Plant Science

Plants growing in soil are exposed to several environmental stresses, such as soil compaction, flooding, or drought, and need to quickly acclimate to these conditions to survive. Such acclimations include suberization and lignification of the outer and inner parts of the root. Visualization of suberin and lignin depositions in roots can be done with several well-established staining techniques followed by microscopy, but these methods are mostly qualitative. Radial water loss assays provide a quick, robust, and quantitative method to evaluate the formation of suberized and/or lignified outer apoplastic barriers and how tightly they regulate water loss. Here, we provide a detailed step-by-step protocol, from harvesting the roots from soil conditions and cleaning them, to preparing them for water loss measurements, while using basic lab equipment. Since the data obtained is water loss as mass, quantitative and statistical comparisons can be performed. Finally, while in this protocol roots were grown in soil, the method can be applied to roots growing in hydroponics, agar plates, or any other growth conditions that allow non-destructive harvesting of roots. In summary, the protocol provides a fast and reliable method to assess differences in outer apoplastic barrier development of root tissues, allowing for fast root tissue screenings while obtaining quantitative data without expensive or highly specialized equipment.

Symbiosomes are plant membrane–bound, organelle-like structures that enclose single or multiple bacteroids (differentiated rhizobia) within legume cells, serving specifically as the site for N₂ fixation. Although isolated symbiosomes have been used to study nitrogen fixation, ion transport, and metabolite translocation, conventional density gradient–based isolation methods for their isolation are labor-intensive. Here, we describe a simplified, density gradient–free protocol for rapid enrichment of symbiosome-containing fractions from mature legume nodules using the Medicago truncatula–Sinorhizobium meliloti symbiosis as an example. The method combines razor blade homogenization, filtration through a 40 μm cell strainer, and sequential low-speed centrifugation to remove debris and enrich symbiosome-associated fractions. The resulting material is compatible with downstream mass spectrometry–based proteomics and mutant phenotypic analyses. Compared with conventional approaches, this protocol is rapid, simple, and suitable for higher-throughput biochemical and proteomic studies of symbiosomes.

Obtaining homozygous mutant and transgenic lines is a critical yet time-consuming step in Arabidopsis thaliana research. Conventional breeding procedures require seeds to undergo complete maturation and natural desiccation before harvest, followed by cold stratification to overcome seed dormancy. This process substantially prolongs generation turnover and delays genetic screening. Here, we describe a rapid germination strategy based on the use of partially dehydrated seeds collected approximately 15 days after pollination (DAP). At this developmental stage, embryos have reached physiological maturity, while the seeds have not yet entered deep desiccation-induced dormancy. After surface sterilization and short-term cold treatment (2–3 days at 4 °C), these seeds readily germinate on 1/2 MS medium and develop into normal seedlings. By bypassing the prolonged maturation and dormancy phases associated with naturally dried seeds, this protocol shortens each generation cycle by approximately 1–2 weeks. The method is particularly useful for accelerating the propagation of transgenic materials and the identification of homozygous mutant lines in Arabidopsis.

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.