First Plant Cell Atlas symposium report

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

Morphological Plant Modeling: Unleashing Geometric and Topological Potential within the Plant Sciences

The geometries and topologies of leaves, flowers, roots, shoots, and their arrangements have fascinated plant biologists and mathematicians alike. As such, plant morphology is inherently mathematical in that it describes plant form and architecture with geometrical and topological techniques. Gaining an understanding of how to modify plant morphology, through molecular biology and breeding, aided by a mathematical perspective, is critical to improving agriculture, and the monitoring of ecosystems is vital to modeling a future with fewer natural resources. In this white paper, we begin with an overview in quantifying the form of plants and mathematical models of patterning in plants. We then explore the fundamental challenges that remain unanswered concerning plant morphology, from the barriers preventing the prediction of phenotype from genotype to modeling the movement of leaves in air streams. We end with a discussion concerning the education of plant morphology synthesizing biological and mathematical approaches and ways to facilitate research advances through outreach, cross-disciplinary training, and open science. Unleashing the potential of geometric and topological approaches in the plant sciences promises to transform our understanding of both plants and mathematics

Morphological diversity and quantitative genetics of the maize shoot apical meristem

The maize shoot apical meristem (SAM) comprises a small pool of stem cells that generate all the organs in the above ground plant. Mutational analyses have identified genetic networks regulating SAM function, although little is known about the genetic determinants of SAM morphological variation in natural populations. We utilized high-throughput image processing to capture rich variation in SAM size for a diverse panel of maize inbred varieties, wild teosinte isolates, and a domesticated maize x wild progenitor teosinte backcross population. Focusing on diverse maize inbred lines, we identified significant correlations between seedling SAM size and agronomically-important adult plant traits such as flowering time, stem size, and leaf node number. Combining SAM phenotype data with a 1.2-million-SNP dataset in a genome-wide association study (GWAS) revealed unexpected SAM morphology candidate genes. We further confirmed correlations between SAM morphology and trait-associated SNP (TAS) alleles of several GWAS-derived SAM candidate genes through in situ hybridization and cell number and size estimation via image segmentation. Our data illustrate that the microscopic seedling SAM is predictive of adult phenotypes and that SAM morphometric variation is associated with genes that were not previously predicted to regulate SAM size. In further exploration of natural variation of SAM shape and size, we implemented rapid and complex morphometric modeling approaches to quantify SAM morphology. Quantitative trait loci (QTL) mapping results suggest that a majority of genetically-attributable SAM shape and size variation can be captured by estimating the SAM as a paraboloid, which has several advantages for high-throughput phenotyping methods. Further application of this model to a broad sampling of evolutionarily-distant plant species suggests that a parabolic SAM may be a universal trait of plant meristems. Future investigations into the mechanisms that orchestrate parabolic SAM parameters may reveal additional correlations between SAM architecture and adult plant morphology that transcend phylogenetic determinants

Modeling the morphometric evolution of the maize shoot apical meristem

The maize (Zea mays subsp. mays L.) shoot apical meristem (SAM) is a self-replenishing pool of stem cells that produces the above-ground plant. Improvements in image acquisition and processing techniques have allowed high-throughput, quantitative genetic analyses of SAM morphology. As with other large-scale phenotyping efforts, meaningful descriptions of genetic architecture depend on the collection of relevant measures. In this study, we tested two quantitative image processing methods to describe SAM morphology within the genus Zea, represented by 33 wild relatives of maize and 841 lines from a domesticated maize by wild teosinte progenitor (MxT) backcross population, along with previously-reported data from several hundred diverse maize inbred lines. Approximating the MxT SAM as a paraboloid derived eight parabolic estimators of SAM morphology that identified highly-overlapping QTL on eight chromosomes, which implicated previously-identified SAM morphology candidate genes along with new QTL for SAM morphological variation. Using a Fourier-transform related method of comprehensive shape analysis, we detected cryptic SAM shape variation that identified QTL on six chromosomes. We found that Fourier transform shape descriptors and parabolic estimation measures are highly correlated and identified similar QTL. Analysis of shoot apex contours from 73 anciently-diverged plant taxa further suggested that parabolic shape may be a universal feature of plant SAMs, regardless of evolutionary clade. Future high-throughput examinations of SAM morphology may benefit from the ease of acquisition and phenotypic fidelity of modeling the SAM as a paraboloid

First Plant Cell Atlas symposium report.

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

Plant single-cell solutions for energy and the environment.

Progress in sequencing, microfluidics, and analysis strategies has revolutionized the granularity at which multicellular organisms can be studied. In particular, single-cell transcriptomics has led to fundamental new insights into animal biology, such as the discovery of new cell types and cell type-specific disease processes. However, the application of single-cell approaches to plants, fungi, algae, or bacteria (environmental organisms) has been far more limited, largely due to the challenges posed by polysaccharide walls surrounding these species' cells. In this perspective, we discuss opportunities afforded by single-cell technologies for energy and environmental science and grand challenges that must be tackled to apply these approaches to plants, fungi and algae. We highlight the need to develop better and more comprehensive single-cell technologies, analysis and visualization tools, and tissue preparation methods. We advocate for the creation of a centralized, open-access database to house plant single-cell data. Finally, we consider how such efforts should balance the need for deep characterization of select model species while still capturing the diversity in the plant kingdom. Investments into the development of methods, their application to relevant species, and the creation of resources to support data dissemination will enable groundbreaking insights to propel energy and environmental science forward

Plant single-cell solutions for energy and the environment.

Progress in sequencing, microfluidics, and analysis strategies has revolutionized the granularity at which multicellular organisms can be studied. In particular, single-cell transcriptomics has led to fundamental new insights into animal biology, such as the discovery of new cell types and cell type-specific disease processes. However, the application of single-cell approaches to plants, fungi, algae, or bacteria (environmental organisms) has been far more limited, largely due to the challenges posed by polysaccharide walls surrounding these species' cells. In this perspective, we discuss opportunities afforded by single-cell technologies for energy and environmental science and grand challenges that must be tackled to apply these approaches to plants, fungi and algae. We highlight the need to develop better and more comprehensive single-cell technologies, analysis and visualization tools, and tissue preparation methods. We advocate for the creation of a centralized, open-access database to house plant single-cell data. Finally, we consider how such efforts should balance the need for deep characterization of select model species while still capturing the diversity in the plant kingdom. Investments into the development of methods, their application to relevant species, and the creation of resources to support data dissemination will enable groundbreaking insights to propel energy and environmental science forward

Morphological plant modeling: Unleashing geometric and topologic potential within the plant sciences

Plant morphology is inherently mathematical. The geometries of leaves and flowers and intricate topologies of the root have fascinated plant biologists and mathematicians alike. Beyond providing aesthetic inspiration, understanding plant morphology has become pressing in an era of climate change and a growing population. Gaining an understanding of how to modify plant architecture through molecular biology and breeding is critical to improving agriculture, and the monitoring of ecosystems and global vegetation is vital to modeling a future with fewer natural resources. In this white paper, we begin by summarizing the rich history and state of the art in quantifying the form of plants, mathematical models of patterning in plants, and how plant morphology manifests dynamically across disparate scales of biological organization. We then explore the fundamental challenges that remain unanswered concerning plant morphology, from the barriers preventing the prediction of phenotype from genotype to modeling the fluttering of leaves in a light breeze. We end with a discussion concerning the education of plant morphology synthesizing biological and mathematical approaches and ways to facilitate research advances through outreach, cross-disciplinary training, and open science. Never has the need to model plant morphology been more imperative. Unleashing the potential of geometric and topological approaches in the plant sciences promises to transform our understanding of both plants and mathematics