Neuronal morphology governs how neurons connect, integrate, and process information, offering critical insights into the functional architecture of the brain. Characterizing the three-dimensional (3D) morphology of individual neurons is key not only for mapping circuit connectivity but also for understanding the cellular diversity that emerges during development. Neural organoids are valuable models of human brain development and disease, yet their morphological complexity remains poorly characterized despite advances in single-cell transcriptomics. Here, we use 3D confocal imaging and manual reconstruction of 735 neurons to analyze forebrain (dorsal and ventral) and thalamic (dorsal and ventral) organoids, as well as forebrain, thalamic, and corticothalamic assembloids. We find that organoids and assembloids exhibit distinct morphologies resembling fetal brain neurons, including immature pyramidal-like, double-bouquet, and bushy-like neurons. Interregional assembloids show greater neuronal morphological complexity than individual organoids, with more extensive dendritic branching, longer projections, and diverse soma shapes. Corticothalamic assembloids further display features of emerging connectivity. We observe dendritic spines with excitatory and inhibitory profiles and varicosities, indicative of maturing synaptic architecture. Together, our work makes an initial effort in describing the diversity of neuronal morphology in human neural organoids and assembloids. It further establishes structural phenotyping as a critical dimension for validating human neural models and underscores their value for modeling morphofunctional disorders.
Fast Approximation Algorithm for Non-Monotone DR-submodular Maximization under Size Constraint
arXiv:2511.02254v1 Announce Type: cross Abstract: This work studies the non-monotone DR-submodular Maximization over a ground set of $n$ subject to a size constraint $k$. We