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Application of pluripotent stem cells for treatment of human neuroendocrine disorders

Suga, Hidetaka
Cell and tissue research 2019 v.375 no.1 pp. 267-278
anterior pituitary, corticotropin, corticotropin-releasing hormone, embryogenesis, humans, medicine, mice, neurons, neurosecretory system, stem cells, vasopressin
The neuroendocrine system is composed of many types of functional cells. Matured cells are generally irreversible to progenitor cells and it is difficult to obtain enough from our body. Therefore, studying specific subtypes of human neuroendocrine cells in vitro has not been feasible. One of the solutions is pluripotent stem cells, such as embryonic stem (ES) cells and induced pluripotent stem (iPS) cells. These are unlimited sources and, in theory, are able to give rise to all cell types of our body. Therefore, we can use them for regenerative medicine, developmental basic research and disease modeling. Based on this idea, differentiation methods have been studied for years. Recent studies have successfully induced hypothalamic-like progenitors from mouse and human ES/iPS cells. The induced hypothalamic-like progenitors generated hypothalamic neurons, for instance, vasopressin neurons. Induction to adenohypophysis was also reported in the manner of self-formation by three-dimensional floating cultures. Rathke’s pouch-like structures, i.e., pituitary anlage, were self-organized in accordance with pituitary development in embryo. Pituitary hormone-producing cells were subsequently differentiated. The induced corticotrophs secreted adrenocorticotropic hormone in response to corticotropin-releasing hormone. When engrafted in vivo, these cells rescued systemic glucocorticoid levels in hypopituitary mice. These culture methods were characterized by replication of stepwise embryonic differentiation. It is based on the idea of mimicking the molecular environment of embryogenesis. Thanks to these improvements, these days, we can generate hormone-secreting neuroendocrine cells from pluripotent stem cells. The next problems that need to be solved are improving differentiation efficiency even further and structuring networks.