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Hyaluronic-Acid Based Hydrogels for 3-Dimensional Culture of Patient-Derived Glioblastoma Cells
Published on: August 24, 2018
Hyaluronic Acid-Based, Double Network Hydrogels With Tunable Viscoelasticity for Neural Cell Culture
Talia Sanazzaro1, Sabrina Pietrosemoli Salazar1, Neha Arvinth1
1Department of Biomedical Engineering, The University of Texas, Austin, Texas, USA.
Abstract:
Brain tissue is the softest, most viscoelastic tissue in mammals and these mechanical properties strongly influence cell phenotypes. However, conventional hydrogels for 3D cultures rarely provide the ability to tune the elasticity (G') independently of the viscosity (G″), making it impossible to decouple the effects of each mechanical component on cell behavior. To address this deficiency, we have developed a hyaluronic acid (HA)-based, double network hydrogel platform, in which G' and G″ can be tuned independently, keeping G' within the range observed in native brain tissue. The double network hydrogel includes a covalently photocrosslinked HA network (thiolene) to control the elasticity and a dynamically crosslinked HA (hydrazone) network to regulate the viscosity. Addition of the dynamic network to the static single networks increased viscoelasticity, as assessed by the stress-relaxation time and dissipation factor (tan(δ)), of the biomaterial fourfold over that of the covalent network alone, without affecting the storage modulus (G'). The proliferation and spreading of two neural cell types, patient-derived glioblastoma (GBM) tumor cells and mouse neural stem cells (mNSCs), were evaluated in single and double network hydrogels with varying elasticities. An increase in viscoelasticity increased cell proliferation in one patient-derived GBM line, independently of elasticity, while the converse was found in mNSCs. In both GBM and mNSCs cultures, increased cell spreading was observed in stiff double network, compared to stiff single network, gels. This double network hydrogel model allows for the orthogonal tuning of elasticity and viscosity to better represent the mechanics of CNS tissue.
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