DOI: 10.5061/dryad.8w9ghx3xn
title: Data from: Optimizing gelation time for cell shape control through active learning

description: 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.
description: # General README for Dataset --- ## Overview This repository contains all
 data, scripts, and analyses supporting the results presented in the
 associated publication: 'Optimizing cell shape control through active
 learning' by Yuxin Luo, Juan Chen, Mengyang Gu, and Yimin Luo. Each
 figure has a corresponding folder, which includes raw data, processed
 data, and analysis scripts used to generate the figure panels. --- ##
 General Notes ### Dataset Organization The dataset is organized by figure,
 with each folder named according to its corresponding figure (e.g.,
 Figure_2, Figure_3, etc.). Each figure folder has contents as follows: ###
 Data Contents * **MATLAB Scripts**: Primarily used for figure generation
 with colormap for better visualization. These scripts are self-contained
 and can reproduce the corresponding figure panels when run. They are
 prefixed with the figure number (e.g., Fig_2abcd.m,
 New_Surface_for_pH74.R). * **R Scripts**: Used for model training such as
 the Gaussian Process Regression model (GPR). * **Processed Data Data (csv
 files)**: Cleaned and prepared datasets for analysis and visualization are
 typically included in Results folders or directly alongside corresponding
 scripts. These files typically include variables relevant to the analysis,
 which are detailed in figure-specific README files  * **Raw Data**:
 Included where applicable, described in specific README files.  ###
 Experimental Conditions * **Experimental Backgrounds and Setup**:
 Descriptions of setups of experiemts such as microrheology, bulk rheology
 and UV-Vis spectroscopy, are briefly described in figure-specific README
 files. --- ## Overview of Figures ### **Figure 2** * **Content**:
 Illustration of MSD shifting procedure at different temperature and
 concentration conditions using both MPT microrheology and AIUQ analysis.
 ### **Figure 3** * **Content**: Comparison of the performances by fitting
 the gelation response surface using both linear regression and GPR. ###
 **Figure 4** * **Content**: Comparison of the out-of-sample RMSE computed
 for surfaces learned from observations at pH = 7.4 and from observations
 at pH =7 and pH = 7.4 jointly. ### **Figure 6** * **Content**:
 Time-dependent bulk rheometry measurements for gels crosslinked at 30 ◦C
 are measured from pure collagen plus TPEG at concentrations 0, 25, 30, and
 40 mg/mL. ### **Figure 7** * **Content**: Measuring the strength of the
 correlation between gelation time and cell shape along with their
 corresponding Pearson R test. ### **Figure 8** * **Content**: One-way
 analysis of variance (one-way ANOVA) to compare whether the aspect ratios
 of the cells that underwent FAK-inhibition are significant different than
 those that didn't undergo inhibition. ### **Figure S1** *
 **Content**: An example (23mg/mL 28◦C) of dedrifting procedure. ###
 **Figure S2** * **Content**: Sequential minimization of error in
 predicting the response surface. Visual representation of the interval
 after sequential selection of 4-10 points in the pH = 7 case. ### **Figure
 S3** * **Content**: The predicted gelation time map at pH = 7 using linear
 regression and Gaussian Process Regression (GPR) models. The models'
 performances are quantified through the changes in interval length and
 RMSE as the number of training data points increases. ### **Figure S4** *
 **Content**: UV-Vis spectroscopy analysis for hydrolysis rates of TPEG-SG
 at pH 7 and 7.4. ### **Figure S5** * **Content**: Frequency-dependent
 modulus data for fully crosslinked collagen-TPEG double networks. ###
 **Figure S6** * **Content**: Time-dependent modulus data (G’, G”) for
 collagen-TPEG double networks during crosslinking. ### **Figure S7** *
 **Content**: Time-dependent modulus data (G’, G”) for TPEG-only networks
 crosslinked at 25°C and 30°C. ### **Figure S9** * **Content**:
 Distributions of cell aspect ratios for different gelation conditions. ###
 **Figure S10** * **Content**: Procedure for calculating cell aspect ratios
 using gyration tensor and correlations with other cell shape metrics. ###
 **Figure S11** * **Content**: Histograms of aspect ratios for cells on 2D
 substrates and in 3D hydrogels. ### **Figure S12** * **Content**: Weighted
 correlation analysis between gelation time and cell aspect ratios. --- ##
 Citation If you use this dataset in your work, we request that you cite
 the corresponding publication: **Title**: Optimizing gelation time for
 cell shape control through active learning\ **Journal**: Soft Matter\
 **DOI**:
 [https://doi.org/10.1039/D4SM01130A](https://doi.org/10.1039/D4SM01130A)
 --- ## Contact For questions regarding the data or scripts: * Refer to the
 figure-specific README files for detailed information. * Contact
 information is provided in the associated publication. ---