MULTIMODAL HIGH-THROUGHPUT PHARMACOLOGICAL PROFILING IN HUMAN IPSC-DERIVED PSYCHIATRIC DISEASE MODELS

This study aims to establish a scalable human iPSC-derived platform for pharmacological profiling in psychiatric disease models. By screening a large panel of over 100 psychotropic or tool compounds across genetically defined patient and control neuronal systems, we seek to assess cell-model-to-human transferability through clustering of compound-specific molecular and electrophysiological signatures. Ultimately, this approach is intended to enable high-throughput discovery of modulators of intracellular signaling and synaptic connectivity capable of rescuing disease-associated phenotypes.
Schizotypic and control human iPSC lines are differentiated into transcription factor-induced excitatory (NGN2) or inhibitory (AD2) neuronal subtypes and co-cultured with murine astrocytes in both 2D and 3D configurations. Network-level functional phenotypes are assessed using microelectrode array (MEA) recordings, while cell population-specific changes of gene expression are captured by single-cell RNA sequencing. Complementary high-content imaging and calcium imaging are employed to increase phenotypic resolution and enable multimodal integration of pharmacological effects.
Pilot experiments demonstrate robust differences in multiple network activity parameters in 3D iAssembloids following treatment with a focused subset of psychotropic compounds, indicating sensitivity of the system to pharmacological modulation. Ongoing analyses of a larger compound panel across multiple cell lines are expected to further delineate compound-specific and genotype-dependent response patterns at both functional and transcriptional levels.
These findings establish the feasibility of combining human iPSC-derived neuronal systems with multimodal phenotyping to capture pharmacological response profiles relevant to psychiatric disease. This platform provides a foundation for systematic compound clustering and translational assessment, with the potential to accelerate identification of therapeutic strategies targeting disease-relevant cellular networks.