Plant Science

Accurately quantifying the areas of necrotic lesions on plant leaves is essential for evaluating plant–pathogen interactions and disease resistance. Although digital image analysis methods using ImageJ are widely employed, they often require case-specific optimization and may not be readily applicable across different experimental conditions. Furthermore, many studies have used ImageJ for lesion measurement without providing methodological details, which limits reproducibility. Here, we present a simple, step-by-step ImageJ workflow for measuring irregular necrotic lesions using a standard personal computer and mouse. The procedure relies on manual lesion selection using the freehand selection tool, followed by Gaussian smoothing, binarization, and automated particle analysis to extract lesion area measurements. By balancing manual isolation with computational thresholding, this protocol eliminates the need for extensive parameter tuning. This approach provides an accessible, reliable alternative to time-consuming color thresholding methods, thereby improving transparency and reproducibility in lesion quantification. The workflow's reproducibility has been confirmed through both intra-user and inter-user analyses.

Accurate quantification of plant disease severity is essential for evaluating host–pathogen interactions and assessing the effectiveness of disease management strategies. Traditional visual scoring methods and manual estimation of infected tissue are widely used but are often subjective and prone to observer bias. Digital image analysis offers an objective alternative by enabling automated identification and quantification of symptomatic plant tissues based on color and spatial characteristics.

Here, we present a MATLAB-based image processing protocol for differentiating diseased and healthy plant tissue from digital leaf images. The workflow involves acquisition of standardized leaf images, conversion of RGB images into hue-saturation-value (HSV) color space, segmentation of diseased tissue using defined HSV thresholds, refinement of the segmented mask through morphological operations, and extraction of the whole leaf area. The protocol then calculates the diseased area and total leaf area in pixels and computes the percentage of infected tissue. The method uses MATLAB together with the Image Processing Toolbox and can be implemented using simple scripts. This protocol enables rapid and reproducible quantification of disease severity in plant leaves exhibiting visually distinct symptoms such as necrotic lesions or blight patches. By minimizing observer bias and providing quantitative measurements of infected area, the protocol offers a practical and reproducible approach for plant disease phenotyping and evaluation of disease management strategies across diverse plant-pathogen systems where diseased tissues can be clearly distinguished from healthy tissues under reasonably controlled imaging conditions.

Ammonium (NH₄⁺) is the primary inorganic nitrogen source for rice (Oryza sativa L.). Substantial progress has been made in characterizing the functions of ammonium transporters (AMTs) in roots; however, the regulatory dynamics governing subcellular ammonium compartmentation after its entry into cells, particularly its vacuolar sequestration and efflux back to the external environment, remain poorly understood. This knowledge gap stems mainly from two factors: the difficulty of applying conventional detection methods at the organellar scale and interference caused by nonspecific ion adsorption to the cell wall of intact roots. To address these challenges, we present a detailed and reproducible protocol for real-time measurement of net NH₄⁺ fluxes in rice roots, root protoplasts, and isolated vacuoles using non-invasive micro-test technology (NMT). The protocol covers the preparation of protoplasts and vacuoles from rice roots, the configuration and calibration of the NMT system, and the step-by-step measurement of net NH₄⁺ fluxes at three distinct biological levels (intact roots, protoplasts, and vacuoles). By employing a unified sample preparation and measurement strategy, this protocol enables quantification of net uptake fluxes across the plasma membrane, characterization of net efflux dynamics under specific conditions, and indirect estimation of vacuolar sequestration capacity using the isolated vacuole system. Overall, this protocol provides a flexible and robust framework for studying NH₄⁺ homeostasis in plants and is readily adaptable to different crop species, treatment conditions, and experimental objectives. Owing to its modular design and compatibility with standard NMT equipment, it can be readily adopted by laboratories seeking to investigate nitrogen transport mechanisms in plants.

Rice lodicules are specialized floral organs located at the base of the ovary that undergo dynamic morphological changes during the flowering period. Water uptake–driven swelling and subsequent dehydration-induced shrinkage of the lodicules trigger floret opening and closure, respectively. Although lodicules play a central role in floret movement, standardized methods for quantitatively monitoring their temporal morphological changes remain limited. Here, we describe a detailed and reproducible workflow for lodicule sampling, dissection, imaging, and quantitative morphometric analysis. Florets are collected at predefined clock time points during the flowering period, and lodicules are carefully isolated under a stereomicroscope. High-resolution imaging is performed under consistent acquisition settings, followed by precise measurement of lodicule length, width, and thickness using image analysis software. This protocol emphasizes positional consistency in sampling, uniform imaging parameters, and standardized data analysis to enhance reproducibility. This method is suitable for evaluating the effects of genetic background or environmental conditions on lodicule morphology. By providing a standardized analytical framework, this protocol enables accurate and quantitative morphometric analysis of rice lodicules during floret opening.

We present a protocol to allow continuous assessment of cell death in Arabidopsis thaliana (L.) seedlings by measuring the release of electrolytes from dying cells upon heat shock. The electrolyte leakage assay is a well-established method to quantify the extent of cell death of plant tissues exposed to pathogen infection, since the activation of the immune response leads to compromised membrane integrity and to the release of ions from the dying cell. This prolonged release of electrolytes is considered a hallmark of regulated cell death in plants. Heat shock in plants induces ferroptosis-like cell death, which can be suppressed either pharmacologically, using inhibitors such as ferrostatin, or genetically through knockout of ferroptosis-related genes. Here, we have adapted the electrolyte leakage assay to quantify cell death in young Arabidopsis seedlings exposed to a heat shock previously shown to induce ferroptosis-like cell death. We also illustrate how this method can be used to assess activation of ferroptosis-like cell death in whole Arabidopsis seedlings using ferrostatin or knockout mutants of potential gene candidates involved in ferroptosis-like cell death.

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.

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.