Exploring <scp>3D</scp> leaf anatomical traits for <scp>C<sub>4</sub></scp> photosynthesis: chloroplast and plasmodesmata pit field size in maize and sugarcane

Lee, Moonsub; Boyd, Ryan A.; Boateng, Kingsley A.; Ort, Donald R.

doi:10.1111/nph.18956

Cited by 7 publications

(2 citation statements)

References 62 publications

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“…The quantification of anatomical traits is of fundamental importance for recent efforts in crop improvement and the generation of cell– and tissue atlases (Lee et al ., 2023; Tolleter et al ., 2024). Anatomics MLT will provide a useful tool for gathering large-scale input data.…”

Section: Discussionmentioning

confidence: 99%

Anatomics MLT, an AI tool for large scale quantification of ultrastructural traits

Brookhouse,

Ji,

Knoblauch

et al. 2024

Preprint

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The ever increasing breadth of biological knowledge has led to recent efforts to combine information from various fields into cell- or tissue atlases. Anatomical features are the structural basis for such efforts, but unfortunately large scale analysis of subcellular anatomical traits is currently a missing feature. Similarly, small phenotypic alterations of organelle- or cell-specific anatomical traits, such as an increase of the total volume or the number of mitochondria in response to certain stimuli, are currently hard to quantify. To provide tools to extract quantitative information from available 3D microscopic datasets generated with methods such as serial block face scanning electron microscopy we a) developed much improved fixation and embedding protocols for plants to drastically reduce processing artifacts and b) generated an easy-to-use AI tool for quantitative analysis and visualization of large-scale data sets. We make this tool available as open source.

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Section: Discussionmentioning

confidence: 99%

Anatomics MLT, an AI tool for large scale quantification of ultrastructural traits

Brookhouse,

Ji,

Knoblauch

et al. 2024

Preprint

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“…Samples were embedded in epoxy with the same method as used for TEM and shipped to University of Illinois Urbana-Champaign for SBF-SEM. A protocol similar to the one previously utilized by the facility was used [91]. Before initiating block face imaging, epoxy-embedded samples were mounted to aluminum pins (Gatan Inc., Pleasanton, CA, USA) using silver epoxy (Ted Pella) and sputter coated with a thin layer of Au/Pd.…”

Section: Sbf-sem Sample Mounting and Imagingmentioning

confidence: 99%

Algorithmic construction of topologically complex biomineral lattices via cellular syncytia

Vyas,

Brannon,

Formery

et al. 2024

Preprint

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Biomineralization is ubiquitous in both unicellular and multicellular living systems [1, 2] and has remained elusive due to a limited understanding of physicochemical and biomolecular processes [3]. Echinoderms, identified with diverse architectures of calcite-based structures in the dermis[4], present an enigma of how cellular processes control shape and form of individual structures. Specifically, in holothurians (sea cucumbers), multi-cellular clusters construct discrete single-crystal calcite ‘ossicles’ (∼100µm length scale), with diverse morphologies both across species and even within an individual animal [5]. The local rules that might encode these unique morphologies in calcite ossicles in holothurians remain largely unknown. Here we show how transport processes in a cellular syncytium impart a top-down control on ossicle geometry via symmetry breaking, branching, and fusion in finite cellular clusters. As a unique example of cellular masonary, we show how coordination within a small cluster of cells builds calcite structures about an order of magnitude larger than any individual participating cell. We establish live imaging of ossicle growth inApostichopus parvimensisjuveniles revealing how individual crystalline seeds (∼1−2µm) grow inside a multi-cellular syncytial complex with the biomineral completely wrapped within a membrane-bound cytoplasmic sheath. Constructing a topological description of ossicle geometries from 3D micro-CT (computational tomography) data reveals the hidden growth history and conserved patterns across ossicle types. We further demonstrate vesicle transport on the surface of the ossicle, rather than cell motility, regulates material transport to the ossicle tips via a unique cytoskeletal architecture. Finally, using reduced order models of conserved transport on self-closing active branching networks, we highlight the hidden universality in the growth process of distinct ossicles. The system presented here serves as a unique playground merging top-down cellular physiology and classical branching morphogenesis [6] with bottom-up non-equilibrium mineralization [7] processes at the interface of living and non-living matter [8].

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INCREASED CHLOROPLAST OCCUPANCY IN BUNDLE SHEATH CELLS OF RICEhap3HMUTANTS REVEALED BY CHLORO-COUNT, A NEW DEEP LEARNING-BASED TOOL

Lambret-Frotte,

Buarque de Gusmão,

Smith

et al. 2024

Preprint

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SUMMARYThere is an increasing demand to boost photosynthesis in rice to increase yield potential. Chloroplasts are the site of photosynthesis, and increasing the number and size of these organelles in the in leaf is a potential route to elevate leaf-level photosynthetic activity. Notably, bundle sheath cells do not make a significant contribution to overall carbon fixation in rice and thus various attempts are being made to increase chloroplast content in this cell type. In this study we developed and applied a deep learning tool named Chloro-Count to demonstrate that loss ofOsHAP3Hfunction in rice increases chloroplast occupancy in bundle sheath cells by 50%. Although limited to a single season, when grown in the fieldOshap3Hmutants exhibited increased numbers of tillers and panicles as compared to controls or gain of function mutants. The implementation of Chloro-Count enabled precise quantification of chloroplasts in loss- and gain-of-functionOsHAP3Hmutants and facilitated a comparison between 2D and 3D quantification methods. In wild-type rice, as the dimensions of bundle sheath cells increase, the volume of individual chloroplasts also increases. However, the larger the chloroplasts the fewer there are per bundle sheath cell. This observation revealed that a mechanism operates in bundle sheath cells to restrict chloroplast occupancy as cell dimensions increase. That mechanism is unperturbed inOshap3Hmutants. The use of Chloro-Count also revealed that 2D quantification, upon which most previous studies have relied, is compromised by the positioning of chloroplasts within the cell. Chloro-Count is therefore a valuable tool for accurate and high-throughput quantification of chloroplasts that has enabled the robust characterization ofOsHAP3Heffects on chloroplast biogenesis in rice. Whereas previous studies have increased chloroplast occupancy in bundle sheath cells by increasing the size of individual chloroplasts, loss ofOsHAP3Hfunction leads to an increase in chloroplast numbers.

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Exploring 3D leaf anatomical traits for C₄ photosynthesis: chloroplast and plasmodesmata pit field size in maize and sugarcane

Cited by 7 publications

References 62 publications

Anatomics MLT, an AI tool for large scale quantification of ultrastructural traits

Anatomics MLT, an AI tool for large scale quantification of ultrastructural traits

Algorithmic construction of topologically complex biomineral lattices via cellular syncytia

INCREASED CHLOROPLAST OCCUPANCY IN BUNDLE SHEATH CELLS OF RICEhap3HMUTANTS REVEALED BY CHLORO-COUNT, A NEW DEEP LEARNING-BASED TOOL

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Exploring 3D leaf anatomical traits for C4 photosynthesis: chloroplast and plasmodesmata pit field size in maize and sugarcane

Cited by 7 publications

References 62 publications

Anatomics MLT, an AI tool for large scale quantification of ultrastructural traits

Anatomics MLT, an AI tool for large scale quantification of ultrastructural traits

Algorithmic construction of topologically complex biomineral lattices via cellular syncytia

INCREASED CHLOROPLAST OCCUPANCY IN BUNDLE SHEATH CELLS OF RICEhap3HMUTANTS REVEALED BY CHLORO-COUNT, A NEW DEEP LEARNING-BASED TOOL

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Exploring 3D leaf anatomical traits for C₄ photosynthesis: chloroplast and plasmodesmata pit field size in maize and sugarcane