Measuring stomatal and guard cell metrics for plant physiology and growth using StoManager1.

Automated guard cell detection and measurement are vital for understanding plant physiological performance and ecological functioning in global water and carbon cycles. Most current methods for measuring guard cells and stomata are laborious, time-consuming, prone to bias, and limited in scale. We developed StoManager1, a high-throughput tool utilizing geometrical, mathematical algorithms, and convolutional neural networks to automatically detect, count, and measure over 30 guard cell and stomatal metrics, including guard cell and stomatal area, length, width, stomatal aperture area/guard cell area, orientation, stomatal evenness, divergence, and aggregation index. Combined with leaf functional traits, some of these StoManager1-measured guard cell and stomatal metrics explained 90% and 82% of tree biomass and intrinsic water use efficiency (iWUE) variances in hardwoods, making them substantial factors in leaf physiology and tree growth. StoManager1 demonstrated exceptional precision and recall (mAP@0.5 over 0.96), effectively capturing diverse stomatal properties across over 100 species. StoManager1 facilitates the automation of measuring leaf stomatal and guard cells, enabling broader exploration of stomatal control in plant growth and adaptation to environmental stress and climate change. This has implications for global gross primary productivity (GPP) modeling and estimation, as integrating stomatal metrics can enhance predictions of plant growth and resource usage worldwide. Easily accessible open-source code and standalone Windows executable applications are available on a GitHub repository (https://github.com/JiaxinWang123/StoManager1) and Zenodo (https://doi.org/10.5281/zenodo.7686022).

REPRISAL: mapping lignification dynamics using chemistry, data segmentation, and ratiometric analysis.

This article describes a methodology for detailed mapping of the lignification capacity of plant cell walls that we have called "REPRISAL" for REPorter Ratiometrics Integrating Segmentation for Analyzing Lignification. REPRISAL consists of the combination of three separate approaches. In the first approach, H*, G*, and S* monolignol chemical reporters, corresponding to p-coumaryl alcohol, coniferyl alcohol, and sinapyl alcohol, are used to label the growing lignin polymer in a fluorescent triple labeling strategy based on the sequential use of three main bioorthogonal chemical reactions. In the second step, an automatic parametric and/or artificial intelligence segmentation algorithm is developed that assigns fluorescent image pixels to three distinct cell wall zones corresponding to cell corners, compound middle lamella and secondary cell walls. The last step corresponds to the exploitation of a ratiometric approach enabling statistical analyses of differences in monolignol reporter distribution (ratiometric method [RM] 1) and proportions (RM 2) within the different cell wall zones. We first describe the use of this methodology to map developmentally related changes in the lignification capacity of wild-type Arabidopsis (Arabidopsis thaliana) interfascicular fiber cells. We then apply REPRISAL to analyze the Arabidopsis peroxidase (PRX) mutant prx64 and provide further evidence for the implication of the AtPRX64 protein in floral stem lignification. In addition, we also demonstrate the general applicability of REPRISAL by using it to map lignification capacity in poplar (Populus tremula × Populus alba), flax (Linum usitatissimum), and maize (Zea mays). Finally, we show that the methodology can be used to map the incorporation of a fucose reporter into noncellulosic cell wall polymers.

Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for maize QTL mapping.

Stomata are adjustable pores on leaf surfaces that regulate the tradeoff of CO2 uptake with water vapor loss, thus having critical roles in controlling photosynthetic carbon gain and plant water use. The lack of easy, rapid methods for phenotyping epidermal cell traits have limited discoveries about the genetic basis of stomatal patterning. A high-throughput epidermal cell phenotyping pipeline is presented here and used for quantitative trait loci (QTL) mapping in field-grown maize (Zea mays). The locations and sizes of stomatal complexes and pavement cells on images acquired by an optical topometer from mature leaves were automatically determined. Computer estimated stomatal complex density (SCD; R2 = 0.97) and stomatal complex area (SCA; R2 = 0.71) were strongly correlated with human measurements. Leaf gas exchange traits were genetically correlated with the dimensions and proportions of stomatal complexes (rg = 0.39-0.71) but did not correlate with SCD. Heritability of epidermal traits was moderate to high (h2 = 0.42-0.82) across two field seasons. Thirty-six QTL were consistently identified for a given trait in both years. Twenty-four clusters of overlapping QTL for multiple traits were identified, with univariate versus multivariate single marker analysis providing evidence consistent with pleiotropy in multiple cases. Putative orthologs of genes known to regulate stomatal patterning in Arabidopsis (Arabidopsis thaliana) were located within some, but not all, of these regions. This study demonstrates how discovery of the genetic basis for stomatal patterning can be accelerated in maize, a C4 model species where these processes are poorly understood.

GridFree: a python package of imageanalysis for interactive grain counting and measuring.

Grain characteristics, including kernel length, kernel width, and thousand kernel weight, are critical component traits for grain yield. Manual measurements and counting are expensive, forming the bottleneck for dissecting these traits' genetic architectures toward ultimate yield improvement. High-throughput phenotyping methods have been developed by analyzing images of kernels. However, segmenting kernels from the image background and noise artifacts or from other kernels positioned in close proximity remain as challenges. In this study, we developed a software package, named GridFree, to overcome these challenges. GridFree uses an unsupervised machine learning approach, K-Means, to segment kernels from the background by using principal component analysis on both raw image channels and their color indices. GridFree incorporates users' experiences as a dynamic criterion to set thresholds for a divide-and-combine strategy that effectively segments adjacent kernels. When adjacent multiple kernels are incorrectly segmented as a single object, they form an outlier on the distribution plot of kernel area, length, and width. GridFree uses the dynamic threshold settings for splitting and merging. In addition to counting, GridFree measures kernel length, width, and area with the option of scaling with a reference object. Evaluations against existing software programs demonstrated that GridFree had the smallest error on counting seeds for multiple crop species. GridFree was implemented in Python with a friendly graphical user interface to allow users to easily visualize the outcomes and make decisions, which ultimately eliminates time-consuming and repetitive manual labor. GridFree is freely available at the GridFree website (https://zzlab.net/GridFree).

Drone-based physiological index reveals long-term acclimation and drought stress responses in trees.

Monitoring early tree physiological responses to drought is key to understanding progressive impacts of drought on forests and identifying resilient species. We combined drone-based multispectral remote sensing with measurements of tree physiology and environmental parameters over two growing seasons in a 100-y-old Pinus sylvestris forest subject to 17-y of precipitation manipulation. Our goal was to determine if drone-based photochemical reflectance index (PRI) captures tree drought stress responses and whether responses are affected by long-term acclimation. PRI detects changes in xanthophyll cycle pigment dynamics, which reflect increases in photoprotective non-photochemical quenching activity resulting from drought-induced photosynthesis downregulation. Here, PRI of never-irrigated trees was up to 10 times lower (higher stress) than PRI of irrigated trees. Long-term acclimation to experimental treatment, however, influenced the seasonal relationship between PRI and soil water availability. PRI also captured diurnal decreases in photochemical efficiency, driven by vapour pressure deficit. Interestingly, 5 years after irrigation was stopped for a subset of the irrigated trees, a positive legacy effect persisted, with lower stress responses (higher PRI) compared with never-irrigated trees. This study demonstrates the ability of remotely sensed PRI to scale tree physiological responses to an entire forest and the importance of long-term acclimation in determining current drought stress responses.

Saving time maintaining reliability: a new method for quantification of Tetranychus urticae damage in Arabidopsis whole rosettes.

BACKGROUND: The model species Tetranychus urticae produces important plant injury and economic losses in the field. The current accepted method for the quantification of the spider mite damage in Arabidopsis whole rosettes is time consuming and entails a bottleneck for large-scale studies such as mutant screening or quantitative genetic analyses. Here, we describe an improved version of the existing method by designing an automatic protocol. The accuracy, precision, reproducibility and concordance of the new enhanced approach are validated in two Arabidopsis accessions with opposite damage phenotypes. Results are compared to the currently available manual method. RESULTS: Image acquisition experiments revealed that the automatic settings plus 10 values of brightness and the black background are the optimal conditions for a specific recognition of spider mite damage by software programs. Among the different tested methods, the Ilastik-Fiji tandem based on machine learning was the best procedure able to quantify the damage maintaining the differential range of damage between accessions. In addition, the Ilastik-Fiji tandem method showed the lowest variability within a set of conditions and the highest stability under different lighting or background surroundings. Bland-Altman concordance results pointed out a negative value for Ilastik-Fiji, which implies a minor estimation of the damage when compared to the manual standard method. CONCLUSIONS: The novel approach using Ilastik and Fiji programs entails a great improvement for the quantification of the specific spider mite damage in Arabidopsis whole rosettes. The automation of the proposed method based on interactive machine learning eliminates the subjectivity and inter-rater-variability of the previous manual protocol. Besides, this method offers a robust tool for time saving and to avoid the damage overestimation observed with other methods.

Hydroprinted Liquid-Alloy-Based Morphing Electronics for Fast-Growing/Tender Plants: From Physiology Monitoring to Habit Manipulation.

Monitoring physiological signals and manipulating growth habits of living plants in real time are important for botany research, biohybrid plant robots, and precision agriculture. Although emerging epidermal electronics that can conveniently acquire vital signals of living organisms exhibit a high potential for such scenarios, it is a significant challenge to adapt such devices for plants, because they are fragile and usually have complex surfaces that can change significantly during rapid growth. A gentle fabrication process is critical in order to employ compliant electronic systems to adapt to this highly dynamic situation. In this study, a hydroprinted liquid-alloy-based morphing electronics (LAME) process is employed for fast-growing plants that will sense physiological signals and even function as a biohybrid to determine plant behavior on demand. Besides various surfaces of inorganic targeting substrates, pinning liquid alloy circuits onto the complex plant epidermis is enhanced by introducing high-surface-energy liquid. Functionally, the new developed LAME can be used to monitor leaf moisture content and length, and manipulate leaf and bean sprout orientation. This study lays the foundation for a new form of morphing electronics for botany or biohybrid plant robots, potentially impacting the next generation of precision agriculture and smart hybrid robots.

An improved centrifuge method for determining water extraction curves and vulnerability curves in the long-vessel species Robinia pseudoacacia.

Significant improvements to the centrifuge water-extraction method of measuring the percentage loss volume of water (PLV) and corresponding vulnerability curves (VCs) are reported. Cochard and Sperry rotors are both incapable of measuring the VCs of species with long vessels because of premature embolism induced by hypothetical nanoparticles that can be drawn into segments during flow measurement. In contrast, water extraction pushes nanoparticles out of the sample. This study focuses on a long-vessel species, Robinia pseudoacacia, for which many VCs have been constructed by different methods, and the daily water relations have been quantified. PLV extraction curves have dual Weibull curves, and this paper focuses on the second Weibull curve because it involves the extraction of water from vessels, as proven by staining methods. We demonstrate an improved water extraction method after evaporation correction that has accuracy to within 0.5%, shows good agreement with two traditional methods that are slower and less accurate, and is immune to nanoparticle artefacts. Using Poiseuille's Law and the geometry of vessels, we argue that the percentage loss of conductivity (PLC) equals 2PLV-PLV2 in a special case where all vessels, regardless of size, have the same vulnerability curve. In this special case, this equation predicts the data reasonably well.

Live confocal imaging of Arabidopsis flower buds.

Recent advances in confocal microscopy, coupled with the development of numerous fluorescent reporters, provide us with a powerful tool to study the development of plants. Live confocal imaging has been used extensively to further our understanding of the mechanisms underlying the formation of roots, shoots and leaves. However, it has not been widely applied to flowers, partly because of specific challenges associated with the imaging of flower buds. Here, we describe how to prepare and grow shoot apices of Arabidopsis in vitro, to perform both single-point and time-lapse imaging of live, developing flower buds with either an upright or an inverted confocal microscope.

Advances in methods for identification and characterization of plant transporter function.

Transport proteins are crucial for cellular function at all levels. Numerous importers and exporters facilitate transport of a diverse array of metabolites and ions intra- and intercellularly. Identification of transporter function is essential for understanding biological processes at both the cellular and organismal level. Assignment of a functional role to individual transporter proteins or to identify a transporter with a given substrate specificity has notoriously been challenging. Recently, major advances have been achieved in function-driven screens, phenotype-driven screens, and in silico-based approaches. In this review, we highlight examples that illustrate how new technology and tools have advanced identification and characterization of plant transporter functions.

A portable fluorescence spectroscopy imaging system for automated root phenotyping in soil cores in the field.

Root architecture traits are a target for pre-breeders. Incorporation of root architecture traits into new cultivars requires phenotyping. It is attractive to rapidly and directly phenotype root architecture in the field, avoiding laboratory studies that may not translate to the field. A combination of soil coring with a hydraulic push press and manual core-break counting can directly phenotype root architecture traits of depth and distribution in the field through to grain development, but large teams of people are required and labour costs are high with this method. We developed a portable fluorescence imaging system (BlueBox) to automate root counting in soil cores with image analysis software directly in the field. The lighting system was optimized to produce high-contrast images of roots emerging from soil cores. The correlation of the measurements with the root length density of the soil cores exceeded the correlation achieved by human operator measurements (R (2)=0.68 versus 0.57, respectively). A BlueBox-equipped team processed 4.3 cores/hour/person, compared with 3.7 cores/hour/person for the manual method. The portable, automated in-field root architecture phenotyping system was 16% more labour efficient, 19% more accurate, and 12% cheaper than manual conventional coring, and presents an opportunity to directly phenotype root architecture in the field as part of pre-breeding programs. The platform has wide possibilities to capture more information about root health and other root traits in the field.

A robust, simple, high-throughput technique for time-resolved plant volatile analysis in field experiments.

Plant volatiles (PVs) mediate interactions between plants and arthropods, microbes and other plants, and are involved in responses to abiotic stress. PV emissions are therefore influenced by many environmental factors, including herbivore damage, microbial invasion, and cues from neighboring plants, and also light regime, temperature, humidity and nutrient availability. Thus, an understanding of the physiological and ecological functions of PVs must be based on measurements reflecting PV emissions under natural conditions. However, PVs are usually sampled in the artificial environments of laboratories or climate chambers. Sampling of PVs in natural environments is difficult, being limited by the need to transport, maintain and provide power to instruments, or use expensive sorbent devices in replicate. Ideally, PVs should be measured in natural settings with high replication, spatio-temporal resolution and sensitivity, and modest costs. Polydimethylsiloxane (PDMS), a sorbent commonly used for PV sampling, is available as silicone tubing for as little as 0.60 € m(-1) (versus 100-550 € each for standard PDMS sorbent devices). Small pieces of silicone tubing (STs) of various lengths from millimeters to centimeters may be added to any experimental setting and used for headspace sampling, with little manipulation of the organism or headspace. STs have sufficiently fast absorption kinetics and large capacity to sample plant headspaces over a timescale of minutes to hours, and thus can produce biologically meaningful 'snapshots' of PV blends. When combined with thermal desorption coupled to GC-MS (a 40-year-old widely available technology), use of STs yields reproducible, sensitive, spatio-temporally resolved quantitative data from headspace samples taken in natural environments.

3D Laser Triangulation for Plant Phenotyping in Challenging Environments.

To increase the understanding of how the plant phenotype is formed by genotype and environmental interactions, simple and robust high-throughput plant phenotyping methods should be developed and considered. This would not only broaden the application range of phenotyping in the plant research community, but also increase the ability for researchers to study plants in their natural environments. By studying plants in their natural environment in high temporal resolution, more knowledge on how multiple stresses interact in defining the plant phenotype could lead to a better understanding of the interaction between plant responses and epigenetic regulation. In the present paper, we evaluate a commercial 3D NIR-laser scanner (PlantEye, Phenospex B.V., Herleen, The Netherlands) to track daily changes in plant growth with high precision in challenging environments. Firstly, we demonstrate that the NIR laser beam of the scanner does not affect plant photosynthetic performance. Secondly, we demonstrate that it is possible to estimate phenotypic variation amongst the growth pattern of ten genotypes of Brassica napus L. (rapeseed), using a simple linear correlation between scanned parameters and destructive growth measurements. Our results demonstrate the high potential of 3D laser triangulation for simple measurements of phenotypic variation in challenging environments and in a high temporal resolution.

Reduction of molecular gas diffusion through gaskets in leaf gas exchange cuvettes by leaf-mediated pores.

There is an ongoing debate on how to correct leaf gas exchange measurements for the unavoidable diffusion leakage that occurs when measurements are done in non-ambient CO2 concentrations. In this study, we present a theory on how the CO2 diffusion gradient over the gasket is affected by leaf-mediated pores (LMP) and how LMP reduce diffusive exchange across the gaskets. Recent discussions have so far neglected the processes in the quasi-laminar boundary layer around the gasket. Counter intuitively, LMP reduce the leakage through gaskets, which can be explained by assuming that the boundary layer at the exterior of the cuvette is enriched with air from the inside of the cuvette. The effect can thus be reduced by reducing the boundary layer thickness. The theory clarifies conflicting results from earlier studies. We developed leaf adaptor frames that eliminate LMP during measurements on delicate plant material such as grass leaves with circular cross section, and the effectiveness is shown with respiration measurements on a harp of Deschampsia flexuosa leaves. We conclude that the best solution for measurements with portable photosynthesis systems is to avoid LMP rather than trying to correct for the effects.

Improved clearing method contributes to deep imaging of plant organs.

Tissue clearing methods are increasingly essential for the microscopic observation of internal tissues of thick biological organs. We previously developed TOMEI, a clearing method for plant tissues; however, it could not entirely remove chlorophylls nor reduce the fluorescent signal of fluorescent proteins. Here, we developed an improved TOMEI method (iTOMEI) to overcome these limitations. First, a caprylyl sulfobetaine was determined to efficiently remove chlorophylls from Arabidopsis thaliana seedlings without GFP quenching. Next, a weak alkaline solution restored GFP fluorescence, which was mainly lost during fixation, and an iohexol solution with a high refractive index increased sample transparency. These procedures were integrated to form iTOMEI. iTOMEI enables the detection of much brighter fluorescence than previous methods in tissues of A. thaliana, Oryza sativa, and Marchantia polymorpha. Moreover, a mouse brain was also efficiently cleared by the iTOMEI-Brain method within 48 h, and strong fluorescent signals were detected in the cleared brain.

Proximity labeling: an emerging tool for probing in planta molecular interactions.

Protein-protein interaction (PPI) networks are key to nearly all aspects of cellular activity. Therefore, the identification of PPIs is important for understanding a specific biological process in an organism. Compared with conventional methods for probing PPIs, the recently described proximity labeling (PL) approach combined with mass spectrometry (MS)-based quantitative proteomics has emerged as a powerful approach for characterizing PPIs. However, the application of PL in planta remains in its infancy. Here, we summarize recent progress in PL and its potential utilization in plant biology. We specifically summarize advances in PL, including the development and comparison of different PL enzymes and the application of PL for deciphering various molecular interactions in different organisms with an emphasis on plant systems.

A solid-state nanopore-based single-molecule approach for label-free characterization of plant polysaccharides.

Polysaccharides are important biomacromolecules existing in all plants, most of which are integrated into a fibrillar structure called the cell wall. In the absence of an effective methodology for polysaccharide analysis that arises from compositional heterogeneity and structural flexibility, our knowledge of cell wall architecture and function is greatly constrained. Here, we develop a single-molecule approach for identifying plant polysaccharides with acetylated modification levels. We designed a solid-state nanopore sensor supported by a free-standing SiN x membrane in fluidic cells. This device was able to detect cell wall polysaccharide xylans at concentrations as low as 5 ng/µL and discriminate xylans with hyperacetylated and unacetylated modifications. We further demonstrated the capability of this method in distinguishing arabinoxylan and glucuronoxylan in monocot and dicot plants. Combining the data for categorizing polysaccharide mixtures, our study establishes a single-molecule platform for polysaccharide analysis, opening a new avenue for understanding cell wall structures, and expanding polysaccharide applications.