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      Activity-Dependent Remodeling of Synaptic Protein Organization Revealed by High Throughput Analysis of STED Nanoscopy Images

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          Abstract

          The organization of proteins in the apposed nanodomains of pre- and postsynaptic compartments is thought to play a pivotal role in synaptic strength and plasticity. As such, the alignment between pre- and postsynaptic proteins may regulate, for example, the rate of presynaptic release or the strength of postsynaptic signaling. However, the analysis of these structures has mainly been restricted to subsets of synapses, providing a limited view of the diversity of synaptic protein cluster remodeling during synaptic plasticity. To characterize changes in the organization of synaptic nanodomains during synaptic plasticity over a large population of synapses, we combined STimulated Emission Depletion (STED) nanoscopy with a Python-based statistical object distance analysis (pySODA), in dissociated cultured hippocampal circuits exposed to treatments driving different forms of synaptic plasticity. The nanoscale organization, characterized in terms of coupling properties, of presynaptic (Bassoon, RIM1/2) and postsynaptic (PSD95, Homer1c) scaffold proteins was differently altered in response to plasticity-inducing stimuli. For the Bassoon - PSD95 pair, treatments driving synaptic potentiation caused an increase in their coupling probability, whereas a stimulus driving synaptic depression had an opposite effect. To enrich the characterization of the synaptic cluster remodeling at the population level, we applied unsupervised machine learning approaches to include selected morphological features into a multidimensional analysis. This combined analysis revealed a large diversity of synaptic protein cluster subtypes exhibiting differential activity-dependent remodeling, yet with common features depending on the expected direction of plasticity. The expanded palette of synaptic features revealed by our unbiased approach should provide a basis to further explore the widely diverse molecular mechanisms of synaptic plasticity.

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            Silhouettes: A graphical aid to the interpretation and validation of cluster analysis

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              UMAP: Uniform Manifold Approximation and Projection

                Author and article information

                Contributors
                Journal
                Front Neural Circuits
                Front Neural Circuits
                Front. Neural Circuits
                Frontiers in Neural Circuits
                Frontiers Media S.A.
                1662-5110
                15 October 2020
                2020
                : 14
                : 57
                Affiliations
                [1] 1CERVO Brain Research Centre , Québec, QC, Canada
                [2] 2Department of Biochemistry, Microbiology and Bioinformatics, Université Laval , Québec, QC, Canada
                [3] 3Department of Psychiatry and Neuroscience, Université Laval , Québec, QC, Canada
                Author notes

                Edited by: Jean-Claude Béïque, University of Ottawa, Canada

                Reviewed by: Thomas A. Blanpied, University of Maryland, Baltimore, United States; Eric Hosy, UMR5297 Institut Interdisciplinaire de Neurosciences (IINS), France; Valentin Nägerl, UMR5297 Institut Interdisciplinaire de Neurosciences (IINS), France

                *Correspondence: Paul De Koninck paul.dekoninck@ 123456neurosciences.ulaval.ca

                †These authors have contributed equally to this work

                Article
                10.3389/fncir.2020.00057
                7594516
                33177994
                bc41b722-e5bf-4840-b10b-a4f2570840f6
                Copyright © 2020 Wiesner, Bilodeau, Bernatchez, Deschênes, Raulier, De Koninck and Lavoie-Cardinal.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 15 March 2020
                : 29 July 2020
                Page count
                Figures: 9, Tables: 1, Equations: 4, References: 63, Pages: 19, Words: 11986
                Funding
                Funded by: Natural Sciences and Engineering Research Council of Canada 10.13039/501100000038
                Award ID: DGECR-2019-00200
                Funded by: Canadian Institutes of Health Research 10.13039/501100000024
                Funded by: Canada Foundation for Innovation 10.13039/501100000196
                Categories
                Neuroscience
                Original Research

                Neurosciences
                super-resolution microscopy,synaptic plasticity,synaptic proteins,quantitative image analysis,synapse organization,unsupervised machine learning

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