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Increased connectivity of hiPSC-derived neural networks in multiphase granular hydrogel scaffolds

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posted on 2023-12-14, 14:09 authored by Chia-Chen Hsu, Julian H George, Sharlayne Waller, Cyril Besnard, David A Nagel, Eric HillEric Hill, Michael D Coleman, Alexander M Korsunsky, Zhanfeng Cui, Hua Ye

To reflect human development, it is critical to create a substrate that can support long-term cell survival, differentiation, and maturation. Hydrogels are promising materials for 3D cultures. However, a bulk structure consisting of dense polymer networks often leads to suboptimal microenvironments that impedes nutrient exchange and cell-to-cell interaction. Herein, granular hydrogel-based scaffolds were used to support 3D human induced pluripotent stem cell (hiPSC)-derived neural networks. A custom designed 3D printed toolset was developed to extrude hyaluronic acid hydrogel through a porous nylon fabric to generate hydrogel granules. Cells and hydrogel granules were combined using a weaker secondary gelation step, forming self-supporting cell laden scaffolds. At three and seven days, granular scaffolds supported higher cell viability compared to bulk hydrogels, whereas granular scaffolds supported more neurite bearing cells and longer neurite extensions (65.52 ± 11.59 μm) after seven days compared to bulk hydrogels (22.90 ± 4.70 μm). Long-term (three-month) cultures of clinically relevant hiPSC-derived neural cells in granular hydrogels supported well established neuronal and astrocytic colonies and a high level of neurite extension both inside and beyond the scaffold. This approach is significant as it provides a simple, rapid and efficient way to achieve a tissue-relevant granular structure within hydrogel cultures.

Funding

Engineering human neural networks

Biotechnology and Biological Sciences Research Council

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China Regenerative Medicine International (CRMI)

Jiangsu Industrial Technology Research Institute (JITRI)

Tackling human dental caries by multi-modal correlative microscopy and multi-physics modelling

Engineering and Physical Sciences Research Council

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Rich Nonlinear Tomography for advanced materials

Engineering and Physical Sciences Research Council

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History

School

  • Science

Department

  • Chemistry

Published in

Bioactive Materials

Volume

9

Pages

358 - 372

Publisher

Elsevier

Version

  • VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

2021-07-07

Publication date

2021-07-15

Copyright date

2021

eISSN

2452-199X

Language

  • en

Depositor

Deposit date: 14 December 2023

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