Deep learning segmentation with curvature consistency reveals astrocyte nanostructure across species

Cellular function depends on the precise deployment and distribution of nanoscale structures, but these features remain difficult to measure and compare between cells and datasets. This challenge is pronounced for astrocytes, whose intricate nanostructures interface with neurons, glia, and vasculature, to control brain development, synaptic development/plasticity, homeostasis, and responses to injury/disease. Here, we developed deep learning approaches with curvature consistency for automated astrocyte segmentation across volume electron microscopy datasets, reducing reconstruction time from manual or semi-automatic methods by 12-fold and enabling brain region and cross-species interrogation of astrocytic nanoarchitecture. This allowed us to uncover organizing principles and motifs alongside ultrastructural divergence between species. While both species exhibit a wide but shallow topological network, marmoset astrocytes display increased process thickness and branching. We further identified extrasynaptic neuronal engulfment and a robust astrocytic endosomal system across species and brain regions. Together, our findings demonstrate previously inaccessible structural principles of astrocytes, offering a framework for understanding structure-function relationships in the central nervous system.