Tissue Engineered Multi-Aggregate Cortical-Hippocampal Neural Networks for Pharmacological Investigations / Victor Pablo Acero.

Format:

Book

Thesis/Dissertation

Author/Creator:

Acero, Victor Pablo, author.

Contributor:

University of Pennsylvania. Bioengineering, degree granting institution.

Language:

English

Subjects (All):

Neurosciences.

Biomedical engineering.

Cellular biology.

Bioengineering.

Bioengineering--Penn dissertations.

Penn dissertations--Bioengineering.

Local Subjects:

Neurosciences.

Biomedical engineering.

Cellular biology.

Bioengineering.

Bioengineering--Penn dissertations.

Penn dissertations--Bioengineering.

Physical Description:

1 online resource (404 pages)

Contained In:

Dissertations Abstracts International 85-12B.

Place of Publication:

[Philadelphia, Pennsylvania] : University of Pennsylvania, 2022.

Ann Arbor : ProQuest Dissertations & Theses, 2024

Language Note:

English

Summary:

Cortical-hippocampal networks are crucial for integrating multisensory experiences into distinct, enduring memories and facilitating memory retrieval. Despite advances in understanding hippocampal function through various experimental techniques and animal models, the complexity of these in vivo networks remains a challenge, and their low-throughput limits utility in pharmacological research. In vitro models, particularly 2D cultures, offer simplified systems for studying hippocampal networks and are low-cost, high-throughput testbeds. However, they fall short in recapitulating key aspects of the native microenvironment, thus their network properties are too dissimilar and their translational value is limited. In this dissertation, we applied tissue engineering techniques to develop biofidelic multi-cellular cortical-hippocampal neural networks as novel models and testbeds for scientific investigations. We employed a forced aggregation technique to generate high-density (>100,000 cells/mm3) multi-cellular three-dimensional (3D) aggregates using rodent embryonic hippocampal tissue. We compared the structural and functional properties of aggregated (3D) and dissociated (2D) cultures over 28 days in vitro (DIV). Aggregates exhibited robust axonal fasciculation, significant neuronal polarization at earlier time points, and astrocytes forming non-overlapping quasi-domains with stellate morphologies resembling in vivo structures. Using multi-electrode arrays (MEAs), we observed that 3D networks developed highly synchronized activity with high burstiness by 28 DIV. These findings suggest that the 3D microenvironment supports emergent biofidelic properties. Building on this, we explored configurations of cortical and hippocampal aggregates to model cortical-hippocampal networks. We created three distinct four-node multi-aggregate configurations (3H1C, 2H2C, 1H3C) and characterized their morphology, structural connectivity, and electrophysiological properties. We hypothesized that distinct network configurations would produce unique emergent properties. All configurations formed robust networks with axonal tracts spanning distinct nodes and similar structural connectivity, however, astrocyte domain formation was attenuated relative to single-aggregate networks. We posit that further analysis, e.g. LME models and functional connectivity, to further characterize these systems will enhance their utility in translational research by elucidating more complex network properties at local (aggregate) and global (multi-aggregate) scales. We found configuration modulated emergent electrophysiological properties and the effects of ketamine. These findings demonstrate that neural aggregates spanned by long-projecting axonal tracts can be used as modular building blocks for complex multi-nodal network topologies.

Notes:

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Advisors: Cullen, Kacy; Kording, Konrad; Committee members: O'Donnell, John; Vitale, Flavia; Christian, Kimberly.

Department: Bioengineering.

Ph.D. University of Pennsylvania 2024.

Local Notes:

School code: 0175

ISBN:

9798382829944

Access Restriction:

Restricted for use by site license.