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模拟衰老细胞在伤口愈合、慢性伤口和纤维化中的时空动态。

Modelling the spatiotemporal dynamics of senescent cells in wound healing, chronic wounds, and fibrosis.

作者信息

Chandrasegaran Sharmilla, Sluka James P, Shanley Daryl

机构信息

Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom.

Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University Bloomington, Bloomington, Indiana, United States of America.

出版信息

PLoS Comput Biol. 2025 Apr 15;21(4):e1012298. doi: 10.1371/journal.pcbi.1012298. eCollection 2025 Apr.

DOI:10.1371/journal.pcbi.1012298
PMID:40233102
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12052216/
Abstract

Cellular senescence is known to drive age-related pathology through the senescence-associated secretory phenotype (SASP). However, it also plays important physiological roles such as cancer suppression, embryogenesis and wound healing. Wound healing is a tightly regulated process which when disrupted results in conditions such as fibrosis and chronic wounds. Senescent cells appear during the proliferation phase of the healing process where the SASP is involved in maintaining tissue homeostasis after damage. Interestingly, SASP composition and functionality was recently found to be temporally regulated, with distinct SASP profiles involved: a fibrogenic, followed by a fibrolytic SASP, which could have important implications for the role of senescent cells in wound healing. Given the number of factors at play a full understanding requires addressing the multiple levels of complexity, pertaining to the various cell behaviours, individually followed by investigating the interactions and influence each of these elements have on each other and the system as a whole. Here, a systems biology approach was adopted whereby a multi-scale model of wound healing that includes the dynamics of senescent cell behaviour and corresponding SASP composition within the wound microenvironment was developed. The model was built using the software CompuCell3D, which is based on a Cellular Potts modelling framework. We used an existing body of data on healthy wound healing to calibrate the model and validation was done on known disease conditions. The model clearly shows how differences in the spatiotemporal dynamics of different senescent cell phenotypes lead to several distinct repair outcomes. These differences in senescent cell dynamics can be attributed to variable SASP composition, duration of senescence and temporal induction of senescence relative to the healing stage. The range of outcomes demonstrated strongly highlight the dynamic and heterogenous role of senescent cells in wound healing, fibrosis and chronic wounds, and their fine-tuned control. Further specific data to increase model confidence could be used to explore senolytic treatments in wound disorders.

摘要

已知细胞衰老通过衰老相关分泌表型(SASP)驱动与年龄相关的病理过程。然而,它也发挥着重要的生理作用,如抑制癌症、胚胎发生和伤口愈合。伤口愈合是一个严格调控的过程,一旦受到干扰,就会导致诸如纤维化和慢性伤口等病症。衰老细胞出现在愈合过程的增殖阶段,在此阶段SASP参与损伤后维持组织内稳态。有趣的是,最近发现SASP的组成和功能受到时间调控,涉及不同的SASP谱:一种是促纤维化的,随后是促纤维溶解的SASP,这可能对衰老细胞在伤口愈合中的作用具有重要意义。鉴于涉及的因素众多,要全面理解就需要解决与各种细胞行为相关的多个复杂层面的问题,先分别研究每个层面,然后再研究这些要素之间的相互作用以及它们对彼此和整个系统的影响。在这里,我们采用了一种系统生物学方法,开发了一个伤口愈合的多尺度模型,该模型包括伤口微环境中衰老细胞行为的动态变化以及相应的SASP组成。该模型是使用基于细胞Potts建模框架的CompuCell3D软件构建的。我们利用关于健康伤口愈合的现有数据集来校准模型,并在已知疾病条件下进行验证。该模型清楚地展示了不同衰老细胞表型的时空动态差异如何导致几种不同的修复结果。衰老细胞动态的这些差异可归因于SASP组成的变化、衰老持续时间以及相对于愈合阶段的衰老时间诱导。所展示的结果范围有力地突出了衰老细胞在伤口愈合、纤维化和慢性伤口中的动态和异质性作用,以及它们的精细调控。可利用进一步的具体数据来提高模型的可信度,以探索针对伤口疾病的衰老细胞溶解疗法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/992a/12052216/53d0d23448d1/pcbi.1012298.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/992a/12052216/c6e6dd248071/pcbi.1012298.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/992a/12052216/c80163620af5/pcbi.1012298.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/992a/12052216/2dfd0cabebf5/pcbi.1012298.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/992a/12052216/9ce85f3c1fc3/pcbi.1012298.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/992a/12052216/1d1cabcb99cc/pcbi.1012298.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/992a/12052216/0fc35d7750a3/pcbi.1012298.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/992a/12052216/b73a05aedc87/pcbi.1012298.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/992a/12052216/b9585dffc383/pcbi.1012298.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/992a/12052216/7c37bdb2b37a/pcbi.1012298.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/992a/12052216/53d0d23448d1/pcbi.1012298.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/992a/12052216/c6e6dd248071/pcbi.1012298.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/992a/12052216/c80163620af5/pcbi.1012298.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/992a/12052216/2dfd0cabebf5/pcbi.1012298.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/992a/12052216/9ce85f3c1fc3/pcbi.1012298.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/992a/12052216/1d1cabcb99cc/pcbi.1012298.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/992a/12052216/0fc35d7750a3/pcbi.1012298.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/992a/12052216/b73a05aedc87/pcbi.1012298.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/992a/12052216/b9585dffc383/pcbi.1012298.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/992a/12052216/7c37bdb2b37a/pcbi.1012298.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/992a/12052216/53d0d23448d1/pcbi.1012298.g010.jpg

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