bioRxivNeurosciencePreprint
Ultrastructurally planar growth cones during neocortical axon navigation
Yu NakanishiiD, Masafumi TsuboiiD, Hiroki KawaiiD, Koyo Shibayama, Kenta YamauchiiD, Sasikarn LooprasertkuliD, KyungEun Lee, Hajime KomakiiD, Takamitsu Egashira, Shogo SugaiD, Tomonari MurakamiiD, Kenichi OhkiiD, Hiroyuki HiokiiD, Nobuhiko OhnoiD, Hidenobu MizunoiD, Yusuke HirabayashiiD
Abstract
Neurons extend axons toward spatially defined but often distant targets to establish brain circuits. Callosal projection neurons exemplify this organization, linking corresponding cortical territories between the two hemispheres. Growing axons rely on growth cones to regulate direction and speed through densely packed tissues. Although growth cone morphology and dynamics have been extensively characterized in cultured neurons and invertebrate models, how growth cones navigate the three-dimensional environment of the developing mammalian brain remains poorly explored. Here, we combined a genetically encoded electron microscopy (EM)-labeling strategy and zero-shot deep-learning-based tracing with high-resolution volume EM to reconstruct growth cones of identified callosal projection neurons in the developing mouse brain. Mediolaterally extending growth cones exhibited a planar, fan-shaped morphology, preferentially expanded along the dorsoventral axis. Filopodia extended from the edges of this plane and aligned predominantly with the same axis but showed no directional bias toward the growing direction. Longitudinal in vivo imaging revealed that increased growth cone complexity associated with filopodial extension coincided with lower instantaneous velocity but preceded accelerated extension. Together, these findings reveal a dorsoventrally oriented planar growth cone architecture in which filopodial extension precedes accelerated axon growth, suggesting a structural constraint that may restrict axon navigation to the callosal plane.
- organism: mouse
- assay: electronic health records
- research focus: cell type annotation
- approach: deep learning
- Citations
- 0
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