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

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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.