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      Botulinum neurotoxin type-A enters a non-recycling pool of synaptic vesicles

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          Abstract

          Neuronal communication relies on synaptic vesicles undergoing regulated exocytosis and recycling for multiple rounds of fusion. Whether all synaptic vesicles have identical protein content has been challenged, suggesting that their recycling ability may differ greatly. Botulinum neurotoxin type-A (BoNT/A) is a highly potent neurotoxin that is internalized in synaptic vesicles at motor nerve terminals and induces flaccid paralysis. Recently, BoNT/A was also shown to undergo retrograde transport, suggesting it might enter a specific pool of synaptic vesicles with a retrograde trafficking fate. Using high-resolution microscopy techniques including electron microscopy and single molecule imaging, we found that the BoNT/A binding domain is internalized within a subset of vesicles that only partially co-localize with cholera toxin B-subunit and have markedly reduced VAMP2 immunoreactivity. Synaptic vesicles loaded with pHrodo-BoNT/A-Hc exhibited a significantly reduced ability to fuse with the plasma membrane in mouse hippocampal nerve terminals when compared with pHrodo-dextran-containing synaptic vesicles and pHrodo-labeled anti-GFP nanobodies bound to VAMP2-pHluorin or vGlut-pHluorin. Similar results were also obtained at the amphibian neuromuscular junction. These results reveal that BoNT/A is internalized in a subpopulation of synaptic vesicles that are not destined to recycle, highlighting the existence of significant molecular and functional heterogeneity between synaptic vesicles.

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          Most cited references 44

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          NIH Image to ImageJ: 25 years of image analysis.

          For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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            A guided tour into subcellular colocalization analysis in light microscopy.

            It is generally accepted that the functional compartmentalization of eukaryotic cells is reflected by the differential occurrence of proteins in their compartments. The location and physiological function of a protein are closely related; local information of a protein is thus crucial to understanding its role in biological processes. The visualization of proteins residing on intracellular structures by fluorescence microscopy has become a routine approach in cell biology and is increasingly used to assess their colocalization with well-characterized markers. However, image-analysis methods for colocalization studies are a field of contention and enigma. We have therefore undertaken to review the most currently used colocalization analysis methods, introducing the basic optical concepts important for image acquisition and subsequent analysis. We provide a summary of practical tips for image acquisition and treatment that should precede proper colocalization analysis. Furthermore, we discuss the application and feasibility of colocalization tools for various biological colocalization situations and discuss their respective strengths and weaknesses. We have created a novel toolbox for subcellular colocalization analysis under ImageJ, named JACoP, that integrates current global statistic methods and a novel object-based approach.
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              Automated electron microscope tomography using robust prediction of specimen movements.

               D Mastronarde (2005)
              A new method was developed to acquire images automatically at a series of specimen tilts, as required for tomographic reconstruction. The method uses changes in specimen position at previous tilt angles to predict the position at the current tilt angle. Actual measurement of the position or focus is skipped if the statistical error of the prediction is low enough. This method allows a tilt series to be acquired rapidly when conditions are good but falls back toward the traditional approach of taking focusing and tracking images when necessary. The method has been implemented in a program, SerialEM, that provides an efficient environment for data acquisition. This program includes control of an energy filter as well as a low-dose imaging mode, in which tracking and focusing occur away from the area of interest. The program can automatically acquire a montage of overlapping frames, allowing tomography of areas larger than the field of the CCD camera. It also includes tools for navigating between specimen positions and finding regions of interest.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                25 January 2016
                2016
                : 6
                Affiliations
                [1 ]The University of Queensland, Queensland Brain Institute, Clem Jones Centre for Ageing Dementia Research , Brisbane, Queensland 4072, Australia
                [2 ]The University of Queensland, Queensland Brain Institute , Brisbane, Queensland 4072, Australia
                [3 ]The University of Queensland, Centre for Microscopy and Microanalysis , Brisbane, Queensland 4072, Australia
                [4 ]Interdisciplinary Institute for Neuroscience, The University of Bordeaux , Bordeaux, 33000, France
                [5 ]Centre for Neuroscience, Indian Institute of Science , Bangalore, 560012, India
                Author notes
                [†]

                These authors contributed equally to this work.

                [*]

                Present address: Centre for Integrative Physiology, University of Edinburgh, Hugh Robson Building, George Square, Edinburgh, Scotland.

                [‡]

                Present address: The University of Queensland, Australian Institute for Bioengineering and Nanotechnology (AIBN), Brisbane, Queensland 4072, Australia.

                Article
                srep19654
                10.1038/srep19654
                4726273
                26805017
                Copyright © 2016, Macmillan Publishers Limited

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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