Data from: Optimizing gelation time for cell shape control through active learning

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.