Search

Abstract The highly complex structure of the brain requires an approach that can unravel its connectivity. Using volume electron microscopy and a dedicated software we can trace and measure all nerve fibers present within different samples of brain tissue. With this software tool, individual dendrites and axons are traced, obtaining a simplified “skeleton” of each fiber, which is linked to its corresponding synaptic contacts. The result is an intricate meshwork of axons and dendrites interconnected by a cloud of synaptic junctions. To test this methodology, we apply it to the stratum radiatum of the hippocampus and layers 1 and 3 of the somatosensory cortex of the mouse. We find that nerve fibers are densely packed in the neuropil, reaching up to 9 kilometers per cubic mm. We obtain the number of synapses, the number and lengths of dendrites and axons, the linear densities of synapses established by dendrites and axons, and their location on dendritic spines and shafts. The quantitative data obtained through this method enable us to identify subtle traits and differences in the synaptic organization of the samples, which might have been overlooked in a qualitative analysis.

The brain contains thousands of millions of synapses, exhibiting diverse structural, molecular, and functional characteristics. However, synapses can be classified into two primary morphological types: Gray’s type I and type II, corresponding to Colonnier’s asymmetric (AS) and symmetric (SS) synapses, respectively. AS and SS have a thick and thin postsynaptic density, respectively. In the cerebral cortex, since most AS are excitatory (glutamatergic), and SS are inhibitory (GABAergic), determining the distribution, size, density, and proportion of the two major cortical types of synapses is critical, not only to better understand synaptic organization in terms of connectivity, but also from a functional perspective. However, several technical challenges complicate the study of synapses. Potassium ferrocyanide has been utilized in recent volume electron microscope studies to enhance electron density in cellular membranes. However, identifying synaptic junctions, especially SS, becomes more challenging as the postsynaptic densities become thinner with increasing concentrations of potassium ferrocyanide. Here we describe a protocol employing Focused Ion Beam Milling and Scanning Electron Microscopy for studying brain tissue. The focus is on the unequivocal identification of AS and SS types. To validate SS observed using this protocol as GABAergic, experiments with immunocytochemistry for the vesicular GABA transporter were conducted on fixed mouse brain tissue sections. This material was processed with different concentrations of potassium ferrocyanide, aiming to determine its optimal concentration. We demonstrate that using a low concentration of potassium ferrocyanide (0.1%) improves membrane visualization while allowing unequivocal identification of synapses as AS or SS.

The structural complexity of nervous tissue makes it very difficult to unravel the connectivity between neural elements at different scales. Numerous methods are available to trace long-range projections at the light microscopic level, and to identify the actual synaptic connections at the electron microscopic level. However, correlating mesoscopic and nanoscopic scales in the same cell, cell population or brain region is a problematic, laborious and technically demanding task. Here we present an effective method for the 3D reconstruction of labeled subcellular structures at the ultrastructural level, after single-neuron labeling in fixed tissue. The brain is fixed by intracardial perfusion of aldehydes and thick vibratome sections (250 μm) are obtained. Single cells in these vibratome sections are intracellularly injected with horseradish peroxidase (HRP), so that the cell body and its processes can be identified. The thick sections are later flat-embedded in epoxy resin and re-sectioned into a series of thinner (7 μm) sections. The sections containing the regions of interest of the labeled cells are then imaged with automated focused ion beam milling and scanning electron microscopy (FIB-SEM), acquiring long series of high-resolution images that can be reconstructed, visualized, and analyzed in 3D. With this methodology, we can accurately select any cellular segment at the light microscopic level (e.g., proximal, intermediate or distal dendrites, collateral branches, axonal segments, etc.) and analyze its synaptic connections at the electron microscopic level, along with other ultrastructural features. Thus, this method not only facilitates the mapping of the synaptic connectivity of single-labeled neurons, but also the analysis of the surrounding neuropil. Since the labeled processes can be located at different layers or subregions, this method can also be used to obtain data on the differences in local synaptic organization that may exist at different portions of the labeled neurons.

Multivesicular bodies (MVBs) are spherical organelles that are surrounded by a single membrane and contain small vesicles in their lumen. They are involved in degradation of cell components as an intermediary of the endosomal pathway. They also recycle cell components and are able to insert receptors in membranes that are far away from the neural soma. MVBs can fuse to plasma membrane and release exosomes that are essential for cell-to-cell communication. It has recently been described that MVBs store and transport damaged cargo from mitochondria and break it down by fusing with lysosomes. MVBs are also known to accumulate the protein aggregates that are involved in pathological conditions like Alzheimer’s disease and Parkinson disease. We used three dimensional electron microscopy with combined focused ion beam milling and scanning electron microscopy (FIB/SEM) to reconstruct MVBs in the rodent somatosensory cortex. We obtained ten stacks of serial sections from the neuropil of layer III. Using specifically developed software (Espina), we segmented and three-dimensionally reconstructed 678 MVBs. This revealed differences in their size and content (dark or clear vesicles). Some of them (7%) contained dark electron-dense laminar material in addition to the usual vesicles. We then classified MVBs based on their location. Our preliminary results indicate that MVBs located in dendrites outnumber those located on axons in approximately a 3:1 proportion. We also determined whether MVBs were related to other subcellular organelles, with special attention to mitochondria. We found that 19% were in contact with mitochondria. These MVBs are probably incorporating the damaged protein cargo contained in mitochondrial-derived vesicles, thereby acting as a defense mechanism that helps maintain normal mitochondrial function. The quantitative method that we have developed here will help determine the possible alterations of the endosomal pathway in pathological conditions.

Physiological and electron microscope studies have shown that synapses are functionally and morphologically heterogeneous and that variations in size of synaptic junctions are related to characteristics such as release probability and density of postsynaptic AMPA receptors. The present article focuses on how these morphological variations impact synaptic transmission. We based our study on Monte Carlo computational simulations of simplified model synapses whose morphological features have been extracted from hundreds of actual synaptic junctions reconstructed by three-dimensional electron microscopy. We have examined the effects that parameters such as synaptic size or density of AMPA receptors have on the number of receptors that open after release of a single synaptic vesicle. Our results indicate that the maximum number of receptors that will open after the release of a single synaptic vesicle may show a ten-fold variation in the whole population of synapses. When individual synapses are considered, there is also a stochastical variability that is maximal in small synapses with low numbers of receptors. The number of postsynaptic receptors and the size of the synaptic junction are the most influential parameters, while the packing density of receptors or the concentration of extrasynaptic transporters have little or no influence on the opening of AMPA receptors.

The advent of transmission electron microscopy (TEM) in the 1950’s represented a fundamental step in the study of neuronal circuits. The application of this technique soon led to the realization that the number of synapses changes during the course of normal life, as well as under certain pathological or experimental circumstances. Since then, one of the main goals in neurosciences has been to define simple and accurate methods to estimate the magnitude of these changes. Contrary to analysing single sections, TEM reconstructions are extremely time-consuming and difficult. Therefore, most quantitative studies use stereological methods to define the three-dimensional characteristics of synaptic junctions that are studied in two dimensions. Here, to count the exact number of synapses per unit of volume we have applied a new three-dimensional reconstruction method that involves the combination of focused ion beam milling and scanning electron microscopy (FIB/SEM). We show that the images obtained with FIB/SEM are similar to those obtained with TEM, but with the advantage that FIB/SEM permits serial reconstructions of large volumes of tissue to be generated rapidly and automatically. Furthermore, we compared the estimates of the number of synapses obtained with stereological methods with the values obtained by FIB/SEM reconstructions. We concluded that FIB/SEM not only provides the actual number of synapses per volume but it is also much easier and faster to use than other currently available TEM methods. More importantly, it also avoids most of the errors introduced by stereological methods and overcomes the difficulties associated with these techniques.

Small-conductance calcium-activated potassium (SK) channels are crucial for learning and memory. However, many aspects of their spatial organization in neurons are still unknown. In this study, we have taken a novel approach to answering these questions combining a pre-embedding immunogold labeling with an automated dual-beam electron microscope that integrates focused ion beam milling and scanning electron microscopy (FIB/SEM) to gather 3D map ultrastructural and biomolecular information simultaneously. Using this new approach, we evaluated the number and variability in the density of extrasynaptic SK2 channels in 3D reconstructions from six dendritic segments of excitatory neurons and six inhibitory neurons present in the stratum radiatum of the CA1 region of the mouse. SK2 immunoparticles were observed throughout the surface of hippocampal neurons, either scattered or clustered, as well as at intracellular sites. Quantitative volumetric evaluations revealed that the extrasynaptic SK2 channel density in spines was seven times higher than in dendritic shafts and thirty-five times higher than in interneurons. Spines showed a heterogeneous population of SK2 expression, some spines having a high SK2 content, others having a low content and others lacking SK2 channels. SK2 immunonegative spines were significantly smaller than those immunopositive. These results show that SK2 channel density differs between excitatory and inhibitory neurons and demonstrates a large variability in the density of SK2 channels in spines. Furthermore, we demonstrated that SK2 expression was associated with excitatory synapses, but not with inhibitory synapses in CA1 pyramidal cells. Consequently, regulation of excitability and synaptic plasticity by SK2 channels is expected to be neuron class- and target-specific. These data show that immunogold FIB/SEM represent a new powerful EM tool to correlate structure and function of ion channels with nanoscale resolution.

Semithin sections are commonly used to examine large areas of tissue with an optical microscope, in order to locate and trim the regions that will later be studied with the electron microscope. Ideally, the observation of semithin sections would be from mesoscopic to nanoscopic scales directly, instead of using light microscopy and then electron microscopy (EM). Here we propose a method that makes it possible to obtain high-resolution scanning EM images of large areas of the brain in the millimeter to nanometer range. Since our method is compatible with light microscopy, it is also feasible to generate hybrid light and electron microscopic maps. Additionally, the same tissue blocks that have been used to obtain semithin sections can later be used, if necessary, for transmission EM, or for focused ion beam milling and scanning electron microscopy (FIB-SEM).

Synaptic activity is regulated and limited by blood flow, which is controlled by blood vessel dilation and contraction. Traditionally, the study of neurovascular coupling has mainly focused on energy consumption and oxygen delivery. However, the mechanical changes that blood vessel movements induce in the surrounding tissue have not been considered. We have modeled the mechanical changes that movements of blood vessels cause in neighboring synapses. Our simulations indicate that synaptic densities increase or decrease during vascular dilation and contraction, respectively, near the blood vessel walls. This phenomenon may alter the concentration of neurotransmitters and vasoactive substances in the immediate vicinity of the vessel wall and thus may have an influence on local blood flow.

Synapses are key elements in the information transmission in the nervous system. Among the different approaches to study them, the use of computational simulations is identified as the most promising technique. Simulations, however, do not provide generalized models of the underlying biochemical phenomena, but a set of observations, or time-series curves, displaying the behavior of the synapse in the scenario represented. Finding a general model of these curves, like a set of mathematical equations, could be an achievement in the study of synaptic behavior. In this paper, we propose an exploratory analysis in which selected curve models are proposed, and state-of-the-art metaheuristics are used and compared to fit the free coefficients of these curves to the data obtained from simulations. Experimental results demonstrate that several models can fit these data, though a deeper analysis from a biological perspective reveals that some are better suited for this purpose, as they represent more accurately the biological process. Based on the results of this analysis, we propose a set of mathematical equations and a methodology, adequate for modeling several aspects of biochemical synaptic behavior.

Chemical synaptic transmission involves the release of a neurotransmitter that diffuses in the extracellular space and interacts with specific receptors located on the postsynaptic membrane. Computer simulation approaches provide fundamental tools for exploring various aspects of the synaptic transmission under different conditions. In particular, Monte Carlo methods can track the stochastic movements of neurotransmitter molecules and their interactions with other discrete molecules, the receptors. However, these methods are computationally expensive, even when used with simplified models, preventing their use in large-scale and multi-scale simulations of complex neuronal systems that may involve large numbers of synaptic connections. We have developed a machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the percentage of open synaptic receptors as a function of time since the release of the neurotransmitter, with considerably lower computational cost compared with the conventional Monte Carlo alternative. The method is designed to learn patterns and general principles from a corpus of previously generated Monte Carlo simulations of synapses covering a wide range of structural and functional characteristics. These patterns are later used as a predictive model of the behavior of synapses under different conditions without the need for additional computationally expensive Monte Carlo simulations. This is performed in five stages: data sampling, fold creation, machine learning, validation and curve fitting. The resulting procedure is accurate, automatic, and it is general enough to predict synapse behavior under experimental conditions that are different to the ones it has been trained on. Since our method efficiently reproduces the results that can be obtained with Monte Carlo simulations at a considerably lower computational cost, it is suitable for the simulation of high numbers of synapses and it is therefore an excellent tool for multi-scale simulations.