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Brains in a Dish: The Fidelity of Cerebral Organoids as Models for Human Disease
Luke Lyons
Division:
Mathematics and Sciences
Department:
Biological Sciences
Mentor:
Allissa Blystone
Mentor's e-mail:
ablystone@occ.cccd.edu
Author's e-mail:
llyons4@student.cccd.edu
Abstract:

To study the complexities of human brain development and disease, researchers make use of a model system known as cerebral organoids—self-organizing brain tissue derived from stem cells. These “mini-brains” are being used with increasing frequency to research complex diseases without a thorough understanding of their accuracy. This literature review focuses on the use of cerebral organoids as models for human disease, specifically looking at the insights gained on COVID-19 and glioblastoma multiforme, a deadly brain cancer. These studies are contextualized with current research on the fidelity of organoids as models of brain development. Cerebral organoid research has authored many critical findings: SARS-CoV-2 infects brain cells, derailing neural physiology, and disrupts the production of cerebrospinal fluid (Pellegrini et al., 2020); and glioblastomas reactivate the formation of outer radial glia, cells critical in brain development, contributing to the heterogeneity of the disease (Bhaduri et al., 2020). Although important, these studies are all predicated on the assumption that cerebral organoids recapitulate features of human cortical development accurately. While most research suggests that they are sound models, recent studies argue that current culturing methods activate stress pathways, impairing the development of characteristic cellular subtypes found in a maturing cortex (Bhaduri et al., 2019). Currently, cerebral organoid research is a work in progress. As culturing techniques continue to be refined, it is paramount that organoid research is not extrapolated to generalizable information about all brain disease and development, rather as an avenue to research important questions that can be studied further using real cortical samples.

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