Multifaceted Changes in Synaptic Composition and Astrocytic Involvement in a Mouse Model of Fragile X Syndrome.

Fragile X Syndrome (FXS), a common inheritable form of intellectual disability, is known to alter neocortical circuits. However, its impact on the diverse synapse types comprising these circuits, or on the involvement of astrocytes, is not well known. We used immunofluorescent array tomography to quantify different synaptic populations and their association with astrocytes in layers 1 through 4 of the adult somatosensory cortex of a FXS mouse model, the FMR1 knockout mouse. The collected multi-channel data contained approximately 1.6 million synapses which were analyzed using a probabilistic synapse detector. Our study reveals complex, synapse-type and layer specific changes in the neocortical circuitry of FMR1 knockout mice. We report an increase of small glutamatergic VGluT1 synapses in layer 4 accompanied by a decrease in large VGluT1 synapses in layers 1 and 4. VGluT2 synapses show a rather consistent decrease in density in layers 1 and 2/3. In all layers, we observe the loss of large inhibitory synapses. Lastly, astrocytic association of excitatory synapses decreases. The ability to dissect the circuit deficits by synapse type and astrocytic involvement will be crucial for understanding how these changes affect circuit function, and ultimately defining targets for therapeutic intervention

Probabilistic Fluorescence-Based Synapse Detection

Brain function results from communication between neurons connected by complex synaptic networks. Synapses are themselves highly complex and diverse signaling machines, containing protein products of hundreds of different genes, some in hundreds of copies, arranged in precise lattice at each individual synapse. Synapses are fundamental not only to synaptic network function but also to network development, adaptation, and memory. In addition, abnormalities of synapse numbers or molecular components are implicated in most mental and neurological disorders. Despite their obvious importance, mammalian synapse populations have so far resisted detailed quantitative study. In human brains and most animal nervous systems, synapses are very small and very densely packed: there are approximately 1 billion synapses per cubic millimeter of human cortex. This volumetric density poses very substantial challenges to proteometric analysis at the critical level of the individual synapse. The present work describes new probabilistic image analysis methods for single-synapse analysis of synapse populations in both animal and human brains.Comment: Current awaiting peer revie

Synaptojanin Forms Two Separate Complexes in the Nerve Terminal: INTERACTIONS WITH ENDOPHILIN AND AMPHIPHYSIN

Endophilin is a recently discovered src homology 3 domain-containing protein that is a major in vitro binding partner for synaptojanin. To further characterize endophilin, we generated an antipeptide antibody. Endophilin is enriched in the brain, and immunofluorescence analysis reveals a high concentration of the protein in synaptic terminals, where it colocalizes with synaptojanin. In vitro binding assays demonstrate that endophilin binds through its src homology 3 domain to synaptojanin, and immunoprecipitation analysis with the antiendophilin antibody reveals that endophilin is stably associated with synaptojanin in the nerve terminal. Immunoprecipitation with an antibody against amphiphysin I and II, which interact through their src homology 3 domains with dynamin and synaptojanin at sites distinct from those for endophilin, reveals a second stable complex, which includes dynamin and synaptojanin but excludes endophilin. These data demonstrate that synaptojanin is present in two separate complexes in the nerve terminal and support an important role for endophilin in the regulation of synaptojanin function

Distinctive Structural and Molecular Features of Myelinated Inhibitory Axons in Human Neocortex.

Numerous types of inhibitory neurons sculpt the performance of human neocortical circuits, with each type exhibiting a constellation of subcellular phenotypic features in support of its specialized functions. Axonal myelination has been absent among the characteristics used to distinguish inhibitory neuron types; in fact, very little is known about myelinated inhibitory axons in human neocortex. Here, using array tomography to analyze samples of neurosurgically excised human neocortex, we show that inhibitory myelinated axons originate predominantly from parvalbumin-containing interneurons. Compared to myelinated excitatory axons, they have higher neurofilament and lower microtubule content, shorter nodes of Ranvier, and more myelin basic protein (MBP) in their myelin sheath. Furthermore, these inhibitory axons have more mitochondria, likely to sustain the high energy demands of parvalbumin interneurons, as well as more 2',3'-cyclic nucleotide 3'-phosphodiesterase (CNP), a protein enriched in the myelin cytoplasmic channels that are thought to facilitate the delivery of nutrients from ensheathing oligodendrocytes. Our results demonstrate that myelinated axons of parvalbumin inhibitory interneurons exhibit distinctive features that may support the specialized functions of this neuron type in human neocortical circuits

Probabilistic fluorescence-based synapse detection

Deeper exploration of the brain’s vast synaptic networks will require new tools for high-throughput structural and molecular profiling of the diverse populations of synapses that compose those networks. Fluorescence microscopy (FM) and electron microscopy (EM) offer complementary advantages and disadvantages for single-synapse analysis. FM combines exquisite molecular discrimination capacities with high speed and low cost, but rigorous discrimination between synaptic and non-synaptic fluorescence signals is challenging. In contrast, EM remains the gold standard for reliable identification of a synapse, but offers only limited molecular discrimination and is slow and costly. To develop and test single-synapse image analysis methods, we have used datasets from conjugate array tomography (cAT), which provides voxel-conjugate FM and EM (annotated) images of the same individual synapses. We report a novel unsupervised probabilistic method for detection of synapses from multiplex FM (muxFM) image data, and evaluate this method both by comparison to EM gold standard annotated data and by examining its capacity to reproduce known important features of cortical synapse distributions. The proposed probabilistic model-based synapse detector accepts molecular-morphological synapse models as user queries, and delivers a volumetric map of the probability that each voxel represents part of a synapse. Taking human annotation of cAT EM data as ground truth, we show that our algorithm detects synapses from muxFM data alone as successfully as human annotators seeing only the muxFM data, and accurately reproduces known architectural features of cortical synapse distributions. This approach opens the door to data-driven discovery of new synapse types and their density. We suggest that our probabilistic synapse detector will also be useful for analysis of standard confocal and super-resolution FM images, where EM cross-validation is not practical

Knowing a synapse when you see one

Recent years have seen a rapidly growing recognition of the complexity and diversity of the myriad individual synaptic connections that define brain synaptic networks. It has also become increasingly apparent that the synapses themselves are a major key to understanding the development, function and adaptability of those synaptic networks. In spite of this growing appreciation, the molecular, structural and functional characteristics of individual synapses and the patterning of their diverse characteristics across functional networks have largely eluded quantitative study with available imaging technologies. Here we offer an overview of new computational imaging methods that promise to bring single-synapse analysis of synaptic networks to the fore. We focus especially on the challenges and opportunities associated with quantitative detection of individual synapses and with measuring individual synapses across network scale populations in mammalian brain

Visualizing the Distribution of Synapses from Individual Neurons in the Mouse Brain

BACKGROUND:Proper function of the mammalian brain relies on the establishment of highly specific synaptic connections among billions of neurons. To understand how complex neural circuits function, it is crucial to precisely describe neuronal connectivity and the distributions of synapses to and from individual neurons. METHODS AND FINDINGS:In this study, we present a new genetic synaptic labeling method that relies on expression of a presynaptic marker, synaptophysin-GFP (Syp-GFP) in individual neurons in vivo. We assess the reliability of this method and use it to analyze the spatial patterning of synapses in developing and mature cerebellar granule cells (GCs). In immature GCs, Syp-GFP is distributed in both axonal and dendritic regions. Upon maturation, it becomes strongly enriched in axons. In mature GCs, we analyzed synapses along their ascending segments and parallel fibers. We observe no differences in presynaptic distribution between GCs born at different developmental time points and thus having varied depths of projections in the molecular layer. We found that the mean densities of synapses along the parallel fiber and the ascending segment above the Purkinje cell (PC) layer are statistically indistinguishable, and higher than previous estimates. Interestingly, presynaptic terminals were also found in the ascending segments of GCs below and within the PC layer, with the mean densities two-fold lower than that above the PC layer. The difference in the density of synapses in these parts of the ascending segment likely reflects the regional differences in postsynaptic target cells of GCs. CONCLUSIONS:The ability to visualize synapses of single neurons in vivo is valuable for studying synaptogenesis and synaptic plasticity within individual neurons as well as information flow in neural circuits