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SUMMARY Maize anthers emerge from male‐only florets, a process that involves complex genetic programming and is affected by environmental factors. Quantifying anther exertion provides a key indicator of male fertility; however, traditional manual scoring methods are often subjective and labor‐intensive. To address this limitation, we developedTasselyzer— an accessible, cost‐effective, and time‐saving method for quantifying maize anther exertion. This image‐based program uses the PlantCV platform to provide a quantitative assessment of anther exertion by capturing regional differences within the tassel based on the distinct color of anthers. We applied this method to 22 maize lines with six genotypes, showing high precision (F₁score > 0.8). Furthermore, we demonstrate that customizing the parameters to assay a specific line is straightforward and practical for enhancing precision in additional genotypes. Tasselyzer is a valuable resource for maize research and breeding programs, enabling automated and efficient assessments of anther exertion. 

Abstract We review how a data infrastructure for the Plant Cell Atlas might be built using existing infrastructure and platforms. The Human Cell Atlas has developed an extensive infrastructure for human and mouse single cell data, while the European Bioinformatics Institute has developed a Single Cell Expression Atlas, that currently houses several plant data sets. We discuss issues related to appropriate ontologies for describing a plant single cell experiment. We imagine how such an infrastructure will enable biologists and data scientists to glean new insights into plant biology in the coming decades, as long as such data are made accessible to the community in an open manner. 

SUMMARY The demand for agricultural production is becoming more challenging as climate change increases global temperature and the frequency of extreme weather events. This study examines the phenotypic variation of 149 accessions ofBrachypodium distachyonunder drought, heat, and the combination of stresses. Heat alone causes the largest amounts of tissue damage while the combination of stresses causes the largest decrease in biomass compared to other treatments. Notably, Bd21‐0, the reference line forB. distachyon, did not have robust growth under stress conditions, especially the heat and combined drought and heat treatments. The climate of origin was significantly associated withB. distachyonresponses to the assessed stress conditions. Additionally, a GWAS found loci associated with changes in plant height and the amount of damaged tissue under stress. Some of these SNPs were closely located to genes known to be involved in responses to abiotic stresses and point to potential causative loci in plant stress response. However, SNPs found to be significantly associated with a response to heat or drought individually are not also significantly associated with the combination of stresses. This, with the phenotypic data, suggests that the effects of these abiotic stresses are not simply additive, and the responses to the combined stresses differ from drought and heat alone. 

Summary Plant responses to abiotic environmental challenges are known to have lasting effects on the plant beyond the initial stress exposure. Some of these lasting effects are transgenerational, affecting the next generation. The plant response to elevated carbon dioxide (CO₂) levels has been well studied. However, these investigations are typically limited to plants grown for a single generation in a high CO₂environment while transgenerational studies are rare.We aimed to determine transgenerational growth responses in plants after exposure to high CO₂by investigating the direct progeny when returned to baseline CO₂levels.We found that both the flowering plantArabidopsis thalianaand seedless nonvascular plantPhyscomitrium patenscontinue to display accelerated growth rates in the progeny of plants exposed to high CO₂. We used the model species Arabidopsis to dissect the molecular mechanism and found that DNA methylation pathways are necessary for heritability of this growth response.More specifically, the pathway of RNA‐directed DNA methylation is required to initiate methylation and the proteins CMT2 and CMT3 are needed for the transgenerational propagation of this DNA methylation to the progeny plants. Together, these two DNA methylation pathways establish and then maintain a cellular memory to high CO₂exposure. 

Abstract High‐throughput phenotyping (HTP) has emerged as one of the most exciting and rapidly evolving spaces within plant science. The successful application of phenotyping technologies will facilitate increases in agricultural productivity. High‐throughput phenotyping research is interdisciplinary and may involve biologists, engineers, mathematicians, physicists, and computer scientists. Here we describe the need for additional interest in HTP and offer a primer for those looking to engage with the HTP community. This is a high‐level overview of HTP technologies and analysis methodologies, which highlights recent progress in applying HTP to foundational research, identification of biotic and abiotic stress, breeding and crop improvement, and commercial and production processes. We also point to the opportunities and challenges associated with incorporating HTP across food production to sustainably meet the current and future global food supply requirements. 

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning. 

Abstract The Plant Cell Atlas (PCA) community hosted a virtual symposium on December 9 and 10, 2021 on single cell and spatial omics technologies. The conference gathered almost 500 academic, industry, and government leaders to identify the needs and directions of the PCA community and to explore how establishing a data synthesis center would address these needs and accelerate progress. This report details the presentations and discussions focused on the possibility of a data synthesis center for a PCA and the expected impacts of such a center on advancing science and technology globally. Community discussions focused on topics such as data analysis tools and annotation standards; computational expertise and cyber‐infrastructure; modes of community organization and engagement; methods for ensuring a broad reach in the PCA community; recruitment, training, and nurturing of new talent; and the overall impact of the PCA initiative. These targeted discussions facilitated dialogue among the participants to gauge whether PCA might be a vehicle for formulating a data synthesis center. The conversations also explored how online tools can be leveraged to help broaden the reach of the PCA (i.e., online contests, virtual networking, and social media stakeholder engagement) and decrease costs of conducting research (e.g., virtual REU opportunities). Major recommendations for the future of the PCA included establishing standards, creating dashboards for easy and intuitive access to data, and engaging with a broad community of stakeholders. The discussions also identified the following as being essential to the PCA's success: identifying homologous cell‐type markers and their biocuration, publishing datasets and computational pipelines, utilizing online tools for communication (such as Slack), and user‐friendly data visualization and data sharing. In conclusion, the development of a data synthesis center will help the PCA community achieve these goals by providing a centralized repository for existing and new data, a platform for sharing tools, and new analytical approaches through collaborative, multidisciplinary efforts. A data synthesis center will help the PCA reach milestones, such as community‐supported data evaluation metrics, accelerating plant research necessary for human and environmental health. 

With growing populations and pressing environmental problems, future economies will be increasingly plant-based. Now is the time to reimagine plant science as a critical component of fundamental science, agriculture, environmental stewardship, energy, technology and healthcare. This effort requires a conceptual and technological framework to identify and map all cell types, and to comprehensively annotate the localization and organization of molecules at cellular and tissue levels. This framework, called the Plant Cell Atlas (PCA), will be critical for understanding and engineering plant development, physiology and environmental responses. A workshop was convened to discuss the purpose and utility of such an initiative, resulting in a roadmap that acknowledges the current knowledge gaps and technical challenges, and underscores how the PCA initiative can help to overcome them.