During lateral root (LR) development, morphological alteration of the developing single LR primordium occurs continuously. Precise observation of this continuous alteration is important for understanding the mechanism involved in single LR development. Recently, we reported that very long-chain fatty acids are important signalling molecules that regulate LR development. In the study, we developed an efficient method to quantify the transition of single LR developmental stages using time-lapse imaging followed by a deep neural network (DNN) analysis. In this ‘insight’ paper, we discuss our DNN method and the importance of time-lapse imaging in studies on plant development. Integrating DNN analysis and imaging is a powerful technique for the quantification of the timing of the transition of organ morphology; it can become an important method to elucidate spatiotemporal molecular mechanisms in plant development.

Quantitative analyses and models are required to connect a plant’s cellular organisation with its metabolism. However, quantitative data are often scattered over multiple studies, and finding such data and converting them into useful information is time-consuming. Consequently, there is a need to centralise the available data and to highlight the remaining knowledge gaps. Here, we present a step-by-step approach to manually extract quantitative data from various information sources, and to unify the data format. First, data from Arabidopsis leaf were collated, checked for consistency and correctness and curated by cross-checking sources. Second, quantitative data were combined by applying calculation rules. They were then integrated into a unique comprehensive, referenced, modifiable and reusable data compendium representing an Arabidopsis reference leaf. This atlas contains the metrics of the 15 cell types found in leaves at the cellular and subcellular levels.

Plant growth requires the integration of internal and external cues, perceived and transduced into a developmental programme of cell division, elongation and wall thickening. Mechanical forces contribute to this regulation, and thigmomorphogenesis typically includes reducing stem height, increasing stem diameter, and a canonical transcriptomic response. We present data on a bZIP transcription factor involved in this process in grasses. Brachypodium distachyon SECONDARY WALL INTERACTING bZIP (SWIZ) protein translocated into the nucleus following mechanostimulation. Classical touch-responsive genes were upregulated in B. distachyon roots following touch, including significant induction of the glycoside hydrolase 17 family, which may be unique to grass thigmomorphogenesis. SWIZ protein binding to an E-box variant in exons and introns was associated with immediate activation followed by repression of gene expression. SWIZ overexpression resulted in plants with reduced stem and root elongation. These data further define plant touch-responsive transcriptomics and physiology, offering insights into grass mechanotranduction dynamics.

As master transcription factors of stomatal development, SPEECHLESS, MUTE, and FAMA, collectively termed SMFs, are primary targets of molecular genetic analyses in the model plant Arabidopsis thaliana. Studies in other model systems identified SMF orthologs as key players in evolutionary developmental biology studies on stomata. However, recent studies on the astomatous liverwort Marchantia polymorpha revealed that the functions of these genes are not limited to the stomatal development, but extend to other types of tissues, namely sporophytic setal and gametophytic epidermal tissues. These studies provide insightful examples of gene-regulatory network co-opting, and highlight SMFs and related transcription factors as general toolkits for novel trait evolution in land plant lineages. Here, we critically review recent literature on the SMF-like gene in M. polymorpha and discuss their implications for plant evolutionary biology.

In this work, we present a quantitative comparison of the cell division dynamics between populations of intact and regenerating root tips in the plant model system Arabidopsis thaliana. To achieve the required temporal resolution and to sustain it for the duration of the regeneration process, we adopted a live imaging system based on light-sheet fluorescence microscopy, previously developed in the laboratory. We offer a straightforward quantitative analysis of the temporal and spatial patterns of cell division events showing a statistically significant difference in the frequency of mitotic events and spatial separation of mitotic event clusters between intact and regenerating roots.

Ion homeostasis is a crucial process in plants that is closely linked to the efficiency of nutrient uptake, stress tolerance and overall plant growth and development. Nevertheless, our understanding of the fundamental processes of ion homeostasis is still incomplete and highly fragmented. Especially at the mechanistic level, we are still in the process of dissecting physiological systems to analyse the different parts in isolation. However, modelling approaches have shown that it is not individual transporters but rather transporter networks (homeostats) that control membrane transport and associated homeostatic processes in plant cells. To facilitate access to such theoretical approaches, the modelling of the potassium homeostat is explained here in detail to serve as a blueprint for other homeostats. The unbiased approach provided strong arguments for the abundant existence of electroneutral H+/K+ antiporters in plants.

Plant postures are affected by environmental stimuli. When the gravitational direction changes, the Arabidopsis thaliana mutants myosin xif xik (xif xik) and atp-binding cassette b19 (abcb19) exhibit aberrantly enhanced organ bending. Whether their phenotypes are due to the same mechanism is unknown. We characterized the primary root postures of these mutants. Their roots exhibited enhanced gravitropic bending with the same root-tip angles. The wavy roots of vertically grown plants were quantitatively evaluated using four indices. The straightness index (root base-to-tip length to total root-length ratio) was similar for xif xik and abcb19, and it slightly decreased for xif xik abcb19. The curvature index was similar for abcb19 and xif xik abcb19, but it decreased for xif xik, suggesting the ABCB19 deficiency caused the roots to curve more sharply. Combination of these indices for quantitative analyses of root postures may distinguish between similar wavy-root phenotypes and clarify genetic relationships.

Noise is a ubiquitous feature for all organisms growing in nature. Noise (defined here as stochastic variation) in the availability of nutrients, water and light profoundly impacts their growth and development. Not only is noise present as an external factor but cellular processes themselves are noisy. Therefore, it is remarkable that organisms can display robust control of growth and development despite noise. To survive, various mechanisms to suppress noise have evolved. However, it is also becoming apparent that noise is not just a nuisance that organisms must suppress but can be beneficial as low noise can facilitate the response of an organism to a sub-threshold input signal in a stochastic resonance mechanism. This review discusses mechanisms capable of noise suppression or noise leveraging that might play a significant role in robust temporal regulation of an organism’s response to their noisy environment.

Trees, living for centuries, accumulate somatic mutations in their growing trunks and branches, causing genetic divergence within a single tree. Stem cell lineages in a shoot apical meristem accumulate mutations independently and diverge from each other. In plants, somatic mutations can alter the genetic composition of reproductive organs and gametes, impacting future generations. To evaluate the genetic variation among a tree’s reproductive organs, we consider three indexes: mean pairwise phylogenetic distance ($\overline{D}$), phylogenetic diversity ($PD$; sum of branch lengths in molecular phylogeny) and parent-offspring phylogenetic distance (${D}_{PO}$). The tissue architecture of trees facilitated the accumulation of somatic mutations, which have various evolutionary effects, including enhancing fitness under strong sib competition and intense host-pathogen interactions, efficiently eliminating deleterious mutations through epistasis and increasing genetic variance in the population. Choosing appropriate indexes for the genetic diversity of somatic mutations depends on the specific aspect of evolutionary influence being assessed.

Hormonal mechanisms associated with cell elongation play a vital role in the development and growth of plants. Here, we report Nextflow-root (nf-root), a novel best-practice pipeline for deep-learning-based analysis of fluorescence microscopy images of plant root tissue from A. thaliana. This bioinformatics pipeline performs automatic identification of developmental zones in root tissue images. This also includes apoplastic pH measurements, which is useful for modeling hormone signaling and cell physiological responses. We show that this nf-core standard-based pipeline successfully automates tissue zone segmentation and is both high-throughput and highly reproducible. In short, a deep-learning module deploys deterministically trained convolutional neural network models and augments the segmentation predictions with measures of prediction uncertainty and model interpretability, while aiming to facilitate result interpretation and verification by experienced plant biologists. We observed a high statistical similarity between the manually generated results and the output of the nf-root.

Plant zygote cells exhibit tip growth, producing a hemisphere-like tip. To understand how this hemisphere-like tip shape is formed, we revisited a viscoelastic–plastic deformation model that enabled us to simultaneously evaluate the shape, stress and strain of Arabidopsis (Arabidopsis thaliana) zygote cells undergoing tip growth. Altering the spatial distribution of cell wall extensibility revealed that cosine-type distribution and growth in a normal direction to the surface create a stable hemisphere-like tip shape. Assuming these as constraints for cell elongation, we determined the best-fitting parameters for turgor pressure and wall extensibility to computationally reconstruct an elongating zygote that retained its hemisphere-like shape using only cell contour data, leading to the formulation of non-dimensional growth parameters. Our computational results demonstrate the different morphologies in elongating zygotes through effective non-dimensional parameters.

Plant growth and development are tightly regulated by cell division, elongation, and differentiation. A visible plant phenotype at the tissue or organ level is coordinated at the cellular level. Among these cellular regulations (cell division, elongation and differentiation), cell division in plants follows the same universal mechanisms across kingdoms of life, and involves conserved cell cycle regulatory proteins (cyclins, cyclin-dependent kinase and cell cycle inhibitors). Cell division is regulated through distinct cell cycle steps (G1, S, G2 and M), and these individual steps are visualised using transgenic marker lines. As a result, a quantitative cell cycle approach in plants during development and stress conditions relies on the accuracy of cell cycle markers. In this perspective article, we highlight the available cell cycle marker lines in plants, common practices within plant biology communities based on existing literature and provide a road map to a thorough quantitative approach of cell cycle regulation in plants.