Plant Science

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.

Despite its significant relevance to drought adaptation, optimization of nutrient acquisition, and carbon sequestration in soil, genetic factors determining root depth remain poorly explored, mostly due to the limitations of the methods currently available to estimate it. Although several such methods have been developed for crops, their applicability to large-scale studies and those involving smaller, more fragile root systems is severely limited. To address this, we have developed ClearDepth, a simple, non-destructive, low-cost method. In ClearDepth, the root system develops naturally inside the soil in clear pots. As it expands, secondary roots reach the transparent walls of the pot ("wall roots"), becoming visible. The shallowness of each wall root is then measured (wall root shallowness, WRS), and the depth of the root system is expressed as the average of all single WRS measurements. We demonstrated the suitability of ClearDepth for root depth studies using Arabidopsis thaliana and Oryza sativa (rice), two species with contrasting root system architecture (RSA) and root size. The robustness and sensitivity of the WRS trait allow us not only to reproducibly discriminate between shallow and deep root systems but also to detect smaller yet significant differences in depth determined by the influence of environmental factors, such as light. Here, we present a comprehensive protocol for utilizing this method.

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.

The root meristem navigates the highly variable soil environment where water availability limits water absorption, slowing or halting growth. Traditional studies use uniform high osmotic potentials, poorly representing natural conditions where roots gradually encounter increasing osmotic potentials. Uniform high osmotic potentials reduce root growth by inhibiting cell division and shortening mature cell length. This protocol describes a simple and effective in vitro system using a gradient mixer that generates a vertical gradient in an agar gel based on the principle of communicating vessels, exploiting gravity to generate a continuous mannitol concentration gradient (from 0 to 400 mM mannitol) reaching osmotic potentials of -1,2 MPa. It enables long-term Arabidopsis root growth analysis under progressive water deficit, improving phenotyping and molecular studies in soil-like conditions.

The identification of chemical compounds that affect intracellular processes has greatly contributed to the understanding of developmental regulation in plants. In this protocol, we describe a method for identifying chemical compounds that affect cold-regulated gene expression in Arabidopsis thaliana. Specifically, we generated Arabidopsis plants harboring a COLD-REGULATED 15A (COR15A) promoter::luciferase (COR15Apro::LUC) construct and grew them in a submerged liquid culture. Using a single true leaf excised from COR15Apro::LUC plants and 96-well culture plates, we performed high-throughput screening of chemical compounds that inhibit cold-induction of COR15Apro::LUC. Luciferase activity was detected using a microplate reader and a chemiluminescence imaging device. This protocol can be easily used for the identification of chemical compounds that regulate other processes, being versatile with respect to equipment.

Cyst and root-knot nematodes are sedentary biotrophic parasites that infect a wide range of plant species, causing significant annual yield and economic losses. Cyst nematodes (genera Heterodera and Globodera) induce specialized feeding structures called syncytia in host plant roots, while root-knot nematodes (Meloidogyne spp.) form galls containing feeding cells known as giant cells. This protocol describes the visualization of lignin in Arabidopsis roots infected by beet cyst nematode H. schachtii and root-knot nematode M. incognita using histochemical staining. We present two distinct approaches for lignin detection: direct staining of root segments containing syncytia and galls and histopathological detection in thin longitudinal sections of the feeding sites.