1
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Wenzl C, Lohmann JU. 3D imaging reveals apical stem cell responses to ambient temperature. Cells Dev 2023; 175:203850. [PMID: 37182581 DOI: 10.1016/j.cdev.2023.203850] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/04/2023] [Accepted: 05/09/2023] [Indexed: 05/16/2023]
Abstract
Plant growth is driven by apical meristems at the shoot and root growth points, which comprise continuously active stem cell populations. While many of the key factors involved in homeostasis of the shoot apical meristem (SAM) have been extensively studied under artificial constant growth conditions, only little is known how variations in the environment affect the underlying regulatory network. To shed light on the responses of the SAM to ambient temperature, we combined 3D live imaging of fluorescent reporter lines that allowed us to monitor the activity of two key regulators of stem cell homeostasis in the SAM namely CLAVATA3 (CLV3) and WUSCHEL (WUS), with computational image analysis to derive morphological and cellular parameters of the SAM. Whereas CLV3 expression marks the stem cell population, WUS promoter activity is confined to the organizing center (OC), the niche cells adjacent to the stem cells, hence allowing us to record on the two central cell populations of the SAM. Applying an integrated computational analysis of our data we found that variations in ambient temperature not only led to specific changes in spatial expression patterns of key regulators of SAM homeostasis, but also correlated with modifications in overall cellular organization and shoot meristem morphology.
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Affiliation(s)
- Christian Wenzl
- Department of Stem Cell Biology, Centre for Organismal Studies, Heidelberg University, D-69120 Heidelberg, Germany
| | - Jan U Lohmann
- Department of Stem Cell Biology, Centre for Organismal Studies, Heidelberg University, D-69120 Heidelberg, Germany.
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2
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Zakieva A, Cerrone L, Greb T. Deep machine learning for cell segmentation and quantitative analysis of radial plant growth. Cells Dev 2023; 174:203842. [PMID: 37080460 DOI: 10.1016/j.cdev.2023.203842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 04/05/2023] [Accepted: 04/13/2023] [Indexed: 04/22/2023]
Abstract
Plants produce the major part of terrestrial biomass and are long-term deposits of atmospheric carbon. This capacity is to a large extent due to radial growth of woody species - a process driven by cambium stem cells located in distinct niches of shoot and root axes. In the model species Arabidopsis thaliana, thousands of cells are produced by the cambium in radial orientation generating a complex organ anatomy enabling long-distance transport, mechanical support and protection against biotic and abiotic stressors. These complex organ dynamics make a comprehensive and unbiased analysis of radial growth challenging and asks for tools for automated quantification. Here, we combined the recently developed PlantSeg and MorphographX image analysis tools, to characterize tissue morphogenesis of the Arabidopsis hypocotyl. After sequential training of segmentation models on ovules, shoot apical meristems and adult hypocotyls using deep machine learning, followed by the training of cell type classification models, our pipeline segments complex images of transverse hypocotyl sections with high accuracy and classifies central hypocotyl cell types. By applying our pipeline on both wild type and phloem intercalated with xylem (pxy) mutants, we also show that this strategy faithfully detects major anatomical aberrations. Collectively, we conclude that our established pipeline is a powerful phenotyping tool comprehensively extracting cellular parameters and providing access to tissue topology during radial plant growth.
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Affiliation(s)
- Alexandra Zakieva
- Centre for Organismal Studies (COS), Heidelberg University, Heidelberg, Germany
| | - Lorenzo Cerrone
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
| | - Thomas Greb
- Centre for Organismal Studies (COS), Heidelberg University, Heidelberg, Germany.
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3
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Braker Scott C, Mjolsness E, Oyen D, Kodera C, Uyttewaal M, Bouchez D. Graph metric learning quantifies morphological differences between two genotypes of shoot apical meristem cells in Arabidopsis. IN SILICO PLANTS 2023; 5:diad001. [PMID: 38938656 PMCID: PMC11210494 DOI: 10.1093/insilicoplants/diad001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Abstract
We present a method for learning 'spectrally descriptive' edge weights for graphs. We generalize a previously known distance measure on graphs (graph diffusion distance [GDD]), thereby allowing it to be tuned to minimize an arbitrary loss function. Because all steps involved in calculating this modified GDD are differentiable, we demonstrate that it is possible for a small neural network model to learn edge weights which minimize loss. We apply this method to discriminate between graphs constructed from shoot apical meristem images of two genotypes of Arabidopsis thaliana specimens: wild-type and trm678 triple mutants with cell division phenotype. Training edge weights and kernel parameters with contrastive loss produce a learned distance metric with large margins between these graph categories. We demonstrate this by showing improved performance of a simple k -nearest-neighbour classifier on the learned distance matrix. We also demonstrate a further application of this method to biological image analysis. Once trained, we use our model to compute the distance between the biological graphs and a set of graphs output by a cell division simulator. Comparing simulated cell division graphs to biological ones allows us to identify simulation parameter regimes which characterize mutant versus wild-type Arabidopsis cells. We find that trm678 mutant cells are characterized by increased randomness of division planes and decreased ability to avoid previous vertices between cell walls.
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Affiliation(s)
- Cory Braker Scott
- Department of Mathematics and Computer Science, Colorado
College, Colorado Springs, CO 80903, USA
- Department of Computer Science, University of California
Irvine, Irvine, CA 92697, USA
- Los Alamos National Laboratory, Los Alamos, NM 87544,
USA
| | - Eric Mjolsness
- Department of Computer Science, University of California
Irvine, Irvine, CA 92697, USA
- Los Alamos National Laboratory, Los Alamos, NM 87544,
USA
| | - Diane Oyen
- Los Alamos National Laboratory, Los Alamos, NM 87544,
USA
| | - Chie Kodera
- Université Paris-Saclay, INRAE, AgroParisTech,
Institut Jean-Pierre Bourgin (IJPB), 78000 Versailles, France
- CryoCapCell, Inserm U1195, Université Paris Saclay,
94270 Le Kremlin-Bicêtre, France
| | - Magalie Uyttewaal
- Université Paris-Saclay, INRAE, AgroParisTech,
Institut Jean-Pierre Bourgin (IJPB), 78000 Versailles, France
| | - David Bouchez
- Université Paris-Saclay, INRAE, AgroParisTech,
Institut Jean-Pierre Bourgin (IJPB), 78000 Versailles, France
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4
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Formosa-Jordan P, Landrein B. Quantifying Gene Expression Domains in Plant Shoot Apical Meristems. Methods Mol Biol 2023; 2686:537-551. [PMID: 37540376 DOI: 10.1007/978-1-0716-3299-4_25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
The shoot apical meristem is the plant tissue that produces the plant aerial organs such as flowers and leaves. To better understand how the shoot apical meristem develops and adapts to the environment, imaging developing shoot meristems expressing fluorescence reporters through laser confocal microscopy is becoming increasingly important. Yet, there are not many computational pipelines enabling a systematic and high-throughput characterization of the produced microscopy images. This chapter provides a simple method to analyze 3D images obtained through laser scanning microscopy and quantitatively characterize radially or axially symmetric 3D fluorescence domains expressed in a tissue or organ by a reporter. Then, it presents different computational pipelines aiming at performing high-throughput quantitative image analysis of gene expression in plant inflorescence and floral meristems. This methodology has notably enabled the quantitative characterization of how stem cells respond to environmental perturbations in the Arabidopsis thaliana inflorescence meristem and will open new avenues in the use of quantitative analysis of gene expression in shoot apical meristems. Overall, the presented methodology provides a simple framework to analyze quantitatively gene expression domains from 3D confocal images at the tissue and organ level, which can be applied to shoot meristems and other organs and tissues.
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Affiliation(s)
- Pau Formosa-Jordan
- Sainsbury Laboratory, University of Cambridge, Cambridge, UK.
- Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research, Cologne, Germany.
- Cluster of Excellence on Plant Science (CEPLAS), Max Planck Institute for Plant Breeding Research, Cologne, Germany.
| | - Benoit Landrein
- Sainsbury Laboratory, University of Cambridge, Cambridge, UK
- Laboratoire Reproduction et Développement des Plantes, Université de Lyon, ENS de Lyon, UCB Lyon 1, CNRS, INRAE, INRIA, Lyon, France
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5
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Vijayan A, Strauss S, Tofanelli R, Mody TA, Lee K, Tsiantis M, Smith RS, Schneitz K. The annotation and analysis of complex 3D plant organs using 3DCoordX. PLANT PHYSIOLOGY 2022; 189:1278-1295. [PMID: 35348744 PMCID: PMC9237718 DOI: 10.1093/plphys/kiac145] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 03/05/2022] [Indexed: 06/14/2023]
Abstract
A fundamental question in biology concerns how molecular and cellular processes become integrated during morphogenesis. In plants, characterization of 3D digital representations of organs at single-cell resolution represents a promising approach to addressing this problem. A major challenge is to provide organ-centric spatial context to cells of an organ. We developed several general rules for the annotation of cell position and embodied them in 3DCoordX, a user-interactive computer toolbox implemented in the open-source software MorphoGraphX. 3DCoordX enables rapid spatial annotation of cells even in highly curved biological shapes. Using 3DCoordX, we analyzed cellular growth patterns in organs of several species. For example, the data indicated the presence of a basal cell proliferation zone in the ovule primordium of Arabidopsis (Arabidopsis thaliana). Proof-of-concept analyses suggested a preferential increase in cell length associated with neck elongation in the archegonium of Marchantia (Marchantia polymorpha) and variations in cell volume linked to central morphogenetic features of a trap of the carnivorous plant Utricularia (Utricularia gibba). Our work demonstrates the broad applicability of the developed strategies as they provide organ-centric spatial context to cellular features in plant organs of diverse shape complexity.
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Affiliation(s)
| | | | - Rachele Tofanelli
- Plant Developmental Biology, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Tejasvinee Atul Mody
- Plant Developmental Biology, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | | | - Miltos Tsiantis
- Department of Comparative Developmental and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Richard S Smith
- Department of Comparative Developmental and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
- The John Innes Centre, Norwich, UK
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6
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Burgess AJ, Majda M. A future in 3D: Analyzing morphology in all dimensions. PLANT PHYSIOLOGY 2022; 189:1175-1176. [PMID: 35478044 PMCID: PMC9237670 DOI: 10.1093/plphys/kiac190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 04/11/2022] [Indexed: 06/14/2023]
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7
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Strauss S, Runions A, Lane B, Eschweiler D, Bajpai N, Trozzi N, Routier-Kierzkowska AL, Yoshida S, Rodrigues da Silveira S, Vijayan A, Tofanelli R, Majda M, Echevin E, Le Gloanec C, Bertrand-Rakusova H, Adibi M, Schneitz K, Bassel G, Kierzkowski D, Stegmaier J, Tsiantis M, Smith RS. Using positional information to provide context for biological image analysis with MorphoGraphX 2.0. eLife 2022; 11:72601. [PMID: 35510843 PMCID: PMC9159754 DOI: 10.7554/elife.72601] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 05/03/2022] [Indexed: 11/13/2022] Open
Abstract
Positional information is a central concept in developmental biology. In developing organs, positional information can be idealized as a local coordinate system that arises from morphogen gradients controlled by organizers at key locations. This offers a plausible mechanism for the integration of the molecular networks operating in individual cells into the spatially coordinated multicellular responses necessary for the organization of emergent forms. Understanding how positional cues guide morphogenesis requires the quantification of gene expression and growth dynamics in the context of their underlying coordinate systems. Here, we present recent advances in the MorphoGraphX software (Barbier de Reuille et al., 2015) that implement a generalized framework to annotate developing organs with local coordinate systems. These coordinate systems introduce an organ-centric spatial context to microscopy data, allowing gene expression and growth to be quantified and compared in the context of the positional information thought to control them.
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Affiliation(s)
- Sören Strauss
- Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Adam Runions
- Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | | | - Dennis Eschweiler
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
| | - Namrata Bajpai
- Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | | | | | - Saiko Yoshida
- Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | | | - Athul Vijayan
- School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Rachele Tofanelli
- School of Life Sciences, Technical University of Munich, Freising, Germany
| | | | - Emillie Echevin
- Department of Biological Sciences, University of Montreal, Montreal, Canada
| | | | | | - Milad Adibi
- Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Kay Schneitz
- School of Life Sciences, Technical University of Munich, Freising, Germany
| | - George Bassel
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
| | - Daniel Kierzkowski
- Department of Biological Sciences, University of Montreal, Montreal, Canada
| | - Johannes Stegmaier
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
| | - Miltos Tsiantis
- Max Planck Institute for Plant Breeding Research, Cologne, Germany
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8
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Vijayan A, Tofanelli R, Strauss S, Cerrone L, Wolny A, Strohmeier J, Kreshuk A, Hamprecht FA, Smith RS, Schneitz K. A digital 3D reference atlas reveals cellular growth patterns shaping the Arabidopsis ovule. eLife 2021; 10:e63262. [PMID: 33404501 PMCID: PMC7787667 DOI: 10.7554/elife.63262] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Accepted: 12/19/2020] [Indexed: 12/23/2022] Open
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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Affiliation(s)
- Athul Vijayan
- Plant Developmental Biology, School of Life Sciences, Technical University of MunichFreisingGermany
| | - Rachele Tofanelli
- Plant Developmental Biology, School of Life Sciences, Technical University of MunichFreisingGermany
| | - Sören Strauss
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding ResearchCologneGermany
| | - Lorenzo Cerrone
- Heidelberg Collaboratory for Image Processing, Dept. of Physics and Astronomy, Heidelberg UniversityHeidelbergGermany
| | - Adrian Wolny
- Heidelberg Collaboratory for Image Processing, Dept. of Physics and Astronomy, Heidelberg UniversityHeidelbergGermany
- European Molecular Biology LaboratoryHeidelbergGermany
| | - Joanna Strohmeier
- Plant Developmental Biology, School of Life Sciences, Technical University of MunichFreisingGermany
| | - Anna Kreshuk
- European Molecular Biology LaboratoryHeidelbergGermany
| | - Fred A Hamprecht
- Heidelberg Collaboratory for Image Processing, Dept. of Physics and Astronomy, Heidelberg UniversityHeidelbergGermany
| | - Richard S Smith
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding ResearchCologneGermany
| | - Kay Schneitz
- Plant Developmental Biology, School of Life Sciences, Technical University of MunichFreisingGermany
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9
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Abstract
A transition from qualitative to quantitative descriptors of morphology has been facilitated through the growing field of morphometrics, representing the conversion of shapes and patterns into numbers. The analysis of plant form at the macromorphological scale using morphometric approaches quantifies what is commonly referred to as a phenotype. Quantitative phenotypic analysis of individuals with contrasting genotypes in turn provides a means to establish links between genes and shapes. The path from a gene to a morphological phenotype is, however, not direct, with instructive information progressing both across multiple scales of biological complexity and through nonintuitive feedback, such as mechanical signals. In this review, we explore morphometric approaches used to perform whole-plant phenotyping and quantitative approaches in capture processes in the mesoscales, which bridge the gaps between genes and shapes in plants. Quantitative frameworks involving both the computational simulation and the discretization of data into networks provide a putative path to predicting emergent shape from underlying genetic programs.
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Affiliation(s)
- Hao Xu
- School of Life Sciences, University of Warwick, Coventry CV4 7AL, United Kingdom;
| | - George W Bassel
- School of Life Sciences, University of Warwick, Coventry CV4 7AL, United Kingdom;
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10
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Jones AR, Band LR, Murray JAH. Double or Nothing? Cell Division and Cell Size Control. TRENDS IN PLANT SCIENCE 2019; 24:1083-1093. [PMID: 31630972 DOI: 10.1016/j.tplants.2019.09.005] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 08/08/2019] [Accepted: 09/06/2019] [Indexed: 06/10/2023]
Abstract
Size is a fundamental property that must be tightly regulated to ensure that cells and tissues function efficiently. Dynamic size control allows unicellular organisms to adapt to environmental changes, but cell size is also integral to multicellular development, affecting tissue size and structure. Despite clear evidence for homeostatic cell size maintenance, we are only now beginning to understand cell size regulation in the actively dividing meristematic tissues of higher plants. We discuss here how coupled advances in live cell imaging and modelling are uncovering dynamic mechanisms for size control mediated at the cellular level. We argue that integrated models of cell growth and division will be necessary to predict cell size and fully understand multicellular growth and development.
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Affiliation(s)
- Angharad R Jones
- Cardiff School of Biosciences, Cardiff University, Sir Martin Evans Building, Museum Avenue, Cardiff CF10 3AX, UK.
| | - Leah R Band
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, UK; Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, UK
| | - James A H Murray
- Cardiff School of Biosciences, Cardiff University, Sir Martin Evans Building, Museum Avenue, Cardiff CF10 3AX, UK
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