Cryptic disease-prone states in human mesocortical assembloids revealed by multimodal profiling
Kim, S.; Kang, R.; Lee, T.; Kim, Y.; Kim, C.-D.; Koo, K.-M.; Na, J.; Lee, S.; Kim, D.; Oh, H.; Lee, A. C.; Kim, T.-H.; Park, B.; Lee, L. P.; Kim, I.; Park, J.-C.
Show abstract
Neurodegenerative diseases are characterized by synaptic failure, aberrant protein accumulation, and neuroglial dysfunction that emerge long before clinical onset. Although brain assembloids, which recapitulate interregional circuit connectivity beyond the scope of single organoids, significantly advance the modeling of circuit pathophysiology, they are predominantly evaluated through single-modality approaches that cannot resolve the functional and molecular heterogeneity underlying differential disease susceptibility. Here we show that morphologically identical human iPSC-derived dorso forebrain-midbrain mesocortical assembloids (MCAs) spontaneously bifurcate into disease-prone and non-prone states under identical culture conditions, revealing that MCA heterogeneity reflects intrinsic neurodegeneration susceptibility rather than stochastic culture variability. Using integrated electrophysiological, molecular, and spatial profiling, we find that disease-prone MCAs exhibit a temporally ordered molecular cascade in which neurofilament light chain elevation precedes tau dysregulation, mirroring the sequential biomarker trajectories observed in pre-symptomatic human neurodegeneration. Disease-prone MCAs further display selective cortical hyperexcitability and aberrant brainwave-like oscillatory dynamics that remain undetectable by any single modality. Spatially, a discrete junction-like neuronal population at the midbrain-forebrain interface shows transcriptional priming for synaptic overactivation alongside impaired astrocytic glutamate clearance, defining a spatially confined neuron-glial uncoupling as a candidate early origin of the disease-prone state. These findings reframe MCA heterogeneity as a biological window into pre-symptomatic neurodegeneration, with broad implications for disease modeling, risk stratification, and therapeutic discovery.
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