Plant Methods最新文献

{"title":"A simple hand-sectioning approach for cellular-resolution imaging of anatomy and gene expression in small developing organs and boundary regions in Arabidopsis.","authors":"Jae-Ung Hwang, Seonghwan Kim, Heejeong Son, June M Kwak","doi":"10.1186/s13007-026-01567-w","DOIUrl":"10.1186/s13007-026-01567-w","url":null,"abstract":"<p><strong>Background: </strong>Precise characterization of gene expression patterns across temporal, cellular, and tissue-specific contexts is fundamental to understanding plant development and function. Recent advances in ClearSee-based tissue clearing have enabled high-resolution visualization of internal structures and fluorescent reporter signals in plant tissues. Although hand sectioning can provide optical access to tissues that are not amenable to whole-mount clearing, its application to submillimeter-scale and fragile Arabidopsis organs and tissues, including developing inflorescence apices, flowers, fruits, and organ boundaries, has remained limited. Consequently, analysis of these tissues has largely depended on specialized microdissection techniques and labor-intensive histological workflows, such as wax- or resin-embedded microtomy, which restrict throughput, accessibility, and routine use.</p><p><strong>Results: </strong>We developed and optimized a simple hand-sectioning and imaging method that enables routine visualization of anatomical organization and gene expression patterns at cellular resolution in small, fragile Arabidopsis tissues. This method relies only on gentle manual tissue processing under a stereomicroscope and readily available reagents, allowing reproducible preparation of delicate tissues without the need for embedding or specialized equipment. Combined with ClearSee-based clearing and fluorescent reporters, the approach enables high-resolution imaging of internal tissue architecture and gene expression, while preserving tissue integrity and fluorescence signals that are often compromised during conventional embedding and microtomy procedures.</p><p><strong>Conclusions: </strong>Our method substantially reduces technical complexity, costs, preparation time, and labor associated with cellular-resolution imaging of small, fragile plant tissues. By providing a simple, scalable, and accessible alternative to conventional histological workflows, this approach facilitates routine analysis of internal developmental processes across diverse plant species.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13660029/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148456544","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 high-performance detection model ISA-YOLO for eggplant pests and diseases.","authors":"Haimin Luo, Jiawei Gao, Ruoqi Li, Qiyu Zhong","doi":"10.1186/s13007-026-01566-x","DOIUrl":"10.1186/s13007-026-01566-x","url":null,"abstract":"<p><p>Eggplant (Solanum melongena) is a major cash crop, yet field detection of its pests and diseases remains difficult because disease evidence is simultaneously occluded by foliage, blurred at lesion boundaries, and highly variable in scale. Fruit rot is especially challenging: the waxy epidermis and purple anthocyanin-rich surface reduce chromatic contrast, while infection spreads gradually from water-soaked tissue to necrotic tissue, producing diffuse borders between diseased and healthy regions. In this work, we reframe eggplant disease detection through a context-boundary-scale coupling principle, which states that accurate field detection should jointly model incomplete contextual cues, ambiguous lesion boundaries, and scale-varying symptom morphology rather than optimize these cues independently. ISA-YOLO is proposed as an implementation of this principle on top of YOLOv13 through coordinated context modeling, boundary-aware aggregation, and progressive multi-scale fusion. Experiments on two public datasets show that ISA-YOLO achieves 78.1 and 77.7% mAP at 30.66 and 31.74 FPS, outperforming mainstream detectors in overall trade-off between accuracy and speed. After pruning and quantization, inference speed increases to about 75 FPS while maintaining strong accuracy. These results indicate that the proposed principle provides an effective pathway for accurate and deployable eggplant pest and disease detection in smart agriculture.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13560390/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148456439","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":"Mapping the dynamic plant interactome: from in vitro assays to in vivo quantitative approaches.","authors":"Muhammad Ans Hussain, Ameer Hamza Hafeez, Iqra Noor, Adeena Shakoor, Hammad Hussain, Fatemeh Gholizadeh, Hamza Sohail","doi":"10.1186/s13007-026-01571-0","DOIUrl":"10.1186/s13007-026-01571-0","url":null,"abstract":"<p><strong>Background: </strong>Protein-protein interactions underpin virtually all biological processes in plants, from signal transduction and immune responses to development and stress adaptation. Despite their fundamental importance, the plant interactome remains far from complete, and existing maps are systematically biased by the technical limitations inherent to conventional detection platforms.</p><p><strong>Main body: </strong>This review critically traces the evolution of protein-protein interaction methodologies, from foundational approaches to advance in vivo and quantitative platforms. Classical techniques such as the yeast two-hybrid system and in vitro pull-down assays operate outside physiological cellular environments and are poorly suited to capturing transient or condition-dependent interactions. Affinity purification coupled with mass spectrometry improves throughput but remains vulnerable to artifacts introduced during cell lysis and to the preferential loss of weak interactors. To address these shortcomings, proximity labeling with engineered biotin ligases, most notably the fast-acting variant TurboID, has emerged as a powerful strategy, enabling covalent biotinylation of protein neighborhoods within living cells prior to lysis and thereby preserving associations that conventional methods routinely miss. Because TurboID reports proximity rather than direct binding, its output requires downstream binary validation. Complementary in planta validation tools are equally critical for moving beyond discovery. Split-luciferase complementation assays based on the NanoLuciferase reporter provide exceptional sensitivity for binary interaction detection under native expression conditions, while Förster Resonance Energy Transfer measured through fluorescence lifetime imaging microscopy offers quantitative biophysical evidence of molecular proximity at endogenous expression levels, serving as a high-confidence validation approach. Emerging technologies, including high-throughput protein microarrays and optogenetically controlled dimerization systems, further expand the methodological repertoire available to the plant biology community.</p><p><strong>Conclusion: </strong>We propose a practical, integrative three-tier framework, combining proximity labeling for broad in vivo discovery, split-luciferase complementation for sensitive binary validation, and fluorescence lifetime imaging microscopy for quantitative confirmation, that systematically funnels candidate interactions from initial identification to physiologically rigorous verification. This framework synthesizes established best practices into a structured workflow applicable to mapping dynamic plant interactomes, though its optimal implementation will depend on the biological question, target protein class, and available resources.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13425787/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148431118","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":"RootHairAreaFinder: an image processing method for quantifying barley root growth and root hairs simultaneously in a flat rhizotron system.","authors":"George Themistokleous, Kirsty Binnie, Blair M McKenzie, Sacha J Mooney, Tracy A Valentine","doi":"10.1186/s13007-026-01564-z","DOIUrl":"10.1186/s13007-026-01564-z","url":null,"abstract":"<p><p>Root system architecture and root hairs highly influence plant resource uptake, yet their simultaneous quantification at the whole-plant scale remains challenging due to the conflicting requirements of high-resolution imaging and non-destructive, repeated measurements. Here, we present a plant growth and imaging system based on A3 sized flatbed rhizotrons, combined with a non-machine-learning image analysis workflow implemented in R, that enables repeated, in situ quantification of barley root system architecture and accompanied visible projected root hair area (VP root hair area) over three weeks of growth. The system produces integrated outputs highlighting both whole-root architecture and the spatial distribution of surrounding root hairs from single images. Validation of the image analysis algorithm showed good segmentation performance, with average Matthews Correlation Coefficient values of 0.68 for projected root area and 0.65 for VP root hair area. To demonstrate its experimental applicability, the system was used to assess root growth and root hair responses under controlled environmental conditions, combining three irrigation regimes (2, 4, and 6 irrigation events per day) with three dry bulk density levels (1.4, 1.5, and 1.6 g cm^-3). In addition to whole-system metrics, the approach enables analysis of root hair expansion at individual root tips. This methodology provides a training-free method for integrated analysis of root traits e.g. projected root size, root growth, root diameter and root hairs under controlled physical conditions similar to soil, facilitating studies of root-soil interactions that require both spatial resolution and temporal continuity.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13644045/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148431124","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":"Stomatalia: a deep learning-based platform for quantitative stomata and pavement cell analysis.","authors":"Dan Jeric Arcega Rustia, Marcello Gazale, Micha Gracianna Devi, Gerard van der Linden, Lucia Sandra Perez-Borroto","doi":"10.1186/s13007-026-01568-9","DOIUrl":"10.1186/s13007-026-01568-9","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Stomata and pavement cells are fundamental components of the leaf epidermis, jointly regulating gas exchange, water loss, and leaf surface expansion. Stomata size, aperture, density, and pavement cell morphology are critical parameters for assessing plant transpiration efficiency, epidermal growth dynamics, and adaptive responses to environmental constraints. Despite their biological importance, quantifying stomatal and pavement-cell traits remains seldom not generalized, simple, and fast enough . Manual or semi-automated approaches limit large-scale phenotyping and restrict the integration of epidermal morphology into crop-improvement pipelines aimed at developing climate-resilient varieties with optimised stomatal patterning. To address such limitations, we developed Stomatalia, a deep learning-based platform designed to automate and standardise the quantification of stomatal and pavement cell traits. The algorithm was trained on epidermal images of cultivated and wild potato and tomato genotypes grown under optimal and abiotic-stress conditions. Stomatalia automatically detects stomata and pavement cells and extracts a broad range of morphological and integrative epidermal parameters, enabling high-throughput phenotyping within a unified workflow.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Prior to platform development, we optimised a rapid, minimally-destructive leaf-printing protocol that generates negative impressions of the leaf surface within 40-100 s. Transparent positive prints were subsequently produced and imaged under a light microscope at scale settings ranging from 20 to 200 μm. The resulting images are analysed using Stomatalia's user-friendly web-based interface, which runs an instance-segmentation deep learning algorithm to detect, count, and calculate stomatal and pavement cell parameters. The platform outputs structured files containing raw measurements, derived integrative traits, and associated metadata, facilitating downstream statistical and physiological analyses. Algorithm evaluation on independent datasets demonstrated high performance within the validated dicot imaging domain, with F&lt;sub&gt;1&lt;/sub&gt;-scores ranging from 0.86 to 0.94 depending on image scale, species, and resolution, and high segmentation overlap for both stomata and pavement cells. The generality of stomatal detection was also tested on spring onion, chickpea, balsam poplar, and wheat in cross-species feasibility tests, although performance was more variable in monocots, and pavement-cell segmentation remained species- and architecture-dependent. Benchmarking against another publicly available app further showed that, under the tested web interface settings and image types, Stomatalia exhibited closer agreement with manual counts and substantially faster processing times. The practical performance of Stomatalia was further tested in a proof-of-concept trial with potato plants subjected to optimal irrigation and a long, gradual drought. ","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13621726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148405422","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":"In-bore climate control chamber for magnetic resonance imaging of living plants.","authors":"N Knorr, P Rovedo, J Leupold, D von Elverfeldt, L Hesse, S Bär","doi":"10.1186/s13007-026-01561-2","DOIUrl":"10.1186/s13007-026-01561-2","url":null,"abstract":"<p><p>Magnetic resonance imaging (MRI) enables non-invasive and non-destructive, three-dimensional anatomical and functional imaging of plant tissues and the quantitative investigation of dynamic processes such as water transport. Despite these advantages, MRI remains underutilized in plant and biomimetic research. One major limitation is the difficulty of maintaining physiologically suitable and stable environmental conditions during prolonged measurements, particularly when using ultra-high-field preclinical MRI scanners that were originally developed for small-animal imaging.In this work, we present a low cost, climate-controlled and MR-compatible growth chamber that includes an in-bore extension for preclinical MRI scanners. The system integrates growth and imaging conditions into a single setup, allowing continuous control of temperature, humidity, and illumination by the same system and removing the need to maintain separate commercial growth chambers alongside custom in-bore extensions. The implementation was optimized for the horizontal bore of a small animal scanner (Bruker PharmaScan 70/16) with 16 cm bore diameter and 72 mm free access but is applicable to other ultra-high-field preclinical MRI systems with comparable dimensions.The performance of the climate chamber and the in-bore extension was characterized with respect to temperature, humidity, and illumination stability. In addition, the potential negative impact of the insert and its electronics on the MRI signal (B₀ homogeneity, RF attenuation as well as potential RF artefacts) were verified.Functional validation in form of sap flow measurements as well as anatomical validation was demonstrated in a naturally transpiring stem of Passiflora quadrangularis. Under controlled in-bore environmental conditions, changes in sap flow velocity were reliably detected using a pulsed field gradient spin-echo sequence. Specifically, increasing the light intensity in the extension resulted in a shift of the maximum flow velocity in individual vascular bundles from 0.21 mm/s and 0.39 mm/s to 1.37 mm/s and 1.17 mm/s, respectively. In addition, high-resolution anatomical imaging (1 mm slices with an in-plane resolution of 25 µm) of branching regions in Dracaena braunii was successfully performed without observable motion artifacts. The presented system provides a low-cost, open-source solution for conducting anatomical and functional MRI studies of intact plants using ultra-high field preclinical MRI scanners.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"22 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13339381/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148405381","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":"PlasmiDB: an open-source and customizable database for plasmid lifecycle management in multi-user, multi-project plant molecular biology laboratories.","authors":"Alexandre Soriano, Martine Bes, Anne-Cécile Meunier, Celine Georget, Phonsiri Saengram, Sergi Navarro-Sanz, Aurore Vernet, Murielle Portefaix, Thomas Mauran, Maryline Summo, Gaetan Droc, Christophe Périn","doi":"10.1186/s13007-026-01555-0","DOIUrl":"10.1186/s13007-026-01555-0","url":null,"abstract":"<p><strong>Background: </strong>Functional genomics in plant biology relies on the generation, reuse, and long-term management of large numbers of plasmids produced through diverse cloning strategies. As collections expand across users and projects, laboratories face increasing challenges in organization, traceability, and preservation of construction histories. Existing cloning and sequence-design software supports plasmid design but does not address collaborative, laboratory-wide plasmid management.</p><p><strong>Results: </strong>We developed PlasmiDB, an open-source, web-based database for managing plasmid collections in multi-user research environments. PlasmiDB provides structured storage of plasmid metadata, explicit tracking of plasmid genealogy, and traceability throughout the plasmid life cycle, including inter-laboratory exchanges. The system implements project-based access control and supports collaborative workflows involving staff, students, and core facilities. Implemented using a standard LAMP architecture and deployable via Docker, PlasmiDB is designed for extensibility without modification of the core database schema. A gene module links genetic targets to associated plasmids, primers, and CRISPR reagents, improving coherence between molecular constructs and experimental objectives. In our laboratory, PlasmiDB currently manages nearly 700 plasmids and facilitates reporting through automated declaration file generation.</p><p><strong>Conclusions: </strong>PlasmiDB complements existing cloning tools by providing traceability, collaboration support, and long-term data stewardship for plant molecular biology laboratories. The source code and Docker image are publicly available.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13599298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148369245","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":"Establishment of a protoplast isolation and transient transformation system for tung tree (Vernicia fordii).","authors":"Jie Cao, Lichuan Xiao, Wenying Li, Zhangqi He, Mengyu Jia, Chengbo Yang, Junjie Chen, Yanli Li, Meilan Liu, Lin Zhang","doi":"10.1186/s13007-026-01553-2","DOIUrl":"10.1186/s13007-026-01553-2","url":null,"abstract":"<p><p>Tung tree (Vernicia fordii) serves as an economically important woody oilseed plant. High-quality protoplasts are essential for applications such as somatic hybridization, studying cell signal transduction and rapid gene function analysis. A reliable protocol for protoplast isolation and transient gene expression was developed using petals, anther-derived callus, and root tips as source materials. Through systematic optimization of key parameters such as enzyme combinations, osmotic pressure, digestion duration, and centrifugation speed, we successfully established tissue-specific isolation protocols. To ensure the availability of widely applicable experimental materials, protoplasts isolated from anther-derived callus were used to optimize transient transformation conditions. Under the optimized parameters (20 µg plasmid, 40% PEG4000, and 30-min incubation), the transformation efficiency reached 44.91%. Using this system, we conducted multiple experimental validations, including subcellular localization of VfWOX1.2 protein, detection of its protein expression, analysis of its transcriptional regulation of phytohormone biosynthetic genes, and assessment of stress-responsive gene expression under various treatment conditions. Collectively, these results confirm the utility of this platform for functional gene studies in tung tree. This study establishes a robust technical foundation, which is helpful for overcoming the limitation posed by the absence of a stable genetic transformation system and will facilitate future research on gene function in woody species.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13587368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148361817","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":"Deep aerenchyma: a transformer-based pipeline for scalable phenotyping of rice root aerenchyma lacunae across environments.","authors":"Hani Atef, Laura Fierro-Dominguez, Paula Andrea Lozano-Montaña, Sergi Navarro Sanz, Julie Bals, Benoît Clerget, Christophe Périn, Maria Camila Rebolledo, Romain Fernandez","doi":"10.1186/s13007-026-01546-1","DOIUrl":"10.1186/s13007-026-01546-1","url":null,"abstract":"<p><strong>Background: </strong>Quantification of rice root anatomical traits such as cortical aerenchyma lacunae is key to understanding rice adaptation to diverse water regimes and to support climate-smart breeding. Aerenchyma lacunae contributes to rice internal gas transport and influences methane emissions from flooded systems and can also limit rice water conductivity. It could be an interesting anatomical trait for breeding, however, large-scale anatomical phenotyping remains limited because manual analysis of root cross-sections is labor-intensive, subjective, and difficult to scale across heterogeneous imaging conditions. Existing pipelines often require parameter tuning and do not generalize well across environments.</p><p><strong>Results: </strong>We developed a deep learning pipeline based on a vision transformer architecture to automatically segment rice root cross-sections and quantify cortical aerenchyma lacunae. The model was trained on 1,760 annotated images collected across multiple countries, growth stages, cultivation systems, and experimental contexts, using a collaboratively defined annotation protocol. The final model achieved high segmentation accuracy, with mean intersection over union values exceeding 0.92 for cortical tissues and lacunae. Quantification of the lacuna-to-cortex ratio showed strong agreement with manual annotations, with a coefficient of determination of 0.98 on an independent test set. An independent expert review indicated that model predictions were at least as consistent as manual annotations and reduced large annotation inconsistencies. The pipeline is released as open-source software and includes an interactive online demonstrator, and is accompanied by an online test dataset to support testing and reproducibility. Application across six experimental use cases revealed reproducible differences in aerenchyma lacunae across genotypes, water regimes, environments, and developmental stages.</p><p><strong>Conclusions: </strong>This work provides a robust, scalable, and transferable tool for automated root anatomical phenotyping under heterogeneous experimental conditions. Transformer-based segmentation enables consistent and high-throughput quantification of lacunae, facilitating integration of these anatomical traits into breeding, physiological studies, and climate-smart crop improvement programs.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13595614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148361805","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":"Comparative analysis of SP3 and S-Trap sample preparation protocols for proteomic profiling associated with somatic embryogenesis efficiency in Olea europaea L.","authors":"Rita Pires, Fátima Milhano Santos, Catarina Campos, Lénia Rodrigues, Sergio Ciordia, Augusto Peixe, Hélia Cardoso","doi":"10.1186/s13007-026-01562-1","DOIUrl":"https://doi.org/10.1186/s13007-026-01562-1","url":null,"abstract":"<p><p>Somatic embryogenesis (SE) is an asexual plant propagation technique in which embryo-like structures are generated from somatic cells. It is a powerful tool for clonal propagation, genetic transformation, and germplasm conservation, but not all species respond efficiently to SE, limiting its application. To overcome this recalcitrance, recent studies have investigated the physiological, molecular, and cytological processes associated with SE in different plant species. Proteomics has emerged as a high-throughput approach to investigate global protein dynamics, and it has been increasingly applied to characterise SE-related molecular mechanisms. However, even using these large-scale proteomic strategies, the impact of sample preparation on data quality has never been systematically demonstrated. Here, we compared, for the first time, two sample preparation strategies for the analysis of SE cultures by liquid chromatography-tandem orbitrap mass spectrometry (LC-MS/MS), including (i) single-pot solid-phase-enhanced sample preparation (SP3), performed on an OT-2 automated platform, and (ii) the Suspension Trapping (S-Trap). For this purpose, we analysed the proteome of Olea europaea embryogenic lines with high and low efficiencies, maintained through secondary SE. More than 9,000 protein groups were identified in each method, most of which (7,735 proteins) were shared between workflows, while only a small proportion were uniquely detected by either workflow. Protein quantification was also highly consistent, with 8,454 and 8,365 protein groups quantified for SP3 and S-Trap, respectively, and 7,075 in common. Both workflows achieved comprehensive proteome coverage, with SP3 capturing additional functional categories, such as mitochondrial components, membrane-associated proteins, and vesicle trafficking. S-Trap provided complementary but comparatively more restricted coverage, while both approaches consistently captured key biological processes related to metabolism and intracellular organisation. Overall, the results demonstrate that SP3 and S-Trap workflows provide highly consistent and complementary proteomic coverage, supporting their combined or alternative use for comprehensive proteomic studies of SE.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148353107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}