American Chemical Society
Browse
ab8b00622_si_008.zip (20.48 MB)

Functional and Sustainable 3D Human Neural Network Models from Pluripotent Stem Cells

Download (20.48 MB)
dataset
posted on 2018-10-01, 00:00 authored by William L. Cantley, Chuang Du, Selene Lomoio, Thomas DePalma, Emily Peirent, Dominic Kleinknecht, Martin Hunter, Min D. Tang-Schomer, Giuseppina Tesco, David L. Kaplan
Three-dimensional (3D) in vitro cell and tissue culture models, particularly for the central nervous system, allow for the exploration of mechanisms of organ development, cellular interactions, and disease progression within defined environments. Here, we describe the development and characterization of human 3D tissue models that promote the differentiation and long-term survival of functional neural networks. This work builds upon previous work where primary rodent neurons were successfully grown in a similar 3D system. The model was adapted to human induced pluripotent stem cells, allowing for a more direct exploration of the human condition. These tissue cultures show diverse cell populations, including neurons and astroglial cells, interacting in 3D and exhibit spontaneous neural activity confirmed through electrophysiological recordings and calcium imaging over at least nine months. This approach allows for the direct integration of pluripotent stem cells into the 3D construct, bypassing early neural differentiation steps (embryoid bodies and neural rosettes). The streamlined process, in combination with the longevity of the cultures, provides a system that can be manipulated to support a variety of experimental applications, including the study of network development, maturation, plasticity, and/or degeneration. This tissue model was tested with stem cells derived from healthy individuals as well as Alzheimer’s and Parkinson’s disease patients. We observed similar growth and gene expression, which indicates the feasibility of generating patient-derived brain tissue models. These could be used to uncover early stage biomarkers of the disease state, in turn supporting earlier diagnosis and improving understanding of disease progression. With additional model development, this approach would have potential use for investigating drug targets in neurodegenerative diseases.

History