AUTHOR=Juneja Deppo S. , Nasuto Slawomir , Delivopoulos Evangelos TITLE=Fast and Efficient Differentiation of Mouse Embryonic Stem Cells Into ATP-Responsive Astrocytes JOURNAL=Frontiers in Cellular Neuroscience VOLUME=13 YEAR=2020 URL=https://www.frontiersin.org/journals/cellular-neuroscience/articles/10.3389/fncel.2019.00579 DOI=10.3389/fncel.2019.00579 ISSN=1662-5102 ABSTRACT=

Astrocytes are multifunctional cells in the CNS, involved in the regulation of neurovascular coupling, the modulation of electrolytes, and the cycling of neurotransmitters at synapses. Induction of astrocytes from stem cells remains a largely underdeveloped area, as current protocols are time consuming, lack granularity in astrocytic subtype generation, and often are not as efficient as neural induction methods. In this paper we present an efficient method to differentiate astrocytes from mouse embryonic stem cells. Our technique uses a cell suspension protocol to produce embryoid bodies (EBs) that are neurally inducted and seeded onto laminin coated surfaces. Plated EBs attach to the surface and release migrating cells to their surrounding environment, which are further inducted into the astrocytic lineage, through an optimized, heparin-based media. Characterization and functional assessment of the cells consists of immunofluorescent labeling for specific astrocytic proteins and sensitivity to adenosine triphosphate (ATP) stimulation. Our experimental results show that even at the earliest stages of the protocol, cells are positive for astrocytic markers (GFAP, ALDH1L1, S100β, and GLAST) with variant expression patterns and purinergic receptors (P2Y). Generated astrocytes also exhibit differential Ca2+ transients upon stimulation with ATP, which evolve over the differentiation period. Metabotropic purinoceptors P2Y1R are expressed and we offer preliminary evidence that metabotropic purinoceptors contribute to Ca2+ transients. Our protocol is simple, efficient and fast, facilitating its use in multiple investigations, particularly in vitro studies of engineered neural networks.