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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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          Most cited references108

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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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            Floral dip: a simplified method forAgrobacterium-mediated transformation ofArabidopsis thaliana

            The Agrobacterium vacuum infiltration method has made it possible to transform Arabidopsis thaliana without plant tissue culture or regeneration. In the present study, this method was evaluated and a substantially modified transformation method was developed. The labor-intensive vacuum infiltration process was eliminated in favor of simple dipping of developing floral tissues into a solution containing Agrobacterium tumefaciens, 5% sucrose and 500 microliters per litre of surfactant Silwet L-77. Sucrose and surfactant were critical to the success of the floral dip method. Plants inoculated when numerous immature floral buds and few siliques were present produced transformed progeny at the highest rate. Plant tissue culture media, the hormone benzylamino purine and pH adjustment were unnecessary, and Agrobacterium could be applied to plants at a range of cell densities. Repeated application of Agrobacterium improved transformation rates and overall yield of transformants approximately twofold. Covering plants for 1 day to retain humidity after inoculation also raised transformation rates twofold. Multiple ecotypes were transformable by this method. The modified method should facilitate high-throughput transformation of Arabidopsis for efforts such as T-DNA gene tagging, positional cloning, or attempts at targeted gene replacement.
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              Genome-wide atlas of gene expression in the adult mouse brain.

              Molecular approaches to understanding the functional circuitry of the nervous system promise new insights into the relationship between genes, brain and behaviour. The cellular diversity of the brain necessitates a cellular resolution approach towards understanding the functional genomics of the nervous system. We describe here an anatomically comprehensive digital atlas containing the expression patterns of approximately 20,000 genes in the adult mouse brain. Data were generated using automated high-throughput procedures for in situ hybridization and data acquisition, and are publicly accessible online. Newly developed image-based informatics tools allow global genome-scale structural analysis and cross-correlation, as well as identification of regionally enriched genes. Unbiased fine-resolution analysis has identified highly specific cellular markers as well as extensive evidence of cellular heterogeneity not evident in classical neuroanatomical atlases. This highly standardized atlas provides an open, primary data resource for a wide variety of further studies concerning brain organization and function.
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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
                : e63262
                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.

                Author information
                https://orcid.org/0000-0003-1837-6359
                http://orcid.org/0000-0002-5196-1122
                http://orcid.org/0000-0003-1334-6388
                https://orcid.org/0000-0001-9220-0787
                https://orcid.org/0000-0001-6688-0539
                Article
                63262
                10.7554/eLife.63262
                7787667
                33404501
                981afc9c-f123-4ce1-b87c-45049c86d837
                © 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.

                History
                : 19 September 2020
                : 19 December 2020
                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
                Life sciences
                3d digital atlas, image analysis, ovule, machine learning, segmentation, plants, a. thaliana

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