A novel approach to increase glial cell populations in brain microphysiological systems

增加大脑微生理系统中神经胶质细胞数量的新方法

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作者:Itzy E Morales Pantoja, Lixuan Ding, Paulo E C Leite, Suelen A Marques, July Carolina Romero, Dowlette-Mary Alam El Din, Donald J Zack, Xitiz Chamling, Lena Smirnova

Abstract

Brain microphysiological systems (bMPS), which recapitulate human brain cellular architecture and functionality more closely than traditional monolayer cultures, have become a practical, non-invasive, and increasingly relevant platform for the study of neurological function in health and disease. These models include 3D spheroids and organoids as well as organ-on-chip models. Currently, however, existing 3D brain models vary in reflecting the relative populations of the different cell types present in the human brain. Most of the models consist mainly of neurons, while glial cells represent a smaller portion of the cell populations. Here, by means of a chemically defined glial-enriched medium (GEM), we present an improved method to expand the population of astrocytes and oligodendrocytes without compromising neuronal differentiation in bMPS. An important finding is that astrocytes not only increased in number but also changed in morphology when cultured in GEM, more closely recapitulating primary culture astrocytes. We demonstrate oligodendrocyte and astrocyte enrichment in GEM bMPS using a variety of complementary methods. We found that GEM bMPS are electro-chemically active and showed different patterns of Ca +2 staining and flux. Synaptic vesicles and terminals observed by electron microscopy were also present. No significant changes in neuronal differentiation were observed by gene expression, however, GEM enhanced neurite outgrowth and cell migration, and differentially modulated neuronal maturation in two different iPSC lines. Our results have the potential to significantly improve in vivo-like functionality of bMPS for the study of neurological diseases and drug discovery, contributing to the unmet need for safe human models.

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