AI-informed Signaling Factor Design for in vitro Rejuvenating Mesenchymal Stromal Cells

用于体外再生间充质基质细胞的人工智能信号因子设计

• 批准号: 10707372
• 负责人: Neil Lin
• 金额: $ 37.16万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-21 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10707372
• 关键词: Address; Age; Aging; Algorithms; Artificial Intelligence platform; Biochemical; Cell Aging; Cell Therapy; Cells; Characteristics; Clinical; DDR2 gene; Data; Disease; Drug Design; EGF gene; Engineering; Ensure; FRAP1 gene; Fibroblast Growth Factor; Gene Expression; Genetic Transcription; Goals; In Vitro; Knowledge; Metabolic; Modeling; Morphology; Outcome; Phenotype; Process; Proliferating; Rejuvenation; Reproducibility; Research; Signal Pathway; Signal Transduction; Technology; Therapeutic; Training; artificial intelligence method; cellular engineering; deep learning; deep learning model; design; improved; in vivo; innovation; mesenchymal stromal cell; response; senescence; tool

ABSTRACT While mesenchymal stromal cells (MSCs) hold enormous promise for treating many challenging diseases, a major barrier toward clinically meaningful MSC therapies is the inability to produce potent MSCs consistently. Specifically, in vitro cultured MSCs often rapidly enter senescence in which they lose their potency. In contrast to natural in vivo senescence, such in vitro aging has been shown to be largely driven by misregulated metabolic signaling in culture. To address this grand challenge, many signaling pathways (e.g., FGF, ATM, SRT, mTOR, EGF, DDR2) have been identified for regulating senescence-related processes. Building upon these discoveries, this R35 MIRA proposal aims to develop an innovative engineering approach to delaying the MSC senescence process by collectively adjusting these signaling pathways. Specifically, we hypothesize that a sufficiently trained AI model can predict the signaling factor combination that effectively slows down or even reverts the senescence-related transcriptional drift. To achieve such a goal, my research aims to address three knowledge/technology gaps in MSC engineering (Fig. 1B): 1) how to accurately phenotype live MSCs (e.g., characteristics, proliferation, and potency); 2) how to predict signaling factors that dictate the desired transcriptional response; and 3) how to ensure the robustness of such predictions. In challenge 1, this proposal will expand our previously developed AI platform by developing approaches to acquiring large-scale AI training data that cover a wide range of MSC phenotypes and interpreting black-box deep learning models. The goal is to decipher the morphology-gene expression relationship in MSCs. In challenge 2, we will utilize deep learning to identify the signaling factor combination and predictively adjust gene expression in MSCs. In the third challenge, we will develop algorithms that improve the robustness of AI models and turn our proof-of-concept AI platforms into reliable tools for practical clinical utilizations. The immediate outcome of our proposed research will lead to a high-throughput phenotyping and engineering platform of MSCs. The proposed experimental platform will also enable us to establish better understandings in MSC mechanobiology and senescence signaling interactions.

抽象的 虽然间充质基质细胞 (MSC) 在治疗许多具有挑战性的疾病方面具有巨大的前景 间充质干细胞治疗的一个主要障碍是无法产生有效的间充质干细胞 始终如一。具体来说，体外培养的间充质干细胞通常会迅速进入衰老状态，并失去其效力。 与体内自然衰老相反，这种体外衰老已被证明很大程度上是由失调引起的 文化中的代谢信号。为了应对这一巨大挑战，许多信号通路（例如 FGF、ATM、SRT、 mTOR、EGF、DDR2）已被确定可调节衰老相关过程。在此基础上 发现，这项 R35 MIRA 提案旨在开发一种创新的工程方法来延迟 MSC 通过共同调节这些信号通路来实现衰老过程。具体来说，我们假设 经过充分训练的人工智能模型可以预测信号因子组合，从而有效减缓甚至 恢复与衰老相关的转录漂移。为了实现这一目标，我的研究旨在解决三个问题 MSC 工程中的知识/技术差距（图 1B）：1）如何准确地对活 MSC 进行表型分析（例如， 特征、增殖和效力）； 2）如何预测决定期望的信号因素 转录反应； 3）如何确保此类预测的稳健性。 在挑战 1 中，该提案将通过开发方法来扩展我们之前开发的 AI 平台 获取涵盖广泛 MSC 表型的大规模 AI 训练数据并解释黑盒 深度学习模型。目标是破译 MSC 中的形态-基因表达关系。在 挑战2，我们将利用深度学习来识别信号因子组合并预测性调整基因 MSC 中的表达。在第三个挑战中，我们将开发提高AI模型鲁棒性的算法 并将我们的概念验证人工智能平台转变为实际临床应用的可靠工具。即时的 我们提出的研究成果将形成一个高通量的间充质干细胞表型分析和工程平台。 拟议的实验平台也将使我们能够更好地理解 MSC 机械生物学和衰老信号相互作用。