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      A digital 3D reference atlas reveals cellular growth patterns shaping the Arabidopsis ovule

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          Abstract

          A fundamental question in biology is how morphogenesis integrates the multitude of processes that act at different scales, ranging from the molecular control of gene expression to cellular coordination in a tissue. Using machine-learning-based digital image analysis, we generated a three-dimensional atlas of ovule development in Arabidopsis thaliana, enabling the quantitative spatio-temporal analysis of cellular and gene expression patterns with cell and tissue resolution. We discovered novel morphological manifestations of ovule polarity, a new mode of cell layer formation, and previously unrecognized subepidermal cell populations that initiate ovule curvature. The data suggest an irregular cellular build-up of WUSCHEL expression in the primordium and new functions for INNER NO OUTER in restricting nucellar cell proliferation and the organization of the interior chalaza. Our work demonstrates the analytical power of a three-dimensional digital representation when studying the morphogenesis of an organ of complex architecture that eventually consists of 1900 cells.

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          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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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                06 January 2021
                2021
                : 10
                Affiliations
                [1 ]Plant Developmental Biology, School of Life Sciences, Technical University of Munich FreisingGermany
                [2 ]Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research CologneGermany
                [3 ]Heidelberg Collaboratory for Image Processing, Dept. of Physics and Astronomy, Heidelberg University HeidelbergGermany
                [4 ]European Molecular Biology Laboratory HeidelbergGermany
                University of California, Berkeley United States
                University of Lausanne Switzerland
                University of California, Berkeley United States
                University of California, Berkeley United States
                Wageningen University Netherlands
                Author notes
                [‡]

                The John Innes Centre, Norwich Research Park, Norwich, United Kingdom.

                [†]

                These authors contributed equally to this work.

                Article
                63262
                10.7554/eLife.63262
                7787667
                33404501
                © 2021, Vijayan 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.

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                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: FOR2581 (TP3)
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: FOR2581 (TP8)
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: FOR2581 (TP7)
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Developmental Biology
                Plant Biology
                Custom metadata
                Deep imaging, machine-learning-based segmentation, and tissue annotation resulted in a developmental series of 3D digital ovules with cellular resolution allowing next-level analysis of the ontogenesis of this complex organ.

                Life sciences

                3d digital atlas, image analysis, ovule, machine learning, segmentation, plants, a. thaliana

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