Collagen methacrylation is a promising approach to generate photo-cross-linkable cell-laden hydrogels with improved mechanical properties. However, the impact of species-based variations in amino acid composition and collagen isolation method on methacrylation degree (MD) and its subsequent effects on the physical properties of methacrylated collagen (CMA) hydrogels and cell response are unknown. Herein, we compared the effects of three collagen species (bovine, human, and rat), two collagen extraction methods (pepsin digestion and acid extraction), and two photoinitiators (lithium phenyl-2,4,6-trimethylbenzoylphosphinate (LAP) and Irgacure-2959 (I-2959)) on the physical properties of CMA hydrogels, printability and mesenchymal stem cell (MSC) response. Human collagen showed the highest MD. LAP was more cytocompatible than I-2959. The compressive modulus and cell viability of rat CMA were significantly higher (p < 0.05) than bovine CMA. Human CMA yielded constructs with superior print fidelity. Together, these results suggest that careful selection of collagen source and cross-linking conditions is essential for biomimetic design of CMA hydrogels for tissue engineering applications.
Astrocytes, highly specialized glial cells, play a critical role in neuronal function. Variations in brain extracellular matrix (ECM) during development and disease can significantly alter astrocyte cell function. Age-related changes in ECM properties have been linked to neurodegenerative diseases such as Alzheimer’s disease. The goal of this study was to develop hydrogel-based biomimetic ECM models with varying stiffness and evaluate the effects of ECM composition and stiffness on astrocyte cell response. Xeno-free ECM models were synthesized by combining varying ratios of human collagen and thiolated hyaluronic acid (HA) crosslinked with polyethylene glycol diacrylate. Results showed that modulating ECM composition yielded hydrogels with varying stiffnesses that match the stiffness of the native brain ECM. Collagen-rich hydrogels swell more and exhibit greater stability. Higher metabolic activity and greater cell spreading was observed in hydrogels with lower HA. Soft hydrogels trigger astrocyte activation indicated by greater cell spreading, high GFAP expression and low ALDH1L1 expression. This work presents a baseline ECM model to investigate the synergistic effects of ECM composition and stiffness on astrocytes, which could be further developed to identify key ECM biomarkers and formulate new therapies to alleviate the impact of ECM changes on the onset and progression of neurodegenerative diseases.
Biomimetic scaffolds composed of bioactive ceramic‐based materials incorporated within a polymeric framework have shown immense promise for use in bone tissue engineering (BTE) applications. However, studies on direct comparison of the efficacy of different bioceramics on bone bioactivity and osteogenic differentiation are lacking. Herein, we performed an in vitro direct comparison of three different bioceramics—Bioglass 45S5 (BG), Laponite XLG (LAP), and β‐Tricalcium Phosphate (TCP)—on the physical properties and bone bioactivity of methacrylated collagen (CMA) hydrogels (10% w/w bioceramic:CMA). In addition, human MSCs (hMSCs) were encapsulated in bioceramic‐laden CMA hydrogels and the effect of different bioceramics on osteogenic differentiation of hMSCs was investigated in two different culture medium—osteoconductive (without dexamethasone [DEX]) and osteoinductive (with DEX). Results showed that the stability of CMA hydrogels was maintained upon bioceramic addition. Compression testing revealed that BG incorporation significantly decreased (p < 0.05) the modulus of photochemically crosslinked CMA hydrogels. Incubation of TCP‐CMA and LAP‐CMA hydrogels in simulated body fluid showed deposition of hydroxycarbonate apatite layer on the surface indicating that these hydrogels may be more bone bioactive than BG‐CMA and CMA only hydrogels. Cell cytoskeleton staining results showed greater cell spreading in TCP‐CMA hydrogels. Furthermore, TCP incorporation significantly increased alkaline phosphatase activity (ALP; p < 0.05) in hMSCs. Together, these results indicate that TCP has superior osteogenic potential compared with BG and LAP and hence should be considered as a bioceramic of preferred choice for use in the biomimetic design of cell‐laden hydrogels for BTE applications.
In this study, a finite element (FE)-based machine learning model was developed to predict the mechanical properties of bioglass (BG)-collagen (COL) composite hydrogels. Based on the experimental observation of BG-COL composite hydrogels with scanning electron microscope, 2000 microstructural images with randomly distributed BG particles were created. The BG particles have diameters ranging from 0.5 µm to 1.5 µm and a volume fraction from 17% to 59%. FE simulations of tensile testing were performed for calculating the Young’s modulus and Poisson’s ratio of 2000 microstructures. The microstructural images and the calculated Young’s modulus and Poisson’s ratio by FE simulation were used for training and testing a convolutional neural network regression model. Results showed that the network developed in this work can effectively predict the mechanical properties of the composite hydrogels. The R-squared values were 95% and 83% for Young’s modulus and Poisson’s ratio, respectively. This work provides a surrogate model of finite element analysis to predict mechanical properties of BG-COL hydrogel using microstructure images, which could be further utilized for characterizing heterogeneous materials in big data-driven material designs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.