Progress 11/01/22 to 10/31/23
Outputs
Target Audience:The project reached multiple groups during this reporting period. One such group is the larger plant sciences community, through online videos showing stages of the project, maize development, stress techniques, and other plant genetic educational content. These videos were provided to the community through social media via @real_time_science. The project has also reached undergraduate students in the classroom and in summer research opportunities. The initial data, in the form of standardized-high quality RGB images of maize plants undergoing different stresses, has been provided and guidance have been given in image analysis and data analysis of high-throughput phenotyping to a course at the Harris Stowe State University in St. Louis, Missouri. In addition, undergraduate researchers were exposed to the research in the project through summer research experiences that focused on experimental design, plant development and stress response, data collection, and data analysis at both the University of Minnesota and the Donald Danforth Plant Science Center. Changes/Problems:We encountered some challenges related to RNA extraction quality and consistency, which caused delays in generating transcriptomic data for certain samples. In addition, working across multiple data types--RGB imaging, ionomics, transcriptomics, and hyperspectral data--has presented integration challenges due to differences in data structure, scale, and timing. To address these issues, we are optimizing our RNA extraction protocols and sample processing workflows to improve yield and quality. We are also developing a more structured data integration framework, including standardized metadata and time-alignment approaches, to facilitate analysis across data types. These steps are helping us move forward with the multi-dimensional analysis as planned. What opportunities for training and professional development has the project provided?This project has provided meaningful training and professional development opportunities for graduate students, undergraduate researchers, and early-career scientists involved in the work. Trainees have gained hands-on experience with high-throughput phenotyping techniques, including the setup and use of imaging equipment, data collection under controlled stress conditions, and the development of standardized protocols for environmental stress application in maize. These skills are highly transferable and valuable in both academic and industry settings. Graduate students have been trained in advanced data analysis approaches, including statistical modeling of growth curves, Bayesian inference, and multivariate analysis of ionomic data. Several trainees are also gaining experience in bioinformatics as we begin integrating transcriptomic data into the project. These interdisciplinary training experiences are strengthening their computational and analytical skills and preparing them for diverse careers in plant science and data-intensive research. The project has also provided professional development opportunities through scientific writing and presentation. Students have contributed to peer-reviewed publications, including two methodological protocol papers, and are co-authors on upcoming manuscripts. They have presented research findings at national and international conferences, gaining experience in science communication and networking with peers and professionals in the field.? Finally, the collaborative nature of this work has fostered mentorship and leadership opportunities. Students have worked closely with faculty and external collaborators, participated in research planning and troubleshooting, and are developing the ability to lead their own research questions within the broader framework of the project. How have the results been disseminated to communities of interest?We have actively disseminated the results of this project to both scientific and applied communities through a variety of channels. Preliminary findings have been presented at national and international conferences, including sessions focused on high-throughput phenotyping and crop stress biology. These presentations have allowed us to engage with researchers, breeders, and technologists working in related areas, fostering discussion and feedback on our methods and results.? To promote reproducibility and broader adoption of our stress phenotyping approach, we published two peer-reviewed protocol papers in a leading maize research journal. These publications provide detailed, standardized methods for applying drought and heat stress in controlled environments and are intended as practical tools for use by other research groups and breeding programs. What do you plan to do during the next reporting period to accomplish the goals?During the next reporting period, we plan to finalize and submit the manuscript based on our RGB imaging analysis, which captures temporal responses of maize to heat, drought, and combined stress conditions. We will also complete the analysis of the ionomics and transcriptomic datasets, and begin preparing manuscripts that explore how nutrient profiles and gene expression relate to observed stress phenotypes. A major focus will be on integrating the RGB, ionomic, and transcriptomic data to identify multi-layered patterns of stress response and uncover potential biomarkers or pathways associated with resilience. In parallel, we will continue developing our hyperspectral imaging pipeline, with particular emphasis on using seed-level hyperspectral profiles to predict downstream stress responses in plants. These activities will move the project toward completion while contributing new tools and insights to the broader plant research and breeding community.
Impacts
What was accomplished under these goals?
Over the past reporting period, we have made substantial progress toward our objectives focused on characterizing maize stress responses using high-throughput imaging, physiological, and molecular data. Our work has advanced in several key areas, with multiple data streams now converging toward an integrated understanding of how maize responds to abiotic stress. We have continued our analysis of RGB images collected from a controlled experiment subjecting diverse maize genotypes to heat, drought, and combined stress conditions. This work has generated a robust dataset of temporal plant responses across treatments. The first manuscript from this experiment is in the final stages of preparation and will be submitted for publication shortly. In this study, we show that all stress treatments affected plant size, color, and developmental trajectories in measurable and distinct ways. Water use efficiency was consistently reduced under all stress conditions, a finding that underscores the broad physiological cost of abiotic stress. Notably, our temporal analysis of growth curves revealed genotype-specific differences in the timing of stress response. We implemented Bayesian growth modeling to determine the point at which the posterior predictive distribution of each stress treatment no longer overlapped with the control condition--offering a statistically rigorous method to define the onset of measurable stress effects across genotypes and treatments. As part of our commitment to supporting the broader research community, we published two peer-reviewed manuscripts in a leading maize research journal that describe our standardized protocols for applying controlled heat and drought stress treatments. These resources are designed to facilitate reproducibility and data sharing across studies, providing the community with practical tools to implement comparable stress environments in growth chamber and greenhouse experiments. The standardization of stress protocols will be valuable for generating more consistent phenotypic data and accelerating comparative studies across labs and programs. In parallel with our imaging analysis, we have now received ionomics data from the same experiment. Initial analyses have identified differentially quantified macro- and micronutrients between control and stress treatments. While we are observing some shared patterns in nutrient accumulation and depletion across stress types, we are also detecting stress-specific responses that suggest distinct metabolic adjustments. This dataset will be critical in linking physiological traits to underlying nutrient dynamics and will support future publications. To complement the phenotypic and ionomic analyses, we are currently generating sequencing data from leaf tissues collected under each treatment. Our goal is to integrate gene expression profiles with imaging and ionomics data to develop a multi-dimensional understanding of maize stress response at the morphological, physiological, and molecular levels. This systems-level approach will allow us to identify candidate genes, pathways, and biomarkers associated with stress resilience or susceptibility. Additionally, we have begun pipeline development for hyperspectral imaging analysis. This work includes both leaf-level stress detection and the innovative use of seed hyperspectral profiles to develop predictive models of future stress responses. By linking early, non-destructive seed imaging to downstream plant performance under stress, we aim to explore novel strategies for stress prediction and resilience screening.? In summary, we have made strong progress on multiple fronts of this project. Our imaging-based phenotyping workflows are yielding valuable insights and are being actively shared with collaborators. Ionomics and transcriptomics data are enhancing the depth of our analyses, and the new hyperspectral imaging work is expanding our methodological toolkit. Collectively, these efforts are moving us toward our goal of developing data-driven strategies to understand and predict maize stress responses, and we are excited by the scientific momentum and forthcoming outputs from this work.
Publications
- Type:
Peer Reviewed Journal Articles
Status:
Published
Year Published:
2024
Citation:
Murphy M, Casto A, Chavez L, Lima L, Qui�ones A, Gehan M, Hirsch C. 2024. Maize abiotic stress treatments in controlled environments. Cold Spring Harbor Protocols. doi: 10.1101/pdb.prot108620
- Type:
Peer Reviewed Journal Articles
Status:
Published
Year Published:
2024
Citation:
Qui�ones A, Lima L, Murphy M, Casto A, Gehan M, Hirsch C. 2024. Optimized methods for applying and assessing heat, drought, and nutrient stress of maize seedlings in controlled environment experiments. Cold Spring Harbor Protocols. doi: 10.1101/pdb.top108467
- Type:
Peer Reviewed Journal Articles
Status:
Published
Year Published:
2024
Citation:
Murphy K, Ludwig E, Gutierrez J, Gehan M. 2024. Deep learning in image-based plant phenotyping. Annual Review of Plant Biology. 75. doi: 10.1146/annurev-arplant-070523-042828
- Type:
Peer Reviewed Journal Articles
Status:
Published
Year Published:
2025
Citation:
Rico o A, Ludwig E, Casto A, Zurich S, Sumner J, Bird K, Edger P, Mockler T, Hegeman A, Gehan M, Greenham K. 2025. Homoeolog expression divergence contributes to time of day changes in transcriptomic and glucosinolate responses to prolonged water limitation in brassica napes. The Plant Journal. doi: 10.1111/tpj.70011
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Progress 11/01/21 to 10/31/22
Outputs
Target Audience:The project reached multiple groups during this reporting period. One such group is the larger plant sciences community, through online videos showing the initial stages of the project, maize development, stress techniques, and other plant genetic educational content. These videos were provided to the community mainly through TikTok (@real_time_science). The project has also reached undergraduate students in the classroom and in summer research opportunities. The initial data, in the form of standardized-high quality RGB images of maize plants undergoing different stresses, has been provided and guidance have been given in image analysis and data analysis of high-throughput phenotyping to a course at the Harris Stowe State University in St. Louis, Missouri. In addition, undergraduate researchers were exposed to the research in the project through summer research experiences that focused on experimental design, plant development and stress response, data collection, and data analysis. Changes/Problems:There have not been or do we foresee any major changes or problems in our approach. The only change that has occurred is the location the plants have been grown to reach project objectives. We originally planned to have paired experiments running at the University of Minnesota and the Donald Danforth Plant Science Center. In this approach half of the experiment replications would have been grown at each location. To have more consistent stress conditions and data collection across experiments we changed to having all plants grown, stressed, and phenotyped at the Donald Danforth Plant Science Center. Along with having more consistent data and plants being stressed at the same time and location, which will hopefully improve our data comparisons, we also have collected the data for all experiments that were planned for the project, so the timeline on data collection has been improved. The only downside of this change was that plants were not imaged using a hyperspectral camera throughout the experiments as that infrastructure wasn't in place, but we did take hyperpectral measurements on the last day of the experiments have that data in hand to complete project objectives. What opportunities for training and professional development has the project provided?Undergraduate and graduate students, and postdoctoral researchers on this project have been trained in experimental design, high-throughput phenotyping techniques, computational genomics, and large data analytics through the research activities associated with this project. This first year of the project have been heavily focused on getting the project running, organized, and in good position to complete project objectives timely. All members of the project have been involved in the process from planning to execution. The postdoctoral research scientist on the project has also gained experience in science communication by making TikTok videos about the project and ongoing work and also in presenting the project in professional scientific settings as well (2 invited seminars). How have the results been disseminated to communities of interest?The project reached multiple groups during this reporting period. One such group is the larger plant sciences community, through online videos showing the initial stages of the project, maize development, stress techniques, and other plant genetic educational content. These videos were provided to the community mainly through TikTok (@real_time_science). The project has released over 20 videos this year and have had over 150,000 views. The project has also reached undergraduate students in the classroom and in summer research opportunities. The initial data, in the form of standardized-high quality RGB images of maize plants undergoing different stresses, has been provided and guidance have been given in image analysis and data analysis of high-throughput phenotyping to a course at the Harris Stowe State University in St. Louis, Missouri. In addition, undergraduate researchers were exposed to the research in the project through summer research experiences that focused on experimental design, plant development and stress response, data collection, and data analysis.The project has also been presented in two different oral presentations that were attended by the broader plant sciences community. What do you plan to do during the next reporting period to accomplish the goals?As we have been making consistent measurable progress towards the goals of this project we plan to continue moving forward using an approach consistent with the previous years of this project. More specifically in the coming year we plan to focus heavily on the analysis side of our collected visible and hyperspectral data. This will include determine the response to stress of each genotype and assess the traits that are effected based on the stress applied. We will also continue to increase the output of our trait acquisition pipeline to be robust and include new important morphological traits. We will also begin to work on a trait extraction and analysis pipeline to begin to assess NIR changes due to different stresses. In the second year we also plan to have all the ionomics and an initial set of gene expression samples to be processed and initial analysis of those datasets to begin as well. If we can avoid delays in processing and initial data analysis we also plan to start to work on models that will integrate all the different data types collected from the project together.
Impacts
What was accomplished under these goals?
Data Collection: The main work accomplished towards completing project goals has been in generating all the data to complete the objectives. The project involves multiple stress environments (heat, drought, reduced nitrogen, and reduced phosphorus). To start the project pilot experiments were run using 3 maize genotypes. The genotypes were then put into varying stress environments for us to be able to test conditions that would produce a measurable stress response. Once the conditions for stressing were understood we continued to run full stress experiments using the Bellwether facility at the Donald Danforth Plant Science Center. This greenhouse is able to hold ~1,200 plants that are precisely environmentally controlled and imaged daily with both an RGB camera and a NIR camera. We have run 47 maize inbred genotypes through control, heat, drought, reduced nitrogen, and reduced phosphorus conditions using the Bellwether facility with six replications for each line. The plants were grown for a totally of 21 days. Stress conditions were applied starting at day 7 and continued until day 21. Each plant was imaged using the two different cameras daily. In addition, each plant had hyperspectral profiles collected (1000 wavelengths from ~400-1000nm) to better assess light reflectance outside the visible range on the last day of stress (day 21). Lastly, three replications for each genotype and each growth condition had leaf samples collected for future gene expression analysis (RNAseq) and element profiling (ionomics). We have also collected data from the seeds of all 47 maize genotypes used in this project. We have successful collected hyperspectral images for ~100 seeds from each genotype that will be used for completion of objective 3. A small subset of these seeds (10) have also been submitted for ionomics analysis. Data Analysis: We have completed initial data analysis of the phenotyping data that this project is generating, namely the RGB and hyperspectral data collected from the different stress environments. We have spent time to complete a image analysis pipeline to quickly and automatically separate the plant and extract morphological traits (height, width) and color traits from the plants. We used this pipeline to originally determine stress response in our pilot study to ensure our conditions were appropriate for the genotypes being studied. We are in the processing and analyzing all the images from our larger stress experiments (~20,000 images). We are currently assessing the level of stress response with replications and between genotypes. We will initially be using this data to help us select lines for further gene expression analysis as we are not able to sequence all samples due to budget constraints. Currently our RGB analysis pipeline extracts ~15 morphological traits and color trait information. These are core traits that are necessary for us to assess maize seedling stress responses. We are also working to improve the tools available and traits extracted in the pipeline. We are working to implement a leaf counting algorithm and a leaf angle algorithm into the pipeline. We believe these traits will help us understand more nuanced stress responses and also to evaluate development variation easier as well. In conjunction with the RGB image analysis pipeline, we have also been completing pipelines to extract wavelength reflectance information from hyperspectral seed images. We have developed a pipeline that will extract information for each individual seed in the image. We have initially used this in a machine learning model to predict what genotype each seed belongs too. This isn't a direct project objective, but is showing the power of hyperspectral to be able to detect seed genotype and is getting project participants familiar with the data extraction and analysis methods to complete future project objectives. Overall: Through this first year of the project we have made a lot of progress towards all objectives of the project. We have completed all the visible and hyperspectral phenotyping for the project, collected samples for ionomics and gene expression analysis, improved traits extracted in our pipelines, and are currently processing ionomic and gene expression samples, and analysis trait responses to stress environments.
Publications
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2022
Citation:
Murphy K. Beat the Heat: How maize responds to heat stress. Donald Danforth Plant Science Center Seminar. October 4, 2022
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2022
Citation:
Murphy K. Correlating maize metabolic and phenotypic responses to heat stress. NSF PGRP Annual Award Meeting. September 7, 2022
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