Egor Dzyubenko1, Andrey Rozenberg2, Dirk M Hermann3, Andreas Faissner4. 1. Department of Cell Morphology and Molecular Neurobiology, Ruhr-University Bochum, Germany; International School of Neuroscience (IGSN), Ruhr-University Bochum, Germany. 2. Department of Animal Ecology, Evolution and Biodiversity, Ruhr-University Bochum, Germany. 3. Department of Neurology, University Hospital Essen, Germany. 4. Department of Cell Morphology and Molecular Neurobiology, Ruhr-University Bochum, Germany. Electronic address: Andreas.faissner@rub.de.
Abstract
BACKGROUND: Quantification of synapses and their morphological analysis are extensively used in network development and connectivity studies, drug screening and other areas of neuroscience. Thus, a number of quantitative approaches were introduced so far. However, most of the available methods are highly tailored to specific applications and have limitations for widespread use. NEW METHOD: We present a new plugin for the open-source software ImageJ to provide a modifiable, high-throughput and easy to use method for synaptic puncta analysis. Our approach is based on colocalization of pre- and postsynaptic protein markers. Structurally completed glutamatergic and GABAergic synapses were identified by VGLUT1-PSD95 and VGAT-gephyrin colocalization, respectively. By combining conventional confocal microscopy with stimulated emission depletion (STED) imaging, we propose a method to quantify the number of scaffolding protein clusters, recruited to a single postsynaptic density. RESULTS: In a proof-of-concept study, we reveal the differential distribution of glutamatergic and GABAergic synapse density with reference to perineuronal net (PNN) expression. Using super-resolution STED imaging, we demonstrate that postsynaptic puncta of completed synapses are composed of significantly more protein clusters, compared to uncompleted synapses. COMPARISON WITH EXISTING METHODS: Our Synapse Counter plugin for ImageJ offers a rapid and unbiased research tool for a broad spectrum of neuroscientists. The proposed method of synaptic protein clusters quantification exploits super-resolution imaging to provide a comprehensive approach to the analysis of postsynaptic density composition. CONCLUSIONS: Our results strongly substantiate the benefits of colocalization-based synapse detection.
BACKGROUND: Quantification of synapses and their morphological analysis are extensively used in network development and connectivity studies, drug screening and other areas of neuroscience. Thus, a number of quantitative approaches were introduced so far. However, most of the available methods are highly tailored to specific applications and have limitations for widespread use. NEW METHOD: We present a new plugin for the open-source software ImageJ to provide a modifiable, high-throughput and easy to use method for synaptic puncta analysis. Our approach is based on colocalization of pre- and postsynaptic protein markers. Structurally completed glutamatergic and GABAergic synapses were identified by VGLUT1-PSD95 and VGAT-gephyrin colocalization, respectively. By combining conventional confocal microscopy with stimulated emission depletion (STED) imaging, we propose a method to quantify the number of scaffolding protein clusters, recruited to a single postsynaptic density. RESULTS: In a proof-of-concept study, we reveal the differential distribution of glutamatergic and GABAergic synapse density with reference to perineuronal net (PNN) expression. Using super-resolution STED imaging, we demonstrate that postsynaptic puncta of completed synapses are composed of significantly more protein clusters, compared to uncompleted synapses. COMPARISON WITH EXISTING METHODS: Our Synapse Counter plugin for ImageJ offers a rapid and unbiased research tool for a broad spectrum of neuroscientists. The proposed method of synaptic protein clusters quantification exploits super-resolution imaging to provide a comprehensive approach to the analysis of postsynaptic density composition. CONCLUSIONS: Our results strongly substantiate the benefits of colocalization-based synapse detection.
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