Role of extracellular matrix in age-related declines of muscle regeneration

项目摘要 随着年龄的增长，肌肉再生能力明显下降。而再生是由肌肉主导的 骨骼肌细胞外基质（ECM）中与年龄相关的复杂变化提供了 驱动异常谱系特化的有力信号。老龄化与人类的 MuSC及其环境生态位由生物力学，结构和动态变化定义， ECM表明，数据驱动的分析可以阐明潜在的机制，增加我们的基本 了解衰老和干细胞生物学，并指出新的治疗策略。在这项研究中， 数据（即，单细胞RNA-seq和成肌标志物的成像流式细胞术评估）， 在不同弹性和细胞粘附性的基质上培养的细胞将用于探测信号通路 包括培养的MuSC中的线粒体/代谢信号传导途径。我们建议， 机器学习/人工智能（ML/AI）范式的发展代表了集成多个 层组学数据集和建立预测模型，将更全面地阐明干细胞 对外在生物物理环境的反应。 本补编的首要目标是测试生物数据和域 与肌肉老化有关的知识可以嵌入贝叶斯优化的框架中， 阐明机制并准确预测再生反应。这一核心假设将是 通过三个具体目标进行测试：具体目标1。为ML模型准备组学数据：Curate 数据集，识别和估算缺失数据，编译元数据，并预处理数据以量化描述符 用于模型构建。采用与最佳实践相关的数据管理协议。具体目标2。到 使用贝叶斯优化执行基准ML建模：识别环境变量（ECM刚度 和组成，信号分子）和细胞特征（年龄，表达标志物）， 表观遗传特征和肌原性，然后开发机制ML模型，并估计后验 分布。具体目标3。为了拓宽ML建模的方法并扩大研究人员的参与， 衰老的生物学：CMU将主办一个黑客会议，由来自以下地区的联合收割机学生和研究人员组成的团队参加： 区域大学和HBCU合作伙伴。

PROJECT SUMMARY The capacity for muscle regeneration decreases markedly with aging. While regeneration is led by muscle stem cells (MuSC), complex age-related changes in the skeletal muscle extracellular matrix (ECM) provide potent signals that drive aberrant lineage specification. The complexity of the interactions between aging MuSC and their environmental niche defined by biomechanical, architectural, and dynamic changes in the ECM suggests a data-driven analysis can elucidate underlying mechanisms, increase our fundamental understanding of aging and stem cell biology, and point to novel therapeutic strategies. In this research, -omics data (i.e., single cell RNA-seq and imaging flow cytometry assessments of myogenic markers) obtained from cells cultured onto substrates of varying elasticity and cell-adhesion will be used to probe signaling pathways including mitochondrial/metabolic signaling pathways in cultured MuSCs. We propose that the implementation of machine learning/artificial intelligence (ML/AI) paradigms represents a critical next step for integrating multi- layer -omics datasets and building predictive models that will more comprehensively elucidate stem cell responses to the extrinsic biophysical environment. The overarching goal of this Supplement is to test the central hypothesis that Biological data and domain knowledge relating to muscle aging can be embedded in a framework of Bayesian optimization will allow for elucidating mechanisms and accurately predicting regenerative responses. This central hypothesis will be tested by conducting three specific aims: Specific Aim 1. To prepare -omics data for ML models: Curate datasets, identify and impute missing data, compile metadata, and pre-process data to quantify descriptors used in model building. Adopt data management protocols associated with best practices. Specific Aim 2. To perform benchmark ML modeling with Bayesian optimization: Identify environmental variables (ECM stiffness and composition, signaling molecules) and cellular characteristics (age, expression markers) that correlate with epigenetic signatures and myogenicity, then develop mechanistic ML models and estimate posterior distributions. Specific Aim 3. To broaden approaches to ML modeling and broaden researcher engagement in the biology of aging: CMU will host a hackathon with teams that combine students and researchers from regional universities and HBCU partners.