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

细胞外基质在年龄相关的肌肉再生衰退中的作用

• 批准号: 10410777
• 负责人: Fabrisia Ambrosio
• 金额: $ 28.14万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10410777
• 关键词: Acute; Administrative Supplement; Adopted; Age; Aging; Architecture; Artificial Intelligence; Bayesian Modeling; Benchmarking; Biological; Biology of Aging; Biomechanics; Biophysics; Cell Adhesion; Characteristics; Complex; Cultured Cells; Data; Data Set; Descriptor; Elasticity; Elderly; Environment; Epigenetic Process; Extracellular Matrix; Flow Cytometry; Goals; Image; Impairment; Individual; Injury; Knowledge; Machine Learning; Metabolic; Metadata; Mitochondria; Modeling; Muscle; Muscle satellite cell; Natural regeneration; Process; Protocols documentation; Regenerative response; Research; Research Personnel; Role; Signal Pathway; Signal Transduction; Signaling Molecule; Skeletal Muscle; Students; Testing; Universities; age related; age-related muscle loss; data management; functional decline; hackathon; healing; model building; muscle aging; muscle regeneration; novel therapeutic intervention; parent grant; predictive modeling; regeneration potential; response; single-cell RNA sequencing; stem cell biology; stem cell function; stem cells

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.

项目概要 肌肉再生的能力随着年龄的增长而显着下降。虽然再生是由肌肉主导的 干细胞 (MuSC)、骨骼肌细胞外基质 (ECM) 中与年龄相关的复杂变化提供 驱动异常谱系规范的有效信号。衰老之间相互作用的复杂性 MuSC 及其由生物力学、建筑和动态变化定义的环境利基 ECM 建议数据驱动的分析可以阐明潜在机制，增强我们的基本面 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，-组学 数据（即单细胞 RNA-seq 和肌源性标记物的成像流式细胞术评估） 培养在不同弹性和细胞粘附力的基质上的细胞将用于探测信号传导途径 包括培养的 MuSC 中的线粒体/代谢信号通路。我们建议实施 机器学习/人工智能 (ML/AI) 范式的发展代表了集成多领域的关键下一步 层组学数据集和构建预测模型将更全面地阐明干细胞 对外部生物物理环境的反应。 本补充的总体目标是检验生物数据和领域的中心假设 与肌肉衰老相关的知识可以嵌入贝叶斯优化框架中，这将允许 阐明机制并准确预测再生反应。这个中心假设将是 通过执行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、编译元数据以及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. 至 使用贝叶斯优化执行基准 ML 建模：识别环境变量（ECM 刚度 以及与相关的成分、信号分子）和细胞特征（年龄、表达标记） 表观遗传特征和肌原性，然后开发机械机器学习模型并估计后验 分布。具体目标 3. 拓宽 ML 建模方法并扩大研究人员参与 衰老生物学：卡耐基梅隆大学将举办一场黑客马拉松，团队成员包括来自不同国家的学生和研究人员 地区大学和 HBCU 合作伙伴。