SynDB is an online resource of proteins known or predicted to be related to the synapse or synaptic activity, and extensive information on the proteins'' functions, sequences, structures, expression, pathways, interactions, and disease associations. It is intended to be a repository of current knowledge and data as well as a starting point for future proteomics research in neurobiology. SynDB is the first focused database of the molecular biology of the synapse proteome. It contains the most comprehensive collection of proteins (13809 unique proteins spanning 1979 species and 104 protein domains, Aug 2006) that are known or predicted to be associated with synaptic activities. It integrates extensive information on protein functions, sequences, structures, expression, pathways, interactions, and disease associations. SynDB was generated using a combination of automated approaches, including keyword- and domain-based searches, and manual curation. It serves as a starting point for future neurobiology, neuropharmacology, and neuroinformatics research. Synapse ontology is a set of standard vocabulary which help to describe all synaptic gene products in a consistant way. As in common ontology, synapse ontolgy is composed of all the terms in a hierarchical structure, but specifically restricted to the function and structure annotation of synapse related gene products. Synapse ontology is a callaborative fruit of bioinformatists and neural biologists. Synapse ontolgy is aimed to describe all the synaptic molecules in terms of structure/biochemistry of synapse and physiology/function at synapse in a specied-independent manner. The controled vocabularies are hierarchically structured, so you can browser the related gene products in different levels: for example, you can find all the gene products of synaptic vesicle cycling or ion channels and receptors, or you can zoom in on all the gene products playing roles in the priming step of synaptic vesicle cycling.

Access to Data SynDB is an online resource of proteins known or predicted to be related to the synapse* or synaptic activity. It is intended to be a repository of current knowledge and data as well as a starting point for future proteomics research in neurobiology. *A synapse is the structure where a nervous impulse passes from one neuron to another and synaptic activity is central to almost all neurobiological processes, including learning, memory, and neuronal development. The study of synaptic function and composition will help in understanding the pathology of neurological diseases and in the search for therapeutic approaches.

A cloud-based collaborative platform which co-locates data, code, and computing resources for analyzing genome-scale data and seamlessly integrates these services allowing scientists to share and analyze data together. Synapse consists of a web portal integrated with the R/Bioconductor statistical package and will be integrated with additional tools. The web portal is organized around the concept of a Project which is an environment where you can interact, share data, and analysis methods with a specific group of users or broadly across open collaborations. Projects provide an organizational structure to interact with data, code and analyses, and to track data provenance. A project can be created by anyone with a Synapse account and can be shared among all Synapse users or restricted to a specific team. Public data projects include the Synapse Commons Repository (SCR) (syn150935) and the metaGenomics project (syn275039). The SCR provides access to raw data and phenotypic information for publicly available genomic data sets, such as GEO and TCGA. The metaGenomics project provides standardized preprocessed data and precomputed analysis of the public SCR data.

Synapse is a collaborative, open-source research platform that allows teams to share data, track analyses, and collaborate.

This item has been replaced by the one which can be found at https://doi.org/10.7488/ds/2796 ## How synapses change molecularly during the lifespan and across all brain circuits is unknown. We analyzed the protein composition of billions of individual synapses from birth to old age on a brain-wide scale in the mouse, revealing a program of changes in the lifespan synaptome architecture spanning individual dendrites to the systems level. Three major phases were uncovered, corresponding to human childhood, adulthood and old age. An arching trajectory of synaptome architecture drives the differentiation and specialization of brain regions to a peak in young adults before dedifferentiation returns the brain to a juvenile state. This trajectory underscores changing network organization and hippocampal physiology that may account for lifespan transitions in intellectual ability and memory, and the onset of behavioral disorders.

Synapses are found in vast numbers in the brain and contain complex proteomes. We developed genetic labeling and imaging methods to examine synaptic proteins in individual excitatory synapses across all regions of the mouse brain. Synapse catalogs were generated from the molecular and morphological features of 0.5 billion synapses. Each synapse subtype showed a unique anatomical distribution and each brain region showed a distinct signature of synapse subtypes. Whole brain synaptome maps revealed spatial architecture from dendritic to global systems levels and previously unknown anatomical features. Synaptome mapping of circuits showed correspondence between synapse diversity and structural and functional connectomes. Behaviorally relevant patterns of neuronal activity trigger spatio-temporal postsynaptic responses sensitive to the structure of synaptome maps. Areas controlling higher cognitive function contain greatest synapse diversity and mutations causing cognitive disorders reorganized synaptome maps. Synaptome mapping technology and resources have wide application in studies of the normal and diseased brain.

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Synaptic molecular characterization is limited for Alzheimer’s disease (AD). Our newly invented mass cytometry-based method, Synaptometry by Time of Flight (SynTOF), was used to measure 38 antibody probes in approximately 17 million single-synapse events from human brains without pathologic change or with pure AD or Lewy body disease (LBD), non-human primates (NHP), and PS/APP mice. Synaptic molecular integrity in humans and NHP was similar. Although not detected in human synapses, Aβ was in PS/APP mice single-synapse events. Clustering and pattern identification of human synapses showed expected disease-specific differences, like increased hippocampal pathologic tau in AD and reduced caudate dopamine transporter in LBD, and revealed novel findings including increased hippocampal CD47 and lowered DJ1 in AD and higher ApoE in AD with dementia. Our results were independently supported by multiplex ion beam imaging of intact tissue. This highlights the higher depth and breadth of insight on neurodegenerative diseases obtainable through SynTOF. Methods The dataset was collected using mass cytometry at The Human Immune Monitoring Center (HIMC) and multiplexed ion beam imaging by time of flight (MIBI-TOF) at Stanford University in 2019-2020 and 2020, respectively. The single-synapse data has been processed by arcsinh transformed with a cofactor of 5 to produce the results published in Science Advances, where a detailed experimental method and preprocessing can be found.

Detailed Active users (daily) metrics and analytics for Synapse, including historical data and trends.

Excitatory synapse detection queries for the AT data.

Mammalian protein-protein interaction database focusing on synaptic proteins. The Protein-Protein Interaction Database was originally a single-person's attempt to integrate a gamut of biological/bibliographical/molecular data and build a framework which might help understanding how cells orchestrate their protein content in order to become what they are: machines with a purpose. This is based on the simple paradigm that functionality like signal cascades are held together in a close space, thereby allowing specific events to occur without the necessity of passive diffusion and random events. The PPID database arose from the need to interpret Proteomic datasets, which were generated analysing the NMDA-receptor complex (see H. Husi, M. A. Ward, J. S. Choudhary, W. P. Blackstock and S. G. Grant (2000). Proteomic analysis of NMDA receptor-adhesion protein signaling complexes. Nat Neurosci 3, 661-669.). To study these clusters of proteins requires unavoidably the handling of large datasets, which PPID is generally aimed and tailored for. This database is unifying molecular entries across three species, namely human, rat and mouse and is is footed on sequence databases such as SwissProt, EMBL, TrEMBL (translated EMBL sequences) and Unigene and the literature database PubMed. A typical entry in PPID holds up to three general entries for the three species, all protein and gene accession numbers associated with them (assembled from Blast2 searches of the databases) and the OMIM entry as maintained by Johns Hopkins University. Furthermore protein sequence information is also included, together with known and novel splice-variants of each molecule as found by ClustalW sequence alignments. Entry points also include protein-binding information together with the literature reference. The whole database is curated manually to insure accuracy and quality. Querying the database will be possible by online browsing and batch-submission for large datasets holding accession number information, as can be generated using software like Mascot for mass-spectrometry. Cluster-analysis of the submitted datasets in the form of a graphical output will be developed as well as an easy-to-use web-interface. An interface is currently being built in collaboration with the Department of Informatics (T. Theodosiou and D. Armstrong) and will be deployed soon The current team of people collating and deploying the database are H. Husi (database mining and information gathering) and T. Theodosiou (web-interface and deployment). Please note that this database is not funded financially, and cannot survive without sponsorship.

We used patch-clamp electrophysiology from mouse brain slices to record miniature excitatory postsynaptic currents (mEPSC), evoked EPSCs, and EPSCs with two-photon glutamate uncaging at single spines (uEPSC) to determine the synaptic properties in visual cortex layer 2/3 pyramidal neurons during development from postnatal day (P) 10 until P30. Different parameters of the synaptic responses, such as amplitude, half-width, and frequency of mEPSCs were determined. These data and their analysis are documented in the Excel file Data_for_Figures. We also constructed a synaptic model to explain our results. The model for silent synapses is documented in the Excel file SS_simulation. The synaptic model was developed in MatLab, and the code is included in the file Figure_7_code. The fitting of the mEPSC amplitude distribution is documented in the file Figure_4_code., , , # Data and code from: Distinct transmission sites within a synapse for strengthening and homeostasis

https://doi.org/10.5061/dryad.7pvmcvf3x

Description of the data and file structure

Files are stored in matlab (.mat) format or Excel format (.xlsx).

They were used to create the fits for the data in Figure 4 and the simulation in Figure 7. See the manuscript for details of parameter space.

The file Data_for_Figures.xlsx is organized into sheets. Each sheet is named after the Figure panel for which it contains the data. The columns are generally named by the data set as described in the Figure. In addition, the mean and standard error of the mean (SEM) are presented. n represents the number of neurons, while m represents the number of animals. In some sheets, e.g., Fig. 2B, the data set name is illustrated before the column for SEM. Then, the column with the data set name contains the respective mean values. In some sheets, e.g., Fig 1H, ...,

A thorough understanding of the brain's diverse molecular architecture and complex neural circuitry is critical to uncovering the mechanisms that underlie higher-level information processing. Synapses are an integral component of neural circuits because they propagate signals from one neuron to the next. However, because of the astounding multitude and diversity of synapses in the brain, a set of high-resolution, high-precision tools is necessary for extracting enough information from a tissue sample to recover its synaptic structure. To this end, many state-of- the-art molecular imaging methods, including array tomography, have recently focused their efforts on building large-scale datasets annotated with synaptic tissue markers. Manual processing of these images, however, is too time-intensive to be practical. Here, we present a computational method for synapse detection and classification that aims to locate and characterize all synapses in a mouse barrel cortex. Our technique uses a nonparametric Bayesian network to represent the underlying biological process in order to allow for flexibility in the total numbers of synapses and synapse types that are found. Using this data-driven approach, we are able to detect subtle but significant patterns with implications for neuroscience research.

Synapse loss is associated with cognitive decline in Alzheimer's disease (AD) and owing to their plastic nature, synapses are an ideal target for therapeutic intervention. Oligomeric amyloid beta (Abeta) around amyloid plaques is known to contribute to synapse loss in mouse models and is associated with synapse loss in human AD brain tissue, but the mechanisms leading from Ab to synapse loss remain unclear. Recent data suggest that the fast-activating and -inactivating voltage-gated potassium channel subtype 3.4 (Kv3.4) may play a role in Abeta-mediated neurotoxicity. Here, we tested whether this channel could also be involved in Ab synaptotoxicity. Using adeno-associated virus and CRISPR (clustered regularly interspaced short palindromic repeats) technology, we reduced Kv3.4 expression in neurons of the somatosensory cortex of APP/PS1 mice. These mice express human familial AD associated mutations in amyloid precursor protein and presenilin 1 and develop amyloid plaques and plaque-associated synapse loss similar to that observed in AD brain. We observe that reducing Kv3.4 levels ameliorates dendritic spine loss and changes spine morphology compared to control virus. In support of translational relevance, Kv3.4 protein was observed in human AD and control brain and is associated with synapses in human iPSC-derived cortical neurons. Interestingly, we observe a decrease in Kv3.4 expression in iPSC derived cortical neurons when they are challenged with human Alzheimer's disease derived brain homogenate either containing Abeta or immunodepleted to remove Abeta These results suggest that approaches to reduce Kv3.4 expression and/or function could be protective against Abeta-induced synaptic alterations. This dataset includes the .csv files and R studio scripts for generating graphs and statistical analyses for the published paper.

This dataset contains the predicted prices of the asset SYNAPSE AI over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

How newly formed memories are preserved while brain plasticity is ongoing has been a source of debate. One idea is that synapses which experienced recent plasticity become resistant to further plasticity, a type of metaplasticity often referred to as saturation. Here, we probe the local dendritic mechanisms that limit plasticity at recently potentiated synapses. We show that recently potentiated individual synapses exhibit a synapse-specific refractory period for further potentiation. We further found that the refractory period is associated with reduced postsynaptic CaMKII signaling; however, stronger synaptic activation fully restored CaMKII signaling but only partially restored the ability for further plasticity. Importantly, the refractory period is released after one hour, a timing that coincides with the enrichment of several postsynaptic proteins to pre-plasticity levels. Notably, increasing the level of the postsynaptic scaffolding protein, PSD95, but not of PSD93, overcomes the refractory period. Our results support a model in which potentiation at a single synapse is sufficient to initiate a synapse-specific refractory period that persists until key postsynaptic proteins regain their steady-state synaptic levels.

Brain function is mediated by the physiological coordination of a vast, intricately connected network of molecular and cellular components. The physiological properties of network components can be quantified with high throughput; the ability to assess many animals per study has been key to relating physiological properties to behavior. Conversely, detailed anatomical properties (e.g., the synaptic connectivity of molecularly-defined cell types across an entire circuit) are presently quantifiable only with low throughput; thus we know very little about how network structure, and structural variation, influences behavior. For neuroanatomical reconstruction there is a methodological gulf between electron-microscopic (EM) methods, which yield dense connectomes (but at great expense and low throughput) and light-microscopic methods, which provide molecular and cell-type specificity with high throughput (but without synaptic resolution). We developed a high-throughput analysis pipeline and imaging protocol using tissue expansion and light sheet microscopy (ExLLSM) to rapidly reconstruct selected circuits across many animals with single-synapse resolution and molecular contrast. Using Drosophila to validate this approach, we demonstrate that it yields synaptic counts similar to those obtained by EM, enables synaptic connectivity to be compared across sex and experience, and can be used to correlate structural connectivity, functional connectivity, and behavior. This approach fills a critical methodological gap in studying variability in the structure and function of neural circuits across individuals within and between species. Here, we share the data used to train the synapse classifier that was utilized in the analysis pipeline. All additional software, code, and usage examples to train and run the classifier can be found at Github: https://github.com/JaneliaSciComp/exllsm-circuit-reconstruction Methods Automatic synapse classification Presynaptic sites can be identified as clusters of BRP proteins (Ehmann et al., 2017). Using 8X ExLLSM and labeling BRP with the nc82 antibody (Wagh et al., 2006) or the STaR-BRP reporter (Chen et al., 2014b), discrete clusters of fluorescent antibodies were present that, as expected (Schneider-Mizell et al., 2016), varied significantly in shape and size across the Drosophila brain (Figure 1H-K). We tested using ilastik (Sommer et al., 2011), a 3D VGG shaped neural network (Simonyan and Zisserman, 2014), and 3D U-Net shaped neural network (Çiçek et al., 2016) to segment these heterogeneous structures from non-specific antibody labels and background signals. On our data, we found that the neural networks performed better than ilastik and similarly to each other, and that the U-Net was faster than the VGG. Therefore, we elected to train a U-Net convolutional neural network to automatically classify presynaptic sites.
To generate ground truth data for training the U-Net, we made 100x100x100 and 500x500x500 pixel crops of BRP staining (as labeled using the nc82 antibody) using the Fiji N5 Viewer. We considered clusters of three or more BRP labels in close proximity that fell along a common plane to be presynaptic sites. We semi-automatically segmented these presynaptic sites from non-specific antibody labels and background signals using VVD Viewer. This semi-automatic segmentation was accomplished similarly to semi-automatic neuron segmentation: the VVD Viewer Component Analyzer tool was used to extract signal from background followed by manual inspection of each potential presynaptic site. In total, we segmented over 10,000 presynaptic sites in image crops from 25 different brains. Crops were made from the optic lobe, mushroom body, lateral horn, central complex, antennal lobe, and protocerebrum. We used these raw image data crops and manually segmented presynaptic sites to train the U-Net for 3000 epochs until the loss, accuracy, and error rates plateaued. The entire synapse classification and assignment pipeline includes a post-U-Net processing workflow. This post-U-Net workflow includes a watershed segmentation step to segment individual synaptic sites and a size filter to remove connected components below a given size threshold. For presynaptic sites labeled by nc82 or STaR-BRP, objects smaller than 400 pixels were removed. We evaluated the results of this synapse detection pipeline (including post-U-Net watershed segmentation and 400 pixel size thresholding) by running it on data crops of BRP labeled by nc82 from the optic lobe, protocerebrum, and lateral horn of three brain samples that were not included in the training. We compared these results to the manually segmented ground truth data (2300 presynaptic sites) of these image volumes. The final synapse detection pipeline had an average precision of 94% and recall of 88% (Fig. 1L-Q). Here, we include the ground truth data used to train the model. On Github, we include the trained model used for classifying synaptic sites, code and instructions to train the classifier, and code and instructions to calculate performance of the classifier (https://github.com/JaneliaSciComp/exllsm-synapse-detector). These components can be run locally or on a compute cluster, and can be run independently or as part of several common use workflows described below (https://github.com/JaneliaSciComp/exllsm-circuit-reconstruction).

Alzheimer's disease (AD) is characterized by memory loss, insidious cognitive decline, profound neurodegeneration and the extracellular accumulation of amyloid-beta (Ab) peptide in senile plaques and intracellular accumulation of tau in neurofibrillary tangles. Loss and dysfunction of synapses are believed to underlie the devastating cognitive decline in AD. A large amount of evidence suggests that oligomeric forms of Ab associated with senile plaques are toxic to synapses, but the precise sub-synaptic localization of Ab and which forms are synaptotoxic remain unknown. Here, we characterize the sub-synaptic localization of Ab oligomers using three high-resolution imaging techniques, stochastic optical reconstruction microscopy, immunogold electron microscopy and Forster resonance energy transfer in a plaque-bearing mouse model of Alzheimer's disease. With all three techniques, we observe oligomeric Ab inside synaptic terminals. Further, we tested a panel of Ab antibodies using the relatively high-throughput array tomography technique to determine which forms are present in synapses. Our results show that different oligomeric Ab species are present in synapses and highlight the potential of array tomography for rapid testing of aggregation state specific Ab antibodies in brain tissue.

Brain function relies on communication via neuronal synapses. Neurons build and diversify synaptic contacts using different protein combinations that define the specificity, function and plasticity potential of synapses. More than a thousand proteins have been globally identified in both pre- and postsynaptic compartments, providing substantial potential for synaptic diversity. While there is ample evidence of diverse synaptic structures, states or functional properties, the diversity of the underlying individual synaptic proteomes remains largely unexplored. Here we used 7 different Cre-driver mouse lines crossed with a floxed mouse line in which the presynaptic terminals were fluorescently labeled (SypTOM) to identify the proteomes that underlie synaptic diversity. We combined microdissection of 5 different brain regions with fluorescent-activated synaptosome sorting to isolate and analyze using quantitative mass spectrometry 18 types of synapses and their underlying synaptic proteomes. We discovered ~1’800 unique synapse type-enriched proteins and allocated thousands of proteins to different types of synapses. We identify commonly shared synaptic protein modules and highlight the hotspots for proteome specialization. A protein-protein correlation network classifies proteins into modules and their association with synaptic traits reveals synaptic protein communities that correlate with either neurotransmitter glutamate or GABA. Finally, we reveal specializations and commonalities of the striatal dopaminergic proteome and outline the proteome diversity of synapses formed by parvalbumin, somatostatin and vasoactive intestinal peptide-expressing cortical interneuron subtypes, highlighting proteome signatures that relate to their functional properties. This study opens the door for molecular systems-biology analysis of synapses and provides a framework to integrate proteomic information for synapse subtypes of interest with cellular or circuit-level experiments.

This dataset contains the predicted prices of the asset Synapse over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.