Proteomics-based receptor-ligand matching enhances differentiation maturity of human-stem-cell-derived neurons

Human-induced pluripotent stem cell (hiPSC) technology enables generation of various cell types, offering significant potential for regenerative medicine and personalized disease modeling. However, optimizing the functional maturity of differentiated cells is crucial for improving their reliability in research. Here, we introduce a deep-proteomics-based "receptor-ligand matching" (RLM) strategy to inventory surface receptors on differentiated cells and adjust the culture conditions accordingly. Focusing on an NGN2-induced neuron (iN) model, which rapidly produces glutamatergic neurons but exhibits modest synaptic activity, we identified 3,934 iN membrane proteins, including dozens of growth factor receptors and, notably, the complete GDNF receptor family (GFRA1, GFRA2, and GFRA3) previously undetected. Supplementing culture media with selected ligands enhanced neuronal health, neurite density, and synaptogenesis. Electrophysiology measurements confirmed greater functional synaptic maturity and responsiveness in optimized iNs compared to conventionally generated iNs. The RLM strategy offers a versatile approach to enhance the health and functionality of potentially any hiPSC-derived cell type.