Plant Methods最新文献

Background: Pyricularia oryzae is a major fungal pathogen responsible for significant yield losses in rice. In recent years, diverse pathotypes have emerged as threats to other economically important grasses, including ryegrass, oats, wheat and foxtail millet. Research on host-pathogen interactions involving this species requires reliable spore production for inoculation. However, as a hemibiotrophic pathogen, P. oryzae often sporulates poorly on artificial media and typically requires specialized two-stage protocols for consistent spore production. Although several such methods have been developed, all were optimized for rice-derived strains and have not been systematically evaluated across strains from other hosts. There is also a practical need for a simple setup that allows advance preparation and frozen storage of spore stocks. Therefore, we developed a new two-stage filter paper method and compared it with four published protocols across 23 strains from 13 grass hosts.

Results: Comparative analysis showed strain specific differences in sporulation across methods, with no consistent link to phylogenetic lineage. The filter paper method reached an inoculum-competent concentration (defined here as [Formula: see text] spores/mL, suitable for routine spray inoculation) without any concentration step in 18 of 23 strains (78%), compared with TARI 16/23 (70%), IRRI 15/23 (65%), corn grain 14/23 (61%), and mycelial mat 3/23 (13%). Spores dried on filter paper were ready to use upon thawing and retained germination with no change in virulence after six months of storage at -40 [Formula: see text]C. Step by step protocols with illustrations are provided for all five methods, together with practical guidance for choosing a method based on laboratory conditions, available resources, and research objectives.

Conclusions: This study provides a comparative evaluation of two-stage sporulation methods for Pyricularia strains across diverse grass hosts. Among the five methods, the newly developed filter paper method shows the broadest applicability across strains while maintaining yields comparable to established protocols. It can be prepared for frozen storage and used directly after thawing, enabling advance preparation and bulk stocking of inoculum for virulence profiling, resistance breeding, and disease management. These findings are particularly relevant for laboratories in regions that are affected by, or at risk of, outbreaks caused by this pathogen.

Background: Black huckleberry (Vaccinium membranaceum) is a native fruit species of high nutritional, medicinal, ecological, and economic value. The black huckleberries, abundant in bioactive compounds, offer significant antioxidants and anti-inflammatory effects and play a key role in maintaining wildlife and forest ecosystems. Despite its importance, protoplast isolation and gene editing have not been reported in this species. These techniques are essential for functional genomics and crop improvement, but the recalcitrant nature of this species, complex genome, and variable ploidy present significant challenges for cellular and molecular manipulation. This study aimed to establish a reliable protocol for efficient mesophyll protoplast isolation and transient gene expression in V. membranaceum using in vitro-grown leaves.

Results: A systematic optimization of enzyme composition, osmotic concentration, antioxidant supplementation, and pH was undertaken to enhance protoplast yield and viability in V. membranaceum. The optimized enzymatic combination of 2% cellulase R-10, 1% hemicellulase, 1% Macerozyme R-10, and 1.5% pectinase facilitated efficient cell wall degradation while maintaining structural integrity. The inclusion of 0.6 M mannitol ensured osmotic stability, and 1% PVP-40 effectively suppressed phenolic oxidation, significantly improving protoplast viability. A near-neutral pH of 5.8 supported optimal enzyme activity without inducing cellular damage. Under these optimized conditions, 14 h enzymatic digestion produced 7.20 × 10⁶ protoplasts g⁻¹ FW with 95.1% viability. Subsequent optimization of PEG-mediated transformation identified 40% PEG-4000 with 30 µg plasmid DNA as the most effective combination, achieving 75.1% transient expression efficiency. Nuclear localization of GFP-tagged proteins, confirmed by DAPI staining, validated the robustness of the optimized system.

Conclusions: This study presents a standardized, PVP-40-enhanced protocol for mesophyll protoplast isolation with notable yield and viability in V. membranaceum, supporting efficient transient gene expression. The method provides a robust platform for functional genomics, gene editing, and biotechnological applications in this underutilized species and other related plant species.

Plant growth and development strongly depend on the uptake of soil minerals and their distribution within plants. Various electrophysiological techniques have been developed to study these ion transport processes and the role of ions in signal transduction pathways. An important non-invasive method is provided by Scanning Ion-Selective Electrodes (SISE), which are used to detect ion fluxes. These SISE-measurements depend on software that coordinates the continuous electrode movement between two positions, as well as data collection and analysis. We developed two LabView-based programs; the SISE-Monitor and SISE-Analyser that enable ion flux recordings and their analysis, respectively. These applications are freely available, both as windows-executable files that enable routine measurements, as well as the LabView source code that allows insights into the routines used for measurement and analysis. Moreover, the source code can be used to develop new functions, such as the combined measurement of extracellular ion fluxes with SISE and cellular ion concentrations with fluorescent dyes, or proteins.

Plant reproduction involves dynamic spatiotemporal changes that occur deep within maternal tissues. In ovules of Arabidopsis thaliana (A. thaliana), one of the two synergid cells degenerates at fertilization, while the fertilized egg cell (zygote) undergoes directional elongation followed by asymmetric division to initiate embryonic patterning. However, morphological analysis of these events has been hampered by the limitations of conventional cell wall staining, which fails to label cells lacking complete walls, and by the requirement for transgenic fluorescent reporters to visualize cell outlines. Here, we report that the membrane-specific fluorescent dye FM4-64 readily permeates ovules, allowing clear visualization of reproductive cell morphology both before and after fertilization. This staining method supports high-resolution time-lapse imaging and quantitative analysis of early embryogenesis in living tissues. Importantly, it is applicable not only to the angiosperm A. thaliana but also to the liverwort Marchantia polymorpha (M. polymorpha) and the fern Ceratopteris richardii (C. richardii), enabling the visualization of live reproductive cell structures within maternal tissues and revealing fertilization-associated morphological changes. This simple and robust method thus provides a valuable tool for spatiotemporal and quantitative analyses of reproductive processes across a broad range of plant species, without the need to generate transgenic lines.

Stomata regulate gas and water exchange in plants and are crucial for plant productivity and survival, making their trait analysis essential for advancing plant biology research. While current machine learning methods enable automated stomatal trait extraction, existing approaches face significant limitations that require extensive manual labeling for training and additional human annotation when applied to new species. This study presents an automated system for extracting stomatal traits from Pisum sativum (pea) leaves that addresses these challenges through generative artificial intelligence. Our pipeline integrates imaging, detection, segmentation, and synthetic data generation processes. A nail polish impression technique was employed to prepare leaf microscopic images, followed by the application of deep learning networks to identify and segment stomata in these images. By including generative AI-produced synthetic data, our system achieves high segmentation accuracy across species, reducing manual relabeling requirements. This approach enables seamless cross-species model adaptation for many cases, alleviating the annotation bottleneck that often limits machine learning applications in plant biology. Our results demonstrate the pipeline's effectiveness for automated stomatal trait extraction and highlight generative AI's transformative potential in advancing stomatal detection methodologies, offering a scalable solution for broad-scale comparative stomatal analysis.

Background: Variety mixtures combining crop varieties with different root system properties have the potential to improve soil exploration through belowground niche complementarity, which can improve soil resource acquisition and crop productivity. However, there is a lack of appropriate methods to distinguish and quantify roots of different varieties, which limits our ability to elucidate belowground processes that underpin soil exploration and resource uptake by plants in variety mixtures.

Results: In the present study, we developed a method to quantify root biomass and distribution patterns of different barley varieties grown together in mixtures using DNA extraction and quantitative PCR with variety-specific genetic markers. Two field experiments, one in Sweden and one in Denmark, were conducted that included two barley varieties grown either alone in pure stands or together in the same plot. The genetic markers were highly variety-specific, enabling accurate detection of the roots of each individual variety in the mixture. We found that the contribution of varieties to total root biomass in the mixture differed between the two locations, indicating the effects of the environment on root distribution patterns in variety mixtures.

Conclusions: The method presented here opens new possibilities for rapid quantification of root biomass and can provide new insights into belowground processes underpinning the functioning of mixed variety systems. Ultimately, such understanding is needed to assess the potential to adopt mixed variety systems in practical agriculture.

Field trials play an essential role in developing genetically modified and genome-edited biotechnology plants, as they assess plant growth, yield, and potential unintended effects. Australian biotechnology field trials are regulated by federal protocols that mandate accurate forecasting of flowering times. Currently, this relies on labour-intensive and subjective visual field inspections of individual wheat plants at defined growth stages (Zadoks growth stages Z37, Z39, and Z41). To enable automatic forecasting, hyperspectral and red-green-blue (RGB) images were captured in the greenhouse, and hyperspectral reflectance data were acquired in a semi-natural environment. Support Vector Machine classification achieved F1 scores (0.832) for pre-anthesis growth stage classification through the combined use and systematic comparison of three spectral transformations, including Standard Normal Variate, Hyper-hue, or Principal Component Analysis, which together outperformed reliance on any single transformation. After feature selection, F1 scores (0.752) could be achieved with only five wavelengths. Furthermore, the SNV transformation demonstrated robust performance under limited training conditions, maintaining high classification accuracy and strong generalizability across varying data sizes. These findings highlight the effectiveness of transformation-enriched data and optimized feature selection for accurate growth stage classification, providing a low-cost approach to reduce manual inspection burdens and strengthen biosafety during biotechnology field trial practices.

Efficient regeneration protocols are essential for large-scale propagation and genetic manipulation of recalcitrant medicinal species such as Cannabis sativa. Existing direct and indirect regeneration methods are highly genotype and explant-dependent, limiting broader applicability. Here, we report a five-stage (S₀-S₄) optimised protocol that is reproducible and achieves high-efficiency direct de novo regeneration using cotyledonary node explants from both hemp and medicinal cannabis genotypes. A 1% (v/v) H₂O₂-based sterilisation method significantly improved seed germination and reduced endophyte contamination. Among embryo-derived explants, the cotyledonary node attached to the cotyledon showed superior regeneration efficiency through two distinct pathways: axillary shoot initiation and de novo regeneration, the latter achieving ~ 70-90% efficiency in six hemp cultivars and three medicinal cannabis lines on TDZ and NAA containing shoot regeneration medium. Histological analysis confirmed true de novo shoot formation from peripheral cortical cells, independent of pre-existing meristems or callus. De novo shoots were initiated within 2 d of shoot regeneration medium treatment, indicating rapid cellular commitment to organogenesis, with optimal regeneration between 7 and 14 d. Prolonged exposure proved detrimental, causing excessive callusing and vitrification. Repeated subculturing during proliferation stage enabled scalable shoot multiplication, yielding an average of 7 shoots per responding explant (~ 11.4 shoots per seed), outperforming previously published cotyledon-based (~ 2-fold) and hypocotyl-based (~ 5-fold) methods under comparable conditions. Regenerated plantlets developed healthy roots (with IAA or IBA) and acclimatised readily, exhibiting normal vegetative and reproductive growth. The protocol's reproducibility across diverse cannabis genotypes and its applicability to other medicinal angiosperm species in this study highlights its value for both research and commercial applications.

Background: Seed quality analysis using X-rays is increasingly explored due to its non-invasive and rapid nature. Yet, the current absence of reliable and standardised imaging protocols has led to contradictory effects of X-ray exposure in previous studies. Our work systematically investigated the effect of low-energy X-rays (peak energy ≲25 keV) with limited doses (< 3 mGy) on a wide range of plant materials.

Results: The baseline of three germination categories was established across seven species before the application of low-dose X-ray exposure under controlled standard germination conditions. The high inter-varietal and inter-lot variabilities, in addition to the strong interaction between X-ray exposure with both variety and lot, reinforced the need to consider genetic and seed quality aspects while evaluating the impacts of low-dose, low-energy X-rays (< 3 mGy, peak energy ≲25 keV). A slight stimulative effect was observed on most of the species (bean, carrot, fennel, maize, radish, and ryegrass), notably, with a repeated reduction in ungerminated seeds led to an increase in normal germination (1.7 ± 1.9%). Intrinsic physical quality holds a crucial value where the minor negative impact observed in soybean originated from its degraded physical quality and not from X-ray exposure; hence, no destructive effects were detected. To understand whether seed size plays a significant role in a seed's response to exposure, linear regression models were built to predict 3D seed traits (volume) from 2D X-ray images. Yet, seed size did not explain the variation in responses to low doses of X-rays. However, the average density of the seven species explained both their natural germination (p < 0.01; R² = 0.82) and their germination outcomes after exposure (p < 0.01; R² = 0.88). Among all species, fennel with notably low density (0.7 g/cm³) demonstrated the most pronounced gains in germination after exposure (4.6 ± 6.3%) due to the stimulative effect.

Conclusion: Low-dose X-ray exposure is non-destructive with a beneficial effect on germination, but can be strongly influenced by underlying genetics and the physical quality of the tested seeds. This work addressed important gaps in evaluating X-ray impacts and proposed a robust design and well-examined radiography protocol for a proven non-destructive seed quality analysis.

Plant diseases can cause heavy yield losses in arable crops resulting in major economic losses. Effective early disease recognition is paramount for modern large-scale farming. Since plants can be infected with multiple concurrent pathogens, it is important to be able to distinguish and identify each disease to ensure appropriate treatments can be applied. Hyperspectral imaging is a state-of-the art computer vision approach, which can improve plant disease classification, by capturing a wide range of wavelengths before symptoms become visible to the naked eye. Whilst a lot of work has been done applying the technique to identifying single infections, to our knowledge, it has not been used to analyse multiple concurrent infections which presents both practical and scientific challenges. In this study, we investigated three wheat pathogens (yellow rust, mildew and Septoria), cultivating co-occurring infections, resulting in a dataset of 1447 hyperspectral images of single and double infections on wheat leaves. We used this dataset to train four disease classification algorithms (based on four neural network architectures: Inception and EfficientNet with either a 2D or 3D convolutional layer input). The highest accuracy was achieved by EfficientNet with a 2D convolution input with 81% overall classification accuracy, including a 72% accuracy for detecting a combined infection of yellow rust and mildew. Moreover, we found that hyperspectral signatures of a pathogen depended on whether another pathogen was present, raising interesting questions about co-existence of several pathogens on one plant host. Our work demonstrates that the application of hyperspectral imaging and deep learning is promising for classification of multiple infections in wheat, even with a relatively small training dataset, and opens opportunities for further research in this area. However, the limited number of Septoria and yellow rust + Septoria samples highlights the need for larger, more balanced datasets in future studies to further validate and extend our findings under field conditions.