Smooth Elongation of Pavement Cells Induced by RIC1 Overexpression Leads to Marginal Protrusions of the Cotyledon in Arabidopsis thaliana

Distinct actin microfilament localization during early cell plate formation through deep learning-based image restoration

KEY MESSAGE

Using deep learning-based image restoration, we achieved high-resolution 4D imaging with minimal photodamage, revealing distinct localization and suggesting Lifeact-RFP-labeled actin microfilaments play a role in initiating cell plate formation. Phragmoplasts are plant-specific intracellular structures composed of microtubules, actin microfilaments (AFs), membranes, and associated proteins. Importantly, they are involved in the formation and the expansion of cell plates that partition daughter cells during cell division. While previous studies have revealed the important role of cytoskeletal dynamics in the proper functioning of the phragmoplast, the localization and the role of AFs in the initial phase of cell plate formation remain controversial. Here, we used deep learning-based image restoration to achieve high-resolution 4D imaging with minimal laser-induced damage, enabling us to investigate the dynamics of AFs during the initial phase of cell plate formation in transgenic tobacco BY-2 cells labeled with Lifeact-RFP or RFP-ABD2 (actin-binding domain 2). This computational approach overcame the limitation of conventional imaging, namely laser-induced photobleaching and phototoxicity. The restored images indicated that RFP-ABD2-labeled AFs were predominantly localized near the daughter nucleus, whereas Lifeact-RFP-labeled AFs were found not only near the daughter nucleus but also around the initial cell plate. These findings, validated by imaging with a long exposure time, highlight distinct localization patterns between the two AF probes and suggest that Lifeact-RFP-labeled AFs play a role in initiating cell plate formation.

Deep learning-based cytoskeleton segmentation for accurate high-throughput measurement of cytoskeleton density

Microscopic analyses of cytoskeleton organization are crucial for understanding various cellular activities, including cell proliferation and environmental responses in plants. Traditionally, assessments of cytoskeleton dynamics have been qualitative, relying on microscopy-assisted visual inspection. However, the transition to quantitative digital microscopy has introduced new technical challenges, with segmentation of cytoskeleton structures proving particularly demanding. In this study, we examined the utility of a deep learning-based segmentation method for accurate quantitative evaluation of cytoskeleton organization using confocal microscopic images of the cortical microtubules in tobacco BY-2 cells. The results showed that, although conventional methods sufficed for measurement of cytoskeleton angles and parallelness, the deep learning-based method significantly improved the accuracy of density measurements. To assess the versatility of the method, we extended our analysis to physiologically significant models in the context of changes in cytoskeleton density, namely Arabidopsis thaliana guard cells and zygotes. The deep learning-based method successfully improved the accuracy of cytoskeleton density measurements for quantitative evaluations of physiological changes in both stomatal movement in guard cells and intracellular polarization in elongating zygotes, confirming its utility in these applications. The results demonstrate the effectiveness of deep learning-based segmentation in providing precise and high-throughput measurements of cytoskeleton density, and has the potential to automate and expedite analyses of large-scale image datasets.

Virtual staining from bright-field microscopy for label-free quantitative analysis of plant cell structures

The applicability of a deep learning model for the virtual staining of plant cell structures using bright-field microscopy was investigated. The training dataset consisted of microscopy images of tobacco BY-2 cells with the plasma membrane stained with the fluorescent dye PlasMem Bright Green and the cell nucleus labeled with Histone-red fluorescent protein. The trained models successfully detected the expansion of cell nuclei upon aphidicolin treatment and a decrease in the cell aspect ratio upon propyzamide treatment, demonstrating its utility in cell morphometry. The model also accurately documented the shape of Arabidopsis pavement cells in both wild type and the bpp125 triple mutant, which has an altered pavement cell phenotype. Metrics such as cell area, circularity, and solidity obtained from virtual staining analyses were highly correlated with those obtained by manual measurements of cell features from microscopy images. Furthermore, the versatility of virtual staining was highlighted by its application to track chloroplast movement in Egeria densa. The method was also effective for classifying live and dead BY-2 cells using texture-based machine learning, suggesting that virtual staining can be applied beyond typical segmentation tasks. Although this method still has some limitations, its non-invasive nature and efficiency make it highly suitable for label-free, dynamic, and high-throughput analyses in quantitative plant cell biology.

Mechanisms Involved in Cell Wall Remodeling in Etiolated Rice Shoots Grown Under Osmotic Stress

Osmotic stress impacts the cell wall properties in plants. This study aimed to elucidate the mechanisms involved in cell wall remodeling in etiolated (dark-grown) rice (Oryza sativa L.) shoots grown under polyethylene glycol (PEG)-induced osmotic stress conditions. Shoot growth was inhibited by 70% by the treatment with 60 mM PEG for 2 days. However, when the stressed seedlings were transferred to a solution without PEG, their shoot growth rate increased significantly. A measurement of the cell wall mechanical properties revealed that the cell walls of the stressed shoots became looser and more extensible than those of unstressed shoots. Among the cell wall constituents, the amounts of cell wall-bound phenolic acids, such as ferulic acid (FA), p-coumaric acid (p-CA), and diferulic acid (DFA), per shoot and per unit of matrix polysaccharide content were significantly reduced in the stressed shoots compared to those in the unstressed shoots. Concerning the formation of cell wall-bound phenolic acids, the activity of cell wall-bound peroxidase (CW-PRX) per unit of cell wall content, which is responsible for the coupling reaction of FA to produce DFA, was 3.5 times higher in stressed shoots than in unstressed shoots, while the activity was reduced by 20% on a shoot basis in stressed shoots compared to that in unstressed shoots. The expression levels of the major class III peroxidase genes in stressed shoots were either comparable to or slightly lower than those in unstressed shoots. Conversely, the phenylalanine ammonia-lyase (PAL) activity, which contributes to the biosynthesis of FA and p-CA, was reduced by 55% and 30% on a shoot and unit-of-protein-content basis, respectively, in stressed shoots compared to that in unstressed shoots. The expression levels of abundantly expressed PAL genes decreased by 14-46% under osmotic stress. Moreover, the gene expression levels of specific BAHD acyltransferases, which are responsible for the addition of FA and p-CA to form ester-linked moieties on cell wall constituents, decreased by 15-33% under osmotic stress. These results suggest that the downregulation of the expression of specific PAL and BAHD acyltransferase genes in osmotically stressed rice shoots is responsible for a reduction in the formation of cell wall-bound phenolic acid monomers. This, in turn, may result in a decrease in the levels of DFAs. The reduction in the formation of DFA-mediated cross-linking structures within the cell wall may contribute to an increase in the mechanical extensibility of the cell wall. The remodeling of cell walls in an extensible and loosened state could assist in maintaining the growth capacity of etiolated rice shoots grown under osmotic stress and contribute to rapid growth recovery following the alleviation of osmotic stress.

Cell geometry regulates tissue fracture

In vascular plants, the epidermal surfaces of leaves and flower petals often display cells with wavy geometries forming intricate jigsaw puzzle patterns. The prevalence and diversity of these complex epidermal patterns, originating from simple polyhedral progenitor cells, suggest adaptive significance. However, despite multiple efforts to explain the evolutionary drivers behind these geometrical features, compelling validation remains elusive. Employing a multidisciplinary approach that integrates microscopic and macroscopic fracture experiments with computational fracture mechanics, we demonstrate that wavy epidermal cells toughen the plants' protective skin. Through a multi-scale framework, we demonstrate that this energy-efficient patterning mechanism is universally applicable for toughening biological and synthetic materials. Our findings reveal a tunable structural-mechanical strategy employed in the microscale design of plants to protect them from deleterious surface fissures while facilitating and strategically directing beneficial ones. These findings hold implications for targeted plant breeding aimed at enhancing resilience in fluctuating environmental conditions. From an engineering perspective, our work highlights the sophisticated design principles the plant kingdom offers to inspire metamaterials.