Integrated Mesh-Based Analysis of Neuronal Morphology, Synaptic Subcompartments, and Laminar Inputs in the H01 Human Temporal Cortex.

Quantitative nanoscale descriptions of human cortical neurons remain limited, particularly when neuronal morphology, synaptic geometry, and laminar input organization are examined within a unified connectomic framework. Using publicly available H01 human temporal cortex data and open-source tools, we developed a reproducible pipeline to decode sharded data, reconstruct neuronal and synaptic mesh models, and integrate mesh-based morphometry with annotation-derived laminar input analysis. The associated custom scripts will be released in a public repository upon publication. Morphometric analysis focused on a layer 5 pyramidal neuron, whereas laminar input profiles were examined in four layer 5 pyramidal neurons and four layer 5 interneurons. The reconstructed pyramidal neuron contained one soma, seven main limbs, and 207 branch-level skeleton segments, with a skeleton length of 10.13 mm and a soma volume of 9191.33 µm3. Branch parameters showed right-skewed heterogeneity, and branch length was strongly correlated with surface area (r = 0.864, p < 0.001). Afferent synaptic subcompartments displayed irregular, flattened morphologies and continuous right-skewed bounding-box distributions without evidence of bimodality. Their volume and surface area were strongly correlated (r = 0.952, p < 0.001), indicating coordinated geometric scaling. In the selected cohort, layer-wise input annotations were concentrated in layers III-V, with a layer V-dominant profile in the selected pyramidal neurons and broader distributions in the selected interneurons. Overall, this H01-based framework links neuronal morphology, synaptic-subcompartment geometry, and laminar input estimates, while broader conclusions require validation across additional neurons and donors.