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      MorphoGraphX: A platform for quantifying morphogenesis in 4D

      research-article
      1 , 2 , 2 , 3 , 4 , 5 , 2 , 2 , 1 , 6 , 7 , 1 , 8 , 2 , 2 , 8 , 9 , 1 , 10 , 9 , 5 , 11 , 9 , 1 , 12 , 2 , 2 , 8 , 4 , 1 , 1 , 2 , *
      eLife
      eLife Sciences Publications, Ltd
      morphogenesis, quantification, image analysis, confocal microscopy, software, tomato, Arabidopsis, D. melanogaster, mouse, other

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Morphogenesis emerges from complex multiscale interactions between genetic and mechanical processes. To understand these processes, the evolution of cell shape, proliferation and gene expression must be quantified. This quantification is usually performed either in full 3D, which is computationally expensive and technically challenging, or on 2D planar projections, which introduces geometrical artifacts on highly curved organs. Here we present MorphoGraphX ( www.MorphoGraphX.org), a software that bridges this gap by working directly with curved surface images extracted from 3D data. In addition to traditional 3D image analysis, we have developed algorithms to operate on curved surfaces, such as cell segmentation, lineage tracking and fluorescence signal quantification. The software's modular design makes it easy to include existing libraries, or to implement new algorithms. Cell geometries extracted with MorphoGraphX can be exported and used as templates for simulation models, providing a powerful platform to investigate the interactions between shape, genes and growth.

          DOI: http://dx.doi.org/10.7554/eLife.05864.001

          eLife digest

          Animals, plants and other multicellular organisms develop their distinctive three-dimensional shapes as they grow. This process—called morphogenesis—is influenced by many genes and involves communication between cells to control the ability of individual cells to divide and grow. The precise timing and location of events in particular cells is very important in determining the final shape of the organism.

          Common techniques for studying morphogenesis use microscopes to take 2-dimensional (2D) and 3-dimensional (3D) time-lapse videos of living cells. Fluorescent tags allow scientists to observe specific proteins, cell boundaries, and interactions between individual cells. These imaging techniques can produce large sets of data that need to be analyzed using a computer and incorporated into computer simulations that predict how a tissue or organ within an organism grows to form its final shape.

          Currently, most computational models of morphogenesis work on 2D templates and focus on how tissues and organs form. However, many patterning events occur on surfaces that are curved or folded, so 2D models may lose important details. Developing 3D models would provide a more accurate picture, but these models are expensive and technically challenging to make.

          To address this problem, Barbier de Reuille, Routier-Kierzkowska et al. present an open-source, customizable software platform called MorphoGraphX. This software extracts images from 3D data to recreate curved 2D surfaces. Barbier de Reuille, Routier-Kierkowska et al. have also developed algorithms to help analyze growth and gene activity in these curved images, and the data can be exported and used in computer simulations.

          Several scientists have already used this software in their studies, but Barbier de Reuille, Routier-Kierzkowska et al. have now made the software more widely available and have provided a full explanation of how it works. How scientists can extend and customize MorphoGraphX to answer their own unique research questions is also described. It is anticipated that MorphoGraphX will become a popular platform for the open sharing of computational tools to study morphogenesis.

          DOI: http://dx.doi.org/10.7554/eLife.05864.002

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          Most cited references54

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          Reaction-diffusion model as a framework for understanding biological pattern formation.

          The Turing, or reaction-diffusion (RD), model is one of the best-known theoretical models used to explain self-regulated pattern formation in the developing animal embryo. Although its real-world relevance was long debated, a number of compelling examples have gradually alleviated much of the skepticism surrounding the model. The RD model can generate a wide variety of spatial patterns, and mathematical studies have revealed the kinds of interactions required for each, giving this model the potential for application as an experimental working hypothesis in a wide variety of morphological phenomena. In this review, we describe the essence of this theory for experimental biologists unfamiliar with the model, using examples from experimental studies in which the RD model is effectively incorporated.
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            V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets

            The V3D system provides three-dimensional (3D) visualization of gigabyte-sized microscopy image stacks in real time on current laptops and desktops. Combined with highly ergonomic features for selecting an X, Y, Z location of an image directly in 3D space and for visualizing overlays of a variety of surface objects, V3D streamlines the on-line analysis, measurement, and proofreading of complicated image patterns. V3D is cross-platform and can be enhanced by plug-ins. We built V3D-Neuron on top of V3D to reconstruct complex 3D neuronal structures from large brain images. V3D-Neuron enables us to precisely digitize the morphology of a single neuron in a fruit fly brain in minutes, with about 17-fold improvement in reliability and 10-fold savings in time compared to other neuron reconstruction tools. Using V3D-Neuron, we demonstrated the feasibility of building a 3D digital atlas of neurite tracts in the fruit fly brain.
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              FibrilTool, an ImageJ plug-in to quantify fibrillar structures in raw microscopy images.

              Cell biology heavily relies on the behavior of fibrillar structures, such as the cytoskeleton, yet the analysis of their behavior in tissues often remains qualitative. Image analysis tools have been developed to quantify this behavior, but they often involve an image pre-processing stage that may bias the output and/or they require specific software. Here we describe FibrilTool, an ImageJ plug-in based on the concept of nematic tensor, which can provide a quantitative description of the anisotropy of fiber arrays and their average orientation in cells, directly from raw images obtained by any form of microscopy. FibrilTool has been validated on microtubules, actin and cellulose microfibrils, but it may also help analyze other fibrillar structures, such as collagen, or the texture of various materials. The tool is ImageJ-based, and it is therefore freely accessible to the scientific community and does not require specific computational setup. The tool provides the average orientation and anisotropy of fiber arrays in a given region of interest (ROI) in a few seconds.
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                Author and article information

                Contributors
                Role: Reviewing editor
                Journal
                eLife
                eLife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                2050-084X
                06 May 2015
                2015
                : 4
                : e05864
                Affiliations
                [1 ]deptInstitute of Plant Sciences , University of Bern , Bern, Switzerland
                [2 ]deptDepartment of Comparative Development and Genetics , Max Planck Institute for Plant Breeding Research , Cologne, Germany
                [3 ]deptSchool of Biosciences , University of Birmingham , Birmingham, United Kingdom
                [4 ]Swiss Institute of Bioinformatics , Lausanne, Switzerland
                [5 ]deptChair of Computational Science , ETH Zurich , Zurich, Switzerland
                [6 ]deptReproduction et Développement des Plantes , Ecole Normale Supérieure de Lyon , Lyon, France
                [7 ]deptLaboratoire Joliot Curie , Ecole Normale Supérieure de Lyon , Lyon, France
                [8 ]deptDepartment of Biophysics and Morphogenesis of Plants , University of Silesia , Katowice, Poland
                [9 ]Institute of Molecular Life Sciences , Zurich, Switzerland
                [10 ]deptLaboratory of Membrane Biogenesis , University of Bordeaux , Bordeaux, France
                [11 ]deptWeill Institute for Cell and Molecular Biology and School of Integrative Plant Science, Section of Plant Biology , Cornell University , Ithaca, United States
                [12 ]deptInstitute of Molecular Plant Sciences , University of Edinburgh , Edinburgh, United Kingdom
                Stanford University , United States
                Stanford University , United States
                Author notes
                [* ]For correspondence: smith@ 123456mpipz.mpg.de
                [†]

                These authors contributed equally to this work.

                Author information
                http://orcid.org/0000-0001-6685-2984
                Article
                05864
                10.7554/eLife.05864
                4421794
                25946108
                c1738636-d661-484a-ac6b-e6750872b1e9
                © 2015, Barbier de Reuille et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 03 December 2014
                : 03 April 2015
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001711, Schweizerische Nationalfonds zur Förderung der Wissenschaftlichen Forschung;
                Award ID: international short research visit
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000854, Human Frontier Science Program (HFSP);
                Award ID: RGP0008/2013
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000781, European Research Council (ERC);
                Award ID: Advanced grant
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000268, Biotechnology and Biological Sciences Research Council (BBSRC);
                Award ID: BB/L010232/1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000855, universityUniversity Of Birmingham;
                Award ID: Research Fellowship
                Award Recipient :
                Funded by: universityUniversitat Zurich;
                Award ID: Forschungskredit
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004281, Narodowe Centrum Nauki;
                Award ID: MAESTRO research grant No 2011/02/A/NZ3/00079
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: SFB 680
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000854, Human Frontier Science Program (HFSP);
                Award ID: RGY0087/2011
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004189, Max-Planck-Gesellschaft;
                Award ID: W2 Minerva program grant
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001711, Schweizerische Nationalfonds zur Förderung der Wissenschaftlichen Forschung;
                Award ID: CR32I3_132586
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001711, Schweizerische Nationalfonds zur Förderung der Wissenschaftlichen Forschung;
                Award ID: CR32I3_143833
                Award Recipient :
                Funded by: The Swiss Initiative in Systems Biology;
                Award ID: RTDs Plant Growth 1 & 2
                Award Recipient :
                Funded by: The Swiss Initiative in Systems Biology;
                Award ID: RTD WingX
                Award Recipient :
                Funded by: The Swiss Initiative in Systems Biology;
                Award ID: RTD SyBIT
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Tools and Resources
                Computational and Systems Biology
                Developmental Biology and Stem Cells
                Custom metadata
                2.3
                MorphoGraphX summarizes full 3D datasets as curved surface images (2.5D), enabling the efficient quantification of growth and gene expression data from large time-lapse volumetric datasets.

                Life sciences
                morphogenesis,quantification,image analysis,confocal microscopy,software,tomato,arabidopsis,d. melanogaster,mouse,other

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