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Designing and developing new biostimulants is a crucial process which requires an accurate testing of the product effects on the morpho-physiological traits of plants and a deep understanding of the mechanism of action of selected products. Product screening approaches using omics technologies have been found to be more efficient and cost effective in finding new biostimulant substances. A screening protocol based on the use of high-throughput phenotyping platform for screening new vegetal-derived protein hydrolysates (PHs) for biostimulant activity followed by a metabolomic analysis to elucidate the mechanism of the most active PHs has been applied on tomato crop. Eight PHs (A-G, I) derived from enzymatic hydrolysis of seed proteins of Leguminosae and Brassicaceae species were foliarly sprayed twice during the trial. A non-ionic surfactant Triton X-100 at 0.1% was also added to the solutions before spraying. A control treatment foliarly sprayed with distilled water containing 0.1% Triton X-100 was also included. Untreated and PH-treated tomato plants were monitored regularly using high-throughput non-invasive imaging technologies. The phenotyping approach we used is based on automated integrative analysis of photosynthetic performance, growth analysis, and color index analysis. The digital biomass of the plants sprayed with PH was generally increased. In particular, the relative growth rate and the growth performance were significantly improved by PHs A and I, respectively, compared to the untreated control plants. Kinetic chlorophyll fluorescence imaging did not allow to differentiate the photosynthetic performance of treated and untreated plants. Finally, MS-based untargeted metabolomics analysis was performed in order to characterize the functional mechanisms of selected PHs. The treatment modulated the multi-layer regulation process that involved the ethylene precursor and polyamines and affected the ROS-mediated signaling pathways. Although further investigation is needed to strengthen our findings, metabolomic data suggest that treated plants experienced a metabolic reprogramming following the application of the tested biostimulants. Nonetheless, our experimental data highlight the potential for combined use of high-throughput phenotyping and metabolomics to facilitate the screening of new substances with biostimulant properties and to provide a morpho-physiological and metabolomic gateway to the mechanisms underlying PHs action on plants.
Automated analysis of small and optically variable plant organs, such as grain spikes, is highly demanded in quantitative plant science and breeding. Previous works primarily focused on the detection of prominently visible spikes emerging on the top of the grain plants growing in field conditions. However, accurate and automated analysis of all fully and partially visible spikes in greenhouse images renders a more challenging task, which was rarely addressed in the past. A particular difficulty for image analysis is represented by leaf-covered, occluded but also matured spikes of bushy crop cultivars that can hardly be differentiated from the remaining plant biomass. To address the challenge of automated analysis of arbitrary spike phenotypes in different grain crops and optical setups, here, we performed a comparative investigation of six neural network methods for pattern detection and segmentation in RGB images, including five deep and one shallow neural network. Our experimental results demonstrate that advanced deep learning methods show superior performance, achieving over 90% accuracy by detection and segmentation of spikes in wheat, barley and rye images. However, spike detection in new crop phenotypes can be performed more accurately than segmentation. Furthermore, the detection and segmentation of matured, partially visible and occluded spikes, for which phenotypes substantially deviate from the training set of regular spikes, still represent a challenge to neural network models trained on a limited set of a few hundreds of manually labeled ground truth images. Limitations and further potential improvements of the presented algorithmic frameworks for spike image analysis are discussed. Besides theoretical and experimental investigations, we provide a GUI-based tool (SpikeApp), which shows the application of pre-trained neural networks to fully automate spike detection, segmentation and phenotyping in images of greenhouse-grown plants.
Fluorescence microscopy images of biological samples contain valuable information but require rigorous analysis for accurate and reliable determination of changes in protein localization, fluorescence intensity, and morphology of the studied objects. Traditionally, cells for microscopy are immobilized using chemicals, which can introduce stress. Analysis often focuses only on colocalization and involves manual segmentation and measurement, which are time-consuming and can introduce bias. Our new workflow addresses these issues by gently immobilizing cells using a small agarose block on a microscope coverslip. This approach is suitable for cell-walled cells (yeast, fungi, plants, bacteria), facilitates their live imaging under conditions close to their natural environment and enables the addition of chemicals during time-lapse experiments. The primary focus of the protocol is on the presented analysis workflow, which is applicable to virtually any cell type-we describe cell segmentation using the Cellpose software followed by automated analysis of a multitude of parameters using custom-written Fiji (ImageJ) macros. The results can be easily processed using the provided R markdown scripts or available graphing software. Our method facilitates unbiased batch analysis of large datasets, improving the efficiency and accuracy of fluorescence microscopy research. The reported sample preparation protocol and Fiji macros were used in our recent publications: Microbiol Spectr (2022), DOI: 10.1128/spectrum.01961-22; Microbiol Spectr (2022), DOI: 10.1128/spectrum.02489-22; J Cell Sci (2023), DOI: 10.1242/jcs.260554.
- Klíčová slova
- Cellpose, Fiji, ImageJ, automated, blinded, cells, data analysis, microscopy, unbiased, yeast,
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
BACKGROUND: Recently emerging approaches to high-throughput plant phenotyping have discovered their importance as tools in unravelling the complex questions of plant growth, development and response to the environment, both in basic and applied science. High-throughput methods have been also used to study plant responses to various types of biotic and abiotic stresses (drought, heat, salinity, nutrient-starving, UV light) but only rarely to cold tolerance. RESULTS: We present here an experimental procedure of integrative high-throughput in-house phenotyping of plant shoots employing automated simultaneous analyses of shoot biomass and photosystem II efficiency to study the cold tolerance of pea (Pisum sativum L.). For this purpose, we developed new software for automatic RGB image analysis, evaluated various parameters of chlorophyll fluorescence obtained from kinetic chlorophyll fluorescence imaging, and performed an experiment in which the growth and photosynthetic activity of two different pea cultivars were followed during cold acclimation. The data obtained from the automated RGB imaging were validated through correlation of pixel based shoot area with measurement of the shoot fresh weight. Further, data obtained from automated chlorophyll fluorescence imaging analysis were compared with chlorophyll fluorescence parameters measured by a non-imaging chlorophyll fluorometer. In both cases, high correlation was obtained, confirming the reliability of the procedure described. CONCLUSIONS: This study of the response of two pea cultivars to cold stress confirmed that our procedure may have important application, not only for selection of cold-sensitive/tolerant varieties of pea, but also for studies of plant cold-response strategies in general. The approach, provides a very broad tool for the morphological and physiological selection of parameters which correspond to shoot growth and the efficiency of photosystem II, and is thus applicable in studies of various plant species and crops.
- Klíčová slova
- Biomass production, Chlorophyll fluorescence imaging, Cold adaptation, Pea (Pisum), Plant phenotyping, RGB digital imaging, Shoot growth,
- Publikační typ
- časopisecké články MeSH
High-throughput plant phenotyping platforms provide new possibilities for automated, fast scoring of several plant growth and development traits, followed over time using non-invasive sensors. Using Arabidopsis as a model offers important advantages for high-throughput screening with the opportunity to extrapolate the results obtained to other crops of commercial interest. In this study we describe the development of a highly reproducible high-throughput Arabidopsis in vitro bioassay established using our OloPhen platform, suitable for analysis of rosette growth in multi-well plates. This method was successfully validated on example of multivariate analysis of Arabidopsis rosette growth in different salt concentrations and the interaction with varying nutritional composition of the growth medium. Several traits such as changes in the rosette area, relative growth rate, survival rate and homogeneity of the population are scored using fully automated RGB imaging and subsequent image analysis. The assay can be used for fast screening of the biological activity of chemical libraries, phenotypes of transgenic or recombinant inbred lines, or to search for potential quantitative trait loci. It is especially valuable for selecting genotypes or growth conditions that improve plant stress tolerance.
- Klíčová slova
- Arabidopsis, high-throughput screening assay, multi-well plates, rosette growth, stress conditions,
- Publikační typ
- časopisecké články MeSH
Reproducible and efficient high-throughput phenotyping approaches, combined with advances in genome sequencing, are facilitating the discovery of genes affecting plant performance. Salinity tolerance is a desirable trait that can be achieved through breeding, where most have aimed at selecting for plants that perform effective ion exclusion from the shoots. To determine overall plant performance under salt stress, it is helpful to investigate several plant traits collectively in one experimental setup. Hence, we developed a quantitative phenotyping protocol using a high-throughput phenotyping system, with RGB and chlorophyll fluorescence (ChlF) imaging, which captures the growth, morphology, color and photosynthetic performance of Arabidopsis thaliana plants in response to salt stress. We optimized our salt treatment by controlling the soil-water content prior to introducing salt stress. We investigated these traits over time in two accessions in soil at 150, 100, or 50 mM NaCl to find that the plants subjected to 100 mM NaCl showed the most prominent responses in the absence of symptoms of severe stress. In these plants, salt stress induced significant changes in rosette area and morphology, but less prominent changes in rosette coloring and photosystem II efficiency. Clustering of ChlF traits with plant growth of nine accessions maintained at 100 mM NaCl revealed that in the early stage of salt stress, salinity tolerance correlated with non-photochemical quenching processes and during the later stage, plant performance correlated with quantum yield. This integrative approach allows the simultaneous analysis of several phenotypic traits. In combination with various genetic resources, the phenotyping protocol described here is expected to increase our understanding of plant performance and stress responses, ultimately identifying genes that improve plant performance in salt stress conditions.
Current methods of in-house plant phenotyping are providing a powerful new tool for plant biology studies. The self-constructed and commercial platforms established in the last few years, employ non-destructive methods and measurements on a large and high-throughput scale. The platforms offer to certain extent, automated measurements, using either simple single sensor analysis, or advanced integrative simultaneous analysis by multiple sensors. However, due to the complexity of the approaches used, it is not always clear what such forms of plant phenotyping can offer the potential end-user, i.e. plant biologist. This review focuses on imaging methods used in the phenotyping of plant shoots including a brief survey of the sensors used. To open up this topic to a broader audience, we provide here a simple introduction to the principles of automated non-destructive analysis, namely RGB, chlorophyll fluorescence, thermal and hyperspectral imaging. We further on present an overview on how and to which extent, the automated integrative in-house phenotyping platforms have been used recently to study the responses of plants to various changing environments.
- Klíčová slova
- Biomass production, Chlorophyll fluorescence imaging, Hyperspectral imaging, Plant phenotyping, RGB digital imaging, Shoot growth, Thermal imaging,
- Publikační typ
- časopisecké články MeSH
BACKGROUND: Non-invasive and high-throughput monitoring of drought in plants from its initiation to visible symptoms is essential to quest drought tolerant varieties. Among the existing methods, chlorophyll a fluorescence (ChlF) imaging has the potential to probe systematic changes in photosynthetic reactions; however, prerequisite of dark-adaptation limits its use for high-throughput screening. RESULTS: To improve the throughput monitoring of plants, we have exploited their light-adaptive strategy, and investigated possibilities of measuring ChlF transients under low ambient irradiance. We found that the ChlF transients and associated parameters of two contrasting Arabidopsis thaliana accessions, Rsch and Co, give almost similar information, when measured either after ~20 min dark-adaptation or in the presence of half of the adaptive growth-irradiance. The fluorescence parameters, effective quantum yield of PSII photochemistry (ΦPSII) and fluorescence decrease ratio (RFD) resulting from this approach enabled us to differentiate accessions that is often not possible by well-established dark-adapted fluorescence parameter maximum quantum efficiency of PSII photochemistry (FV/FM). Further, we screened ChlF transients in rosettes of well-watered and drought-stressed six A. thaliana accessions, under half of the adaptive growth-irradiance, without any prior dark-adaptation. Relative water content (RWC) in leaves was also assayed and compared to the ChlF parameters. As expected, the RWC was significantly different in drought-stressed from that in well-watered plants in all the six investigated accessions on day-10 of induced drought; the maximum reduction in the RWC was obtained for Rsch (16%), whereas the minimum reduction was for Co (~7%). Drought induced changes were reflected in several features of ChlF transients; combinatorial images obtained from pattern recognition algorithms, trained on pixels of image sequence, improved the contrast among drought-stressed accessions, and the derived images were well-correlated with their RWC. CONCLUSIONS: We demonstrate here that ChlF transients and associated parameters measured even in the presence of low ambient irradiance preserved its features comparable to that of measured after dark-adaptation and discriminated the accessions having differential geographical origin; further, in combination with combinatorial image analysis tools, these data may be readily employed for early sensing and mapping effects of drought on plant's physiology via easy and fully non-invasive means.
- Klíčová slova
- Chlorophyll fluorescence transients, Drought, Natural accessions, Non-invasive methods, Plant phenotyping, Whole plant rosettes,
- Publikační typ
- časopisecké články MeSH
High-throughput root phenotyping in the soil became an indispensable quantitative tool for the assessment of effects of climatic factors and molecular perturbation on plant root morphology, development and function. To efficiently analyse a large amount of structurally complex soil-root images advanced methods for automated image segmentation are required. Due to often unavoidable overlap between the intensity of fore- and background regions simple thresholding methods are, generally, not suitable for the segmentation of root regions. Higher-level cognitive models such as convolutional neural networks (CNN) provide capabilities for segmenting roots from heterogeneous and noisy background structures, however, they require a representative set of manually segmented (ground truth) images. Here, we present a GUI-based tool for fully automated quantitative analysis of root images using a pre-trained CNN model, which relies on an extension of the U-Net architecture. The developed CNN framework was designed to efficiently segment root structures of different size, shape and optical contrast using low budget hardware systems. The CNN model was trained on a set of 6465 masks derived from 182 manually segmented near-infrared (NIR) maize root images. Our experimental results show that the proposed approach achieves a Dice coefficient of 0.87 and outperforms existing tools (e.g., SegRoot) with Dice coefficient of 0.67 by application not only to NIR but also to other imaging modalities and plant species such as barley and arabidopsis soil-root images from LED-rhizotron and UV imaging systems, respectively. In summary, the developed software framework enables users to efficiently analyse soil-root images in an automated manner (i.e. without manual interaction with data and/or parameter tuning) providing quantitative plant scientists with a powerful analytical tool.
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
BACKGROUND: With increasing pollution, herbicide application and interest in plant phenotyping, sensors capturing early responses to toxic stress are demanded for screening susceptible or resistant plant varieties. Standard toxicity tests on plants are laborious, demanding in terms of space and material, and the measurement of growth-inhibition based endpoints takes relatively long time. The aim of this work was to explore the potential of photoautotrophic cell suspension cultures for high-throughput early toxicity screening based on imaging techniques. The investigation of the universal potential of fluorescence imaging methods involved testing of three toxicants with different modes of action (DCMU, glyphosate and chromium). RESULTS: The increased pace of testing was achieved by using non-destructive imaging methods-multicolor fluorescence (MCF) and chlorophyll fluorescence (ChlF). These methods detected the negative effects of the toxicants earlier than it was reflected in plant growth inhibition (decrease in leaf area and final dry weight). Moreover, more subtle and transient effects not resulting in growth inhibition could be detected by fluorescence. The pace and sensitivity of stress detection was further enhanced by using photoautotrophic cell suspension cultures. These reacted sooner, more pronouncedly and to lower concentrations of the tested toxicants than the plants. Toxicant-specific stress signatures were observed as a combination of MCF and ChlF parameters and timing of the response. Principal component analysis was found to be useful for reduction of the collected multidimensional data sets to a few informative parameters allowing comparison of the toxicant signatures. CONCLUSIONS: Photoautotrophic cell suspension cultures have proved to be useful for rapid high-throughput screening of toxic stress and display a potential for employment as an alternative to tests on whole plants. The MCF and ChlF methods are capable of distinguishing early stress signatures of at least three different modes of action.
- Klíčová slova
- Abiotic stress, Chlorophyll fluorescence, Chromium, DCMU, Fv/Fm, Glyphosate, Herbicide, Imaging, Multicolor fluorescence, Phenotyping,
- Publikační typ
- časopisecké články MeSH
