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

ABSTRACT: 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. Graphical abstract Image 1 Highlights • In longer-term culture, granular hydrogels supported the development of neural culture with extensive neurite outgrowth.• Granular hydrogels supported significantly higher cell viability and neurite outgrowth over 7 days of culture.• A simple 3D printed extrusion and mixing toolset is used to generate and homogenously seed cells into granular hydrogels.• The toolset designs are provided for 3D printing, enabling use and customization of the techniques described.

SUBMITTER: Hsu C

PROVIDER: S-EPMC8586009 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:In recent years, the application of network analysis to neuroimaging data has provided useful insights about the brain's functional and structural organization in both health and disease. This has proven a significant paradigm shift from the study of individual brain regions in isolation. Graph-based models of the brain consist of vertices, which represent distinct brain areas, and edges which encode the presence (or absence) of a structural or functional relationship between each pair of vertices. By definition, any graph metric will be defined upon this dyadic representation of the brain activity. It is however unclear to what extent these dyadic relationships can capture the brain's complex functional architecture and the encoding of information in distributed networks. Moreover, because network representations of global brain activity are derived from measures that have a continuous response (i.e., interregional BOLD signals), it is methodologically complex to characterize the architecture of functional networks using traditional graph-based approaches. In the present study, we investigate the relationship between standard network metrics computed from dyadic interactions in a functional network, and a metric defined on the persistence homological scaffold of the network, which is a summary of the persistent homology structure of resting-state fMRI data. The persistence homological scaffold is a summary network that differs in important ways from the standard network representations of functional neuroimaging data: (i) it is constructed using the information from all edge weights comprised in the original network without applying an ad hoc threshold and (ii) as a summary of persistent homology, it considers the contributions of simplicial structures to the network organization rather than dyadic edge-vertices interactions. We investigated the information domain captured by the persistence homological scaffold by computing the strength of each node in the scaffold and comparing it to local graph metrics traditionally employed in neuroimaging studies. We conclude that the persistence scaffold enables the identification of network elements that may support the functional integration of information across distributed brain networks.

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

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