Synthesis of geometrically realistic and watertight neuronal ultrastructure manifolds forin silicomodeling

Understanding the intracellular dynamics of brain cells entails performing three-dimensional molecular simulations incorporating ultrastructural models that can capture cellular membrane geometries at nanometer scales. While there is an abundance of neuronal morphologies available online, e.g. from NeuroMorpho.Org , converting those fairly abstract point-and-diameter representations into geometrically realistic and simulation-ready, i.e. watertight, manifolds is challenging. Many neuronal mesh reconstruction methods have been proposed, however, their resulting meshes are either biologically unplausible or non-watertight. We present an effective and unconditionally robust method capable of generating geometrically realistic and watertight surface manifolds of spiny cortical neurons from their morphological descriptions. The robustness of our method is assessed based on a mixed dataset of cortical neurons with a wide variety of morphological classes. The implementation is seamlessly extended and applied to synthetic astrocytic morphologies that are also plausibly biological in detail. Resulting meshes are ultimately used to create volumetric meshes with tetrahedral domains to perform scalable in silico reaction-diffusion simulations for revealing cellular structure–function relationships. Availability and implementation Our method is implemented in NeuroMorphoVis, a neuroscience-specific open source Blender add-on, making it freely accessible for neuroscience researchers. Key points A plethora of neuronal morphologies is available in a point-and-diameter format, but there are no robust techniques capable of converting these morphologies into geometrically realistic models that can be used to conduct subcellular simulations. We present a scalable method capable of synthesizing high fidelity watertight ultrastructural manifolds of complete neuronal models from their one-dimensional descriptions using the synaptic data obtained from the digitally reconstructed neuronal circuits of the Blue Brain Project. Resulting manifold models comprise geometrically realistic somata and spine geometries, enabling accurate in silico experiments that can probe intricate structure-function relationships. Our method is extensible and can be seamlessly applied to other cellular structures such as astroglial morphologies and even large networks of cerebral vasculature.