Editorial: Women in plant science - linking genome to phenome

Le résumé fourni par la source

The escalating impacts of climate change are intensifying the urgency for plant scientists to identify and5breed plant varieties that are able to withstand increasingly harsh environmental conditions while supporting6a growing human population. A key area of focus is in assessing plant performance and linking it with7genomic data to speed up breeding efforts and bridge the gap between the lab and field. In recent years, the8development of new technologies have led to a rapid drop in the cost of genotyping paired with increased9speed (Pootakham, 2023; Thomas et al., 2023; Scheben et al., 2018). The development of high-throughput10crop phenotyping technologies has lagged, creating the so called phenotyping bottleneck (Ninomiya, 2022).11Traditional phenotyping is slow, destructive, and susceptible to human errors (Gill et al., 2022). The12field of plant phenomics aims to overcome these limitations by utilising imaging technologies and high13performance computing to make phenotyping faster, cheaper, and more accurate (Kumar and Kaushik,142023). This special edition underscores research endeavours aiming to achieve these advancements through15various means, including the construction of platforms for image capture, the development of segmentation16tools for precise data extraction, and the creation of data analysis and management tools to optimize data17utilization and accessibility. Moreover, it emphasizes the integration of these tools with genotypic analysis18to synergize both fields and extract biologically significant information for plant science and breeding.19Additionally, this issue aims to highlight the invaluable contributions of women in the field. The 202120UNESCO science report highlights the continued underrepresentation of women in the sciences, particularly21in computer science and computational biology (Lewis et al., 2021; Bonham and Stefan, 2017). Deep-rooted22biases and gender stereotypes persistently discourage girls and women from pursuing careers in science,23technology, engineering, and mathematics (STEM) fields. Therefore, this special issue seeks to prominently24showcase the outstanding research conducted by female researchers in these domains, shedding light on25their significant contributions and advocating for greater gender inclusivity in science.261Bern ́ad et al. Women in Plant Science - Linking Genome to PhenomeINNOVATIVE IMAGING SET-UPS AND PLATFORMSPlant roots play a crucial role in plant adaptations to stress, yet phenotyping them in a high-throughput27and non-destructive manner remains challenging (Ye et al., 2023). Claussen et al. (2024) introduced28”Chamber 8,” a novel high-throughput, non-destructive root phenotyping system. It uses an automated29imaging platform to automatically X-ray individual plants in a field-like substance. To overcome possible30downstream issues with data processing and analysis, the system takes a holistic approach incorporating31data collection, image processing and trait derivation, with complete automatization from initial irrigation32to trait computation.33Similarly, measuring maize stem diameter is a critical phenotyping trait essential for yield prediction34and resistance assessment (Zhou et al., 2023; Zhang et al., 2020). Manual measurements are laborious,35prompting the introduction of a non-invasive, rapid, field-based system. Zhou et al. (2024) developed a new36method relying on RGB-D cameras that is cost-effective, computationally efficient, and comprehensive,37encompassing data collection, processing, and analysis.38OPTIMIZING DATA UTILIZATIONImage segmentation is often challenging, requiring extensive training and preprocessing, especially with39plants due to their varying colors and shapes that change over time. Despite these difficulties, segmentation40is essential for accurately reflecting plant conditions through imaging data. Qui ̃nones et al. (2023) introduces41a new end-to-end unsupervised deep learning framework called Object State Change using Coattention42Cosegmentation (OSC-CO2). This framework utilizes coattention-based CNNs and cosegmentation-based43dense conditional random fields (CRFs). As the first co-segmentation-based algorithm in plant phenotyping,44OSC-CO2 is trained on high-throughput imaging data, including infrared, visible, fluorescence, and45multiple views, without requiring additional data annotations.46As the volume of data generated annually continues to increase, managing this data effectively becomes47more challenging. Vargas-Rojas et al. (2024) addressed this issue by developing two open-source tools:48AgTC and AgETL, to enhance data collection and management. AgTC generates standardized data49collection templates for use with lab computers or mobile devices, while AgETL handles Extract-Transform-50Load (ETL) processes, integrating data from various formats into a database. These tools simplify data51management and sharing, offering flexibility without requiring programming knowledge.52The importance of (1) careful calibration set design prior to data collection and (2) hyperparameter53optimization for robust model development in future studies is emphasized by Ting et al. (2023). The54authors conducted nitrogen stress experiments with high-throughput phenotyping in rice using hyperspectral55imaging. Their study assessed the ability of HSI-derived data to classify subpopulations and treatment56groups over time, identify plant traits with the highest potential for prediction, and evaluated the57transferability of models developed within one subpopulation or treatment group to predict values in58another. Their findings demonstrate the viability of utilizing canopy-level hyperspectral imaging data to59estimate leaf-level nitrogen (N) and carbon:nitrogen ratio (C:N) across diverse rice varieties.60LINKING GENOME TO PHENOME THROUGH ADVANCED TECHNOLOGIESOur understanding of the plant root system is often limited by relying on 2D imaging and a few simple61traits to characterize a complex 3D structure. Li et al. (2024) used a gel-based optical tomography imaging62platform to capture 3D images for maize roots, than measured 84 univariate traits to fully characterize63Frontiers 2Bern ́ad et al. Women in Plant Science - Linking Genome to Phenomethe root system. Through genome-wide association studies (GWAS) they determined that different traits64captured distinct root system variation as evidenced by non-overlapping quantitative trait loci (QTLs).65These studies corroborate the idea that broadening the range of observed plant traits leads to a more66comprehensive understanding of the plant phenome, thereby improving our capability to link it to the67underlying genetic diversity.68In another study, Agnew et al. (2024) utilize a longitudinal GWAS strategy, with the aim of deepening69our understanding of how plants adapt to stress over time. This approach facilitated the identification of70early QTLs capable of predicting biomass accumulation in sorghum under cold stress conditions. Sorghum71accessions were stratified into distinct clusters based on each heritable trait, showcasing diverse growth72profiles. The authors found that the top-performing accessions, exhibiting superior growth-related traits73across varying temperatures and time frames, offer avenues for further genetic exploration and breeding74endeavors aimed at bolstering biomass yield.75CONCLUSIONThis special edition includes research articles describing critical advancements in plant phenomics that76were made possible by the pivotal role of women researchers in this field. By addressing the multifaceted77challenges posed by climate change, the featured studies demonstrate the integration of innovative imaging78platforms, advanced data management tools, and comprehensive genomic analyses to accelerate breeding79efforts and enhance plant resilience. These interdisciplinary approaches, combining engineering, computer80science, bioinformatics, and plant biology, push the boundaries of our understand

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