Navigating Human Astrocyte Differentiation:Direct and Rapid One-Step Differentiation of Induced Pluripotent Stem Cells to Functional Astrocytes Supporting Neuronal Network Development

Astrocytes play a pivotal role in neuronal network development. Despite the well-known role of astrocytes in the pathophysiology of neurologic disorders, the utilization of induced pluripotent stem cell (iPSC)-derived astrocytes in neuronal networks remains limited. Here, we present a streamlined one-step protocol for the differentiation of iPSCs directly into functional astrocytes without the need for ectopic gene expression or neural progenitor cell generation. We found that culturing iPSCs directly in commercial astrocyte medium, was sufficient to differentiate iPSCs into functional astrocytes within 5 weeks. More than 60 iPSC lines were successfully differentiated into astrocytes by independent researchers across 10 independent laboratories. Validation of the iPSC-astrocyte cultures demonstrated consistent astrocyte differentiation with minimal batch-to-batch variability. In dept. characterization of a subset of iPSC lines confirmed astrocyte identity and functionality of the iPSC-astrocyte monocultures by immunofluorescence, flow cytometry, RNA sequencing, glutamate uptake assays and calcium signaling recordings. Optimization of the protocol enabled co-culture of iPSC-astrocytes with Ngn2 iPSC-derived neurons (iNeurons), promoting neuronal differentiation and synapse formation. Lastly, we used single-cell electrophysiology and multi-electrode arrays, by four independent researchers, to confirm robust neuronal network development in 5-week-old iPSC-astrocyte and iNeuron co-cultures. This protocol offers a rapid and efficient method to establish all-human astrocyte-neuron co-cultures, facilitating the investigation of cell-type-specific contributions to disease pathogenesis. Its validation across numerous iPSC lines in 10 independent laboratories highlights the reproducibility of the protocol and positions it as a platform for advancing disease modeling in human neural networks.