Plant Methods最新文献

{"title":"pSIG plasmids, MoClo-compatible vectors for efficient production of chimeric double-stranded RNAs in Escherichia coli HT115 (DE3) strain.","authors":"Ching-Feng Wu, Li-Pang Chang, Chan Lee, Ioannis Stergiopoulos, Li-Hung Chen","doi":"10.1186/s13007-025-01413-5","DOIUrl":"10.1186/s13007-025-01413-5","url":null,"abstract":"<p><strong>Background: </strong>Spray-induced gene silencing (SIGS) is a promising strategy for controlling plant diseases caused by pests, fungi, and viruses. The method involves spraying on plant surfaces double-stranded RNAs (dsRNAs) that target pathogen genes and inhibit pathogen growth via activation of the RNA interference machinery. Despite its potential, significant challenges remain in the application of SIGS, including producing large quantities of dsRNAs for field applications. While industrial-scale dsRNA production is feasible, most research laboratories still rely on costly and labor-intensive in vitro transcription kits that are difficult to scale up for field trials. Therefore, there is a critical need for highly efficient and scalable methods for producing diverse dsRNAs in research laboratories.</p><p><strong>Results: </strong>This study introduces pSIG plasmids, MoClo-compatible vectors designed for efficient dsRNA production in the Escherichia coli RNase III-deficient strain HT115 (DE3). The pSIG vectors enable the assembly of multiple DNA fragments in a single reaction using highly efficient Golden Gate cloning, thereby allowing the production of chimeric dsRNAs to simultaneously silence multiple genes in target pests and pathogens. To demonstrate the efficacy of this system, we generated 12 dsRNAs targeting essential genes in Botrytis cinerea. The results revealed that silencing the Bcerg1, Bcerg2, and Bcerg27 genes involved in the ergosterol biosynthesis pathway, significantly reduced fungal infection in plant leaves. Furthermore, we synthesized a chimeric dsRNA, Bcergi, that incorporates target fragments from Bcerg1, Bcerg2, and Bcerg27. Nevertheless, the Bcerg1 dsRNA alone achieved greater disease suppression than the chimeric Bcergi dsRNA.</p><p><strong>Conclusions: </strong>Here, we developed a highly efficient and scalable method for producing chimeric dsRNAs in E. coli HT115 (DE3) in research laboratories using our homemade pSIG plasmid vectors. This approach addresses key challenges in SIGS research, including the need to produce large quantities of dsRNA and identify effective dsRNAs, thus enhancing the feasibility of SIGS as a sustainable strategy for controlling plant diseases and pests in crops.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"96"},"PeriodicalIF":4.7,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12247436/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144619696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new phenotyping method for root growth studies in compacted soil validated by GWAS in barley.","authors":"Giorgia Carletti, Agostino Fricano, Elisabetta Mazzucotelli, Luigi Cattivelli","doi":"10.1186/s13007-025-01408-2","DOIUrl":"10.1186/s13007-025-01408-2","url":null,"abstract":"<p><strong>Background: </strong>Soil compaction is defined as the reduction of air-filled pore space affecting soil density, water conductivity and nutrient availability. These conditions negatively influence root morphology, root development and plant growth leading to yield loss. To date, the ability of roots to penetrate compacted soil has been investigated using high density agar or wax-petrolatum layers as a proxy for compaction. Nevertheless, these methods are not realistic and fail to account for the root-soil interaction that influences root growth ability.</p><p><strong>Results: </strong>Artificially compacted soil lumps were prepared using natural field soil mixed with sand and vermiculite in a 1:1:0.2 ratio and adjusted to a final water content of 31%. A Genome Wide Association Study (GWAS) was performed to validate this new methodology, combining a panel of 139 barley cultivars with a Single Nucleotide Polymorphism (SNP) dataset of 5,317 polymorphic markers. The panel was evaluated at seedling stage for four traits: total root length, average of diameter width, seminal root number, shoot: root weight ratio and two novel Quantitative Trait Loci (QTLs) associated with total root length were identified on Chr 4 H and 5 H. Four genes (a Nitrate Transporter1 (NRT1)/Peptide Transporter (PTR) family protein 2.2, a Hedgehog-interacting-like protein, an expansin and a cyclic nucleotide-gated channel) were hypothesized as plausible candidates for further investigation, given their implication in root development. In addition, the new phenotyping method revealed an altered plagiogravitropism phenomenon in barley during root emergence in compact substrates. In uncompacted soil, only the primary root exhibits vertical gravitropic set-point angle while a variable number of embryonic seminal roots develop with a shallower growth angle. In contrast, in compacted substrate all roots developed vertically to restore the growth angle after reaching a length of 4-5 millimetres.</p><p><strong>Conclusions: </strong>A methodology based on root-soil interaction is presented as a new method for root growth evaluation and genomic studies in seedlings growing in compacted soil.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"93"},"PeriodicalIF":4.7,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12239455/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144601222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stomata morphology measurement with interactive machine learning: accuracy, speed, and biological relevance?","authors":"Tomke S Wacker, Abraham G Smith, Signe M Jensen, Theresa Pflüger, Viktor G Hertz, Eva Rosenqvist, Fulai Liu, Dorte B Dresbøll","doi":"10.1186/s13007-025-01416-2","DOIUrl":"10.1186/s13007-025-01416-2","url":null,"abstract":"<p><p>Stomatal morphology plays a critical role in regulating plant gas exchange influencing water use efficiency and ecological adaptability. While traditional methods for analyzing stomatal traits rely on labor-intensive manual measurements, machine learning (ML) tools offer a promising alternative. In this study, we evaluate the suitability of a U-Net-based interactive ML software with corrective annotation for stomatal morphology phenotyping. The approach enables non-ML experts to efficiently segment stomatal structures across diverse datasets, including images from different plant species, magnifications, and imprint methods. We trained a single model based on images from five datasets and tested its performance on unseen data, achieving high accuracy for stomatal density (R² = 0.98) and size (R² = 0.90). Thresholding approaches applied to the U-Net segmentations further improved accuracy, particularly for density measurements. Despite significant variability between datasets, our findings demonstrate the feasibility of training a single segmentation model to analyze diverse stomatal data sets. Validation approaches showed that a semi-automatic approach involving correcting segmentations was five times faster than manual annotation while maintaining comparable accuracy. Our results also illustrate that ML metrics, such as the F1 score, correlate with accuracy in the statistical analysis of trait measurements with improvements diminishing after 2:30 h model training. The final model achieved high precision, allowing the detection of highly significant biological differences in stomatal morphology within plant, between genotypes and across growing environments. This study highlights interactive ML with corrective annotation as a robust and accessible tool for accelerating phenotyping in plant sciences, reducing technical barriers and promoting high-throughput analysis.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"95"},"PeriodicalIF":4.7,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12243431/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144601223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing X-ray radiography for non-destructive assessment of paddy rice grain quality traits.","authors":"Murugesan Tharanya, Debarati Chakraborty, Anand Pandravada, Raman Babu, Mahantesh Gangashetti, Swapna Paidi, Sunita Choudhary, Kaliamoorthy Sivasakthi, Krithika Anbazhagan, Bhavani Vaditandra, Michael Waininger, Mareike Weule, Eva Hufnagel, Joelle Claußen, Jiří Vaněk, Thomas Wittenberg, Jana Kholova, Stefan Gerth","doi":"10.1186/s13007-025-01405-5","DOIUrl":"10.1186/s13007-025-01405-5","url":null,"abstract":"<p><strong>Background: </strong>Agricultural systems are under extreme pressure to meet the global food demand, hence necessitating faster crop improvement. Rapid evaluation of the crops using novel imaging technologies coupled with robust image analysis could accelerate crops research and improvement. This proof-of-concept study investigated the feasibility of using X-ray imaging for non-destructive evaluation of rice grain traits. By analyzing 2D X-ray images of paddy grains, we aimed to approximate their key physical Traits (T) important for rice production and breeding: (1) T₁ chaffiness, (2) T₂ chalky rice kernel percentage (CRK%), and (3) T₃ head rice recovery percentage (HRR%). In the future, the integration of X-ray imaging and data analysis into the rice research and breeding process could accelerate the improvement of global agricultural productivity.</p><p><strong>Results: </strong>The study indicated, computer-vision based methods (X-ray image segmentation, features-based multi-linear models and thresholding) can predict the physical rice traits (chaffiness, CRK%, HRR%). We showed the feasibility to predict all three traits with reasonable accuracy (chaffiness: R² = 0.9987, RMSE = 1.302; CRK%: R² = 0.9397, RMSE = 8.91; HRR%: R² = 0.7613, RMSE = 6.83) using X-ray radiography and image-based analytics via PCA based prediction models on individual grains.</p><p><strong>Conclusions: </strong>Our study demonstrated the feasibility to predict multiple key physical grain traits important in rice research and breeding (such as chaffiness, CRK%, and HRR%) from single 2D X-ray images of whole paddy grains. Such a non-destructive rice grain trait inference is expected to improve the robustness of paddy rice evaluation, as well as to reduce time and possibly costs for rice grain trait analysis. Furthermore, the described approach can also be transferred and adapted to other grain crops.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"94"},"PeriodicalIF":4.7,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12243194/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144601224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optical coherence tomography for early detection of crop infection.","authors":"Ghada Salem Sasi, Stephen J Matcher, Adrien Alexis Paul Chauvet","doi":"10.1186/s13007-025-01411-7","DOIUrl":"10.1186/s13007-025-01411-7","url":null,"abstract":"<p><strong>Background: </strong>Fungal diseases are among the most significant threats to global crop production, often leading to substantial yield losses. Early detection of crop infection by fungus is the very first step to deploying a timely and effective treatment. Early and reliable detection is thus key to improving yields, sustainability, and achieving food security. Conventional diagnostic methods are however often destructive, slow, or requiring visible symptoms which appear late in the infection process. To overcome these challenges, we propose using optical coherence tomography (OCT) as an innovative imaging tool to provide cross-sectional and three-dimensional images of the plant internal microstructure non-invasively, in vivo, and in real-time.</p><p><strong>Results: </strong>We demonstrate the use of low-cost OCT to monitoring wheat (cultivar AxC 169) when infected by Septoria tritici. We show that OCT analysis can effectively detect signs of infection before any external symptoms appear. Although OCT cannot directly visualize fungal hyphae, OCT reveals apparent morphological changes of the mesophyll where the fungal filaments are expected to develop. This study thus focuses on monitoring and correlating changes within the mesophyll structural organisation with the state of infection. It results in distinct statistical difference between intact and infected wheat plants two days only after infection. We then demonstrate the use of machine learning (ML) for high throughput segmentation of OCT scans, providing a foundation for future automated fungus-detection analysis.</p><p><strong>Conclusions: </strong>This work highlights the potential of OCT, combined with ML tools, to enable rapid, non-invasive, and early diagnosis of crop fungal infections, opening new avenues for precision agriculture and sustainable disease management.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"92"},"PeriodicalIF":4.7,"publicationDate":"2025-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12232840/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144576109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel method for measuring heat injury in leaves provides insights into the sequence of processes of heat injury development.","authors":"Clara Bertel, Gilbert Neuner","doi":"10.1186/s13007-025-01404-6","DOIUrl":"10.1186/s13007-025-01404-6","url":null,"abstract":"<p><strong>Background: </strong>Global warming is currently occurring at a rapid rate and is having a particularly severe impact on plants, which, as sessile organisms, have a limited ability to escape high temperatures. This requires a better understanding of the thermal limits for different plant species and a better understanding of the processes involved in the development of heat injury in plant leaves. Heat injury results from multiple processes and occurs at the molecular level, involving increased membrane fluidity, lipid peroxidation, and protein aggregation and denaturation.</p><p><strong>Results: </strong>We have tested whether the DSC method allows the detection of heat-induced denaturation and aggregation of molecules in intact leaves. During controlled heating a consistent and repeatable pattern was observed in the DSC plot, from which critical heat thresholds could be derived. These critical temperatures were in good agreement with the temperatures determined using classical methods and also clearly mark the thermal limits of molecular structures. The advantage of the DCS method is the precise, rapid and easy detection of heat thresholds. Finally, taken all thresholds together, we can draw a better image of the sequence of events associated with heat injury in plant leaves: heat injury begins with membrane leakage and continues with protein denaturation and aggregation at high (sublethal, lethal) temperatures.</p><p><strong>Conclusion: </strong>Since heat injury results from multiple processes, a holistic understanding requires the acquisition of parameters indicative of different processes. The presented DSC method, which allows the detection of denaturation and aggregation of cellular compounds, therefore complements well the classical methods that reflect photosynthetic impairment and whole leaf tissue damage. The new simple and rapid method requires only a minimal amount of leaf material and allows rapid collection of data on damaging temperatures for different plants, which is particularly important in the face of rapidly progressing climatic changes.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"89"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12210732/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144541891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attomolar electrochemical direct and sandwich immunoassays for the ultrasensitive detection of tomato brown rugose fruit virus.","authors":"Negin Rezaei, Ahmad Moshaii, Mohammad Reza Safarnejad, Reza H Sajedi, Mahsa Rahmanipour, Masoud Shams-Bakhsh","doi":"10.1186/s13007-025-01407-3","DOIUrl":"10.1186/s13007-025-01407-3","url":null,"abstract":"","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"91"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12211782/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144541892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Whole mount multiplexed visualization of DNA, mRNA, and protein in plant-parasitic nematodes.","authors":"Alexis L Sperling, Sebastian Eves-van den Akker","doi":"10.1186/s13007-025-01409-1","DOIUrl":"10.1186/s13007-025-01409-1","url":null,"abstract":"","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"90"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12210477/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144541893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"YOLOV8-CMS: a high-accuracy deep learning model for automated citrus leaf disease classification and grading.","authors":"Hongyan Zhu, Dani Wang, Yuzhen Wei, Pengcheng Wang, Min Su","doi":"10.1186/s13007-025-01396-3","DOIUrl":"10.1186/s13007-025-01396-3","url":null,"abstract":"<p><strong>Background: </strong>Citrus leaf diseases significantly affect production efficiency and fruit quality in the citrus industry. To effectively identify and classify citrus leaf diseases, this study proposed a classification approach leveraging deep learning techniques (YOLOV8 equipped with CSPPC, MultiDimen, SpatialConv, YOLOV8-CMS). Additionally, a segmentation method was utilized to extract leaf and lesion areas for disease severity grading based on their pixel ratio.</p><p><strong>Results: </strong>By collecting and preprocessing a citrus leaf image dataset, the YOLOV8-CMS model was trained for disease classification. The model integrated MultiDimen attention, SpatialConv, and the CSPPC module to enhance performance. Furthermore, a segmentation approach was applied to precisely segment both leaf and lesion areas, enabling a quantitative assessment of disease severity. To verify the effectiveness of the proposed approach, multiple YOLO-based architectures, including different YOLOV8 series models, YOLOV5, and YOLOV3, were compared and analyzed. Results demonstrated that the proposed method achieved outstanding performance in citrus leaf disease classification, with an mAP50 of 98.2% in distinguishing healthy and diseased leaves and an accuracy of 97.9% in multi-class disease classification tasks.</p><p><strong>Conclusions: </strong>The proposed YOLOV8-CMS model outperformed traditional methods in citrus leaf disease classification, while the segmentation-based approach enabled an accurate and quantitative assessment of disease severity. These findings highlighted the potential of deep learning in precision agriculture, contributing to more effective disease management in citrus production.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"88"},"PeriodicalIF":4.7,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12186444/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144476345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SeedSeg: image-based transgenic seed counting for segregation analysis of T-DNA loci.","authors":"Santiago Hernández, Vivian Zhong, Jennifer A N Brophy","doi":"10.1186/s13007-025-01406-4","DOIUrl":"10.1186/s13007-025-01406-4","url":null,"abstract":"<p><strong>Background: </strong>Transgenic plants are essential for both basic and applied plant biology. Recently, fluorescent and colorimetric markers were developed to enable nondestructive identification of transformed seeds and accelerate the generation of transgenic plant lines. Yet, transformation often results in the integration of multiple copies of transgenes in the plant genome. Multiple transgene copies can lead to transgene silencing and complicate the analysis of transgenic plants by requiring researcher to track multiple T-DNA loci in future generations. Thus, to simplify analysis of transgenic lines, plant researchers typically screen transformed plants for lines where the T-DNA inserted in a single locus - an analysis that involves laborious manual counting of fluorescent and non-fluorescent seeds for screenable markers.</p><p><strong>Results: </strong>To expedite T-DNA segregation analysis, we developed SeedSeg, an image analysis tool that uses a segmentation algorithm to count the number of transformed and wild-type seeds in an image. SeedSeg runs a chi-squared test to determine the number of T-DNA loci. Parameters can be adjusted to optimize for different brightness intensities and seed sizes.</p><p><strong>Conclusions: </strong>By automating the seed counting process, SeedSeg reduces the manual labor associated with identifying transgenic lines containing a single T-DNA locus. SeedSeg is adaptable to different seed sizes and visual transgene markers, making it a versatile tool for accelerating plant research.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"87"},"PeriodicalIF":4.7,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12186423/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144476344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}