Editorial: Omics in seed development: challenges and opportunities for improving of seed quality and yield in model and crop plants

Seeds are a fundamental component in the life cycle of sexually reproducing plants, marking both the beginning of a new generation through germination and the culmination of the reproductive phase through seed production. Plant species exhibit remarkable diversity in seed characteristics, particularly in size, number, and composition, with seeds developing through precisely coordinated programs across embryo, endosperm, and seed coat compartments. In crop plants, seeds represent the most economically significant products, directly influencing both crop quality and yield. Recent advances in omics technologies including genomics, transcriptomics, proteomics, and metabolomics have dramatically enhanced our understanding of seed biology (Liu et al., 2022). These comprehensive findings have provided unprecedented insights into the genetic and molecular mechanisms underlying seed development, germination, and composition (Chen et al., 2023;Yu et al., 2023;Klcova et al., 2024). Seed omics studies have facilitated the identification of key genes and pathways associated with essential traits, contributing to the development of advanced breeding strategies that improve desirable attributes such as nutritional content, fertilization efficiency, and yield (Yuan et al., 2024). Moreover, these studies play a crucial role in optimizing seed quality and enhancing crop resilience to various environmental and climatic challenges. As global food demand continues to rise, insights from seed omics research have become increasingly vital for achieving sustainable agriculture and food security goals.This research topic focuses on recent advances in seed omics research, comprising twelve articles that explore diverse aspects of the field. Five review articles examine recent progress in seed omics, while seven research articles present findings on omics data processing, new methodological developments, and seed trait analysis, as detailed below.Several articles in this research topic have developed new approaches in seed structural imaging, machine learning detection, and breeding procedures. The study by Ashe et al., presented a suite of Synchrotron radiation (SR) related imaging methodologies for applications in research studies using plants seeds. The datasets generated from this study represent diverse plant species that include Citrullus sp. (watermelon), Brassica sp. (canola), Pisum sativum (pea), and Triticum durum (wheat). The authors have introduced the SR micro-computed tomography (SR-µCT) non-destructive imaging method, with advanced capabilities for unveiling detailed internal seed microstructures and their three-dimensional morphologies. Additionally, presented methods for using synchrotron X-rays, including X-ray absorption spectroscopy (XAS) and X-ray fluorescence (XRF) imaging to reveal elemental structural distributions that allow the spatial mapping of micronutrients in seed sub-compartments to determine their speciation characteristics.Once the large omics datasets are acquired, the researchers focus on processing technologies. In this context, a deep learning network SGR-YOLO, proposed by Yao et al., specifically designed to detect seed germination rates in wild rice. The backbone of the network is based on YOLOv7 with the addition of a bi-directional feature pyramid network (BiFPN) and ECA lightweight attention mechanism. The trained model presented in this study will facilitate processing of images of wild rice grains in hydroponic boxes and Petri dishes, outputting bounding boxes that identify each grain, irrespective of germination status.Moreover, Song et al. employed a non-GMO breeding approach to reduce food allergen Len c3 in Lens culinaris seeds. The study, first identified a lipid transfer protein (LTP) gene Lcu.2RBY.4g013600 that encodes the lentil allergen Len c3. The authors of this study introduced gene screening to search for natural mutations of the Len c3 allergen-encoding gene for mitigation of food allergen Len c3. This research resulted in the selection of lentil hybrids with reduced allergenic traits.Nearly every important trait of seeds, such as nutritional content, is regulated by a multitude of factors. ) and eggplant (Solanum melongena L.). A total of 116 NtU-box genes from tobacco and 56 SmU-box genes from eggplant were identified in the genome, which were also categorized into five subfamilies. In addition, phylogenetic analysis also suggested a shared ancestor predating the divergence of six species (tobacco, eggplant, potato, tomato, Arabidopsis thaliana, and pepper).In the other study, Zhang et al. employed genomics and genetics approach to identify the genetic regulatory network for lignin content in Brassica napus seeds using network-based systems that integrate genotype, phenotype, and molecular phenotypes, four QTLs, eighty-three subnetworks, and three modules with 910 genes have been shown to be associated with lignin content.Altogether, the studies within this research topic have showcased innovative techniques in seed research, analyzed seed omics data, and also reviewed current advancements in the field. The progress in seed research methodologies and the analysis of omics data provide comprehensive insights into seed development, germination, and nutritional content. These advancements will assist researchers to identify key proteins, metabolic pathways, and genes associated with desirable seed traits, paving the way for the development of more nutritious and higher yielding seed varieties. With ongoing research and interdisciplinary collaboration, seed omics datasets hold great potential for driving further future innovations in the field.

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