Characterizing 3D inflorescence architecture in grapevine using X-ray imaging and advanced morphometrics: implications for understanding cluster density

Modern phenomics to empower holistic crop science, agronomy, and breeding research

Crop phenomics enables the collection of diverse plant traits for a large number of samples along different time scales, representing a greater data collection throughput compared with traditional measurements. Most modern crop phenomics use different sensors to collect reflective, emitted, and fluorescence signals, etc., from plant organs at different spatial and temporal resolutions. Such multi-modal, high-dimensional data not only accelerates basic research on crop physiology, genetics, and whole plant systems modeling, but also supports the optimization of field agronomic practices, internal environments of plant factories, and ultimately crop breeding. Major challenges and opportunities facing the current crop phenomics research community include developing community consensus or standards for data collection, management, sharing, and processing, developing capabilities to measure physiological parameters, and enabling farmers and breeders to effectively use phenomics in the field to directly support agricultural production.

Segment Anything for Comprehensive Analysis of Grapevine Cluster Architecture and Berry Properties

Grape cluster architecture and compactness are complex traits influencing disease susceptibility, fruit quality, and yield. Evaluation methods for these traits include visual scoring, manual methodologies, and computer vision, with the latter being the most scalable approach. Most of the existing computer vision approaches for processing cluster images often rely on conventional segmentation or machine learning with extensive training and limited generalization. The Segment Anything Model (SAM), a novel foundation model trained on a massive image dataset, enables automated object segmentation without additional training. This study demonstrates out-of-the-box SAM's high accuracy in identifying individual berries in 2-dimensional (2D) cluster images. Using this model, we managed to segment approximately 3,500 cluster images, generating over 150,000 berry masks, each linked with spatial coordinates within their clusters. The correlation between human-identified berries and SAM predictions was very strong (Pearson's r2 = 0.96). Although the visible berry count in images typically underestimates the actual cluster berry count due to visibility issues, we demonstrated that this discrepancy could be adjusted using a linear regression model (adjusted R 2 = 0.87). We emphasized the critical importance of the angle at which the cluster is imaged, noting its substantial effect on berry counts and architecture. We proposed different approaches in which berry location information facilitated the calculation of complex features related to cluster architecture and compactness. Finally, we discussed SAM's potential integration into currently available pipelines for image generation and processing in vineyard conditions.

Topological data analysis expands the genotype to phenotype map for 3D maize root system architecture

A central goal of biology is to understand how genetic variation produces phenotypic variation, which has been described as a genotype to phenotype (G to P) map. The plant form is continuously shaped by intrinsic developmental and extrinsic environmental inputs, and therefore plant phenomes are highly multivariate and require comprehensive approaches to fully quantify. Yet a common assumption in plant phenotyping efforts is that a few pre-selected measurements can adequately describe the relevant phenome space. Our poor understanding of the genetic basis of root system architecture is at least partially a result of this incongruence. Root systems are complex 3D structures that are most often studied as 2D representations measured with relatively simple univariate traits. In prior work, we showed that persistent homology, a topological data analysis method that does not pre-suppose the salient features of the data, could expand the phenotypic trait space and identify new G to P relations from a commonly used 2D root phenotyping platform. Here we extend the work to entire 3D root system architectures of maize seedlings from a mapping population that was designed to understand the genetic basis of maize-nitrogen relations. Using a panel of 84 univariate traits, persistent homology methods developed for 3D branching, and multivariate vectors of the collective trait space, we found that each method captures distinct information about root system variation as evidenced by the majority of non-overlapping QTL, and hence that root phenotypic trait space is not easily exhausted. The work offers a data-driven method for assessing 3D root structure and highlights the importance of non-canonical phenotypes for more accurate representations of the G to P map.

Tapping into the plasticity of plant architecture for increased stress resilience

Plant architecture develops post-embryonically and emerges from a dialogue between the developmental signals and environmental cues. Length and branching of the vegetative and reproductive tissues were the focus of improvement of plant performance from the early days of plant breeding. Current breeding priorities are changing, as we need to prioritize plant productivity under increasingly challenging environmental conditions. While it has been widely recognized that plant architecture changes in response to the environment, its contribution to plant productivity in the changing climate remains to be fully explored. This review will summarize prior discoveries of genetic control of plant architecture traits and their effect on plant performance under environmental stress. We review new tools in phenotyping that will guide future discoveries of genes contributing to plant architecture, its plasticity, and its contributions to stress resilience. Subsequently, we provide a perspective into how integrating the study of new species, modern phenotyping techniques, and modeling can lead to discovering new genetic targets underlying the plasticity of plant architecture and stress resilience. Altogether, this review provides a new perspective on the plasticity of plant architecture and how it can be harnessed for increased performance under environmental stress.

Genetic analysis of grapevine root system architecture and loci associated gene networks

Own-rooted grapevines and grapevine rootstocks are vegetatively propagated from cuttings and have an adventitious root system. Unraveling the genetic underpinnings of the adventitious root system architecture (RSA) is important for improving own-rooted and grafted grapevine sustainability for a changing climate. Grapevine RSA genetic analysis was conducted in an Vitis sp. 'VRS-F2' population. Nine root morphology, three total root system morphology, and two biomass traits that contribute to root anchorage and water and nutrient uptake were phenotyped. Quantitative trait loci (QTL) analysis was performed using a high density integrated GBS and rhAmpSeq genetic map. Thirty-one QTL were detected for eleven of the RSA traits (surface area, root volume, total root length, fresh weight, number of tips, forks or links, longest root and average root diameter, link length, and link surface area) revealing many small effects. Several QTL were colocated on chromosomes 1, 9, 13, 18, and 19. QTL with identical peak positions on chromosomes 1 or 13 were enriched for AP2-EREBP, AS2, C2C2-CO, HMG, and MYB transcription factors, and QTL on chromosomes 9 or 13 were enriched for the ALFIN-LIKE transcription factor and regulation of autophagy pathways. QTL modeling for individual root traits identified eight models explaining 13.2 to 31.8% of the phenotypic variation. 'Seyval blanc' was the grandparent contributing to the allele models that included a greater surface area, total root length, and branching (number of forks and links) traits promoting a greater root density. In contrast, V. riparia 'Manitoba 37' contributed the allele for greater average branch length (link length) and diameter, promoting a less dense elongated root system with thicker roots. LATERAL ORGAN BOUNDARY DOMAIN (LBD or AS2/LOB) and the PROTODERMAL FACTOR (PFD2 and ANL2) were identified as important candidate genes in the enriched pathways underlying the hotspots for grapevine adventitious RSA. The combined QTL hotspot and trait modeling identified transcription factors, cell cycle and circadian rhythm genes with a known role in root cell and epidermal layer differentiation, lateral root development and cortex thickness. These genes are candidates for tailoring grapevine root system texture, density and length in breeding programs.

Phenotyping Complex Plant Structures with a Large Format Industrial Scale High-Resolution X-Ray Tomography Instrument

Phenotyping specific plant traits is difficult when the samples to be measured are architecturally complex. Inflorescence and root system traits are of great biological interest, but these structures present unique phenotyping challenges due to their often complicated and three-dimensional (3D) forms. We describe how a large industrial scale X-ray tomography (XRT) instrument can be used to scan architecturally complex plant structures for the goal of rapid and accurate measurement of traits that are otherwise cumbersome or not possible to capture by other means. The combination of a large imaging cabinet that can accommodate a wide range of sample size geometries and a variable microfocus reflection X-ray source allows noninvasive X-ray imaging and 3D volume generation of diverse sample types. Specific sample fixturing (mounting) and scanning conditions are presented. These techniques can be moderate to high throughput and still provide unprecedented levels of accuracy and information content in the 3D volume data they generate.

Quantitative dissection of color patterning in the foliar ornamental coleus

Coleus (Coleus scutellarioides) is a popular ornamental plant that exhibits a diverse array of foliar color patterns. New cultivars are currently hand selected by both amateur and experienced plant breeders. In this study, we reimagine breeding for color patterning using a quantitative color analysis framework. Despite impressive advances in high-throughput data collection and processing, complex color patterns remain challenging to extract from image datasets. Using a phenotyping approach called "ColourQuant," we extract and analyze pigmentation patterns from one of the largest coleus breeding populations in the world. Working with this massive dataset, we can analyze quantitative relationships between maternal plants and their progeny, identify features that underlie breeder-selections, and collect and compare public input on trait preferences. This study is one of the most comprehensive explorations into complex color patterning in plant biology and provides insights and tools for exploring the color pallet of the plant kingdom.

The Potential of Grape Pomace Varieties as a Dietary Source of Pectic Substances

Grape pomace is one of the most abundant solid by-products generated during winemaking. A lot of products, such as ethanol, tartrates, citric acid, grape seed oil, hydrocolloids, bioactive compounds and dietary fiber are recovered from grape pomace. Grape pomace represents a major interest in the field of fiber extraction, especially pectin, as an alternative source to conventional ones, such as apple pomace and citrus peels, from which pectin is obtained by acid extraction and precipitation using alcohols. Understanding the structural and functional components of grape pomace will significantly aid in developing efficient extraction of pectin from unconventional sources. In recent years, natural biodegradable polymers, like pectin has invoked a big interest due to versatile properties and diverse applications in food industry and other fields. Thus, pectin extraction from grape pomace could afford a new reason for the decrease of environmental pollution and waste generation. This paper briefly describes the structure and composition of grape pomace of different varieties for the utilization of grape pomace as a source of pectin in food industry.

Complementary Phenotyping of Maize Root System Architecture by Root Pulling Force and X-Ray Imaging

The root system is critical for the survival of nearly all land plants and a key target for improving abiotic stress tolerance, nutrient accumulation, and yield in crop species. Although many methods of root phenotyping exist, within field studies, one of the most popular methods is the extraction and measurement of the upper portion of the root system, known as the root crown, followed by trait quantification based on manual measurements or 2D imaging. However, 2D techniques are inherently limited by the information available from single points of view. Here, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample. This approach improves estimates of the genetic contribution to root system architecture and is refined enough to detect various changes in global root system architecture over developmental time as well as more subtle changes in root distributions as a result of environmental differences. We demonstrate that root pulling force, a high-throughput method of root extraction that provides an estimate of root mass, is associated with multiple 3D traits from our pipeline. Our combined methodology can therefore be used to calibrate and interpret root pulling force measurements across a range of experimental contexts or scaled up as a stand-alone approach in large genetic studies of root system architecture.

Analysis of Shoot Architecture Traits in Edamame Reveals Potential Strategies to Improve Harvest Efficiency

Edamame is a type of green, vegetable soybean and improving shoot architecture traits for edamame is important for breeding of high-yield varieties by decreasing potential loss due to harvesting. In this study, we use digital imaging technology and computer vision algorithms to characterize major traits of shoot architecture for edamame. Using a population of edamame PIs, we seek to identify underlying genetic control of different shoot architecture traits. We found significant variations in the shoot architecture of the edamame lines including long-skinny and candle stick-like structures. To quantify the similarity and differences of branching patterns between these edamame varieties, we applied a topological measurement called persistent homology. Persistent homology uses algebraic geometry algorithms to measure the structural similarities between complex shapes. We found intriguing relationships between the topological features of branching networks and pod numbers in our plant population, suggesting combination of multiple topological features contribute to the overall pod numbers on a plant. We also identified potential candidate genes including a lateral organ boundary gene family protein and a MADS-box gene that are associated with the pod numbers. This research provides insight into the genetic regulation of shoot architecture traits and can be used to further develop edamame varieties that are better adapted to mechanical harvesting.

Differential expression of transcription factor- and further growth-related genes correlates with contrasting cluster architecture in Vitis vinifera 'Pinot Noir' and Vitis spp. genotypes

Grapevine (Vitis vinifera L.) is an economically important crop that needs to comply with high quality standards for fruit, juice and wine production. Intense plant protection is required to avoid fungal damage. Grapevine cultivars with loose cluster architecture enable reducing protective treatments due to their enhanced resilience against fungal infections, such as Botrytis cinerea-induced gray mold. A recent study identified transcription factor gene VvGRF4 as determinant of pedicel length, an important component of cluster architecture, in samples of two loose and two compact quasi-isogenic 'Pinot Noir' clones. Here, we extended the analysis to 12 differently clustered 'Pinot Noir' clones from five diverse clonal selection programs. Differential gene expression of these clones was studied in three different locations over three seasons. Two phenotypically opposite clones were grown at all three locations and served for standardization. Data were correlated with the phenotypic variation of cluster architecture sub-traits. A set of 14 genes with consistent expression differences between loosely and compactly clustered clones-independent from season and location-was newly identified. These genes have annotations related to cellular growth, cell division and auxin metabolism and include two more transcription factor genes, PRE6 and SEP1-like. The differential expression of VvGRF4 in relation to loose clusters was exclusively found in 'Pinot Noir' clones. Gene expression studies were further broadened to phenotypically contrasting F1 individuals of an interspecific cross and OIV reference varieties of loose cluster architecture. This investigation confirmed PRE6 and six growth-related genes to show differential expression related to cluster architecture over genetically divergent backgrounds.

Comprehensive 3D phenotyping reveals continuous morphological variation across genetically diverse sorghum inflorescences

●Inflorescence architecture in plants is often complex and challenging to quantify, particularly for inflorescences of cereal grasses. Methods for capturing inflorescence architecture and for analyzing the resulting data are limited to a few easily captured parameters that may miss the rich underlying diversity. ●Here, we apply X-ray computed tomography combined with detailed morphometrics, offering new imaging and computational tools to analyze three-dimensional inflorescence architecture. To show the power of this approach, we focus on the panicles of Sorghum bicolor, which vary extensively in numbers, lengths, and angles of primary branches, as well as the three-dimensional shape, size, and distribution of the seed. ●We imaged and comprehensively evaluated the panicle morphology of 55 sorghum accessions that represent the five botanical races in the most common classification system of the species, defined by genetic data. We used our data to determine the reliability of the morphological characters for assigning specimens to race and found that seed features were particularly informative. ●However, the extensive overlap between botanical races in multivariate trait space indicates that the phenotypic range of each group extends well beyond its overall genetic background, indicating unexpectedly weak correlation between morphology, genetic identity, and domestication history.

Imaging flowers: a guide to current microscopy and tomography techniques to study flower development

Developmental biology relies heavily on our ability to generate three-dimensional images of live biological specimens through time, and to map gene expression and hormone response in these specimens as they undergo development. The last two decades have seen an explosion of new bioimaging technologies that have pushed the limits of spatial and temporal resolution and provided biologists with invaluable new tools. However, plant tissues are difficult to image, and no single technology fits all purposes; choosing between many bioimaging techniques is not trivial. Here, we review modern light microscopy and computed projection tomography methods, their capabilities and limitations, and we discuss their current and potential applications to the study of flower development and fertilization.

The shape of things to come: Topological data analysis and biology, from molecules to organisms

Shape is data and data is shape. Biologists are accustomed to thinking about how the shape of biomolecules, cells, tissues, and organisms arise from the effects of genetics, development, and the environment. Less often do we consider that data itself has shape and structure, or that it is possible to measure the shape of data and analyze it. Here, we review applications of topological data analysis (TDA) to biology in a way accessible to biologists and applied mathematicians alike. TDA uses principles from algebraic topology to comprehensively measure shape in data sets. Using a function that relates the similarity of data points to each other, we can monitor the evolution of topological features-connected components, loops, and voids. This evolution, a topological signature, concisely summarizes large, complex data sets. We first provide a TDA primer for biologists before exploring the use of TDA across biological sub-disciplines, spanning structural biology, molecular biology, evolution, and development. We end by comparing and contrasting different TDA approaches and the potential for their use in biology. The vision of TDA, that data are shape and shape is data, will be relevant as biology transitions into a data-driven era where the meaningful interpretation of large data sets is a limiting factor.

3D characterization of walnut morphological traits using X-ray computed tomography

BACKGROUND

Walnuts are grown worldwide in temperate areas and producers are facing an increasing demand. In a climate change context, the industry also needs cultivars that provide fruits of quality. This quality includes satisfactory filling ratio, thicker shell, ease of cracking, smooth shell and round-shaped walnut, and larger nut size. These desirable traits have been analysed so far using calipers or micrometers, but it takes a lot of time and requires the destruction of the sample. A challenge to take up is to develop an accurate, fast and non-destructive method for quality-related and morphometric trait measurements of walnuts, that are used to characterize new cultivars or collections in any germplasm management process.

RESULTS

In this study, we develop a method to measure different morphological traits on several walnuts simultaneously such as morphometric traits (nut length, nut face and profile diameters), traits that previously required opening the nut (shell thickness, kernel volume and filling kernel/nut ratio) and traits that previously were difficult to quantify (shell rugosity, nut sphericity, nut surface area and nut shape). These measurements were obtained from reconstructed 3D images acquired by X-ray computed tomography (CT). A workflow was created including several steps: noise elimination, walnut individualization, properties extraction and quantification of the different parts of the fruit. This method was applied to characterize 50 walnuts of a part of the INRAE walnut germplasm collection made of 161 unique accessions, obtained from the 2018 harvest. Our results indicate that 50 walnuts are sufficient to phenotype the fruit quality of one accession using X-ray CT and to find correlations between the morphometric traits. Our imaging workflow is suitable for any walnut size or shape and provides new and more accurate measurements.

CONCLUSIONS

The fast and accurate measurement of quantitative traits is of utmost importance to conduct quantitative genetic analyses or cultivar characterization. Our imaging workflow is well adapted for accurate phenotypic characterization of a various range of traits and could be easily applied to other important nut crops.