AI-Informed Signaling Factor Design for In Vitro Rejuvenating Mesenchymal Stromal Cells

摘要 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， 疾病，临床上有意义的MSC疗法的主要障碍是不能产生有效的MSC 始终如一。具体地，体外培养的MSC通常快速进入衰老，其中它们失去效力。 与自然的体内衰老相反，这种体外衰老已被证明主要是由失调的细胞因子引起的。 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）已被鉴定用于调节衰老相关过程。在此基础上， 这项R35 MIRA提案旨在开发一种创新的工程方法，以推迟MSC 通过共同调节这些信号通路来促进衰老过程。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素组合， 逆转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进行表型化（例如， 特征、增殖和效力）; 2）如何预测决定所需的信号传导因子 转录反应;和3）如何确保这种预测的鲁棒性。 在挑战1中，该提案将通过开发方法来扩展我们先前开发的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 深度学习模型我们的目标是破译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地调整基因 在MSC中表达。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AI平台转变为可靠的工具，用于实际的临床应用。立即 我们提出的研究结果将导致高通量表型和工程平台的MSC。 拟议的实验平台也将使我们能够在MSC中建立更好的理解 机械生物学和衰老信号相互作用。

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.