Spatial Distribution Of Synapses Research Articles

The distribution and structural plasticity of excitatory synapses along interneuron dendrites in the adult hippocampus

Hippocampal interneurons form a large, heterogeneous group with differing structural, physiological, and chemical phenotypes. Most CA1 interneuron dendrites, however, lack spines. To investigate structural synaptic plasticity along interneuron dendrites, we generated three-dimensional (3D) reconstructions from serial section electron microscopy (EM) of excitatory synapses along aspiny dendrites in CA1 stratum radiatum of young adult rats under control conditions and at 2 h during LTP induced with theta-burst stimulation. In total, 43 aspiny dendritic segments and their synapses were identified and reconstructed in 3D. Aspiny dendrites were categorized as smooth or varicose based on dendritic volume contours along each segment. We further sub-segmented varicose dendrites varicosities and inter-varicose regions using automated and manual methods. We found synapses and total synaptic input per length were distributed uniformly along smooth dendrites. Synapses along varicose dendrites, however, occurred preferentially at varicosities. While synaptic input per length along varicosities was similar to that along smooth dendrites, both synapse number and total synaptic input per length along inter-varicose regions were lower. Dendritic segments were also analyzed for mitochondria and glycogen content. We found that mitochondria and glycogen were also distributed uniformly along smooth dendrites. In varicose dendrites, however, mitochondrial volume and glycogen granule numbers were highest in varicosities, while inter-varicose regions were comparatively devoid of these resources. The spatial distribution of synapses and dendritic resources in both smooth and varicose dendrites was preserved at 2 h during LTP when compared to control conditions. These findings suggest that synapses occur along interneuron dendrites where dendritic resources are most available. When dendritic resources are distributed uniformly throughout the dendrite, as occurs in smooth dendrites, synapse distribution is also uniform. Varicosities, on the other hand, represent resource-rich sites along varicose dendrites where synaptic clustering occurs. This work was supported by the National Science Foundation (NSF 1707356, NSF 2014862) and the National Institutes of Health (R01 5R01MH09598). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Toolkits for detailed and high-throughput interrogation of synapses in C. elegans.

Visualizing synaptic connectivity has traditionally relied on time-consuming electron microscopy-based imaging approaches. To scale the analysis of synaptic connectivity, fluorescent protein-based techniques have been established, ranging from the labeling of specific pre- or post-synaptic components of chemical or electrical synapses to transsynaptic proximity labeling technology such as GRASP and iBLINC. In this paper, we describe WormPsyQi, a generalizable image analysis pipeline that automatically quantifies synaptically localized fluorescent signals in a high-throughput and robust manner, with reduced human bias. We also present a resource of 30 transgenic strains that label chemical or electrical synapses throughout the nervous system of the nematode Caenorhabditis elegans, using CLA-1, RAB-3, GRASP (chemical synapses), or innexin (electrical synapse) reporters. We show that WormPsyQi captures synaptic structures in spite of substantial heterogeneity in neurite morphology, fluorescence signal, and imaging parameters. We use these toolkits to quantify multiple obvious and subtle features of synapses - such as number, size, intensity, and spatial distribution of synapses - in datasets spanning various regions of the nervous system, developmental stages, and sexes. Although the pipeline is described in the context of synapses, it may be utilized for other 'punctate' signals, such as fluorescently tagged neurotransmitter receptors and cell adhesion molecules, as well as proteins in other subcellular contexts. By overcoming constraints on time, sample size, cell morphology, and phenotypic space, this work represents a powerful resource for further analysis of synapse biology in C. elegans.

Toolkits for detailed and high-throughput interrogation of synapses in C. elegans

Visualizing synaptic connectivity has traditionally relied on time-consuming electron microscopy-based imaging approaches. To scale the analysis of synaptic connectivity, fluorescent protein-based techniques have been established, ranging from the labeling of specific pre- or post-synaptic components of chemical or electrical synapses to transsynaptic proximity labeling technology such as GRASP and iBLINC. In this paper, we describe WormPsyQi, a generalizable image analysis pipeline that automatically quantifies synaptically localized fluorescent signals in a high-throughput and robust manner, with reduced human bias. We also present a resource of 30 transgenic strains that label chemical or electrical synapses throughout the nervous system of the nematode Caenorhabditis elegans, using CLA-1, RAB-3, GRASP (chemical synapses), or innexin (electrical synapse) reporters. We show that WormPsyQi captures synaptic structures in spite of substantial heterogeneity in neurite morphology, fluorescence signal, and imaging parameters. We use these toolkits to quantify multiple obvious and subtle features of synapses – such as number, size, intensity, and spatial distribution of synapses – in datasets spanning various regions of the nervous system, developmental stages, and sexes. Although the pipeline is described in the context of synapses, it may be utilized for other ‘punctate’ signals, such as fluorescently tagged neurotransmitter receptors and cell adhesion molecules, as well as proteins in other subcellular contexts. By overcoming constraints on time, sample size, cell morphology, and phenotypic space, this work represents a powerful resource for further analysis of synapse biology in C. elegans.

Three-dimensional analysis of synaptic organization in the hippocampal CA1field in Alzheimer's disease.

Alzheimer’s disease is the most common form of dementia, characterized by a persistent and progressive impairment of cognitive functions. Alzheimer’s disease is typically associated with extracellular deposits of amyloid-β peptide and accumulation of abnormally phosphorylated tau protein inside neurons (amyloid-β and neurofibrillary pathologies). It has been proposed that these pathologies cause neuronal degeneration and synaptic alterations, which are thought to constitute the major neurobiological basis of cognitive dysfunction in Alzheimer’s disease. The hippocampal formation is especially vulnerable in the early stages of Alzheimer’s disease. However, the vast majority of electron microscopy studies have been performed in animal models. In the present study, we performed an extensive 3D study of the neuropil to investigate the synaptic organization in the stratum pyramidale and radiatum in the CA1 field of Alzheimer’s disease cases with different stages of the disease, using focused ion beam/scanning electron microscopy (FIB/SEM). In cases with early stages of Alzheimer’s disease, the synapse morphology looks normal and we observed no significant differences between control and Alzheimer’s disease cases regarding the synaptic density, the ratio of excitatory and inhibitory synapses, or the spatial distribution of synapses. However, differences in the distribution of postsynaptic targets and synaptic shapes were found. Furthermore, a lower proportion of larger excitatory synapses in both strata were found in Alzheimer’s disease cases. Individuals in late stages of the disease suffered the most severe synaptic alterations, including a decrease in synaptic density and morphological alterations of the remaining synapses. Since Alzheimer’s disease cases show cortical atrophy, our data indicate a reduction in the total number (but not the density) of synapses at early stages of the disease, with this reduction being much more accentuated in subjects with late stages of Alzheimer’s disease. The observed synaptic alterations may represent a structural basis for the progressive learning and memory dysfunctions seen in Alzheimer’s disease cases.

Three-dimensional analysis of synapses in the transentorhinal cortex of Alzheimer\u2019s disease patients

Synaptic dysfunction or loss in early stages of Alzheimer’s disease (AD) is thought to be a major structural correlate of cognitive dysfunction. Early loss of episodic memory, which occurs at the early stage of AD, is closely associated with the progressive degeneration of medial temporal lobe (MTL) structures of which the transentorhinal cortex (TEC) is the first affected area. However, no ultrastructural studies have been performed in this region in human brain samples from AD patients.In the present study, we have performed a detailed three-dimensional (3D) ultrastructural analysis using focused ion beam/scanning electron microscopy (FIB/SEM) to investigate possible synaptic alterations in the TEC of patients with AD. Surprisingly, the analysis of the density, morphological features and spatial distribution of synapses in the neuropil showed no significant differences between AD and control samples. However, light microscopy studies showed that cortical thickness of the TEC was severely reduced in AD samples, but there were no changes in the volume occupied by neuronal and glial cell bodies, blood vessels, and neuropil. Thus, the present results indicate that there is a dramatic loss of absolute number of synapses, while the morphology of synaptic junctions and synaptic spatial distribution are maintained. How these changes affect cognitive impairment in AD remains to be elucidated.

Dendrites Enable a Robust Mechanism for Neuronal Stimulus Selectivity.

Hearing, vision, touch: underlying all of these senses is stimulus selectivity, a robust information processing operation in which cortical neurons respond more to some stimuli than to others. Previous models assume that these neurons receive the highest weighted input from an ensemble encoding the preferred stimulus, but dendrites enable other possibilities. Nonlinear dendritic processing can produce stimulus selectivity based on the spatial distribution of synapses, even if the total preferred stimulus weight does not exceed that of nonpreferred stimuli. Using a multi-subunit nonlinear model, we demonstrate that stimulus selectivity can arise from the spatial distribution of synapses. We propose this as a general mechanism for information processing by neurons possessing dendritic trees. Moreover, we show that this implementation of stimulus selectivity increases the neuron's robustness to synaptic and dendritic failure. Importantly, our model can maintain stimulus selectivity for a larger range of loss of synapses or dendrites than an equivalent linear model. We then use a layer 2/3 biophysical neuron model to show that our implementation is consistent with two recent experimental observations: (1) one can observe a mixture of selectivities in dendrites that can differ from the somatic selectivity, and (2) hyperpolarization can broaden somatic tuning without affecting dendritic tuning. Our model predicts that an initially nonselective neuron can become selective when depolarized. In addition to motivating new experiments, the model's increased robustness to synapses and dendrites loss provides a starting point for fault-resistant neuromorphic chip development.

Dendritic distribution of synaptic input creates a trade-off between input selectivity and flexibility

Information processing by cortical pyramidal neurons is shaped by the spatial distribution of synapses across the dendrites. A prominent hypothesis is that synapses with similar selectivities cluster on dendritic branches. This enables cooperative interactions between neighboring synapses through activation of voltage-dependent membrane currents, and helps establish independent integrative subunits, thereby expanding the computational power of a single neuron [1]. Some recent in vivo recordings argue against this hypothesis, suggesting that inputs that share the same stimulus selectivity are randomly distributed throughout the dendritic tree (e.g., [2]). Other studies seem to support clustered configurations of input selectivities, showing for example that the activity of a synaptic input is more strongly correlated with its neighbors than with more distant inputs [3]. One fundamental feature of the nervous system that has received little attention in this ongoing discussion is learning; specifically, how is the ability to change a neuron's selectivity (i.e., its flexibility) affected by the spatial distribution of synapses? This is highly relevant because the selectivity of many cortical pyramidal neurons is subject to ongoing modification, not only during development, but throughout adulthood (e.g., [4]). Here, we show that the distribution of synapses across active dendrites shapes both the stimulus selectivity of the neuron, as well as the flexibility of the neuron's selectivity. Using cable theoretic analysis and numerical simulations of detailed neuron models we show that synapses that are randomly distributed across the dendrites allow for a modest stimulus selectivity that can be flexibly modified through synaptic plasticity. In contrast, a clustered distribution of synapses that encode the same stimulus allows for very strong stimulus selectivity, however, it hinders adjustment of this selectivity through synaptic plasticity, which requires slow and metabolically costly rearrangement of synaptic projections. Hence, the distribution of synapses across active dendrites creates a trade-off between selectivity and plasticity. We suggest that pyramidal neurons with different functions in the cortical information processing hierarchy exploit different ends of this spectrum.

Synaptic input patterns triggering local dendritic spikes in vivo

Theoretical modelling and experiments in vitro have shown that the computations performed by single neurons critically depend on the spatiotemporal patterns of synaptic input to the neuron [1,2]. While recording the spike output of neuronal populations in vivo is now routinely possible, the spatiotemporal pattern of synaptic inputs to an entire neuron is still largely inaccessible to experiment. However, recent experiments in which sensory-evoked local dendritic spikes were observed in vivo have provided valuable constraints on possible spatiotemporal patterns of synaptic input [3-5]. For example, the synaptic input to layer 2/3 pyramidal neurons in mouse primary visual cortex evokes dendritic spikes at high frequencies during visual stimulation at the preferred orientation, but not at non-preferred orientations [3]. Here, we use a biophysical model to explore which spatiotemporal patterns of synaptic inputs can drive the observed spikes, and in particular the high frequencies in dendritic spike bursts. First, we adapted a detailed active compartmental model of a neocortical layer 2/3 pyramidal neuron [3] to reproduce biophysical properties and firing statistics observed in vivo [6], such as firing threshold, mean membrane potentials for UP and DOWN states, and sparse action potential firing in the presence of Poisson distributed background synaptic input. The model generates fast dendritic spikes heterogeneous in amplitude, time course and spatial extent, as observed in in vivo experiments [3]. The dendritic spikes, together with somatic action potential firing, are abolished by blocking the NMDA receptor-mediated conductance, and their frequency is reduced by hyperpolarization via the dendritic recording site, again as observed in experiments [3]. Next we analyzed which spatiotemporal synaptic input patterns precede the generation of local dendritic events. We find that local spikes are preferentially triggered by excitatory synapses which are spatially and temporally clustered in the local dendritic branch and neighbouring branches. While there is great variability in the spatial distribution of synapses triggering local dendritic spikes, temporal patterns of excitatory synaptic input are stereotyped and tend to be sparse. High-frequency dendritic spikes similar to those observed in vivo occur preferentially when excitatory synaptic inputs are interspersed with inhibitory synaptic inputs. Furthermore, local spikes preceding backpropagating action potentials lead to the highest instantaneous dendritic spike frequencies. The highest sustained frequencies are generated by local spikes initiated sequentially in several neighbouring dendritic branches. Notably, only some of the high frequency dendritic events are effective in evoking action potential output, while others remain local. We use cluster analysis to establish the local and global conditions under which specific spatiotemporal patterns of synaptic input can influence neuronal output. In summary, we have derived spatial and temporal rules for synaptic input and identify input patterns that locally exploit the non-linear integration capacities of dendrites. Which computations can be implemented by spiking dendrites receiving such synaptic input patterns? These constraints on synaptic input patterns, together with the realistic intrinsic properties displayed by the model, put us in a position to investigate the role of dendritic excitability in shaping the input-output relation of the neuron.

Automatic discovery of cell types and microcircuitry from neural connectomics.

Neural connectomics has begun producing massive amounts of data, necessitating new analysis methods to discover the biological and computational structure. It has long been assumed that discovering neuron types and their relation to microcircuitry is crucial to understanding neural function. Here we developed a non-parametric Bayesian technique that identifies neuron types and microcircuitry patterns in connectomics data. It combines the information traditionally used by biologists in a principled and probabilistically coherent manner, including connectivity, cell body location, and the spatial distribution of synapses. We show that the approach recovers known neuron types in the retina and enables predictions of connectivity, better than simpler algorithms. It also can reveal interesting structure in the nervous system of Caenorhabditis elegans and an old man-made microprocessor. Our approach extracts structural meaning from connectomics, enabling new approaches of automatically deriving anatomical insights from these emerging datasets.

Three-dimensional distribution of cortical synapses: a replicated point pattern-based analysis.

The biggest problem when analyzing the brain is that its synaptic connections are extremely complex. Generally, the billions of neurons making up the brain exchange information through two types of highly specialized structures: chemical synapses (the vast majority) and so-called gap junctions (a substrate of one class of electrical synapse). Here we are interested in exploring the three-dimensional spatial distribution of chemical synapses in the cerebral cortex. Recent research has showed that the three-dimensional spatial distribution of synapses in layer III of the neocortex can be modeled by a random sequential adsorption (RSA) point process, i.e., synapses are distributed in space almost randomly, with the only constraint that they cannot overlap. In this study we hypothesize that RSA processes can also explain the distribution of synapses in all cortical layers. We also investigate whether there are differences in both the synaptic density and spatial distribution of synapses between layers. Using combined focused ion beam milling and scanning electron microscopy (FIB/SEM), we obtained three-dimensional samples from the six layers of the rat somatosensory cortex and identified and reconstructed the synaptic junctions. A total volume of tissue of approximately 4500μm3 and around 4000 synapses from three different animals were analyzed. Different samples, layers and/or animals were aggregated and compared using RSA replicated spatial point processes. The results showed no significant differences in the synaptic distribution across the different rats used in the study. We found that RSA processes described the spatial distribution of synapses in all samples of each layer. We also found that the synaptic distribution in layers II to VI conforms to a common underlying RSA process with different densities per layer. Interestingly, the results showed that synapses in layer I had a slightly different spatial distribution from the other layers.

Population stochastic dynamics for synaptic depression

Synaptic transmission between neocortical neurons often shows activity-dependent synaptic depression. Moreover, it is revealed recently that the synaptic depression has a more active role in information processing. The population density approach is a viable method to describe the large populations of neurons and has generated considerable interest recently. In this paper, we use the population stochastic dynamics to analyze the population of neurons with synaptic depression in the stochastic environment. We have derived an evolution equation of the membrane potential density function with synaptic depression, and obtain several formulas for analytic computing the response of instantaneous fire rate. Through a technical analysis, we arrive at several significant conclusions, which show that the background inputs and the spatial distribution of synapses and the spatial–temporal relation of inputs play an important role in information processing.

Comparative electrotonic analysis of three classes of rat hippocampal neurons.

We present a comparative analysis of electrotonus in the three classes of principal neurons in rat hippocampus: pyramidal cells of the CA1 and CA3c fields of the hippocampus proper, and granule cells of the dentate gyrus. This analysis used the electrotonic transform, which combines anatomic and biophysical data to map neuronal anatomy into electrotonic space, where physical distance between points is replaced by the logarithm of voltage attenuation (log A). The transforms were rendered as "neuromorphic figures" by redrawing the cell with branch lengths proportional to log A along each branch. We also used plots of log A versus anatomic distance from the soma; these reveal features that are otherwise less apparent and facilitate comparisons between dendritic fields of different cells. Transforms were always larger for voltage spreading toward the soma (V(in)) than away from it (V(out)). Most of the electrotonic length in V(out) transforms was along proximal large diameter branches where signal loss for somatofugal voltage spread is greatest. In V(in) transforms, more of the length was in thin distal branches, indicating a steep voltage gradient for signals propagating toward the soma. All transforms lengthened substantially with increasing frequency. CA1 neurons were electrotonically significantly larger than CA3c neurons. Their V(out) transforms displayed one primary apical dendrite, which bifurcated in some cases, whereas CA3c cell transforms exhibited multiple apical branches. In both cell classes, basilar dendrite V(out) transforms were small, indicating that somatic potentials reached their distal ends with little attenuation. However, for somatopetal voltage spread, attenuation along the basilar and apical dendrites was comparable, so the V(in) transforms of these dendritic fields were nearly equal in extent. Granule cells were physically and electrotonically most compact. Their V(out) transforms at 0 Hz were very small, indicating near isopotentiality at DC and low frequencies. These transforms resembled those of the basilar dendrites of CA1 and CA3c pyramidal cells. This raises the possibility of similar functional or computational roles for these dendritic fields. Interpreting the anatomic distribution of thorny excrescences on CA3 pyramidal neurons with this approach indicates that synaptic currents generated by some mossy fiber inputs may be recorded accurately by a somatic patch clamp, providing that strict criteria on their time course are satisfied. Similar accuracy may not be achievable in somatic recordings of Schaffer collateral synapses onto CA1 pyramidal cells in light of the anatomic and biophysical properties of these neurons and the spatial distribution of synapses.

Spatial distribution of synapses onto thoracic motor neurones in locusts.

Aggregates of synaptic vesicles, stained black by the zinc iodideosmium procedure, can be visualised with the light microscope in 1 mum plastic sections. This allows the main branches of a neurone to be reconstructed relatively rapidly and the associated vesicle aggregates to be plotted. By resectioning, the identity of the vesicle aggregates has been confirmed with the electron microscope. Two flight motor neurones in the mesothoracic ganglion of the locust have been examined. One is identified as a dorsal longitudinal muscle motor neurone (muscle 112) and the other is probably a subalar neurone (muscle 99). Both have a large density of vesicle aggregates on the neuropilar segment, the widest part of the main neuronal axis, but few on the neurite within 250 mum of the cell body. The larger branches arising from the neuropilar segment tend to have a lower density of aggregates than fine branches, which suggests that synapses to the branches may occur mainly on the distal twigs. These results are an important preliminary step in determining the integrative functions of such neurons and have immediate implications in the interpretation of microelectrode recordings.