A human neuron-microglia tri-culture platform to study the influence of microglia on developing neuronal networks in vitro

Human brain function is dependent on synaptic architecture and function between a range of different cell types. Glutamatergic and GABAergic neurons provide the basis by which the excitatory and inhibitory balance is achieved in cortical networks, and microglia interact with them to shape synaptic architecture and neural networks. Understanding the interactions between these cell types is crucial to elucidating mechanisms relevant to brain physiology and, potentially, to neurodevelopmental and neurological disorders. Here, we establish a rapid and reproducible human tri-culture platform comprising deterministically-programmed glutamatergic neurons, GABAergic neurons, and microglia to facilitate cell-cell interaction studies during human cortical development. Using these deterministically-programmed ioCells, we systematically optimised neuronal ratios, culture conditions, and the timing of microglial integration to generate a stable neuronal network prior to microglia incorporation. Multi-electrode arrays (MEAs) recordings identified an 80:20 glutamatergic-to-GABAergic ratio as the most robust configuration for sustained and reproducible network activity in this context. Structural characterisation using automated high-content imaging confirmed the formation of both excitatory and inhibitory synapses, while longitudinal MEA recordings demonstrated stable network maturation following microglial incorporation. Microglia incorporation influenced neuronal firing dynamics, increasing burst activity without disrupting early synapse formation. As a proof of concept for disease modelling, we incorporated microglia carrying the Alzheimers disease-associated TREM2 R47H mutation and detected subtle but reproducible alterations in neuronal burst dynamics. Together, this work establishes a defined human neuron-microglia triculture platform that enables scalable investigation of neuroimmune interactions and genetic variants, laying the foundations for more complex future models.