Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.
Ophthalmology, University of Virginia, Charlottesville, Virginia, United States of America.
PLoS Comput Biol. 2021 May 10;17(5):e1008937. doi: 10.1371/journal.pcbi.1008937. eCollection 2021 May.
Skeletal muscle possesses a remarkable capacity for repair and regeneration following a variety of injuries. When successful, this highly orchestrated regenerative process requires the contribution of several muscle resident cell populations including satellite stem cells (SSCs), fibroblasts, macrophages and vascular cells. However, volumetric muscle loss injuries (VML) involve simultaneous destruction of multiple tissue components (e.g., as a result of battlefield injuries or vehicular accidents) and are so extensive that they exceed the intrinsic capability for scarless wound healing and result in permanent cosmetic and functional deficits. In this scenario, the regenerative process fails and is dominated by an unproductive inflammatory response and accompanying fibrosis. The failure of current regenerative therapeutics to completely restore functional muscle tissue is not surprising considering the incomplete understanding of the cellular mechanisms that drive the regeneration response in the setting of VML injury. To begin to address this profound knowledge gap, we developed an agent-based model to predict the tissue remodeling response following surgical creation of a VML injury. Once the model was able to recapitulate key aspects of the tissue remodeling response in the absence of repair, we validated the model by simulating the tissue remodeling response to VML injury following implantation of either a decellularized extracellular matrix scaffold or a minced muscle graft. The model suggested that the SSC microenvironment and absence of pro-differentiation SSC signals were the most important aspects of failed muscle regeneration in VML injuries. The major implication of this work is that agent-based models may provide a much-needed predictive tool to optimize the design of new therapies, and thereby, accelerate the clinical translation of regenerative therapeutics for VML injuries.
骨骼肌在受到各种损伤后，具有很强的修复和再生能力。在成功的情况下，这个高度协调的再生过程需要几种肌肉常驻细胞群体的贡献，包括卫星干细胞 (SSCs)、成纤维细胞、巨噬细胞和血管细胞。然而，体积性肌肉损失损伤 (VML) 涉及到多种组织成分的同时破坏（例如，由于战场伤害或车辆事故），并且范围如此广泛，以至于它们超出了无疤痕愈合的内在能力，并导致永久性的美容和功能缺陷。在这种情况下，再生过程失败，由无效的炎症反应和伴随的纤维化主导。考虑到对 VML 损伤中驱动再生反应的细胞机制的不完全理解，当前再生治疗方法未能完全恢复功能性肌肉组织并不令人惊讶。为了开始解决这个深刻的知识差距，我们开发了一个基于代理的模型，以预测 VML 损伤后手术引起的组织重塑反应。一旦该模型能够在没有修复的情况下重现组织重塑反应的关键方面，我们通过模拟植入脱细胞细胞外基质支架或切碎的肌肉移植物后 VML 损伤的组织重塑反应来验证该模型。该模型表明，SSC 微环境和缺乏促分化 SSC 信号是 VML 损伤中肌肉再生失败的最重要方面。这项工作的主要意义是，基于代理的模型可能提供急需的预测工具，以优化新疗法的设计，从而加速再生治疗方法在 VML 损伤中的临床转化。
