植物科学

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.

Mal de Río Cuarto disease, caused by a Fijivirus, is a major constraint for maize production in Argentina. The traditional evaluation of resistant hybrids is limited by the low efficiency of natural virus transmission and the lack of standardized field inoculation methods. We developed a protocol that combines laboratory mass-rearing of the planthopper vector Delphacodes kuscheli with a controlled field transmission system. The method involves the synchronized production of large insect populations, acquisition of viruliferous vectors under controlled conditions, and their safe transport to the field using specialized containers. Transmission is achieved through individual cages placed on maize seedlings, ensuring high inoculation pressure under field-like conditions. This protocol enables reliable and reproducible virus transmission, facilitating large-scale screening of maize hybrids and other cereals. Its main advantages are the high throughput of vector production, improved transmission efficiency, and adaptability to diverse experimental designs.

Accurate profiling of soil and root-associated bacterial communities is essential for understanding ecosystem functions and improving sustainable agricultural practices. Here, a comprehensive, modular workflow is presented for the analysis of full-length 16S rRNA gene amplicons generated with Oxford Nanopore long-read sequencing. The protocol integrates four standardized steps: (i) quality assessment and filtering of raw reads with NanoPlot and NanoFilt, (ii) removal of plant organelle contamination using a curated Viridiplantae Kraken2 database, (iii) species-level taxonomic assignment with Emu, and (iv) downstream ecological analyses, including rarefaction, diversity metrics, and functional inference. Leveraging high-performance computing resources, the workflow enables parallel processing of large datasets, rigorous contamination control, and reproducible execution across environments. The pipeline’s efficiency is demonstrated on full-length 16S rRNA gene datasets from yellow pea rhizosphere and root samples, with high post-filter read retention and high-resolution community profiles. Automated SLURM scripts and detailed documentation are provided in a public GitHub repository (https://github.com/henrimdias/emu-microbiome-HPC; release v1.0.2, emu-pipeline-revised) and archived on Zenodo (DOI: 10.5281/zenodo.17764933).

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.

The rhizosphere, a 2–10 mm region surrounding the root surface, is colonized by numerous microorganisms, known as the rhizosphere microbiome. These microorganisms interact with each other, leading to emergent properties that affect plant fitness. Mapping these interactions is crucial to understanding microbial ecology in the rhizosphere and predicting and manipulating plant health. However, current methods do not capture the chemistry of the rhizosphere environment, and common plant–microbe interaction study setups do not map bacterial interactions in this niche. Additionally, studying bacterial interactions may require the creation of transgenic bacterial lines with markers for antibiotic resistance/fluorescent probes and even isotope labeling. Here, we describe a protocol for both in silico prediction and in vitro validation of bacterial interactions that closely recapitulate the major chemical constituents of the rhizosphere environment using a widely used Murashige & Skoog (MS)-based gnotobiotic plant growth system. We use the auto-fluorescent Pseudomonas, abundantly found in the rhizosphere, to estimate their interactions with other strains, thereby avoiding the need for the creation of transgenic bacterial strains. By combining artificial root exudate medium, plant cultivation medium, and a synthetic bacterial community (SynCom), we first simulate their interactions using genome-scale metabolic models (GSMMs) and then validate these interactions in vitro, using growth assays. We show that the GSMM-predicted interaction scores correlate moderately, yet significantly, with their in vitro validation. Given the complexity of interactions among rhizosphere microbiome members, this reproducible and efficient protocol will allow confident mapping of interactions of fluorescent Pseudomonas with other bacterial strains within the rhizosphere microbiome.

Salt-tolerant bacteria can enhance plant growth through various mechanisms, including the production of auxin, siderophores, hydrogen cyanide, and the solubilization of insoluble phosphate. This study investigated the production of these growth-stimulating factors by salt- and drought-tolerant bacteria isolated from the arid and saline farmlands of Jiroft. Initially, we screened for bacterial strains that exhibited the highest levels of these factors. We then evaluated their effects on improving the growth indices of cucumber seedlings. Additionally, we optimized the protocols for isolating auxin, siderophores, hydrogen cyanide, and phosphate solubilization, which can also be applied to other host rhizobacteria to assess their growth-promoting compounds.

Fusarium crown rot (FCR), mainly caused by Fusarium pseudograminearum, is a devastating soil-borne disease of wheat that results in severe yield and quality reduction. FCR is characterized by stem base necrosis and whitehead development. In recent years, FCR has escalated in both incidence and severity, emerging as a critical threat to global wheat production, particularly within key cultivation zones such as China's Huang-Huai-Hai Plain. The development of resistant cultivars is an effective and environmentally sustainable strategy for FCR disease control. However, the lack of standardized and reproducible inoculation protocols has hindered the accurate assessment and screening of disease-resistant wheat germplasms. To address this limitation, we established a robust FCR inoculation system utilizing F. pseudograminearum propagated on a millet grain substrate, facilitating rapid and reliable evaluation of both host resistance and fungal pathogenicity. Laboratory validation demonstrated high infection efficiency and strong reproducibility of this method.

Oomycetes are a predominantly plant-pathogenic group of organisms often considered and managed as fungi; however, due to their evolutionary divergence from true fungi, many conventional fungicides are ineffective against them. Their unique physiological characteristics make them challenging to work with, highlighting the need for a standardized and reproducible procedure for anti-oomycete assays. Previous studies describe methods to obtain sporulation forms in the laboratory, but there remains a disconnect between spore production and the subsequent screening process for potential biological pesticides based on microbial organic extracts. This protocol bridges that gap by providing a complete and reliable workflow from spore production to screening. In this study, we present an efficient in vitro protocol to identify microbial extracts with activity against Phytophthora capsici and Pythium ultimum. The protocol includes a method for obtaining zoospores of P. capsici and oospores of P. ultimum, followed by a simple and rapid screening assay to detect microbial extracts that inhibit the growth of these pathogens. The extracts are dispensed onto plates in two concentrations and allowed to dry. This facilitates pauses in the protocol and allows for storage of the plates until the biological material is ready for the assay. The protocol’s effectiveness has been validated with these two oomycetes, resulting in the identification of active extracts in both cases. Moreover, it can be adapted to other pathogens.

Rice (Oryza sativa), a staple crop sustaining half of humanity’s caloric intake, is threatened by numerous insect-vector-transmitted diseases, such as rice stripe disease, caused by the rice stripe virus (RSV). Most genetic studies on plant antiviral defense mechanisms rely on natural or artificial infection and transgenic approaches, which require months of plant transformation. Here, we present a streamlined protocol that enables rapid analysis of RSV–host interactions within three days. The method encompasses three key phases: (1) polyethylene glycol (PEG)-based precipitation of RSV virions from infected plant tissues, (2) sequential purification through differential ultracentrifugation with glycerol cushion optimization, and (3) high-efficiency transfection of purified virions into rice protoplasts via PEG-mediated delivery. Viral replication is quantitatively assessed using RT-qPCR targeting viral RNA and immunoblotting with RSV nucleocapsid protein-specific monoclonal antibodies. This approach eliminates dependency on stable transgenic lines, allowing the simultaneous introduction of exogenous plasmids for functional studies. Compared with conventional methods requiring several months for transgenic plant generation, our protocol delivers analyzable results within three days, significantly accelerating the exploration of antiviral mechanisms and resistance gene screening.