Data from: Optimizing gelation time for cell shape control through active learning
Data files
Jan 10, 2025 version files 5.14 GB
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Dryad_Data.zip
5.14 GB
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README.md
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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.
README: 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
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.