Early-stage quality-late-stage confidence: Neural induction quality control as a key to reproducible MEA-based...

Human induced pluripotent stem cell (hiPSC)-derived neural models combined with microelectrode array (MEA)-based readouts are increasingly used in next-generation neurotoxicity assessment. However, neural induction of hiPSCs into multipotent neural progenitor cells (hiNPCs) remains highly variable, and current quality control (QC) efforts focus largely on the pluripotent starting material. As a result, failed neural inductions are often recognized only after weeks of differentiation during functional network analysis, causing substantial resource loss. Here, we present a tiered QC framework spanning the entire workflow from hiPSC banking, neural induction into proliferative hiNPCs, the differentiation of hiNPCs into 3D neuron-glia BrainSpheres, and their subsequent organization into functional 2D networks on MEAs, with emphasis on early detection of induction failures. Using eleven independent dual-SMAD inductions derived from a single hiPSC line, we show that hiPSCore, a machine-learning-based classifier for early cell-fate decisions, reliably distinguishes successful from unsuccessful neuroectodermal inductions at early stages (days 6-12). Successful induction depends on timely neuroectodermal commitment, reflected in hiPSCore trajectories and PAX6 expression, as well as preserved hiNPC proliferation. Inductions passing these early QC checkpoints generated BrainSphere-derived networks with reproducible MEA maturation trajectories, balanced neurotransmitter-responsive unit distributions, and conserved subtype-specific pharmacological responses across GABAergic, glutamatergic, dopaminergic, and serotonergic modalities. In contrast, signaling pathway-related markers associated with dual SMAD inhibition (BMP, TGFβ, and MAPK) were not predictive of downstream QC performance. Together, these findings demonstrate that early fate-level QC is predictive of the functional performance of hiPSC-derived neural networks. Our adaptable QC framework allows early termination of failed inductions, reduces resource burden, and strengthens confidence in hiPSC-based neural network assays for neurotoxicity testing.