The synapses in the cerebral cortex can be classified into two main types, Gray’s type I and type II, which correspond to asymmetric (mostly glutamatergic excitatory) and symmetric (inhibitory GABAergic) synapses, respectively. Hence, the quantification and identification of their different types and the proportions in which they are found, is extraordinarily important in terms of brain function. The ideal approach to calculate the number of synapses per unit volume is to analyze three-dimensional samples reconstructed from serial sections. However, obtaining serial sections by transmission electron microscopy is an extremely time consuming and technically demanding task. Using FIB/SEM microscopy, we recently showed that virtually all synapses can be accurately identified as asymmetric or symmetric synapses when they are visualized, reconstructed and quantified from large three-dimensional tissue samples obtained in an automated manner. Nevertheless, the analysis, segmentation and quantification of synapses is still a labor intensive procedure. Thus, novel solutions are currently necessary to deal with the large volume of data that is being generated by automated 3D electron microscopy. Accordingly, we have developed ESPINA, a software tool that performs the automated segmentation and counting of synapses in a reconstructed 3D volume of the cerebral cortex, and that greatly facilitates and accelerates these processes.

Geometrical features of chemical synapses are relevant to their function. Two critical components of the synaptic junction are the active zone and the postsynaptic density, as they are related to the probability of synaptic release and the number of postsynaptic receptors, respectively. Morphological studies of these structures are greatly facilitated by the use of recent electron microscopy techniques, such as combined focused ion beam milling and scanning electron microscopy (FIB/SEM), and software tools that permit reconstruction of large numbers of synapses in three dimensions. Since the active zone and the postsynaptic density are in close apposition and have a similar surface area, they can be represented by a single surface — the synaptic apposition surface (SAS). We have developed an efficient computational technique to automatically extract this surface from synaptic junctions that have previously been three-dimensionally reconstructed from actual tissue samples imaged by automated FIB/SEM. Given its relationship with the release probability and the number of postsynaptic receptors, the surface area of the SAS is a functionally relevant measure of the size of a synapse that can complement other geometrical features like the volume of the reconstructed synaptic junction, the equivalent ellipsoid size and the Feret’s diameter.

Dendritic spines establish most excitatory synapses in the brain and are located in Purkinje cell's dendrites along helical paths, perhaps maximizing the probability to contact different axons. To test whether spine helixes also occur in neocortex, we reconstructed >500 dendritic segments from adult human cortex obtained from autopsies. With Fourier analysis and spatial statistics, we analyzed spine position along apical and basal dendrites of layer 3 pyramidal neurons from frontal, temporal, and cingulate cortex. Although we occasionally detected helical positioning, for the great majority of dendrites we could not reject the null hypothesis of spatial randomness in spine locations, either in apical or basal dendrites, in neurons of different cortical areas or among spines of different volumes and lengths. We conclude that in adult human neocortex spine positions are mostly random. We discuss the relevance of these results for spine formation and plasticity and their functional impact for cortical circuits.