Optimizing gelation time for cell shape control through active learning

Abstract

Hydrogels are popular platforms for cell encapsulation in biomedicine and tissue engineering due to their soft, porous structures, high water content, and excellent tunability. Recent studies highlight that the timing of network formation can be just as important as mechanical properties in influencing cell morphologies. Conventionally, time-dependent properties can be achieved through multi-step processes. In contrast, one-pot synthesis can improve both the efficiency and uniformity of cell encapsulation. Reaction kinetics are sensitive to temperatures and pH conditions, thus, monitoring gelation time across different conditions is essential for formulation. In this work, we choose tetra-poly(ethylene glycol) (TPEG) macromers as a model system to examine the relationship between the rate of polymer network formation and cell morphology. Previous studies of this system focused on reactions at neutral pH and room temperature, leaving much of the formulation space underexplored. We use Gaussian process regression (GPR) to minimize response surface errors by strategically selecting additional investigation points based on prior knowledge. Then we extend the knowledge from pre-trained data at neutral pH to a new surface at physiological pH. We find that the gelation time surface can effectively predict the aspect ratio of the encapsulated cells. Additionally, through focal adhesion kinase inhibition, we show that cell shape is influenced by the properties of the forming network in the initial hours as cells develop connections with the matrix. We demonstrate the utility of a high-throughput microrheology approach in enhancing fabrications of synthetic extracellular matrix and cell assemblies.