Crop Seed Phenomics: Focus on Non-Destructive Functional Trait Phenotyping Methods and Applications.

Seeds play a critical role in ensuring food security for the earth's 8 billion people. There is great biodiversity in plant seed content traits worldwide. Consequently, the development of robust, rapid, and high-throughput methods is required for seed quality evaluation and acceleration of crop improvement. There has been considerable progress in the past 20 years in various non-destructive methods to uncover and understand plant seed phenomics. This review highlights recent advances in non-destructive seed phenomics techniques, including Fourier Transform near infrared (FT-NIR), Dispersive-Diode Array (DA-NIR), Single-Kernel (SKNIR), Micro-Electromechanical Systems (MEMS-NIR) spectroscopy, Hyperspectral Imaging (HSI), and Micro-Computed Tomography Imaging (micro-CT). The potential applications of NIR spectroscopy are expected to continue to rise as more seed researchers, breeders, and growers successfully adopt it as a powerful non-destructive method for seed quality phenomics. It will also discuss the advantages and limitations that need to be solved for each technique and how each method could help breeders and industry with trait identification, measurement, classification, and screening or sorting of seed nutritive traits. Finally, this review will focus on the future outlook for promoting and accelerating crop improvement and sustainability.

Compositional analysis in sorghum (Sorghum bicolor) NIR spectral techniques based on mean spectra from single seeds.

Sorghum (Sorghum bicolor) is an economically important cereal crop that can be used as human food, animal feed, and for industrial use such as bioenergy. In sorghum breeding programs, development of cultivars with desirable seed quality characteristics is important and development of rapid low-cost screening methods for seed nutritional traits are desired, since most standard methods are destructive, slow, and less environmentally friendly. This study investigates the feasibility of single kernel NIR spectroscopy (SKNIRS) for rapid determination of individual sorghum seed components. We developed successful multivariate prediction models based on partial least squares (PLS) regression for protein, oil, and weight in sorghum. The results showed that for sorghum protein content ranging from 8.92% to 18.7%, the model coefficients of determination obtained were R C A L 2 = 0.95 (RMSEC= 0.44) and R P R E D 2 = 0.87 (RMSEP= 0.69). The model coefficients of determination for oil prediction were R C A L 2 = 0.92 (RMSEC= 0.23) and R P R E D 2 = 0.71 (RMSEP= 0.41) for oil content ranging from 1.96% to 5.61%. For weight model coefficients of determination were R C A L 2 = 0.81 (RMSEC= 0.007) and R P R E D 2 = 0.63 (RMSEP= 0.007) for seeds ranging from 4.40 mg to 77.0 mg. In conclusion, mean spectra SKNIRS can be used to rapidly determine protein, oil, and weight in intact single seeds of sorghum seeds and can provide a nondestructive and quick method for screening sorghum samples for these traits for sorghum breeding and industry use.

Flax and Sorghum: Multi-Element Contents and Nutritional Values within 210 Varieties and Potential Selection for Future Climates to Sustain Food Security.

The Dietary Guidelines for Americans recommends giving priority to nutrient-dense foods while decreasing energy-dense foods. Although both flax (Linum usitatissimum) and sorghum (Sorghum bicolor) are rich in various essential minerals, their ionomes have yet to be investigated. Furthermore, previous studies have shown that elevated CO2 levels could reduce key nutrients in crops. In this study, we analyzed 102 flax and 108 sorghum varieties to investigate their ionomic variations (N, P, K, Ca, Mg, S, B, Zn, Mn, Fe, Cu, and Mo), elemental level interactions, and nutritional value. The results showed substantial genetic variations and elemental correlations in flax and sorghum. While a serving size of 28 g of flax delivers 37% daily value (DV) of Cu, 31% of Mn, 28% of Mg, and 19% of Zn, sorghum delivers 24% of Mn, 16% of Cu, 11% of Mg, and 10% of Zn of the recommended daily value (DV). We identified a set of promising flax and sorghum varieties with superior seed mineral composition that could complement breeding programs for improving the nutritional quality of flax and sorghum. Overall, we demonstrate additional minerals data and their corresponding health and food security benefits within flax and sorghum that could be considered by consumers and breeding programs to facilitate improving seed nutritional content and to help mitigate human malnutrition as well as the effects of rising CO2 stress.

Unraveling the Mechanisms of Zinc Efficiency in Crop Plants: From Lab to Field Applications.

Global food security and sustainability in the time of pandemics (COVID-19) and a growing world population are important challenges that will require optimized crop productivity under the anticipated effects of climate change [...].

Characterization of pea seed nutritional value within a diverse population of Pisum sativum.

Micronutrient malnutrition is a global concern that affects more than two billion people worldwide. Pea (Pisum sativum) is a nutritious pulse crop with potential to assist in tackling hidden hunger. Here we report seed ionomic data of 96 diverse pea accessions collected via inductively coupled plasma mass spectrometry (ICP-MS). We found a 100 g serving of peas provides the following average percent daily value for U.S. recommendations: 8% Ca, 39% Mg, 73% Cu, 37% Fe, 63% Mn, 45% Zn, 28% K, and 43% P. Correlations were observed between the majority of minerals tested suggesting strong interrelationships between mineral concentration levels. Hierarchical clustering identified fifteen accessions with high-ranking mineral concentrations. Thirty accessions could be compared to earlier inductively coupled optical emission spectrometry (ICP-OES) data, which revealed significant differences particularly for elements at extreme low or high levels of accumulation. These results improve our understanding of the range of variation in mineral content found in peas and provide additional mineral data resources for germplasm selection.

Zinc (Zn): The Last Nutrient in the Alphabet and Shedding Light on Zn Efficiency for the Future of Crop Production under Suboptimal Zn.

At a global scale, about three billion people have inadequate zinc (Zn) and iron (Fe) nutrition and 500,000 children lose their lives due to this. In recent years, the interest in adopting healthy diets drew increased attention to mineral nutrients, including Zn. Zn is an essential micronutrient for plant growth and development that is involved in several processes, like acting as a cofactor for hundreds of enzymes, chlorophyll biosynthesis, gene expression, signal transduction, and plant defense systems. Many agricultural soils are unable to supply the Zn needs of crop plants, making Zn deficiency a widespread nutritional disorder, particularly in calcareous (pH > 7) soils worldwide. Plant Zn efficiency involves Zn uptake, transport, and utilization; plants with high Zn efficiency display high yield and significant growth under low Zn supply and offer a promising and sustainable solution for the production of many crops, such as rice, beans, wheat, soybeans, and maize. The goal of this review is to report the current knowledge on key Zn efficiency traits including root system uptake, Zn transporters, and shoot Zn utilization. These mechanisms will be valuable for increasing the Zn efficiency of crops and food Zn contents to meet global needs for food production and nutrition in the 21st century. Furthermore, future research will address the target genes underlying Zn efficiency and the optimization of Zn efficiency phenotyping for the development of Zn-efficient crop varieties for more sustainable crop production under suboptimal Zn regimes, as well food security of the future.

Protein, weight, and oil prediction by single-seed near-infrared spectroscopy for selection of seed quality and yield traits in pea (Pisum sativum).

BACKGROUND: Pea (Pisum sativum) is a prevalent cool-season crop that produces seeds valued for their high protein content. Modern cultivars have incorporated several traits that improved harvested yield. However, progress toward improving seed quality has received less emphasis, in part due to the lack of tools for easily and rapidly measuring seed traits. In this study we evaluated the accuracy of single-seed near-infrared spectroscopy (NIRS) for measuring pea-seed weight, protein, and oil content. A total of 96 diverse pea accessions were analyzed using both single-seed NIRS and wet chemistry methods. To demonstrate field relevance, the single-seed NIRS protein prediction model was used to determine the impact of seed treatments and foliar fungicides on the protein content of harvested dry peas in a field trial. RESULTS: External validation of partial least squares (PLS) regression models showed high prediction accuracy for protein and weight (R2 = 0.94 for both) and less accuracy for oil (R2 = 0.74). Single-seed weight was weakly correlated with protein and oil content in contrast with previous reports. In the field study, the single-seed NIRS predicted protein values were within 10 mg g-1 of an independent analytical reference measurement and were sufficiently precise to detect small treatment effects. CONCLUSION: The high accuracy of protein and weight estimation show that single-seed NIRS could be used in the dual selection of high-protein, high-weight peas early in the breeding cycle, allowing for faster genetic advancement toward improved pea nutritional quality. © 2020 Society of Chemical Industry.

Modulation of early maize seedling performance via priming under sub-optimal temperatures.

Seeds planted in early spring frequently experience low temperature stress in the soil during germination and early plant growth. Seed pretreatments such as priming have been shown to ameliorate the negative effects of cold soil in some crops. However, the potential beneficial effects of priming have not been widely investigated for Zea mays (maize). To investigate seed priming effects, 24 diverse maize inbred lines were primed using a synthetic solid matrix, Micro-Cel E, and then exposed to 10°C soil conditions. Six DSLR cameras captured time lapsed images of emerging seedlings. Manual scoring was used to determine treatment effects on three seedling emergence metrics. Chilling substantially reduced total emergence for two of 24 genotypes evaluated. For these genotypes, priming provided protection allowing nearly full emergence. Priming significantly reduced mean emergence time and increased the emergence uniformity of chilling sensitive genotypes. The results suggest that the cold sensitive genotypes may benefit from priming pretreatment. Kernel density, weight, oil, protein, and starch traits, as determined by single-kernel near infrared spectroscopy, were not correlated with seedling emergence traits supporting a conclusion that early seedling performance cannot be determined from these maize kernel characteristics.

The use of an active learning approach in a SCALE-UP learning space improves academic performance in undergraduate General Biology.

Active learning is a pedagogical approach that involves students engaging in collaborative learning, which enables them to take more responsibility for their learning and improve their critical thinking skills. While prior research examined student performance at majority universities, this study focuses on specifically Historically Black Colleges and Universities (HBCUs) for the first time. Here we present work that focuses on the impact of active learning interventions at Florida A&M University, where we measured the impact of active learning strategies coupled with a SCALE-UP (Student Centered Active Learning Environment with Upside-down Pedagogies) learning environment on student success in General Biology. In biology sections where active learning techniques were employed, students watched online videos and completed specific activities before class covering information previously presented in a traditional lecture format. In-class activities were then carefully planned to reinforce critical concepts and enhance critical thinking skills through active learning techniques such as the one-minute paper, think-pair-share, and the utilization of clickers. Students in the active learning and control groups covered the same topics, took the same summative examinations and completed identical homework sets. In addition, the same instructor taught all of the sections included in this study. Testing demonstrated that these interventions increased learning gains by as much as 16%, and students reported an increase in their positive perceptions of active learning and biology. Overall, our results suggest that active learning approaches coupled with the SCALE-UP environment may provide an added opportunity for student success when compared with the standard modes of instruction in General Biology.

Quantitative trait loci associated with soybean seed weight and composition under different phosphorus levels.

Seed size and composition are important traits in food crops and can be affected by nutrient availability in the soil. Phosphorus (P) is a non-renewable, essential macronutrient, and P deficiency limits soybean (Glycine max) yield and quality. To investigate the associations of seed traits in low- and high-P environments, soybean recombinant inbred lines (RILs) from a cross of cultivars Fiskeby III and Mandarin (Ottawa) were grown under contrasting P availability environments. Traits including individual seed weight, seed number, and intact mature pod weight were significantly affected by soil P levels and showed transgressive segregation among the RILs. Surprisingly, P treatments did not affect seed composition or weight, suggesting that soybean maintains sufficient P in seeds even in low-P soil. Quantitative trait loci (QTLs) were detected for seed weight, intact pods, seed volume, and seed protein, with five significant QTLs identified in low-P environments and one significant QTL found in the optimal-P environment. Broad-sense heritability estimates were 0.78 (individual seed weight), 0.90 (seed protein), 0.34 (seed oil), and 0.98 (seed number). The QTLs identified under low P point to genetic regions that may be useful to improve soybean performance under limiting P conditions.

Enhanced Single Seed Trait Predictions in Soybean (Glycine max) and Robust Calibration Model Transfer with Near-Infrared Reflectance Spectroscopy.

Single seed near-infrared reflectance (NIR) spectroscopy predicts soybean (Glycine max) seed quality traits of moisture, oil, and protein. We tested the accuracy of transferring calibrations between different single seed NIR analyzers of the same design by collecting NIR spectra and analytical trait data for globally diverse soybean germplasm. X-ray microcomputed tomography (µCT) was used to collect seed density and shape traits to enhance the number of soybean traits that can be predicted from single seed NIR. Partial least-squares (PLS) regression gave accurate predictive models for oil, weight, volume, protein, and maximal cross-sectional area of the seed. PLS models for width, length, and density were not predictive. Although principal component analysis (PCA) of the NIR spectra showed that black seed coat color had significant signal, excluding black seeds from the calibrations did not impact model accuracies. Calibrations for oil and protein developed in this study as well as earlier calibrations for a separate NIR analyzer of the same design were used to test the ability to transfer PLS regressions between platforms. PLS models built from data collected on one NIR analyzer had minimal differences in accuracy when applied to spectra collected from a sister device. Model transfer was more robust when spectra were trimmed from 910 to 1679 nm to 955-1635 nm due to divergence of edge wavelengths between the two devices. The ability to transfer calibrations between similar single seed NIR spectrometers facilitates broader adoption of this high-throughput, nondestructive, seed phenotyping technology.

Near-infrared reflectance spectroscopy predicts protein, starch, and seed weight in intact seeds of common bean ( Phaseolus vulgaris L.).

The objective of this study was to explore the potential of near-infrared reflectance (NIR) spectroscopy to determine individual seed composition in common bean ( Phaseolus vulgaris L.). NIR spectra and analytical measurements of seed weight, protein, and starch were collected from 267 individual bean seeds representing 91 diverse genotypes. Partial least-squares (PLS) regression models were developed with 61 bean accessions randomly assigned to a calibration data set and 30 accessions assigned to an external validation set. Protein gave the most accurate PLS regression, with the external validation set having a standard error of prediction (SEP) = 1.6%. PLS regressions for seed weight and starch had sufficient accuracy for seed sorting applications, with SEP = 41.2 mg and 4.9%, respectively. Seed color had a clear effect on the NIR spectra, with black beans having a distinct spectral type. Seed coat color did not impact the accuracy of PLS predictions. This research demonstrates that NIR is a promising technique for simultaneous sorting of multiple seed traits in single bean seeds with no sample preparation.

An innovative plant genomics and gene annotation program for high school, community college, and university faculty.

Today's biology educators face the challenge of training their students in modern molecular biology techniques including genomics and bioinformatics. The Dolan DNA Learning Center (DNALC) of Cold Spring Harbor Laboratory has developed and disseminated a bench- and computer-based plant genomics curriculum for biology faculty. In 2007, a five-day "Plant Genomics and Gene Annotation" workshop was held at Florida A&M University in Tallahassee, FL, to enhance participants' knowledge and understanding of plant molecular genetics and assist them in developing and honing their laboratory and computer skills. Florida A&M University is a historically black university with over 95% African-American student enrollment. Sixteen participants, including high school (56%) and community college faculty (25%), attended the workshop. Participants carried out in vitro and in silico experiments with maize, Arabidopsis, soybean, and food products to determine the genotype of the samples. Benefits of the workshop included increased awareness of plant biology research for high school and college level students. Participants completed pre- and postworkshop evaluations for the measurement of effectiveness. Participants demonstrated an overall improvement in their postworkshop evaluation scores. This article provides a detailed description of workshop activities, as well as assessment and long-term support for broad classroom implementation.

The role of shoot-localized processes in the mechanism of Zn efficiency in common bean.

Zn efficiency (ZE) is the ability of plants to maintain high yield under Zn-deficiency stress in the soil. Two bean ( Phaseolus vulgaris L.) genotypes that differed in ZE, Voyager (Zn-efficient) and Avanti (Zn-inefficient), were used for this investigation. Plants were grown under controlled-environment conditions in chelate-buffered nutrient solution where Zn(2+) activities were controlled at low (0.1 pM) or sufficient (150 pM) levels. To investigate the relative contribution of the root versus the shoot to ZE, observations of Zn-deficiency symptoms in reciprocal grafts of the two genotypes were made. After growth under low-Zn conditions, plants of nongrafted Avanti, self-grafted Avanti and reciprocal grafts that had the Avanti shoot scion exhibited Zn-deficiency symptoms. However nongrafted and self-grafted Voyager, as well as reciprocal grafts with the Voyager shoot scion, were healthy with no visible Zn-deficiency symptoms under the same growth conditions. More detailed investigations into putative shoot-localized ZE mechanisms involved determinations of leaf biomass production and Zn accumulation, measurements of subcellular Zn compartmentation, activities of two Zn-requiring enzymes, carbonic anhydrase and Cu/Zn-dependent superoxide dismutase (Co/ZnSOD), as well as the non-Zn-requiring enzyme nitrate reductase. There were no differences in shoot tissue Zn concentrations between the Zn-inefficient and Zn-efficient genotypes grown under the low-Zn conditions where differences in ZE were exhibited. Shoot Zn compartmentation was investigated using radiotracer ((65)Zn) efflux analysis and suggested that the Zn-efficient genotype maintains higher cytoplasmic Zn concentrations and less Zn in the leaf-cell vacuole, compared to leaves from the Zn-inefficient genotype under Zn deficiency. Analysis of Zn-requiring enzymes in bean leaves revealed that the Zn-efficient genotype maintains significantly higher levels of carbonic anhydrase and Cu/ZnSOD activity under Zn deficiency. While these data are not sufficient to allow us to determine the specific mechanisms underlying ZE, they certainly point to the shoot as a key site where ZE mechanisms are functioning, and could involve processes associated with Zn compartmentation and biochemical Zn utilization.

Zinc efficiency is correlated with enhanced expression and activity of zinc-requiring enzymes in wheat.

Zinc (Zn) is an essential micronutrient for plants. The ability of plants to maintain significant yields under low Zn is termed Zn efficiency (ZE) and its genetic and mechanistic basis is still not well understood. Previously, we showed that root Zn uptake did not play a role in ZE. In the current study, Zn-efficient and -inefficient wheat (Triticum aestivum) genotypes were grown for 13 d in chelate buffer nutrient solutions at low (0.1 pM), sufficient (150 pM), and high (1 microM) Zn(2+) activities and analyzed for root-to-shoot translocation of Zn, subcellular leaf Zn distribution, and activity and expression of the Zn-requiring enzymes in leaves. No correlation between ZE and Zn translocation to the shoot was found. Furthermore, total and water-soluble concentrations of leaf Zn were not associated with ZE, and no differences in subcellular Zn compartmentation were found between Zn-efficient and -inefficient genotypes. However, the expression and activity of the Zn-requiring enzymes copper (Cu)/Zn superoxide dismutase (SOD) and carbonic anhydrase did correlate with differences in ZE. Northern analysis suggested that Cu/ZnSOD gene expression was up-regulated in the Zn-efficient genotype, Kirgiz, but not in inefficient BDME. Under Zn deficiency stress, the very Zn-efficient genotype Kirgiz and moderately Zn-efficient Dagdas exhibited an increased activity of Cu/ZnSOD and carbonic anhydrase when compared with Zn-inefficient BDME. These results suggest that Zn-efficient genotypes may be able to maintain the functioning of Zn-requiring enzymes under low Zn conditions; thus, biochemical Zn utilization may be an important component of ZE in wheat.

How do some plants tolerate low levels of soil zinc? Mechanisms of zinc efficiency in crop plants.

Researchers are beginning to understand how some plant genotypes can maintain reasonable growth and yields under low soil Zn, a trait termed zinc efficiency (ZE). Several studies have shown no correlation between ZE and root Zn uptake, Zn translocation to shoot, or shoot Zn accumulation. Furthermore, it has not been possible to conclusively link differences in leaf subcellular Zn compartmentation with ZE. However, biochemical Zn utilization, including the ability to maintain the activity of Zn requiring enzymes in response to Zn deficiency may be a key component of ZE. The next logical step in investigations of this trait is research on the genetic and molecular basis for ZE, in order to better understand the relationship between Zn utilization and ZE, and to identify the gene(s) controlling ZE. Progress in this research area could provide the knowledge to facilitate the engineering of Zn-efficient plant varieties, which could help both crop production on marginal soils as well as possibly improve the micronutrient density of food crops to help address significant human nutrition problems related to micronutrient deficiency.