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 中充质基质细胞（MSC）具有巨大的前途，可以治疗许多具有挑战性 疾病，临床上有意义的MSC疗法的主要障碍是无法产生有效的MSC 一贯。具体而言，体外培养的MSC通常会迅速进入衰老，在这些衰老中失去效力。 与天然体内衰老相反，这种体外衰老已被证明在很大程度上受到了不良调节的驱动 培养中的代谢信号传导。为了应对这一巨大的挑战，许多信号通路（例如FGF，ATM，SRT， MTOR，EGF，DDR2）已被确定用于调节与衰老相关的过程。基于这些 发现R35 MIRA提案旨在开发一种创新的工程方法来推迟MSC 通过共同调整这些信号通路，衰老过程。具体来说，我们假设 足够训练的AI模型可以预测有效减慢甚至 恢复与衰老相关的转录漂移。为了实现这样一个目标，我的研究旨在解决三个 MSC工程中的知识/技术差距（图1B）：1）如何准确表型现场MSC（例如， 特征，增殖和效力）； 2）如何预测决定所需的信号因子 转录响应； 3）如何确保此类预测的鲁棒性。 在挑战1中，该提案将通过开发方法扩展我们先前开发的AI平台 获取涵盖多种MSC表型并解释黑盒的大规模AI训练数据 深度学习模型。目的是破译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确定信号传导因子组合并进行预测调整基因 在MSC中的表达。在第三个挑战中，我们将开发算法来改善AI模型的鲁棒性 并将我们的概念验证AI平台变成可靠的临床利用工具。直接 我们提出的研究的结果将导致MSC的高通量表型和工程平台。 拟议的实验平台还将使我们能够在MSC中建立更好的理解 机械生物学和衰老信号传导相互作用。