville, United States.
Purdue University, West Lafayette, United States.
Elife. 2024 Jun 3;13:RP91924. doi: 10.7554/eLife.91924.
Muscle regeneration is a complex process due to dynamic and multiscale biochemical and cellular interactions, making it difficult to identify microenvironmental conditions that are beneficial to muscle recovery from injury using experimental approaches alone. To understand the degree to which individual cellular behaviors impact endogenous mechanisms of muscle recovery, we developed an agent-based model (ABM) using the Cellular-Potts framework to simulate the dynamic microenvironment of a cross-section of murine skeletal muscle tissue. We referenced more than 100 published studies to define over 100 parameters and rules that dictate the behavior of muscle fibers, satellite stem cells (SSCs), fibroblasts, neutrophils, macrophages, microvessels, and lymphatic vessels, as well as their interactions with each other and the microenvironment. We utilized parameter density estimation to calibrate the model to temporal biological datasets describing cross-sectional area (CSA) recovery, SSC, and fibroblast cell counts at multiple timepoints following injury. The calibrated model was validated by comparison of other model outputs (macrophage, neutrophil, and capillaries counts) to experimental observations. Predictions for eight model perturbations that varied cell or cytokine input conditions were compared to published experimental studies to validate model predictive capabilities. We used Latin hypercube sampling and partial rank correlation coefficient to identify in silico perturbations of cytokine diffusion coefficients and decay rates to enhance CSA recovery. This analysis suggests that combined alterations of specific cytokine decay and diffusion parameters result in greater fibroblast and SSC proliferation compared to individual perturbations with a 13% increase in CSA recovery compared to unaltered regeneration at 28 days. These results enable guided development of therapeutic strategies that similarly alter muscle physiology (i.e. converting extracellular matrix [ECM]-bound cytokines into freely diffusible forms as studied in cancer therapeutics or delivery of exogenous cytokines) during regeneration to enhance muscle recovery after injury.
肌肉再生是一个复杂的过程，涉及动态的多尺度生化和细胞相互作用，因此仅通过实验方法很难确定有利于肌肉损伤后恢复的微环境条件。为了了解单个细胞行为对肌肉内源性恢复机制的影响程度，我们使用基于细胞的模型（ABM）和基于单元的模型（ Cellular-Potts 框架）来模拟鼠骨骼肌组织横截面上的动态微环境。我们参考了 100 多项已发表的研究，定义了 100 多个参数和规则，这些参数和规则决定了肌纤维、卫星干细胞（SSC）、成纤维细胞、中性粒细胞、巨噬细胞、微血管和淋巴管的行为，以及它们彼此之间和与微环境的相互作用。我们利用参数密度估计来校准模型，以使其适应描述损伤后多个时间点横截面积（CSA）恢复、SSC 和成纤维细胞计数的时间生物学数据集。通过将其他模型输出（巨噬细胞、中性粒细胞和毛细血管计数）与实验观察结果进行比较，对校准后的模型进行了验证。将八个模型扰动（改变细胞或细胞因子输入条件）的预测结果与已发表的实验研究进行了比较，以验证模型的预测能力。我们使用拉丁超立方抽样和偏秩相关系数来识别细胞因子扩散系数和衰减率的模拟扰动，以增强 CSA 恢复。该分析表明，与单独的扰动相比，特定细胞因子衰减和扩散参数的联合改变会导致成纤维细胞和 SSC 的增殖增加，与未经改变的再生相比，28 天时 CSA 的恢复增加了 13%。这些结果使我们能够制定治疗策略，这些策略类似地改变肌肉生理学（即，将细胞外基质 [ECM] 结合的细胞因子转化为自由扩散形式，如癌症治疗学中所研究的，或输送外源性细胞因子），以增强损伤后肌肉的恢复。
