A Semi-automated Program for Axonal Reconstructions from Time-lapse 2-Photon Images

Abstract

Event Abstract Back to Event A Semi-automated Program for Axonal Reconstructions from Time-lapse 2-Photon Images Zhilun Yang1*, Qian Wang2, Ge Gao2, Xuesong Li2, Federico Grillo3, Valentina Ferretti3, Vincenzo De Paola3 and Sen Song2 1 School of Software, Tsinghua University, China 2 Department of Biomedical Engineering, School of Medicine, Tsinghua University, China 3 MRC Clinical Sciences Centre, Imperial College London, Du Cane road, London, United Kingdom Tracking individual axons and synapses over extended periods of time has recently become possible by combining time lapse two-photon microscopy and transgenic animals expressing fluorescent proteins in subsets of neurons. These live imaging studies are providing unique insights into the development and plasticity of neural circuits in both basal conditions and in the context of disease. However, the process of data analysis to extract morphological features of neuronal arbors and the strengths (i.e. size) of individual synapses is currently manual and tedious. Here we present a semi-automated reconstruction program called NeuronMatrix with a graphical GUI written in matlab (figure A). Neuron reconstruction typically follows four steps: 1) pre-processing, 2) tracing, 3) post-processing, and 4) measurement. We use two-photon time-lapse images of GFP-expressing axons from mouse barrel cortex taken in the De Paola lab. Firstly, we use median filtering to get rid of shot noise (figure B). To get rid of remaining unwanted background fluorescence and resolve individual neurites, we follow a recent machine learning approach proposed by the Sebastian Seung lab (Sumbul U et al, Neuroscience Abstracts 2011) and use a convolutional neural network for enhancement (figure C). Secondly, we incorporated the algorithm from the Simple Neuron Tracer Fiji plugin to automatically trace a path between two points (Figure B and C). The result shows that the convolutional neural network is able to help resolve junction points and reduce unspecific background. The much cleaner image led to a dramatic improvement in tracing speed (2.92±0.29s versus 15.7±4.7s for each axon), making the user experience more efficient. In some instances, tracing on the median filtered image gave the wrong result due to axons coming close to each other while the result from the enhanced image is correct (figure B versus C). Thirdly, we extract the axonal backbone intensity and align it over imaging sessions using fiducial points (figure D ). Lastly, we use local intensity peak finding to extract bouton strength values. We are currently using this software to analyze the synaptic strength changes underlying cortical remapping. Future improvements in over-day alignment are expected to further reduce the amount of user efforts. We plan to release NeuronMatrix to the general community as a Fiji plugin in the future. Figure 1 Keywords: Neuroimaging, live imaging studies, neuroinformatics, background fluorescence, NeuronMatrix Conference: 5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012. Presentation Type: Poster Topic: Neuroinformatics Citation: Yang Z, Wang Q, Gao G, Li X, Grillo F, Ferretti V, Paola V and Song S (2014). A Semi-automated Program for Axonal Reconstructions from Time-lapse 2-Photon Images. Front. Neuroinform. Conference Abstract: 5th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2014.08.00001 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 Mar 2013; Published Online: 27 Feb 2014. * Correspondence: Dr. Zhilun Yang, School of Software, Tsinghua University, Beijing, China, yzl10@mails.tsinghua.edu.cn Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Zhilun Yang Qian Wang Ge Gao Xuesong Li Federico Grillo Valentina Ferretti Vincenzo De Paola Sen Song Google Zhilun Yang Qian Wang Ge Gao Xuesong Li Federico Grillo Valentina Ferretti Vincenzo De Paola Sen Song Google Scholar Zhilun Yang Qian Wang Ge Gao Xuesong Li Federico Grillo Valentina Ferretti Vincenzo De Paola Sen Song PubMed Zhilun Yang Qian Wang Ge Gao Xuesong Li Federico Grillo Valentina Ferretti Vincenzo De Paola Sen Song Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.