The size and organization of the total pool of synaptic vesicles is another critical determinant in synaptic efficacy, strength and plasticity (reviewed by Südhof, 2012). In this study an average number of 923.95 ± 761.33 synaptic vesicles per synaptic bouton, with a minimum of 64 and a maximum of ~5000 vesicles per bouton was counted. The extrema and the given standard deviation indicate a wide variability in the size of the total synaptic vesicle pool. As already discussed in Chapter 4.3 also the size of synaptic boutons was found occupying a broad range.
Thus, the correlation coefficients between total number of synaptic vesicles and surface area (R²=0.87) as well as the bouton volume (R²=0.92) are important indicators for the proportional increase of the synaptic vesicle pool with the size of a synaptic bouton.
When compared with synaptic boutons of comparable size, for example in the rat ‘barrel’
cortex L4 (Rollenhagen et al., 2014) the vesicle pool of human L1 synaptic boutons is ~2fold larger. Another study in cortical L2/3 in humans demonstrated a similar large vesicle pool in line with our findings (Hesse et al., unpublished), while L5 synaptic boutons showed a larger total synaptic vesicle pool with 1518.52 ± 303.18 vesicles per bouton (Yakoubi et al., manuscript submitted). Nevertheless, all studies of synaptic vesicles in the human neocortex revealed nearly the same diameter of synaptic vesicles (~30nm) and dense core vesicles (~60nm) (Yakoubi et al., manuscript submitted; Hesse et al., unpublished).
The relative large total pool of synaptic vesicles also suggests comparably large RRPs, RPs and resting pools at L1 synaptic boutons and are thus an integral part of the PhD thesis.
O UTLOOK
This study is included in a series of ongoing research to investigate the structural, in particular the synaptic organization, of each cortical layer as exemplified for the temporal lobe human neocortex. The final aim is to directly compare findings obtained in different animal species with those of the human brain. In ongoing experiments, the number of reconstructed synaptic boutons will be increased to meet the requirements for a first publication. As already stated above a detailed analysis of the synaptic density in L1a and L1b, the quantification of the number, size and shape of the AZ, and the three synaptic vesicle pools, the RRP, RP and resting pool will be performed. Both parameters are critical determinants of synaptic transmission and plasticity. This lead to quantitative 3D models of synapses that can be used for numerical and/or Monte Carlo Simulations of various synaptic parameters that in humans are not accessible to experiment. In further studies, also a new generation of electron microscopes, a so-called Scanning-focus ion beam EM (FIB-SEM). This microscope allows the definition of a much larger ROI when compared with a standard transmission EM (TEM) and the generation of much larger z-stacks (up to 1000 images) as possible with ultrathin sectioning. The overall advantage of this technique is the sectioning (milling down to 10nm per step) of the specimen during image acquisition, resulting in a higher number of consecutive sections of a greater ROI with a nearly perfect alignment at a meanwhile qualitative high resolution. This increases the possibility to identify and analyze almost every synaptic bouton and its subelements in a given ROI and hence the implementation of a reference number, e.g. amount of synaptic contacts per unit of volume, to quantify the comparison of different layers and between species.
In the future, we will also concentrate on the molecular composition of the AZ, namely the density, distribution pattern and possible co-localization of neurotransmitter receptors and their subunits (postsynaptic) and the number and localization of Calcium domains (presynaptic) at human neocortical synapses. This will be achieved by a combination using high-sensitive single and multiple postimmunogold labeling on freeze fracture replica.
The aim is to better understand how synapses in different layers of the human neocortex contribute to the computational properties of cortical networks.
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