植物科学

The protochlorophyllide (Pchlide) level is a crucial indicator of plant fitness. Precise quantification of Pchlide content is necessary not only in studies of flu-related mutants that over-accumulate Pchlide in the dark but also for research on plants suffering from environmental stresses. Due to its low content and interference of chlorophylls, quantitative determination of Pchlide content is a challenge. Here, we describe an optimized protocol for Pchlide extraction from Arabidopsis thaliana seedlings and subsequent analysis using high-performance liquid chromatography (HPLC) coupled with fluorescence detection. Divinyl-Protochlorophyllide (DV-Pchlide, the major form of Pchlide in plants) quantification is achieved by interpolating fluorescence peak areas against an experimentally derived standard curve. This protocol provides a reliable workflow for Pchlide quantification, facilitating the deciphering of the underlying mechanism of plant environmental resilience.

Plants move chloroplasts in response to light, changing the optical properties of leaves. Low irradiance induces chloroplast accumulation, while high irradiance triggers chloroplast avoidance. Chloroplast movements may be monitored through changes in leaf transmittance and reflectance, typically in red light. We present a step-by-step procedure for the detection of chloroplast positioning using reflectance hyperspectral imaging in white light. We show how to employ machine learning methods to classify leaves according to the chloroplast positioning. The convolutional network is a method of choice for the analysis of the reflectance spectra, as it allows low levels of misclassification. As a complementary approach, we propose a vegetation index, called the Chloroplast Movement Index (CMI), which is sensitive to chloroplast positioning. Our method offers a high-throughput, contactless way of chloroplast movement detection.

In recent years, the calcifying properties of some cyanobacteria have been used in the production of living building materials (LBMs), such as bio-concrete, as a CO₂-friendly alternative for cement. This microbially induced calcium carbonate precipitation (MICP) technique can act as a novel platform technology for carbon capture strategies. Consequently, various research articles have been conducted based on a diverse set of workflows, including several modifications, to manufacture LBMs. However, such articles contain only fragmentary descriptions of the materials and methods used. This protocol is meant to act as a detailed, step-by-step operational manual for the production of LBMs using the cyanobacterial model strain Picosynechococcus sp. PCC 7002. The process is divided into several steps, such as the activation of the cyanobacterial-gel solution with CaCl₂ × 2H₂O and NaHCO₃, casting standardized prisms (160 × 40 × 40 mm), and demolding LBMs. Subsequently, bending tensile and compressive strength tests are performed according to the procedures commonly used in concrete and material testing as proof of concept.

A simple and effective method to identify genetic markers of yield response to nitrogen (N) fertilizer among maize hybrids is urgently needed. In this article, we describe a detailed methodology to identify genetic markers and develop associated assays for the prediction of yield N-response in maize. We first outline an in silico workflow to identify high-priority single-nucleotide polymorphism (SNP) markers from genome-wide association studies (GWAS). We then describe a detailed methodology to develop cleaved amplified polymorphic sequences (CAPS) and derived CAPS (dCAPS)-based assays to quickly and effectively test genetic marker subsets. This protocol is expected to provide a robust approach to determine N-response type among maize germplasm, including elite commercial varieties, allowing more appropriate on-farm N application rates, minimizing N fertilizer waste.

The tomato (Solanum lycopersicum) is a widely cultivated crop worldwide that serves as a model system for fruit development studies. Agrobacterium tumefaciens–mediated transformation of tomato has played a central role as a tool for analyzing the function of candidate genes and producing transgenic lines with enhanced resistance to pathogens, tolerance to abiotic stresses, and improved fruit quality traits. Among the many tomato varieties, the miniature dwarf cultivar Micro-Tom (MT) has been increasingly adopted as a model system for tomato research due to its short life cycle, small size, and high transformation efficiency. This protocol outlines a replicable methodology for A. tumefaciens–mediated transformation of Micro-Tom from cotyledon explants, utilizing cost-effective plant growth regulators for shoot regeneration, high transformation rates, reduced regeneration time, and enhanced rooting conditions.

Lipid droplets have emerged as dynamic organelles involved in diverse cellular processes beyond simple lipid storage. In plants and cyanobacteria, growing evidence highlights their importance in stress adaptation and signaling, yet methods to study their structure and purity remain limited. Traditionally, in situ transmission electron microscopy (TEM) has been used to visualize lipid droplets within intact cells. While powerful, this approach cannot easily evaluate isolated lipid droplets or confirm their purity. In this protocol, we describe a rapid method for preparing and visualizing cyanoglobule lipid droplets isolated from cyanobacteria. The isolated droplets are directly processed for TEM using negative staining with uranyl acetate, providing a straightforward and efficient workflow. The procedure can be applied broadly to lipid droplets from diverse organisms, independent of species or cellular origin. This protocol offers a simple, fast, and widely applicable approach to assessing lipid droplets, expanding the toolkit for researchers studying their structure and function.

Roots are essential organs for plants, facilitating water and nutrient uptake from the soil to support growth. Traditional methods for studying root systems, such as rhizoboxes and rhizotrons, have provided valuable insights. However, advanced methods such as fabricated ecosystems (EcoFAB) combined with new generation microscopes now enable a more detailed investigation of the rhizosphere, the microenvironment surrounding roots, allowing a deeper understanding of root tissue, exudates, and plant–soil interactions. This microenvironment can be used to investigate the adaptation of plants to environmental stress (salinity, drought, higher temperatures). Our procedure focuses on establishing standardized protocols for plant growth tailored to the EcoFAB system, which offers a controlled environment to study root dynamics. This work also contributes new insights into the early stages of plant germination, an area currently underexplored in the literature. While numerous studies focus on plant growth or genetic aspects, such as gene induction, the germination phase remains underexplored. We have developed optimized germination protocols for multiple plant species, ensuring uniform seedling size and sufficient development for seamless integration into the EcoFAB system.

In plants, the apoplast contains a diverse set of proteins that underpin mechanisms for maintaining cell homeostasis, cell wall remodeling, cell signaling, and pathogen defense. Apoplast protein composition is highly regulated, primarily through the control of secretory traffic in response to endogenous and environmental factors. Dynamic changes in apoplast proteome facilitate plant survival in a changing climate. Even so, the apoplast proteome profiles in plants remain poorly characterized due to technological limitations. Recent progress in quantitative proteomics has significantly advanced the resolution of proteomic profiling in mammalian systems and has the potential for application in plant systems. In this protocol, we provide a detailed and efficient protocol for tandem mass tag (TMT)-based quantitative analysis of Arabidopsis thaliana secretory proteome to resolve dynamic changes in leaf apoplast proteome profiles. The protocol employs apoplast flush collection followed by protein cleaning using filter-aided sample preparation (FASP), protein digestion, TMT-labeling of peptides, and mass spectrometry (MS) analysis. Subsequent data analysis for peptide detection and quantification uses Proteome Discoverer software (PD) 3.0. Additionally, we have incorporated in silico–generated spectral libraries using PD 3.0, which enables rapid and efficient analysis of proteomic data. Our optimized protocol offers a robust framework for quantitative secretory proteomic analysis in plants, with potential applications in functional proteomics and the study of trafficking systems that impact plant growth, survival, and health.

When plants undergo senescence or experience carbon starvation, leaf cells degrade proteins in the chloroplasts on a massive scale via autophagy, an evolutionarily conserved process in which intracellular components are transported to the vacuole for degradation to facilitate nutrient recycling. Nonetheless, how portions of chloroplasts are released from the main chloroplast body and mobilized to the vacuole remains unclear. Here, we developed a method to observe the autophagic transport of chloroplast proteins in real time using confocal laser-scanning microscopy on transgenic plants expressing fluorescently labeled chloroplast components and autophagy-associated membranes. This protocol enabled us to track changes in chloroplast morphology during chloroplast-targeted autophagy on a timescale of seconds, and it could be adapted to monitor the dynamics of other intracellular processes in plant leaves.

Sheath blight, caused by Rhizoctonia solani, is a major fungal disease of rice that leads to significant yield losses globally. Conventional inoculation methods often fail to achieve consistent and uniform infection, limiting their applicability in antifungal screening studies. This protocol describes a reliable in planta inoculation method for R. solani using mature sclerotia placed at the internodal region of tillering-stage rice seedlings. The procedure includes step-by-step instructions for seed germination, seedling preparation, pathogen culture, artificial inoculation, and post-infection application of antifungal treatments, including botanical compounds such as Ocimum gratissimum essential oil and thymol. Lesion development is monitored and quantified over time, and data are analyzed statistically to evaluate treatment efficacy. The protocol is optimized for reproducibility, scalability, and compatibility with sustainable disease management approaches. It provides a robust platform for evaluating antifungal agents in a biologically relevant and controlled environment.