11 research outputs found

    Nanoscale molecular reorganization of the inhibitory postsynaptic density is a determinant of gabaergic synaptic potentiation

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    Gephyrin is a key scaffold protein mediating the anchoring of GABAA receptors at inhibitory synapses. Here, we exploited superresolution techniques combined with proximity-based clustering analysis and model simulations to investigate the single-molecule gephyrin reorganization during plasticity of inhibitory synapses in mouse hippocampal cultured neurons. This approach revealed that, during the expression of inhibitory LTP, the increase of gephyrin density at postsynaptic sites is associated with the promoted formation of gephyrin nanodomains. We demonstrate that the gephyrin rearrangement in nanodomains stabilizes the amplitude of postsynaptic currents, indicating that, in addition to the number of synaptic GABAA receptors, the nanoscale distribution of GABAA receptors in the postsynaptic area is a crucial determinant for the expression of inhibitory synaptic plasticity. In addition, the methodology implemented here clears the way to the application of the graph-based theory to single-molecule data for the description and quantification of the spatial organization of the synapse at the single-molecule level

    Quantitative Super-Resolution Microscopy of Proteins at the Synaptic Level

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    Single-molecule localization (SML) techniques provide a powerful tool to answer biological questions requiring the observation of subcellular structures at the nanoscale. Quantitative single-molecule analysis allows quantifying the number and distribution of molecules in several biological systems beyond the diffraction limit [1]. In the last few years, many computational methods employing clustering analysis algorithms have been developed to extract quantitative information from SML data sets. In neuroscience, quantitative SML has been applied to reveal density and spatial organization of synaptic protein

    Unveiling the Inhibitory Synapse Organization Using Superresolution Microscopy

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    The advent of super-resolution microscopy provided both a substantial improvement of the spatial resolution and the possibility to perform quantitative measurements at a nanometric level. In particular, single-molecule localization (SML) techniques provide a powerful tool to answer biological questions that require the observation of subcellular structures. Quantitative single-molecule analysis allows quantifying the number and observing the distribution of molecules in several biological systems beyond the diffraction limit [1]. In the last few years, many computational methods employing clustering analysis algorithms [2] have been developed to extract quantitative information from SML data sets
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