264 research outputs found

    Thermally activated delayed fluorescence in an optically accessed soft matter environment

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    Probabilistic Fluorescence-Based Synapse Detection

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    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

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    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

    Probabilistic fluorescence-based synapse detection

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    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
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