Targeted and Long-Term Fluorescence Imaging of Plant Cytomembranes Using Main-Chain Charged Polyelectrolytes with Aggregation-Induced Emission

Fluorescent polyelectrolytes have attracted tremendous attention due to their unique properties and wide applications. However, current research objects of fluorescent polyelectrolytes mainly focus on side-chain charged polyelectrolytes, and the applications of polyelectrolytes in plant cytomembrane imaging with long time and high specificity still remain challenging. Herein, long-time and targeted fluorescence imaging of plant cytomembranes was achieved for the first time using main-chain charged polyelectrolytes (MCCPs) with aggregation-induced emission (AIE). A series of MCCPs were designed and synthesized, among which the red-emissive and AIE-active MCCP with a triphenylamine linker and a cyano group around the cationic ring-fused heterocyclic core showed the best fluorescence imaging performance of plant cells. Unlike other MCCPs and its neutral form of polymer, this cyano-substituted conjugated polyelectrolyte can specifically target the cytomembrane of plant cells within a short staining time with many advantages, including wash-free staining, high photostability and imaging integrity, excellent durability (at least 12 h), and low biotoxicity. In addition to onion epidermal cells, this AIE fluorescence probe also shows good imaging capabilities for other kinds of plant cells such as Glycine max and Vigna radiata. Such an AIE-active MCCP-based imaging system provides an effective design strategy to develop fluorescence probes with high specificity and long-term imaging ability toward plant plasma membranes.

Highly Selective and Rapid "Turn-On" Fluorogenic Chemosensor for Detection of Salicylic Acid in Plants and Food Samples

Salicylic acid (SA) is one of the chemical molecules, involved in plant growth and immunity, thereby contributing to the control of pests and pathogens, and even applied in fruit and vegetable preservation. However, only a few tools have ever been designed or executed to understand the physiological processes induced by SA or its function in plant immunity and residue detection in food. Hence, three Rh6G-based fluorogenic chemosensors were synthesized to detect phytohormone SA based on the "OFF-ON" mechanism. The probes showed high selectivity, ultrafast response time (<60 s), and nanomolar detection limit for SA. Moreover, the probe possessed outstanding profiling that can be successfully used for SA imaging of callus and plants. Furthermore, the fluorescence pattern indicated that SA could occur in the distal transport in plants. These remarkable results contribute to improving our understanding of the multiple physiological and pathological processes involved in SA for plant disease diagnosis and for the development of immune activators. In addition, SA detection in some agricultural products used probes to extend the practical application because its use is prohibited in some countries and is harmful to SA-sensitized persons. Interestingly, the as-obtained test paper displayed that SA could be imaged by ultraviolet (UV) and was directly visible to the naked eye. Given the above outcomes, these probes could be used to monitor SA in vitro and in vivo, including, but not limited to, plant biology, food residue detection, and sewage detection.

Phenotyping for waterlogging tolerance in crops: current trends and future prospects

Yield losses to waterlogging are expected to become an increasingly costly and frequent issue in some regions of the world. Despite the extensive work that has been carried out examining the molecular and physiological responses to waterlogging, phenotyping for waterlogging tolerance has proven difficult. This difficulty is largely due to the high variability of waterlogging conditions such as duration, temperature, soil type, and growth stage of the crop. In this review, we highlight use of phenotyping to assess and improve waterlogging tolerance in temperate crop species. We start by outlining the experimental methods that have been utilized to impose waterlogging stress, ranging from highly controlled conditions of hydroponic systems to large-scale screenings in the field. We also describe the phenotyping traits used to assess tolerance ranging from survival rates and visual scoring to precise photosynthetic measurements. Finally, we present an overview of the challenges faced in attempting to improve waterlogging tolerance, the trade-offs associated with phenotyping in controlled conditions, limitations of classic phenotyping methods, and future trends using plant-imaging methods. If effectively utilized to increase crop resilience to changing climates, crop phenotyping has a major role to play in global food security.

Single-Molecule Imaging in Living Plant Cells: A Methodological Review

Single-molecule imaging is emerging as a revolutionary approach to studying fundamental questions in plants. However, compared with its use in animals, the application of single-molecule imaging in plants is still underexplored. Here, we review the applications, advantages, and challenges of single-molecule fluorescence imaging in plant systems from the perspective of methodology. Firstly, we provide a general overview of single-molecule imaging methods and their principles. Next, we summarize the unprecedented quantitative details that can be obtained using single-molecule techniques compared to bulk assays. Finally, we discuss the main problems encountered at this stage and provide possible solutions.

Past and Future of Plant Stress Detection: An Overview From Remote Sensing to Positron Emission Tomography

Plant stress detection is considered one of the most critical areas for the improvement of crop yield in the compelling worldwide scenario, dictated by both the climate change and the geopolitical consequences of the Covid-19 epidemics. A complicated interconnection of biotic and abiotic stressors affect plant growth, including water, salt, temperature, light exposure, nutrients availability, agrochemicals, air and soil pollutants, pests and diseases. In facing this extended panorama, the technology choice is manifold. On the one hand, quantitative methods, such as metabolomics, provide very sensitive indicators of most of the stressors, with the drawback of a disruptive approach, which prevents follow up and dynamical studies. On the other hand qualitative methods, such as fluorescence, thermography and VIS/NIR reflectance, provide a non-disruptive view of the action of the stressors in plants, even across large fields, with the drawback of a poor accuracy. When looking at the spatial scale, the effect of stress may imply modifications from DNA level (nanometers) up to cell (micrometers), full plant (millimeters to meters), and entire field (kilometers). While quantitative techniques are sensitive to the smallest scales, only qualitative approaches can be used for the larger ones. Emerging technologies from nuclear and medical physics, such as computed tomography, magnetic resonance imaging and positron emission tomography, are expected to bridge the gap of quantitative non-disruptive morphologic and functional measurements at larger scale. In this review we analyze the landscape of the different technologies nowadays available, showing the benefits of each approach in plant stress detection, with a particular focus on the gaps, which will be filled in the nearby future by the emerging nuclear physics approaches to agriculture.

Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping

This study aims to test the performances of a low-cost and automatic phenotyping platform, consisting of a Red-Green-Blue (RGB) commercial camera scanning objects on rotating plates and the reconstruction of main plant phenotypic traits via the structure for motion approach (SfM). The precision of this platform was tested in relation to three-dimensional (3D) models generated from images of potted maize, tomato and olive tree, acquired at a different frequency (steps of 4°, 8° and 12°) and quality (4.88, 6.52 and 9.77 µm/pixel). Plant and organs heights, angles and areas were extracted from the 3D models generated for each combination of these factors. Coefficient of determination (R²), relative Root Mean Square Error (rRMSE) and Akaike Information Criterion (AIC) were used as goodness-of-fit indexes to compare the simulated to the observed data. The results indicated that while the best performances in reproducing plant traits were obtained using 90 images at 4.88 µm/pixel (R² = 0.81, rRMSE = 9.49% and AIC = 35.78), this corresponded to an unviable processing time (from 2.46 h to 28.25 h for herbaceous plants and olive trees, respectively). Conversely, 30 images at 4.88 µm/pixel resulted in a good compromise between a reliable reconstruction of considered traits (R² = 0.72, rRMSE = 11.92% and AIC = 42.59) and processing time (from 0.50 h to 2.05 h for herbaceous plants and olive trees, respectively). In any case, the results pointed out that this input combination may vary based on the trait under analysis, which can be more or less demanding in terms of input images and time according to the complexity of its shape (R² = 0.83, rRSME = 10.15% and AIC = 38.78). These findings highlight the reliability of the developed low-cost platform for plant phenotyping, further indicating the best combination of factors to speed up the acquisition and elaboration process, at the same time minimizing the bias between observed and simulated data.

Showcasing the application of synchrotron-based X-ray computed tomography in host-pathogen interactions: The role of wheat rachilla and rachis nodes in Type-II resistance to Fusarium graminearum

Fusarium head blight, caused primarily by Fusarium graminearum (Fg), is one of the most devastating diseases of wheat. Host resistance in wheat is classified into five types (Type-I to Type-V), and a majority of moderately resistant genotypes carry Type-II resistance (resistance to pathogen spread in the rachis) alleles, mainly from the Chinese cultivar Sumai 3. Histopathological studies in the past failed to identify the key tissue in the spike conferring resistance to pathogen spread, and most of the studies used destructive techniques, potentially damaging the tissue(s) under study. In the present study, nondestructive synchrotron-based phase contrast X-ray imaging and computed tomography techniques were used to confirm the part of the wheat spike conferring Type-II resistance to Fg spread, thus showcasing the application of synchrotron-based techniques to image host-pathogen interactions. Seven wheat genotypes of moderate resistance to Fusarium head blight were studied for changes in the void space volume fraction and grayscale/voxel intensity following Fg inoculation. Cell-wall biopolymeric compounds were quantified using Fourier-transform midinfrared spectroscopy for all genotype-treatment combinations. The study revealed that the rachilla and rachis nodes together are structurally important in conferring Type-II resistance. The structural reinforcement was not necessarily observed from lignin deposition but rather from an unknown mechanism.

Un-collimated single-photon imaging system for high-sensitivity small animal and plant imaging

In preclinical single-photon emission computed tomography (SPECT) system development the primary objective has been to improve spatial resolution by using novel parallel-hole or multi-pinhole collimator geometries. However, such high-resolution systems have relatively poor sensitivity (typically 0.01-0.1%). In contrast, a system that does not use collimators can achieve very high-sensitivity. Here we present a high-sensitivity un-collimated detector single-photon imaging (UCD-SPI) system for the imaging of both small animals and plants. This scanner consists of two thin, closely spaced, pixelated scintillator detectors that use NaI(Tl), CsI(Na), or BGO. The performance of the system has been characterized by measuring sensitivity, spatial resolution, linearity, detection limits, and uniformity. With (99m)Tc (140 keV) at the center of the field of view (20 mm scintillator separation), the sensitivity was measured to be 31.8% using the NaI(Tl) detectors and 40.2% with CsI(Na). The best spatial resolution (FWHM when the image formed as the geometric mean of the two detector heads, 20 mm scintillator separation) was 19.0 mm for NaI(Tl) and 11.9 mm for CsI(Na) at 140 keV, and 19.5 mm for BGO at 1116 keV, which is somewhat degraded compared to the cm-scale resolution obtained with only one detector head and a close source. The quantitative accuracy of the system's linearity is better than 2% with detection down to activity levels of 100 nCi. Two in vivo animal studies (a renal scan using (99m)Tc MAG-3 and a thyroid scan with (123)I) and one plant study (a (99m)TcO4(-) xylem transport study) highlight the unique capabilities of this UCD-SPI system. From the renal scan, we observe approximately a one thousand-fold increase in sensitivity compared to the Siemens Inveon SPECT/CT scanner. UCD-SPI is useful for many imaging tasks that do not require excellent spatial resolution, such as high-throughput screening applications, simple radiotracer uptake studies in tumor xenografts, dynamic studies where very good temporal resolution is critical, or in planta imaging of radioisotopes at low concentrations.

Optical sensing method for screening disease in melon seeds by using optical coherence tomography

We report a noble optical sensing method to diagnose seed abnormalities using optical coherence tomography (OCT). Melon seeds infected with Cucumber green mottle mosaic virus (CGMMV) were scanned by OCT. The cross-sectional sensed area of the abnormal seeds showed an additional subsurface layer under the surface which is not found in normal seeds. The presence of CGMMV in the sample was examined by a blind test (n = 140) and compared by the reverse transcription-polymerase chain reaction. The abnormal layers (n = 40) were quantitatively investigated using A-scan sensing analysis and statistical method. By utilizing 3D OCT image reconstruction, we confirmed the distinctive layers on the whole seeds. These results show that OCT with the proposed data processing method can systemically pick up morphological modification induced by viral infection in seeds, and, furthermore, OCT can play an important role in automatic screening of viral infections in seeds.