Burgess, Alexandra J

Majda, Mateusz

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A future in 3D: Analyzing morphology in all dimensions

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A future in 3D: Analyzing morphology in alldimensionsAlexandra J. Burgess 1,* and Mateusz Majda 2,*1 Agriculture and Environmental Sciences, School of Biosciences, University of Nottingham, Loughborough, UK2 Department of Computational and Systems Biology, John Innes Centre, Norwich, UK*Author for correspondence: alexandra.burgess@nottingham.ac.uk (A.J.B.); mateusz.majda@jic.ac.uk (M.M.)Analysis of plant morphology is of interest across all scalesof order; from molecule to cell through organism, field andeven landscape levels (Burgess et al., 2017; Sapala et al.,2019; Strauss et al., 2021). Morphology is critical in deter-mining function, and as such, accurately characterizing mor-phology in 3D is a major focus of many research programs.However, segmentation of 3D models is the most substantialchallenge in analysis pipelines, and automated methodshave been sought to improve the throughput of segmenta-tion (Strauss et al., 2021).For tissue-level morphology, shape is determined by cellu-lar growth rates and proliferation along a designated axis(Whitewoods and Coen, 2017). Differences in cell divisionand cell expansion (i.e. asymmetric growth) lead to altera-tions in morphology due to changes in curvature (Rueda-Contreras et al., 2018; Zhou and Zhang, 2022). However,large-scale analysis of morphology in multicellular organismsrequires an accurate estimate of the shape of all cells acrossseveral representative specimens. Capturing these structuresis gaining traction through the generation of realistic 3Dmodels through confocal laser scanning microscopy or lightsheet fluorescence microscopy. However, the rapid andlarge-scale analysis of such models has been restricted dueto the complexity of segmenting different tissue shapes(Wolny et al., 2020).Plant tissues consist of a small number of different celltypes arranged in distinct layers. Thus, analyzing the geomet-ric and molecular parameters of such cells becomes feasibleusing automated approaches to differentiate the layers(Strauss et al., 2021). Up to now, this has been generallyachieved through distance measurements and does not ac-count for the growth axis (Montenegro-Johnson et al.,2019). However, as plant structures become more complex,such as through curvature, folds, or dome-shaped protru-sions, automating cellular differentiation becomes morecomplex.In this issue, Vijayan et al. (2022) present a tool for the au-tomatic characterization and analysis of plant tissue mor-phology. By incorporating information on the organ axisand extracting the cells in different tissue layers based onthe organ surface, the authors were able to automaticallyannotate cellular layers of even complex morphology; exem-plified by ovule and funiculus (ovule stalk) development inArabidopsis (Arabidopsis thaliana), the archegonium (femalereproductive structure) of the model species the commonliverwort (Marchantia polymopha), and the cup-shaped trapof the aquatic carnivorous plant the floating bladderwort(Utricularia gibba).Implemented using 3DCoordX, an add-on toolbox inMorphoGraphX 2.0, meshes of 3D segmented cells can beautomatically labeled and assigned to different tissue layersbased on surface cells. Beside tissue annotation, 3DCoordXcreates organ-based coordinates using a Bezier ring for 3Dmeshes, allowing analysis of highly curved multi-layer organs.This minimalizes manual work during cell annotation andallows quantitative analysis of cellular features based on or-gan position.The principle behind this annotation is given in Figure 1.The initial outer L1 layer of cells (i.e. the red layer) is distin-guished based upon the relative proportion of surface areathat is exposed, or not adjoining another cell, termed the“outside wall area ratio”. Layers below this are then sequen-tially labeled based on the number of cells between themand the outer layer. Using exposed area also enables the seg-mentation of adjoining structures, such as neighboringovules, even if the surface mesh of the structures isNewsandViewsReceived April 04, 2022. Accepted April 11, 2022. Advance access publication April 27, 2022VC The Author(s) 2022. Published by Oxford University Press on behalf of American Society of Plant Biologists.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution,and reproduction in any medium, provided the original work is properly cited.Open Accesshttps://doi.org/10.1093/plphys/kiac190 PLANT PHYSIOLOGY 2022: 00: 1–2Downloaded from https://academic.oup.com/plphys/advance-article/doi/10.1093/plphys/kiac190/6574920 by guest on 27 May 2022touching. The authors then place a reference point (i.e. theblack dashed line) based on important developmental regu-lators such as gene or hormone expression.Separation of the model into sequential layers with re-spect to the different growth axes allows for automaticanalysis of cell division and expansion to understand theprocesses shaping morphology. This indicates different pro-cesses governing curvature across the diverse plant speciesstudied, from preferential cell proliferation in the anteriordomain of ovule primordia in Arabidopsis, to increased celllength during axial neck growth in the liverwort archego-nium, and to distinct cellular patterns resulting in the fold-ing of the complex floating bladderwort trap.With continued advances in hardware and software, it isfeasible that intelligent model annotation, as achieved here,will be replicated across other 3D models. For example, seg-mentation of organs at the whole plant level poses a consid-erable task for studying whole-plant morphological traits(Lomte and Janwale, 2017).FundingA.J.B. is supported by The Leverhulme Trust.Conflict of interest statement. The authors declare no conflict ofinterest.ReferencesBurgess A, Retkute R, Herman T, Murchie E (2017) Exploringrelationships between canopy architecture, light distribution,and photosynthesis in contrasting rice genotypes using 3D canopyreconstruction. Front Plant Sci 8: 734Lomte S, Janwale A (2017) Plant leaves image segmentation techni-ques: a review. Artic Int J Comput Sci Eng 5: 147–150Montenegro-Johnson T, Strauss S, Jackson M, Walker L, Smith R,Bassel G (2019) 3DCellAtlas meristem: a tool for the global cellularannotation of shoot apical meristems. Plant Methods 15: 33Rueda-Contreras M, Romero-Arias J, Aragón J, Barrio R (2018)Curvature-driven spatial patterns in growing 3D domains: a mech-anochemical model for phyllotaxis. PLoS One 13: e0201746Sapala A, Runions A, Smith R (2019) Mechanics, geometry and ge-netics of epidermal cell shape regulation: different pieces of thesame puzzle. Curr Opin Plant Biol 47: 1–8Strauss S, Runions A, Lane B, Eschweiler D, Bajpai N, Trozzi N,Routier-Kierzkowska A-L, Yoshida S, da Silveira S, Vijayan A, etal. (2021) MorphoGraphX 2.0: providing context for biologicalimage analysis with positional information. bioRxiv, doi:10.1101/2021.08.12.456042 (March 26, 2022)Vijayan A, Strauss S, Tofanelli R, Mody T, Lee K, Tsiantos M,Smith R, Schneitz K (2022) The annotation and analysis of com-plex 3D plant organs using 3DCoordX. Plant Physiol https://doi.org/10.1093/plphys/kiac145Whitewoods C, Coen E (2017) Growth and development ofthree-dimensional plant form. Curr Biol 27: R910–R918Wolny A, Cerrone L, Vijayan A, Tofanelli R, Barro A, Louveaux M,Wenzl C, Strauss S, Wilson-Sánchez D, Lymbouridou R (2020)Accurate and versatile 3D segmentation of plant tissues at cellularresolution. eLife 9: e57613Zhou W, Zhang X (2022) Small bending, big curvature. J Integr PlantBiol 64: 3Figure 1 Schematic overview of tissue and cell layer annotation ac-counting for the growth axis as implemented by Vijayan et al. (2022)A, straight tissue structure and B, curved tissue structure. In straighttissues, the distance from a reference point (dashed line) remains con-stant for each cell layer (i.e. the left versus right layer). However, in acurved structure, distance will differ depending on the layer asdenoted by the arrows.2 | PLANT PHYSIOLOGY 2022: Page 2 of 2 Burgess and MajdaDownloaded from https://academic.oup.com/plphys/advance-article/doi/10.1093/plphys/kiac190/6574920 by guest on 27 May 2022

A future in 3D: Analyzing morphology in all dimensions

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