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Abstract

Neuron morphology is frequently used to classify cell-types in the
mammalian cortex. Apart from the shape of the soma and the axonal
projections, morphological classification is largely defined by the
dendrites of a neuron and their subcellular compartments, referred to
as dendritic spines. The dimensions of a neuron's dendritic
compartment, including its spines, is also a major determinant of the
passive and active electrical excitability of dendrites. Furthermore,
the dimensions of dendritic branches and spines change during postnatal
development and, possibly, following some types of neuronal activity
patterns, changes depending on the activity of a neuron. Due to their
small size, accurate quantitation of spine number and structure is
difficult to achieve (Larkman, J Comp Neurol 306:332, 1991). Here we
follow an analysis approach using high-resolution EM techniques. Serial
block-face scanning electron microscopy (SBFSEM) enables automated
imaging of large specimen volumes at high resolution. The large data
sets generated by this technique make manual reconstruction of neuronal
structure laborious. Here we present NeuroStruct, a reconstruction
environment developed for fast and automated analysis of large SBFSEM
data sets containing individual stained neurons using optimized
algorithms for CPU and GPU hardware. NeuroStruct is based on 3D
operators and integrates image information from image stacks of
individual neurons filled with biocytin and stained with osmium
tetroxide. The focus of the presented work is the reconstruction of
dendritic branches with detailed representation of spines. NeuroStruct
delivers both a 3D surface model of the reconstructed structures and a
1D geometrical model corresponding to the skeleton of the reconstructed
structures. Both representations are a prerequisite for analysis of
morphological characteristics and simulation signalling within a neuron
that capture the influence of spines.