Plant Methods最新文献

Background: The root-knot nematode Meloidogyne incognita, is a highly destructive parasite that manipulates host plant processes through effector proteins, affecting agriculture globally. Despite advances in genomic and transcriptomic studies, the regulatory mechanisms controlling effector gene expression, especially at the chromatin level, are still poorly understood. Gene regulation studies in plant-parasitic nematodes (PPN) face several challenges, including the absence of transformation systems and technical barriers in chromatin preparation, particularly for transcription factors (TFs) expressed in secretory gland cells. Conventional methods like Chromatin Immunoprecipitation (ChIP) are limited in PPN due to low chromatin yields, the impermeability of nematode cuticles, and difficulties in producing antibodies for low-abundance TFs. These issues call for alternative approaches, such as dCas9-based CAPTURE (CRISPR Affinity Purification in siTU of Regulatory Elements) that allows studying chromatin interactions by using a catalytically inactive dCas9 protein to target specific genomic loci without relying on antibodies.

Results: This study presents an optimized in vitro dCas9-based CAPTURE for second stage juvenile (J2) M. incognita that addresses key challenges in chromatin extraction and stability. The protocol focuses on the promoter region of the 6F06 effector gene, a critical gene for parasitism. Several optimizations were made, including improvements in nematode disruption, chromatin extraction, and protein-DNA complex stability. This method successfully isolated chromatin-protein complexes and identified four putative chromatin-associated proteins, including BANF1, linked to chromatin remodelling complexes like SWI/SN.

Conclusion: The optimized in vitro dCas9-based CAPTURE protocol offers a new tool for investigating chromatin dynamics and regulatory proteins in non-transformable nematodes. This method expands the scope of effector gene regulation research and provides new insights into M. incognita parasitism. Future research will aim to validate these regulatory proteins and extend the method to other effector loci, potentially guiding the development of novel nematode control strategies.

Background: Single-cell transcriptomics is a powerful approach to resolve cellular heterogeneity, yet its application in plants is constrained by challenges in tissue preparation, nuclei isolation, and transcriptome quality. Optimized experimental and computational workflows are essential to achieve robust results in plant systems.

Results: We systematically benchmarked bulk and single-cell transcriptomic workflows in maize and established an integrated, optimized framework. First, we developed an improved bulk RNA-seq protocol, providing higher consistency and serving as a reference for single-cell datasets. Second, we compared three input types, protoplasts, fresh nuclei, and frozen nuclei, across tissues, demonstrating overall comparability of their transcriptomic profiles and offering guidance for studies with limited material. Third, by leveraging bulk RNA-seq as a reference, these complementary data provide additional biological context that helps to interpret and validate findings derived from single-cell transcriptomic analyses. A combination of these strategies resulted in high transcriptome integrity and clear clustering resolution in the final dataset, supporting robust identification of plant cell types. While all experimental data are derived from maize, the principles and strategies described here provide practical guidance and inspiration for single-cell studies in other plant species.

Conclusions: Our study establishes optimized experimental and computational workflows for plant single-cell transcriptomics. By validating input comparability and addressing the limitations of nuclear data, we provide methodological guidance that extends beyond maize and supports future single-cell investigations across diverse plant species.

Background: In Shandong Province of China, where annual precipitation is below 800 mm, tea plants face persistent drought stress exacerbated by global warming. Breeding drought-tolerant tea cultivars is one of the effective ways to cope with this challenge. However, traditional breeding approaches are still limited by prolonged cycles, low efficiency, and subjective evaluation. To overcome these limitations, the development of rapid and objective germplasm evaluation methods has become critical‌.

Results: In this study, hyperspectral images of leaves from 12 widely cultivated 'Lucha series' tea cultivars in Shandong Province during different drought periods were collected, and the drought-related physiological indicators were measured simultaneously. Then, a tea drought tolerance index (TDTI) with enhanced accuracy was established by integrating the rate of change of indicators with temporal weights and indicator weights. Subsequently, we developed a novel lightweight Transformer-based hybrid integrated architecture to establish prediction models for the physiological indicators and TDTI. The Transformer-based models synergistically combined a Transformer encoder with XGBoost and LightGBM within a lightweight framework that leverages ensemble learning, data augmentation, and regularization to ensure robustness on limited datasets. Finally, we compared the performance of Transformer-based models against traditional machine learning models.​ The optimal models for MRP, MDA, Pro, SS, ChlT and TDTI were identified as 1D-CARS-TF, 2D-UVE-SVM, 2D-UVE-BRR, 2D-CARS-SVM, 1D-UVE-TF-CNN, and 2D-UVE-TF, respectively, achieving determination coefficient (R²) of 0.8992, 0.8307, 0.8929, 0.8373, 0.7894, and 0.7614, on an independent test set.​ The results demonstrated that the lightweight Transformer-based models equipped with multi-head self-attention mechanism exhibited outstanding capabilities in processing indicators requiring multi-band correlation mining. ​​Simultaneously, feature selection algorithms and overfitting-mitigation optimization strategies played a critical role in enhancing both the accuracy and stability of the Transformer-based models.​.

Conclusions: This study established a robust technical foundation for rapid, accurate, and non-destructive comprehensive evaluation of drought tolerance for tea plant germplasm resources. However, it should be noted that they were based on a specific set of greenhouse-cultivated samples, and further validation under field conditions with expanded germplasm resources would strengthen generalizability. Anyway, the demonstrated potential of the Transformer-based model in our study advances phenomics of tea plants toward greater intelligence and efficiency.

Bakanae is a fungal rice disease that is threatening global rice production, causing severe yield losses. The plant microbiome plays a significant role in plant stress resistance, but its high-dimensional characteristics have not been fully exploited. Therefore, we integrated the microbiome and machine learning (ML) to diagnose bakanae disease in this study. We found significant correlations between Gammaproteobacteria and Bacteroidia and the severity of bakanae disease. We constructed different diagnosis models based on random forests (RF), support vector machines (SVM), and convolutional neural networks (CNN) on 88 biological replicates with an independent test set. We found that the RF model demonstrated strong performance across four taxonomic levels, with an accuracy of 88.9% and an F1 score of 94.1%. Notably, a Bray-Curtis dissimilarity-based extraction method was proposed to rapidly screen practical information from the original microbial community, which can enhance the model performance to a certain extent. According to phenotypic data, the disease severity of infected samples was classified into two levels (high and low infected levels) using the K-means clustering method. In the diagnosis of infection severity based on the family level, the model's prediction accuracy reached 77.8%. Collectively, these findings highlight that the combination of microbiome with ML can advance diagnostic strategies for bakanae disease, providing new avenues for precision agriculture.

Genomic selection (GS) in plant breeding aims to identify individuals with superior genetic merit while maintaining genetic diversity within populations. In plant breeding, considering multiple traits simultaneously makes optimizing selection complex, especially under genetic relatedness constraints. In this study, we propose a binary quadratic programming framework for constructing a multi-trait selection index that maximizes genetic gain while minimizing average pairwise relatedness appropriate for identifying superior candidates for advancement in the breeding pipeline. The approach combines estimated breeding values (EBVs) across multiple traits by applying trait-specific economic weights, while simultaneously accounting for coancestry through the genomic relationship matrix. By formulating the selection problem as a constrained Quadratic Programing Multi-trait Selection Index (QPMSI), our method enables the identification of a fixed number of candidate individuals that jointly optimize selection index values and control genetic relatedness. We evaluated the performance of the proposed method using five real genomic datasets and demonstrated that it provides a more effective balance between selection response and control of genetic relatedness than the Linear Programming Multi-trait Selection Index (LPMSI). In particular, the QPMSI consistently outperformed the LPMSI in terms of the MV metric (gain-to-degree of relatedness ratio), achieving improvements of at least 53.8%. This framework offers a practical and computationally efficient tool for sustainable breeding strategies in multi-trait selection contexts.

Timely and precise insect pest detection is critical in areas with high agricultural intensity and climates that favor continuous pest activity. Traditional pest identification methods, such as manual inspection or expert guided analysis, are labor intensive and time consuming. These approaches lack scalability and hinder timely intervention, particularly in resource-constrained settings. Furthermore, the high visual similarity between pest species and intra-species variability across developmental stages further challenge detection efforts in real-world agricultural conditions. To address these limitations, we propose Attention-PestNet, a novel one-stage object detection network designed for insect pest detection. Our method consist of two key attention-based modules to enhance feature extraction and improve detection performance. First, the Hierarchical Scaled Dot-Product Attention module leverages a multi-level attention mechanism to capture salient pest features at different scales. Second, the Multi-Scale Spatial Attention module refines spatial feature representations by incorporating horizontal and vertical attention pathways with multi-scale max-pooling operation to enhance contextual understanding. Extensive experiments were conducted on two public benchmarks, IP102 and R2000 datasets, which represent agricultural conditions in Asia. The results demonstrate that Attention-PestNet outperforms state-of-the-art models in both visualization outputs and quantitative metrics. Attention-PestNet shows strong potential as a scalable and cost-effective solution for intelligent pest monitoring in modern precision agriculture. Our code and data for this paper are made available at: https://github.com/thinhdoanvu/HSDPA .

Peanut leaf diseases have a major impact on peanut yield and quality. Timely, rapid, and accurate early diagnosis and control of peanut leaf diseases are key to ensuring high quality and yield of peanuts. This work focuses on the early diagnosis of peanut diseases and pests and conducts systematic research on the hardware system for imaging and spectral sensing of peanut plant leaves, as well as the software for deep learning classification algorithms. First, we designed a system that can separately obtain multispectral reflectance and fluorescence images and collect multispectral images of three asymptomatic peanut leaf diseases, including scab, scorch spot, and anthracnose. Second, we constructed a convolutional neural network to extract the basic features of spectral images. Third, an adaptive channel attention mechanism is introduced to update the weights of different channels. Fourth, a sparse second-order attention mechanism driving network is constructed to enhance the discriminative ability of deep feature information. Finally, the classification is completed utilizing the Softmax classifier. The experimental results demonstrate that the spectral image information improves the robustness of deep learning models to data transformation and achieves a high-precision classification score of 98.45% for asymptomatic peanut leaf diseases. Compared to traditional optical devices and software algorithms, the proposed multispectral imaging system and deep learning algorithm significantly improve detection ability and classification accuracy, which can assist botanists in making more accurate diagnoses of peanut leaf diseases.

Background: Plant extracellular vesicles (EVs), hold significant therapeutic potential due to their roles in intercellular communication and cross-kingdom regulation, primarily mediated by their microRNA (miRNA) cargo. However, isolating high-purity plant EVs from complex plant tissues, such as the tuberous roots of Ophiopogon japonicus, is challenging due to the dense cell wall matrix and high content of contaminants like polysaccharides. Existing isolation methods, including differential ultracentrifugation (DUC) and density gradient ultracentrifugation (DGUC), involve trade-offs between yield, purity, and vesicle integrity, necessitating the development of optimized protocols.

Results: We developed and systematically optimized an integrated protocol for isolating high-purity EVs from O. japonicus roots. Key optimizations included: (1) refining the DUC protocol by incorporating a double ultracentrifugation step; (2) implementing a modified DGUC approach with a pre-clearing step for superior debris removal; and (3) evaluating enzymatic pre-treatment with cellulase and pectinase to enhance EVs release. Comparative analysis demonstrated that the optimized method, particularly utilizing enzymatic pre-processing and double ultracentrifugation, significantly improved plant EVs yield and purity. Small RNA (sRNA) sequencing of the resulting high-purity EVs successfully characterized their functional miRNA cargo profile, validating the efficacy of the isolation strategy.

Conclusions: This study establishes a robust and adaptable pipeline for isolating high-quality, functionally intact plant EVs from challenging plant root tissues. The optimized protocol effectively addresses the critical methodological challenges of yield and purity, enabling reliable downstream functional characterization and advancing therapeutic investigations of plant-derived EVs.

Background: Extraction of plant genomic DNA is a critical step for PCR-based genotyping, mapping, and breeding applications. Conventional CTAB protocols and commercial kits provide reliable DNA but are labour-intensive, costly, and generate substantial plastic waste. Simplified crude-extract methods are available, yet their performance is often compromised by PCR inhibition from salts and cellular debris. A rapid, low-cost, and high-throughput method is therefore needed for routine molecular applications.

Results: We developed a single-tube DNA extraction protocol that eliminates supernatant transfers, thereby reducing handling errors, plastic consumption, and processing time. The method consistently produces DNA of sufficient yield and purity for PCR-based assays. Validation in wheat and wheat-wild relative introgression lines demonstrated robust amplification in KASP assays. Cross-species testing in maize, Arabidopsis, and tomato using two Tris-salt extraction buffers confirmed broad applicability, supported by NanoDrop and Qubit measurements. Freeze-dried and frozen tissue produced higher yields than fresh samples, confirming their suitability for high-throughput and large-scale studies.

Conclusions: This streamlined protocol provides a cost-effective, reliable, and scalable approach for extracting plant genomic DNA suitable for PCR-based genotyping, marker development, and diversity analysis. Its simplicity and throughput make it particularly valuable for breeding programmes, although it is not intended for applications requiring highly pure DNA, such as whole-genome resequencing.