An overview of plant division‐plane orientation

Rasmussen, Carolyn G.; Bellinger, Marschal

doi:10.1111/nph.15183

Cited by 76 publications

(61 citation statements)

References 85 publications

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“…Combined with the effect on stomata, this suggests that CYCA3;4 might play an important role in the process of formative cell divisions, which might correspond to the need for its destruction by APC/C CCS52A2 to obtain a well-organized stem cell niche. Its targeted destruction during early prophase, the moment when the division plane orientation is determined through positioning of the preprophase band (Rasmussen and Bellinger, 2018; Facette et al, 2019), fits the idea that CYCA3;4 and CCS52A2 might play a role in the positioning of the division plane. However, the phenotype of the CYCA3;4 -overexpressing plants does not completely mimic that of the ccs52a2-1 knockout, again suggesting that the stabilization of targets other than CYCA3;4 might account for a big part of the disorganized stem cell niche phenotype of ccs52a2-1 plants.…”

Section: Discussionsupporting

confidence: 61%

CYCA3;4 Is a Post-Prophase Target of the APC/C^CCS52A2E3-Ligase Controlling Formative Cell Divisions in Arabidopsis

Willems

Heyman

Eekhout

et al. 2020

Preprint

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32The Anaphase Promoting Complex/Cyclosome (APC/C) controls unidirectional progression 33 through the cell cycle by marking key cell cycle proteins for proteasomal turnover. Its 34 activity is temporally regulated by the docking of different activating subunits, known in 35 plants as CDC20 and CCS52. Despite the importance of the APC/C during cell 36 proliferation, the number of identified targets in the plant cell cycle is limited. Here, we 37 used the growth and meristem phenotypes of Arabidopsis CCS52A2-deficient plants in a 38 suppressor mutagenesis screen to identify APC/C CCS52A2 substrates or regulators, resulting 39 in the identification of a mutant cyclin CYCA3;4 allele. CYCA3;4 deficiency partially 40 rescues the early ccs52a2-1 phenotypes, whereas increased CYCA3;4 levels enhances them. 41 Furthermore, whereas CYCA3;4 proteins are promptly broken down after prophase in 42 wild-type plants, they remain present in later stages of mitosis in ccs52a2-1 mutant plants, 43 marking them as APC/C CCS52A2 substrates. Strikingly, CYCA3;4 overexpression results in 44 aberrant root meristem and stomatal divisions, mimicking phenotypes of plants with 45 reduced RBR1 activity. Correspondingly, RBR1 hyperphosphorylation was observed in 46 CYCA3;4-overproducing plants. Our data thus demonstrate that an inability to timely 47 destroy CYCA3;4 attributes to disorganized formative divisions, likely in part caused by 48 the inactivation of RBR1.49 4 109 ENHANCER (TE) / TILLERING AND DWARF (TAD1) was shown to mediate the 110 ubiquitination and subsequent degradation of MONOCULM 1 (MOC1), called LATERAL 111 6SUPPRESSOR (LAS) in Arabidopsis, a GRAS-family transcription factor that promotes shoot 112 branching and tillering (Lin et al., 2012; Xu et al., 2012). 113 Here, we have utilized an ethyl methanesulfonate (EMS) suppressor screen to identify 114 novel APC/C CCS52A2 targets, based on the growth inhibitory phenotype of ccs52a2-1 knockout 115 plants. We show that one of the identified revertants encodes a mutant allele of the CYCA3;4 116 gene and demonstrate this cyclin to be a specific target of APC/C CCS52A2 to ensure correct stem 117 cell organization. 118 119 RESULTS 120 Identification of pkn2 as a ccs52a2-1 Suppressor Mutant 121 Compared to wild type (WT, Col-0) plants, ccs52a2-1 mutant seedlings display a short root 122 phenotype (Figures 1A and 1B; Supplemental Figures 1A and 1B) (Vanstraelen et al., 2009; 123 Heyman et al., 2013). This phenotype was used to screen for putative targets or regulators of the 124 APC/C CCS52A2 ubiquitin ligase complex through a mutagenesis revertant screen. Therefore, ethyl 125 methanesulfonate (EMS)-mutagenized ccs52a2-1 plants were screened in the M 2 generation for a 126 recovered root growth. Out of a total of 260 initially identified revertants, 33 were confirmed in 127 the next generation. Among these, one revertant mutation, named pikmin 2 (pkn2), yielded a root 128 length in between that of WT and ccs52a2-1 mutant plants (Figures 1A to 1C; Supplemental 129 Figures 1A to 1C). 1...

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Section: Discussionsupporting

confidence: 61%

CYCA3;4 Is a Post-Prophase Target of the APC/C^CCS52A2E3-Ligase Controlling Formative Cell Divisions in Arabidopsis

Willems

Heyman

Eekhout

et al. 2020

Preprint

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“…Accurate and versatile extraction of cell outlines rendered possible by PlantSeg opens the door to rapid and robuts quantitative morphometric analysis of plant cell geometry in complex tissue. This is particularly relevant given the central role plant cell shape plays in the control of cell growth and division [43].…”

Section: Discussionmentioning

confidence: 99%

Accurate and Versatile 3D Segmentation of Plant Tissues at Cellular Resolution

Wolny

Cerrone

Vijayan

et al. 2020

Preprint

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Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, and acquisition settings. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface.Recently an approach combining the output of two neural networks and watershed to detect individual cells showed promising results on segmentation of cells in 2D [19]. Although this method can in principle be generalized to 3D images, the necessity to train two separate networks poses additional difficulty for non-experts.While deep learning-based methods define the state-of-the-art for all image segmentation problems, only a handful of software packages strives to make them accessible to non-expert users in biology (reviewed in [20]). Notably, the U-Net segmentation plugin for ImageJ [21] conveniently exposes U-Net predictions and computes the final segmentation from simple thresholding of the probability maps. CDeep3M [22] and DeepCell [23] enable, via the command-line, the thresholding of the probability maps given by the network, and DeepCell allows instance segmentation as described in [19]. More advanced post-processing methods are provided by the ilastik Multicut workflow [24], however, these are not integrated with CNN-based prediction.Here, we present PlantSeg, a deep learning-based pipeline for volumetric instance segmentation of dense plant tissues at single-cell resolution. PlantSeg processes the output from the microscope with a CNN to produce an accurate prediction of cell boundaries. Building on the insights from previous work on cell segmentation in electron microscopy volumes of neural tissue [13,15], the second step of the pipeline delivers an accurate segmentation by solving a graph partitioning problem. We trained PlantSeg on 3D confocal images of fixed Arabidopsis thaliana ovules and 3D+t light sheet microscope images of developing lateral roots, two standard imaging modalities in the studies of plant morphogenesis. We investigated a range of network architectures and graph partitioning algorithms and selected the ones which performed best with regard to extensive manually annotated groundtruth. We benchmarked PlantSeg on a variety of datasets covering a range of plant organs and image resolutions. Overall, PlantSeg delivers excellent results on ...

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“…In addition to predicting multiple potential division plane orientations, the predicted divisions were directly compared to the location of the PPB, a structure known to predict the future division site (Smertenko et al, 2017;Rasmussen et al, 2013;Rasmussen and Bellinger, 2018). Several elegant studies used time-lapse imaging to compare cells before and after division (von Wangenheim et al, 2016;Shapiro et al, 2015;Louveaux et al, 2016) but they did not directly assess PPB location in comparison to predicted divisions.…”

Section: Discussionmentioning

confidence: 99%

Predicting division planes of three-dimensional cells by soap-film minimization

Martínez

Allsman

Brakke

et al. 2017

Preprint

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One key aspect of cell division in multicellular organisms is the orientation of the division plane. Proper division plane establishment contributes to normal organization of the plant body. To determine the importance of cell geometry in division plane orientation, we designed a threedimensional probabilistic mathematical modeling approach to directly test the century-old hypothesis that cell divisions mimic “soap-film minima” or that daughter cells have equal volume and the resulting division plane is a local surface area minimum. Predicted division planes were compared to a plant microtubule array that marks the division site, the preprophase band (PPB). PPB location typically matched one of the predicted divisions. Predicted divisions offset from the PPB occurred when a neighboring cell wall or PPB was observed directly adjacent to the predicted division site, to avoid creating a potentially structurally unfavorable four-way junction. By comparing divisions of differently shaped plant and animal cells to divisions simulated in silico, we demonstrate the generality of this model to accurately predict in vivo division. This powerful model can be used to separate the contribution of geometry from mechanical stresses or developmental regulation in predicting division plane orientation.

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An overview of plant division‐plane orientation

Cited by 76 publications

References 85 publications

CYCA3;4 Is a Post-Prophase Target of the APC/C^CCS52A2E3-Ligase Controlling Formative Cell Divisions in Arabidopsis

CYCA3;4 Is a Post-Prophase Target of the APC/C^CCS52A2E3-Ligase Controlling Formative Cell Divisions in Arabidopsis

Accurate and Versatile 3D Segmentation of Plant Tissues at Cellular Resolution

Predicting division planes of three-dimensional cells by soap-film minimization

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An overview of plant division‐plane orientation

Cited by 76 publications

References 85 publications

CYCA3;4 Is a Post-Prophase Target of the APC/CCCS52A2E3-Ligase Controlling Formative Cell Divisions in Arabidopsis

CYCA3;4 Is a Post-Prophase Target of the APC/CCCS52A2E3-Ligase Controlling Formative Cell Divisions in Arabidopsis

Accurate and Versatile 3D Segmentation of Plant Tissues at Cellular Resolution

Predicting division planes of three-dimensional cells by soap-film minimization

Contact Info

Product

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CYCA3;4 Is a Post-Prophase Target of the APC/C^CCS52A2E3-Ligase Controlling Formative Cell Divisions in Arabidopsis

CYCA3;4 Is a Post-Prophase Target of the APC/C^CCS52A2E3-Ligase Controlling Formative Cell Divisions in Arabidopsis