High-throughput Phenotyping Tool Research Articles

Elasticity of Carrier Fluid: A Key Factor Affecting Mechanical Phenotyping in Deformability Cytometry.

Recently, microfluidics deformability cytometry has emerged as a powerful tool for high-throughput mechanical phenotyping of large populations of cells. These methods characterize cells by their mechanical fingerprints by exerting hydrodynamic forces and monitoring the resulting deformation. These devices have shown great promise for label-free cytometry, yet there is a critical need to improve their accuracy and reconcile any discrepancies with other methods, such as atomic force microscopy. In this study, we employ computational fluid dynamics simulations and uncover how the elasticity of frequently used carrier fluids, such as methylcellulose dissolved in phosphate-buffered saline, is significantly influential to the resulting cellular deformation. We conducted CFD simulations conventionally used within the deformability cytometry field, which neglect fluid elasticity. Subsequently, we incorporated a more comprehensive model that simulates the viscoelastic nature of the carrier fluid. A comparison of the predicted stresses between these two approaches underscores the significance of the emerging elastic stresses in addition to the well-recognized viscous stresses along the channel. Furthermore, we utilize a two-phase flow model to predict the deformation of a promyelocyte (i.e., HL-60 cell type) within a hydrodynamic constriction channel. The obtained results highlight a substantial impact of the elasticity of carrier fluid on cellular deformation and raise questions about the accuracy of mechanical property estimates derived by neglecting elastic stresses.

Adapting the High-Throughput Phenotyping Tool ALPHI® to Potatoes: First Results and Lessons

Adapting the High-Throughput Phenotyping Tool ALPHI® to Potatoes: First Results and Lessons

Dare to be resilient: the key to future pesticide-free orchards?

In a context of urgent need for a more sustainable fruit tree production, it's high time to find durable alternatives to the systematic use of phytosanitary products in orchards. To this aim, resilience can deliver a number of benefits. Relying on a combination of tolerance, resistance and recovery traits, disease resilience appears as a corner stone to cope with the multiple pest and disease challenge over the orchard's lifetime. Here, we propose to describe resilience as the capacity of a tree to be minimally affected by external disturbances or to rapidly bounce back to normal functioning after being exposed to these disturbances. Based on a literature survey largely inspired from research on livestock, we highlight different approaches for dissecting resilience phenotypic and genotypic components. In particular, multisite experimental designs and longitudinal measures of so-called 'resilience biomarkers' are required. We identified a list of promising biomarkers relying on eco-physiological and digital measurements. Recent advances in high-throughput phenotyping and genomics tools will likely facilitate the fine and temporal monitoring of tree health, allowing to identify resilient genotypes with the calculation of specific resilience indicators. Although resilience can appear as 'black box' trait, we demonstrate how it could become a realistic breeding goal.

Near infrared spectroscopy for cooking time classification of cassava genotypes.

Cooking time is a crucial determinant of culinary quality of cassava roots and incorporating it into the early stages of breeding selection is vital for breeders. This study aimed to assess the potential of near-infrared spectroscopy (NIRS) in classifying cassava genotypes based on their cooking times. Five cooking times (15, 20, 25, 30, and 40 minutes) were assessed and 888 genotypes evaluated over three crop seasons (2019/2020, 2020/2021, and 2021/2022). Fifteen roots from five plants per plot, featuring diameters ranging from 4 to 7 cm, were randomly chosen for cooking analysis and spectral data collection. Two root samples (15 slices each) per genotype were collected, with the first set aside for spectral data collection, processed, and placed in two petri dishes, while the second set was utilized for cooking assessment. Cooking data were classified into binary and multiclass variables (CT4C and CT6C). Two NIRs devices, the portable QualitySpec® Trek (QST) and the benchtop NIRFlex N-500 were used to collect spectral data. Classification of genotypes was carried out using the K-nearest neighbor algorithm (KNN) and partial least squares (PLS) models. The spectral data were split into a training set (80%) and an external validation set (20%). For binary variables, the classification accuracy for cassava cooking time was notably high ( ranging from 0.72 to 0.99). Regarding multiclass variables, accuracy remained consistent across classes, models, and NIR instruments (~0.63). However, the KNN model demonstrated slightly superior accuracy in classifying all cooking time classes, except for the CT4C variable (QST) in the NoCook and 25 min classes. Despite the increased complexity associated with binary classification, it remained more efficient, offering higher classification accuracy for samples and facilitating the selection of the most relevant time or variables, such as cooking time ≤ 30 minutes. The accuracy of the optimal scenario for classifying samples with a cooking time of 30 minutes reached = 0.86 and = 0.84, with a Kappa value of 0.53. Overall, the models exhibited a robust fit for all cooking times, showcasing the significant potential of NIRs as a high-throughput phenotyping tool for classifying cassava genotypes based on cooking time.

A Review on High Throughput Phenotyping for Vegetable Crops

Abstract Conventional phenotyping approaches for vegetable crops such as Solanaceae, Bulb, and Root crops have contributed significantly to the development of numerous varieties. Despite this, traditional phenotyping procedures are insufficient because of the longer time required to produce a variety, poor genetic gain, environmental influences, and other externalities that impact phenotype-based selection. A novel recent approach of high throughput phenotyping (HTP) is regarded a potential tool for addressing the problems of traditional phenotyping. The advent of sensor, computer vision, automation, and sophisticated machine learning technologies sparked the creation of high-throughput phenotyping technology in the prior decade. HTP platforms are being used to conduct non-destructive evaluations of the whole plant system in a variety of crops. HTP provides precise measurements and suggests the collection of high-quality and accurate data, which is required for standardizing phenotyping for genetic dissection and genomic assisted breeding techniques such as genome-wide association studies (GWAS), linkage mapping, marker-assisted selection (MAS), and genomic selection (GS). The rest of this chapter examines the application of high-throughput phenotyping tools in genomic-assisted breeding for vegetable crops.

Developing and evaluating pediatric phecodes (Peds-Phecodes) for high-throughput phenotyping using electronic health records.

Pediatric patients have different diseases and outcomes than adults; however, existing phecodes do not capture the distinctive pediatric spectrum of disease. We aim to develop specialized pediatric phecodes (Peds-Phecodes) to enable efficient, large-scale phenotypic analyses of pediatric patients. We adopted a hybrid data- and knowledge-driven approach leveraging electronic health records (EHRs) and genetic data from Vanderbilt University Medical Center to modify the most recent version of phecodes to better capture pediatric phenotypes. First, we compared the prevalence of patient diagnoses in pediatric and adult populations to identify disease phenotypes differentially affecting children and adults. We then used clinical domain knowledge to remove phecodes representing phenotypes unlikely to affect pediatric patients and create new phecodes for phenotypes relevant to the pediatric population. We further compared phenome-wide association study (PheWAS) outcomes replicating known pediatric genotype-phenotype associations between Peds-Phecodes and phecodes. The Peds-Phecodes aggregate 15533 ICD-9-CM codes and 82949 ICD-10-CM codes into 2051 distinct phecodes. Peds-Phecodes replicated more known pediatric genotype-phenotype associations than phecodes (248 vs 192 out of 687 SNPs, P < .001). We introduce Peds-Phecodes, a high-throughput EHR phenotyping tool tailored for use in pediatric populations. We successfully validated the Peds-Phecodes using genetic replication studies. Our findings also reveal the potential use of Peds-Phecodes in detecting novel genotype-phenotype associations for pediatric conditions. We expect that Peds-Phecodes will facilitate large-scale phenomic and genomic analyses in pediatric populations. Peds-Phecodes capture higher-quality pediatric phenotypes and deliver superior PheWAS outcomes compared to phecodes.

The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia

Very high (spatial and temporal) resolution satellite (VHRS) and high-resolution unmanned aerial vehicle (UAV) imagery provides the opportunity to develop new crop disease detection methods at early growth stages with utility for early warning systems. The capability of multispectral UAV, SkySat and Pleiades imagery as a high throughput phenotyping (HTP) and rapid disease detection tool for wheat rusts is assessed. In a randomized trial with and without fungicide control, six bread wheat varieties with differing rust resistance were monitored using UAV and VHRS. In total, 18 spectral features served as predictors for stem and yellow rust disease progression and associated yield loss. Several spectral features demonstrated strong predictive power for the detection of combined wheat rust diseases and the estimation of varieties’ response to disease stress and grain yield. Visible spectral (VIS) bands (Green, Red) were more useful at booting, shifting to VIS–NIR (near-infrared) vegetation indices (e.g., NDVI, RVI) at heading. The top-performing spectral features for disease progression and grain yield were the Red band and UAV-derived RVI and NDVI. Our findings provide valuable insight into the upscaling capability of multispectral sensors for disease detection, demonstrating the possibility of upscaling disease detection from plot to regional scales at early growth stages.

Impact of Intron and Retransformation on Transgene Expression in Leaf and Fruit Tissues of Field-Grown Pear Trees.

Stable and high expression of introduced genes is a prerequisite for using transgenic trees. Transgene stacking enables combining several valuable traits, but repeated transformation increases the risk of unintended effects. This work studied the stability and intron-mediated enhancement of uidA gene expression in leaves and different anatomical parts of pear fruits during field trials over 14 years. The stability of reporter and herbicide resistance transgenes in retransformed pear plants, as well as possible unintended effects using high-throughput phenotyping tools, were also investigated. The activity of β-glucuronidase (GUS) varied depending on the year, but silencing did not occur. The uidA gene was expressed to a maximum in seeds, slightly less in the peel and peduncles, and much less in the pulp of pear fruits. The intron in the uidA gene stably increased expression in leaves and fruits by approximately twofold. Retransformants with the bar gene showed long-term herbicide resistance and exhibited no consistent changes in leaf size and shape. The transgenic pear was used as rootstock and scion, but grafted plants showed no transport of the GUS protein through the graft in the greenhouse and field. This longest field trial of transgenic fruit trees demonstrates stable expression under varying environmental conditions, the expression-enhancing effect of intron and the absence of unintended effects in single- and double-transformed woody plants.

Phenotyping wood properties of Corymbia torelliana x Corymbia citriodora and Eucalyptus dunnii based on NIR spectra

ABSTRACT Selecting individuals in breeding programs for pulp production, the physical and chemical wood traits must be considered in the genotype selection. In this study, trees from hybrid progeny tests of Corymbia torelliana x Corymbia citriodora (CTOxCCT) and progenies of Eucalyptus dunnii (EDU) were investigated. Sawdust of genetically selected standing trees was collected, classified and prepared for near infrared (NIR) spectra readings. The chemical properties of the selected trees were determined through kraft pulping. Predictive models for each property were developed based on the reference data and NIR spectra. Two approaches for models were developed. The first approach, models were fitted with 25 samples of CTOxCCT, and in the second approach, models were fitted using 61 samples (25 of CTOxCCT and 36 of EDU). The estimated R²cv values were 0.60 and 0.73 for basic chip density, 0.37 and 0.65 for extractive contents, 0.56 and 0.53 for total lignin contents, 0.63 and 0.66 for S/G ratio, and 0.52 and 0.77 for screened pulp yield for the first and second approaches, respectively. All developed models have potential for ranking trees in breeding programs. NIR spectroscopy can potentially be applied as a high-throughput field phenotyping tool where thousands of varieties need to be evaluated.

Hyperspectral imaging for the determination of relevant cooking quality traits of boiled cassava.

The purpose of this study was to investigate the potential of hyperspectral imaging for the characterization of cooking quality parameters, dry matter content (DMC), water absorption (WAB), and texture in cassava genotypes contrasting for their cooking quality. Hyperspectral images were acquired on cooked and fresh intact longitudinal and transversal slices from 31 cassava genotypes harvested in March 2022 in Colombia. Different chemometric methods were tested for the quantification of DMC, WAB, and texture parameters. Data analysis was conducted through partial least squares regression, K nearest neighbors regression, support vector machine regression and CovSel multiple linear regression (CovSel_MLR). Efficient performances were obtained for DMC using CovSel_MLR with, coefficient of multiple determination , root-mean-square error of prediction RMSEP = 0.96 g/100 g, and ratio of the standard deviation values RPD = 3.60. High heterogeneity was observed between contrasting genotypes. The predicted distribution of DMC within the root can be homogeneous or heterogeneous depending on the genotype. Weak predictions were obtained for WAB and texture parameters. This study showed that hyperspectral imaging could be used as a high-throughput phenotyping tool for the visualization of DMC in contrasting cooking quality genotypes. Further improvement of protocols and larger datasets are required for WAB and texture quality traits. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Volatilomics of raspberry fruit germplasm by combining chromatographic and direct-injection mass spectrometric techniques.

The application of direct-injection mass spectrometric (DI-MS) techniques, like Proton Transfer Reaction Time of Flight Mass Spectrometry (PTR-ToF-MS) has been suggested as a reliable phenotyping tool for fruit volatilome assessment in both genetic and quality-related studies. In this study the complexity of raspberry aroma was investigated by a comprehensive untargeted VOC analysis, done by combining SPME-GC-MS and PTR-ToF-MS assessments with multi-block discriminant analysis using the DIABLO mixOmics framework. The aim was to acquire an exhaustive characterization of the raspberry volatilome according to different fruit ripening stages (pink, ripe, and overripe) and genetic variances (50 accessions), as well as to investigate the potential of PTR-ToF-MS as a rapid and high throughput VOC phenotyping tool to address issues related to raspberry fruit quality. Results of this study demonstrated the complementarity between SPME-GC-MS and PTR-ToF-MS techniques to evaluate the raspberry aroma composition. PTR-ToF-MS generates reliable raspberry VOC fingerprints mainly due to a reduced compound fragmentation and precise content estimation. In addition, the high collinearity between isomers of monoterpenes and norisoprenoids, discovered by GC analysis, reduces the main analytic limitation of PTR-ToF-MS of not being able to separate isomeric molecules. The high similarity between the VOC matrices obtained by applying PTR-ToF-MS and SPME-GC-MS confirmed the possibility of using PTR-ToF-MS as a reliable high throughput phenotyping tool for raspberry volatiolome assessment. In addition, results provided by the germplasm collection investigation enabled to distinguish the best performing accessions, based on VOCs composition, to be used as superior parental lines for future breeding programs.

A leaf-level spectral library to support high-throughput plant phenotyping: predictive accuracy and model transfer.

Leaf-level hyperspectral reflectance has become an effective tool for high-throughput phenotyping of plant leaf traits due to its rapid, low-cost, multi-sensing, and non-destructive nature. However, collecting samples for model calibration can still be expensive, and models show poor transferability among different datasets. This study had three specific objectives: first, to assemble a large library of leaf hyperspectral data (n=2460) from maize and sorghum; second, to evaluate two machine-learning approaches to estimate nine leaf properties (chlorophyll, thickness, water content, nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur); and third, to investigate the usefulness of this spectral library for predicting external datasets (n=445) including soybean and camelina using extra-weighted spiking. Internal cross-validation showed satisfactory performance of the spectral library to estimate all nine traits (mean R2=0.688), with partial least-squares regression outperforming deep neural network models. Models calibrated solely using the spectral library showed degraded performance on external datasets (mean R2=0.159 for camelina, 0.337 for soybean). Models improved significantly when a small portion of external samples (n=20) was added to the library via extra-weighted spiking (mean R2=0.574 for camelina, 0.536 for soybean). The leaf-level spectral library greatly benefits plant physiological and biochemical phenotyping, whilst extra-weight spiking improves model transferability and extends its utility.

Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning.

Weeds can cause significant yield losses and will continue to be a problem for agricultural production due to climate change. Dicamba is widely used to control weeds in monocot crops, especially genetically engineered dicamba-tolerant (DT) dicot crops, such as soybean and cotton, which has resulted in severe off-target dicamba exposure and substantial yield losses to non-tolerant crops. There is a strong demand for non-genetically engineered DT soybeans through conventional breeding selection. Public breeding programs have identified genetic resources that confer greater tolerance to off-target dicamba damage in soybeans. Efficient and high throughput phenotyping tools can facilitate the collection of a large number of accurate crop traits to improve the breeding efficiency. This study aimed to evaluate unmanned aerial vehicle (UAV) imagery and deep-learning-based data analytic methods to quantify off-target dicamba damage in genetically diverse soybean genotypes. In this research, a total of 463 soybean genotypes were planted in five different fields (different soil types) with prolonged exposure to off-target dicamba in 2020 and 2021. Crop damage due to off-target dicamba was assessed by breeders using a 1-5 scale with a 0.5 increment, which was further classified into three classes, i.e., susceptible (≥3.5), moderate (2.0 to 3.0), and tolerant (≤1.5). A UAV platform equipped with a red-green-blue (RGB) camera was used to collect images on the same days. Collected images were stitched to generate orthomosaic images for each field, and soybean plots were manually segmented from the orthomosaic images. Deep learning models, including dense convolutional neural network-121 (DenseNet121), residual neural network-50 (ResNet50), visual geometry group-16 (VGG16), and Depthwise Separable Convolutions (Xception), were developed to quantify crop damage levels. Results show that the DenseNet121 had the best performance in classifying damage with an accuracy of 82%. The 95% binomial proportion confidence interval showed a range of accuracy from 79% to 84% (p-value ≤ 0.01). In addition, no extreme misclassifications (i.e., misclassification between tolerant and susceptible soybeans) were observed. The results are promising since soybean breeding programs typically aim to identify those genotypes with 'extreme' phenotypes (e.g., the top 10% of highly tolerant genotypes). This study demonstrates that UAV imagery and deep learning have great potential to high-throughput quantify soybean damage due to off-target dicamba and improve the efficiency of crop breeding programs in selecting soybean genotypes with desired traits.

Hyperspectral Remote Sensing for Phenotyping the Physiological Drought Response of Common and Tepary Bean.

Proximal remote sensing offers a powerful tool for high-throughput phenotyping of plants for assessing stress response. Bean plants, an important legume for human consumption, are often grown in regions with limited rainfall and irrigation and are therefore bred to further enhance drought tolerance. We assessed physiological (stomatal conductance and predawn and midday leaf water potential) and ground- and tower-based hyperspectral remote sensing (400 to 2,400 nm and 400 to 900 nm, respectively) measurements to evaluate drought response in 12 common bean and 4 tepary bean genotypes across 3 field campaigns (1 predrought and 2 post-drought). Hyperspectral data in partial least squares regression models predicted these physiological traits (R 2 = 0.20 to 0.55; root mean square percent error 16% to 31%). Furthermore, ground-based partial least squares regression models successfully ranked genotypic drought responses similar to the physiologically based ranks. This study demonstrates applications of high-resolution hyperspectral remote sensing for predicting plant traits and phenotyping drought response across genotypes for vegetation monitoring and breeding population screening.

A Pipeline to Characterize Structural Heart Defects in the Fetal Mouse

Congenital heart diseases (CHDs) are major causes of infant death in the United States. In the 1980s and earlier, most patients with moderate or severe CHD died before adulthood, with the maximum mortality during the first week of life. Remarkable advances in surgical techniques, diagnostic approaches, and medical management have led to marked improvements in outcomes. To address the critical research needs of understanding congenital heart defects, murine models have provided an ideal research platform, as they have very similar heart anatomy to humans and short gestation rates. The combination of genetic engineering with high-throughput phenotyping tools has allowed for the replication and diagnosis of structural heart defects to further elucidate the molecular pathways behind CHDs. The use of noninvasive fetal echocardiography to screen the cardiac phenotypes in mouse models coupled with the high fidelity of Episcopic fluorescence image capture (EFIC) using Episcopic confocal microscopy (ECM) histopathology with three-dimensional (3D) reconstructions enables a detailed view into the anatomy of various congenital heart defects. This protocol outlines a complete workflow of these methods to obtain an accurate diagnosis of murine congenital heart defects. Applying this phenotyping protocol to model organisms will allow for accurate CHD diagnosis, yielding insights into the mechanisms of CHD. Identifying the underlying mechanisms of CHD provide opportunities for potential therapies and interventions.

UAV-LiDAR and RGB Imagery Reveal Large Intraspecific Variation in Tree-Level Morphometric Traits across Different Pine Species Evaluated in Common Gardens

Remote sensing is increasingly used in forest inventories. However, its application to assess genetic variation in forest trees is still rare, particularly in conifers. Here we evaluate the potential of LiDAR and RGB imagery obtained through unmanned aerial vehicles (UAVs) as high-throughput phenotyping tools for the characterization of tree growth and crown structure in two representative Mediterranean pine species. To this end, we investigated the suitability of these tools to evaluate intraspecific differentiation in a wide array of morphometric traits for Pinus nigra (European black pine) and Pinus halepensis (Aleppo pine). Morphometric traits related to crown architecture and volume, primary growth, and biomass were retrieved at the tree level in two genetic trials located in Central Spain and compared with ground-truth data. Both UAV-based methods were then tested for their accuracy to detect genotypic differentiation among black pine and Aleppo pine populations and their subspecies (black pine) or ecotypes (Aleppo pine). The possible relation between intraspecific variation of morphometric traits and life-history strategies of populations was also tested by correlating traits to climate factors at origin of populations. Finally, we investigated which traits distinguished better among black pine subspecies or Aleppo pine ecotypes. Overall, the results demonstrate the usefulness of UAV-based LiDAR and RGB records to disclose tree architectural intraspecific differences in pine species potentially related to adaptive divergence among populations. In particular, three LiDAR-derived traits related to crown volume, crown architecture, and main trunk—or, alternatively, the latter (RGB-derived) two traits—discriminated the most among black pine subspecies. In turn, Aleppo pine ecotypes were partly distinguishable by using two LiDAR-derived traits related to crown architecture and crown volume, or three RGB-derived traits related to tree biomass and main trunk. Remote-sensing-derived-traits related to main trunk, tree biomass, crown architecture, and crown volume were associated with environmental characteristics at the origin of populations of black pine and Aleppo pine, thus hinting at divergent environmental stress-induced local adaptation to drought, wildfire, and snowfall in both species.

Use of near-infrared spectroscopy to estimate physical, anatomical and hydraulic properties of Eucalyptus wood.

Tree breeding programs and wood industries require simple, time- and cost-effective techniques to process large volumes of samples. In recent decades, near-infrared spectroscopy (NIRS) has been acknowledged as one of the most powerful techniques for wood analysis, making it the most used tool for high-throughput phenotyping. Previous studies have shown that a significant number of anatomical, physical, chemical and mechanical wood properties can be estimated through NIRS, both for angiosperm and gymnosperm species. However, the ability of this technique to predict functional traits related to drought resistance has been poorly explored, especially in angiosperm species. This is particularly relevant since determining xylem hydraulic properties by conventional techniques is complex and time-consuming, clearly limiting its use in studies and applications that demand large amounts of samples. In this study, we measured several wood anatomical and hydraulic traits and collected NIR spectra in branches of two Eucalyptus L'Hér species. We developed NIRS calibration models and discussed their ability to accurately predict the studied traits. The models generated allowed us to adequately calibrate the reference traits, with high R2 (≥0.75) for traits such as P12, P88, the slope of the vulnerability curves to xylem embolism or the fiber wall fraction, and with lower R2 (0.39-0.52) for P50, maximum hydraulic conductivity or frequency of ray parenchyma. We found that certain wavenumbers improve models' calibration, with those in the range of 4000-5500cm-1 predicting the highest number of both anatomical and functional traits. We concluded that the use of NIRS allows calibrating models with potential predictive value not only for wood structural and chemical variables but also for anatomical and functional traits related to drought resistance in wood types with complex structure as eucalypts. These results are promising in light of the required knowledge about species and genotypes adaptability to global climatic change.

Tracing Back the History of Pepper (Capsicum annuum) in the Iberian Peninsula from a Phenomics Point of View.

The Iberian Peninsula was the place where pepper (Capsicum annuum) entered Europe and dispersed to other continents but was also an important secondary center for its diversification. The current work evaluated the phenotypic diversity existing in this region and investigated how that evolved from Capsicum native areas (Mexico and Andean Region). For that purpose, the high-throughput phenotyping tool Tomato Analyzer was employed. Descriptors related to size and shape were the most distinctive among fruit types, reflecting a broad diversity for Iberian peppers. These traits likely reflected those suffering from more intensive human selections, driving the worldwide expansion of C. annuum. Iberian peppers maintained close proximity to the American accessions in terms of fruit phenomics. The highest similarities were observed for those coming from the southeastern edge of the Peninsula, while northwestern accessions displayed more significant differences. Common fruit traits (small, conical) suggested that Portuguese and Spanish landraces may have arisen from an ancient American population that entered the south of Spain and promptly migrated to the central and northern territories, giving rise to larger, elongated, and blocky pods. Such lineages would be the result of adaptations to local soil-climate factors prevailing in different biogeographic provinces.

High‐Throughput Phenotyping Tool for Protein Biofortification in Pulses

CSA NewsVolume 67, Issue 10 p. 6-6 SOCIETY SCIENCE High-Throughput Phenotyping Tool for Protein Biofortification in Pulses First published: 01 September 2022 https://doi.org/10.1002/csan.20845Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume67, Issue10October 2022Pages 6-6 RelatedInformation

Exploring Chickpea Germplasm Diversity for Broadening the Genetic Base Utilizing Genomic Resourses

Legume crops provide significant nutrition to humans as a source of protein, omega-3 fatty acids as well as specific macro and micronutrients. Additionally, legumes improve the cropping environment by replenishing the soil nitrogen content. Chickpeas are the second most significant staple legume food crop worldwide behind dry bean which contains 17%–24% protein, 41%–51% carbohydrate, and other important essential minerals, vitamins, dietary fiber, folate, β-carotene, anti-oxidants, micronutrients (phosphorus, calcium, magnesium, iron, and zinc) as well as linoleic and oleic unsaturated fatty acids. Despite these advantages, legumes are far behind cereals in terms of genetic improvement mainly due to far less effort, the bottlenecks of the narrow genetic base, and several biotic and abiotic factors in the scenario of changing climatic conditions. Measures are now called for beyond conventional breeding practices to strategically broadening of narrow genetic base utilizing chickpea wild relatives and improvement of cultivars through advanced breeding approaches with a focus on high yield productivity, biotic and abiotic stresses including climate resilience, and enhanced nutritional values. Desirable donors having such multiple traits have been identified using core and mini core collections from the cultivated gene pool and wild relatives of Chickpea. Several methods have been developed to address cross-species fertilization obstacles and to aid in inter-specific hybridization and introgression of the target gene sequences from wild Cicer species. Additionally, recent advances in “Omics” sciences along with high-throughput and precise phenotyping tools have made it easier to identify genes that regulate traits of interest. Next-generation sequencing technologies, whole-genome sequencing, transcriptomics, and differential genes expression profiling along with a plethora of novel techniques like single nucleotide polymorphism exploiting high-density genotyping by sequencing assays, simple sequence repeat markers, diversity array technology platform, and whole-genome re-sequencing technique led to the identification and development of QTLs and high-density trait mapping of the global chickpea germplasm. These altogether have helped in broadening the narrow genetic base of chickpeas.