Plant Methods最新文献

{"title":"Bioluminescent imaging of an oomycete pathogen empowers chemical selections and rational fungicide applications.","authors":"Han Chen, Jiana Mao, Yujie Fang, Waqas Raza, Zhi Li, Chongyuan Zhang, Yingguang Zhu, Yuanchao Wang, Suomeng Dong","doi":"10.1186/s13007-025-01374-9","DOIUrl":"https://doi.org/10.1186/s13007-025-01374-9","url":null,"abstract":"<p><p>Fungicides play an indispensable role in ensuring food security. However, rational chemical selection and fungicide precision application guidance remain constrained by the limitations in real-time monitoring of tracking pathogens within plant tissues. In the current study, we generated a genetically stable Phytophthora infestans strain (PiLuc) expressing luciferase gene, which serves as a dual-mode quantification platform for both in vitro and in vivo throughput screening. Consequently, we designed a 96-well plate high-throughput screening system to assess compounds inhibitory efficacy using PiLuc. Crucially, bioluminescence imaging enabled visualization of PiLuc in potato leaves and tubers during early infection stage, which is invisible to the naked eye. Capitalizing on the semi non-destructive and visual advantages, we developed a system for fungicide bioavailability evaluation and dosage-response assessment in tuber tissues, integrating real-time dynamic monitoring of pathogen. The development of bioluminescent imaging of late blight pathogen establishes an enabling platform for high-throughput fungicide screening while improving the precision bioavailability assessments.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"57"},"PeriodicalIF":4.7,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12060300/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144001209","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":"Phenotype microarray-based assessment of metabolic variability in plant protoplasts.","authors":"Alice Checcucci, Francesca Decorosi, Giulia Alfreducci, Roberto Natale, Agnese Bellabarba, Stefano Biricolti, Donatella Paffetti, Alessio Mengoni, Carlo Viti","doi":"10.1186/s13007-025-01378-5","DOIUrl":"https://doi.org/10.1186/s13007-025-01378-5","url":null,"abstract":"<p><strong>Background: </strong>Productivity and fitness of cultivated plants are influenced by genetic heritage and environmental interactions, shaping certain phenotypes. Phenomics is the -omics methodology providing applicative approaches for the analysis of multidimensional phenotypic information, essential to understand and foresee the genetic potential of organisms relevant to agriculture. While plant phenotyping provides information at the whole organism level, cellular level phenotyping is crucial for identifying and dissecting the metabolic basis of different phenotypes and the effect of metabolic-related genetic modifications. Phenotype Microarray (PM) is a high-throughput technology developed by Biolog^™ for metabolic characterization studies at cellular level, which is based on colorimetric reactions to monitor cellular respiration under different conditions. Nowadays, PM is widely used for bacteria, fungi, and mammalian cells, but a procedure for plant cells characterization has not yet been developed, due to difficulties linked in identifying a suitable reporter of cell activities.</p><p><strong>Results: </strong>Here, we tested for the first time, PM technology on plant cells using protoplasts as a means of evaluating metabolic activity. Indeed, studying the metabolism of plant protoplasts can be a valuable method for predicting the inherent metabolic potential of an entire plant organism. Protoplasts are indeed valuable tools in plant research and biotechnology because they offer a simplified, isolated cellular system where researchers can focus on intracellular processes without interference from the cell wall. As a proof-of-principle, we used protoplasts of Solanum tuberosum L. as model system. Protoplasts were isolated from leaf tissue of in vitro-grown plants, purified and then diluted until desired concentration. Microplates were inoculated with protoplasts suspension and various markers of redox potential as indicators of cell activity were tested. After identifying the optimal conditions for PM testing, metabolic tests were extended to protoplasts from S. lycopersicum L., evaluating plant response to different NaCl concentrations and some of the toxic compounds present in pre-configured Biolog^™ microplates.</p><p><strong>Conclusions: </strong>The standardized high-throughput system developed was effective for the metabolic characterization of plant protoplasts. This method lays the foundation for plant cell metabolic phenotype studies enabling comparative studies at cellular level among cultivars, species, wild-type organisms, and genome-edited plants.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"58"},"PeriodicalIF":4.7,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12060552/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144006272","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":"Novel cultivation techniques for water lily (Nymphaea micrantha Guill. & Perr) production based on in vitro technology.","authors":"Fei Lin, Yong Kang, Arwa Abdulkreem Al-Huqail, Dikhnah Alshehri, Yamei Li, Yuhua Guo, Guangsui Yang, Fenling Tang, Junmei Yin, Zheli Ding, Mamdouh A Eissa","doi":"10.1186/s13007-025-01377-6","DOIUrl":"https://doi.org/10.1186/s13007-025-01377-6","url":null,"abstract":"<p><p>Water lily (Nymphaea micrantha Guill. & Perr) is an aquatic plant that is well known for its nutritional value and medicinal uses. The lack of adequate information regarding propagation and farming techniques has led to the low utilization of this valuable plant. To address the knowledge gap in the use of water lily rhizomes as explants in tissue culture, in this study, the processes of sterilization, induction, proliferation, and rooting in water lily tissue culture are examined. Laboratory experiments were conducted to determine the best methods, materials, and concentrations to develop an ideal method for producing water lily plants. Disinfection with 75% C₂H₅OH for 2 min + 0.1% HgCl₂ for 15 min produced the best results, with a contamination rate of 30% and a browning rate of 25%, according to the data. The results indicated that indole-3-butyric acid (IBA) is the optimal plant growth regulator for the induction of water lily rhizomes. Medium containing 3 mg L^-1 of 6-BA was most suitable for the induction of water lily adventitious shoots, with an induction rate of up to 80% and a yield of 2 to 8 shoots. The induction rate of water lily adventitious shoots approached 80% in medium supplemented with 3 mg L^-1 6-benzylaminopurine (6-BA). The best medium for inducing root development contained IBA at a concentration of 0.5 mg L^-1, which resulted in rapid root elongation. The best tissue culture techniques identified in the present study were successful in growing full water lily plants with good and vigorous growth from tuberous rhizomes to flowering plants. This early success in water lily tissue culture technology provides crucial technical assistance for ex vitro preservation and water lily seedling growth. This study offers a workable answer to one of the most significant obstacles preventing the spread of the use of water lily culture by outlining a good technique that results in robust and healthy seedlings, which helps lower the cost of cultivation.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"56"},"PeriodicalIF":4.7,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12042527/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144015658","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":"BioCurve Analyzer: a web-based shiny app for analyzing biological response curves.","authors":"Zenan Xing, James Eckhardt, Aditya S Vaidya, Sean R Cutler","doi":"10.1186/s13007-025-01372-x","DOIUrl":"https://doi.org/10.1186/s13007-025-01372-x","url":null,"abstract":"<p><strong>Background: </strong>Dose-response and time-to-event data are common in enzymology, pharmacology, and agronomy studies. Diverse biological response curves can be generated from such data. The features of these curves can be elucidated through parameters such as ED₅₀ (the effective dose that gives 50% of the maximum response) and T₅₀ (the time required to reach 50% of the maximum response). Properly estimating these parameters is crucial for inferring the potency of compounds or the relative timings of biological processes.</p><p><strong>Results: </strong>We present an open-source Shiny application, BioCurve Analyzer, that simplifies the process of inferring ED₅₀ and T₅₀ parameters from response curves exhibiting various patterns, including classic monotonic sigmoidal curves and more complicated biphasic curves. BioCurve Analyzer provides access to several packages and commonly used models for characterizing response patterns, assists users in identifying the models that best describe their data, and includes options for inferring ED₅₀ values on both sides of biphasic curves. BioCurve Analyzer also facilitates the visualization of response patterns and allows users to customize their final graphical representation to deliver publication-quality graphs of the data.</p><p><strong>Conclusion: </strong>BioCurve Analyzer integrates multiple R packages in an easy-to-use web-based interface to facilitate dose-response and time-to-event analyses.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"55"},"PeriodicalIF":4.7,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12036208/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144039252","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":"DM_CorrMatch: a semi-supervised semantic segmentation framework for rapeseed flower coverage estimation using UAV imagery.","authors":"Jie Li, Chengyong Zhu, Chenbo Yang, Quan Zheng, Binhui Wang, Jingmin Tu, Qian Zhang, Sheng Liu, Xinfa Wang, Jiangwei Qiao","doi":"10.1186/s13007-025-01373-w","DOIUrl":"https://doi.org/10.1186/s13007-025-01373-w","url":null,"abstract":"<p><p>Rapeseed (Brassica napus L.) inflorescence coverage is a crucial phenotypic parameter for assessing crop growth and estimating yield. Accurate crop cover assessment is typically performed using Unmanned Aerial Vehicles (UAVs) in combination with semantic segmentation methods. However, the irregular and variable morphology of rapeseed inflorescences presents significant challenges in segmentation. To address these challenges, advanced methods that can improve segmentation accuracy, particularly under limited data conditions, are needed. In this study, we propose a cost-effective and high-throughput approach using a semi-supervised learning framework, DM_CorrMatch. This method enhances input images through strong and weak data augmentation techniques, while leveraging the Denoising Diffusion Probabilistic Model (DDPM) to generate additional samples in data-scarce scenarios. We propose an automatic update strategy for labeled data to dilute the proportion of erroneous labels in manual segmentation. Furthermore, a novel network architecture, Mamba-Deeplabv3+, is proposed, combining the strengths of Mamba and Convolutional Neural Networks (CNNs) for both global and local feature extraction. This architecture effectively captures key inflorescence features, even under varying poses, while reducing the influence of complex backgrounds. The proposed method is validated on the Rapeseed Flower Segmentation Dataset (RFSD), which consists of 720 UAV images from the Yangluo experimental station of the Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences (CAAS). The experimental results showed that our method outperforms four traditional segmentation methods and eleven deep learning methods, achieving an Intersection over Union (IoU) of 0.886, Precision of 0.942, and Recall of 0.940. The proposed semi-supervised learning-based method, combined with the Mamba-Deeplabv3+ architecture, demonstrates superior performance in accurately segmenting rapeseed inflorescences under challenging conditions. Our approach effectively handles complex backgrounds and various poses of inflorescences, providing a reliable tool for rapeseed flower cover estimation. This method can aid in the development of high-yield cultivars and improve crop monitoring through UAV-based technologies.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"54"},"PeriodicalIF":4.7,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12032800/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144020988","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":"PlantCareNet: an advanced system to recognize plant diseases with dual-mode recommendations for prevention.","authors":"Muhaiminul Islam, Akm Azad, Shifat E Arman, Salem A Alyami, Md Mehedi Hasan","doi":"10.1186/s13007-025-01366-9","DOIUrl":"https://doi.org/10.1186/s13007-025-01366-9","url":null,"abstract":"<p><p>Plant diseases adversely affect the agricultural sector by substantially affecting food security and limiting production. We introduce PlantCareNet, a novel, automated, end-to-end diagnostic system for plant diseases that can also offer interactive guidance to users. The system utilizes a dual mode strategy that integrates advanced deep learning algorithms for precise disease diagnosis with a knowledge-based framework guided by experts for preventive measures. The proposed architecture utilizes a convolutional neural network (CNN) to examine images of plant leaves, with the final block flattened and subsequently forwarded to Dense-100 and ultimately Dense-35 for the precise classification of various plant diseases. Subsequently, PlantCareNet promptly offers two types of recommendations: automated suggestions based on identified symptoms and expert-guided advice for personalized treatment. Both categories of recommendations are accessible immediately. The experimental findings indicate that PlantCareNet can accurately diagnose diseases in five well-known datasets, with an accuracy between 82% and 97%, outperforming notable models like Inception and ResNet in most cases. The overall approach demonstrates advancement by surpassing lightweight CNN models with 97% precision and an average inference time of 0.0021 s, hence offering farmers precise and quick actions for remedy. This study emphasises a novel blend of artificial intelligence-driven recognition and expert consultation, which contributes to the advancement of sustainable agriculture practices.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"52"},"PeriodicalIF":4.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12016399/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144038344","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":"Pest detection in dynamic environments: an adaptive continual test-time domain adaptation strategy.","authors":"Rui Fu, Shiyu Wang, Mingqiu Dong, Hao Sun, Mohammed Abdulhakim Al-Absi, Kaijie Zhang, Qian Chen, Liqun Xiao, Xuewei Wang, Ye Li","doi":"10.1186/s13007-025-01371-y","DOIUrl":"https://doi.org/10.1186/s13007-025-01371-y","url":null,"abstract":"<p><p>Pest management is essential for agricultural production and food security, as pests can cause significant crop losses and economic impact. Early pest detection is key to timely intervention. While object detection models perform well on various datasets, they assume i.i.d. data, which is often not the case in diverse real-world environments, leading to decreased accuracy. To solve the problem, we propose the CrossDomain-PestDetect (CDPD) method, which is based on the YOLOv9 model and incorporates a test-time adaptation (TTA) framework. CDPD includes Dynamic Data Augmentation (DynamicDA), a Dynamic Adaptive Gate (DAG), and a Multi-Task Dynamic Adaptation Model (MT-DAM). Our DynamicDA enhances images for each batch by combining strong and weak augmentations. The MT-DAM integrates an object detection model with an image segmentation model, exchanging information through feature fusion at the feature extraction layer. During testing, test-time adaptation updates both models, continuing feature fusion during forward propagation. DAG adaptively controls the degree of feature fusion to improve detection capabilities. Self-supervised learning enables the model to adapt during testing to changing environments. Experiments show that without test-time adaptation, our method achieved a 7.6% increase in mAP50 over the baseline in the original environment and a 16.1% increase in the target environment. Finally, with test-time adaptation, the mAP50 score in the unseen target environment reaches 73.8%, which is a significant improvement over the baseline.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"53"},"PeriodicalIF":4.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12020128/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144018393","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":"PFLO: a high-throughput pose estimation model for field maize based on YOLO architecture.","authors":"Yuchen Pan, Jianye Chang, Zhemeng Dong, Bingwen Liu, Li Wang, Hailin Liu, Jue Ruan","doi":"10.1186/s13007-025-01369-6","DOIUrl":"https://doi.org/10.1186/s13007-025-01369-6","url":null,"abstract":"<p><p>Posture is a critical phenotypic trait that reflects crop growth and serves as an essential indicator for both agricultural production and scientific research. Accurate pose estimation enables real-time tracking of crop growth processes, but in field environments, challenges such as variable backgrounds, dense planting, occlusions, and morphological changes hinder precise posture analysis. To address these challenges, we propose PFLO (Pose Estimation Model of Field Maize Based on YOLO Architecture), an end-to-end model for maize pose estimation, coupled with a novel data processing method to generate bounding boxes and pose skeleton data from a\"keypoint-line\"annotated phenotypic database which could mitigate the effects of uneven manual annotations and biases. PFLO also incorporates advanced architectural enhancements to optimize feature extraction and selection, enabling robust performance in complex conditions such as dense arrangements and severe occlusions. On a fivefold validation set of 1,862 images, PFLO achieved 72.2% pose estimation mean average precision (mAP50) and 91.6% object detection mean average precision (mAP50), outperforming current state-of-the-art models. The model demonstrates improved detection of occluded, edge, and small targets, accurately reconstructing skeletal poses of maize crops. PFLO provides a powerful tool for real-time phenotypic analysis, advancing automated crop monitoring in precision agriculture.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"51"},"PeriodicalIF":4.7,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11998432/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144009833","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":"Advancing radiation-induced mutant screening through high-throughput technology: a preliminary evaluation of mutant screening in Arabidopsis thaliana.","authors":"Zhe Li, Jinhu Mu, Yan Du, Xiao Liu, Lixia Yu, Jianing Ding, Jing Long, Jingmin Chen, Libin Zhou","doi":"10.1186/s13007-025-01367-8","DOIUrl":"https://doi.org/10.1186/s13007-025-01367-8","url":null,"abstract":"<p><p>Identifying mutant traits is essential for improving crop yield, quality, and stress resistance in plant breeding. Historically, the efficiency of breeding has been constrained by throughput and accuracy. Recent significant advancements have been made through the development of automated, high-accuracy, and high-throughput equipment. However, challenges remain in the post-processing of large-scale image data and its practical application and evaluation in breeding. This study presents a comparative analysis of human and machine recognition, with validation of a randomly selected mutant at the physiological level performed on wild-type Arabidopsis thaliana and a candidate mutant of the M₃ generation, which was generated through mutagenesis with heavy ion beams (HIBs) and ⁶⁰Co-γ radiation. The mutant populations were subjected to image acquisition and automated screening using the High-throughput Plant Imaging System (HTPIS), generating approximately 10 GB of data (4,635 image datasets). We performed Principal Components Analysis (PCA), scatter matrix clustering, and Logistic Growth Curve (LGC) analyses, and compared these results with those obtained from traditional manual screening based on human visual assessment, and randomly selected #197 candidate mutants for validation in terms of growth and development, chlorophyll fluorescence, and subcellular structure. Our findings demonstrate that as the confidence interval level increases from 75 to 99.9%, the accuracy of machine-based mutant identification decreases from 1 to 0.446, while the false positive rate decreases from 0.817 to 0.118, and the false negative rate increases from 0 to 0.554. Nevertheless, machine-based screening remains more accurate and efficient than human assessment. This study evaluated and validated the efficiency (greater than 80%) of high-throughput techniques for screening mutants in complex populations of radiation-induced progeny, and presented a graphical data processing procedure for high-throughput screening of mutants, providing a basis for breeding techniques utilizing HIBs and γ-ray radiation, and offering innovative approaches and methodologies for radiation-induced breeding in the context of high-throughput big data.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"50"},"PeriodicalIF":4.7,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11998337/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144015685","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":"Citrus phloem specific transcriptional profiling through the development of a citrus tristeza virus expressed translating ribosome affinity purification system.","authors":"James N Culver, Meinhart Vallar, Erik Burchard, Sophie Kamens, Sebastien Lair, Yiping Qi, Tamara D Collum, Christopher Dardick, Choaa A El-Mohtar, Elizabeth E Rogers","doi":"10.1186/s13007-025-01368-7","DOIUrl":"https://doi.org/10.1186/s13007-025-01368-7","url":null,"abstract":"<p><strong>Background: </strong>The analysis of translationally active mRNAs, or translatome, is a useful approach for monitoring cellular and plant physiological responses. One such method is the translating ribosome affinity purification (TRAP) system, which utilizes tagged ribosomal proteins to isolate ribosome-associated transcripts. This approach enables spatial and temporal gene expression analysis by driving the expression of tagged ribosomal proteins with tissue- or development-specific promoters. In plants, TRAP has enhanced our understanding of physiological responses to various biotic and abiotic factors. However, its utility is hampered by the necessity to generate transgenic plants expressing the tagged ribosomal protein, making this approach particularly challenging in perennial crops such as citrus.</p><p><strong>Results: </strong>This study involved the construction of a citrus tristeza virus (CTV) vector to express an immuno-tagged ribosome protein (CTV-hfRPL18). CTV, limited to the phloem, has been used for expressing marker and therapeutic sequences, making it suitable for analyzing citrus vascular tissue responses, including those related to huanglongbing disease. CTV-hfRPL18 successfully expressed a clementine-derived hfRPL18 peptide, and polysome purifications demonstrated enrichment for the hfRPL18 peptide. Subsequent translatome isolations from infected Nicotiana benthamiana and Citrus macrophylla showed enrichment for phloem-associated genes.</p><p><strong>Conclusion: </strong>The CTV-hfRPL18 vector offers a transgene-free and rapid system for TRAP expression and translatome analysis of phloem tissues within citrus.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"49"},"PeriodicalIF":4.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11983876/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144039368","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}