@misc{buckner_tong_ottley_williams_2021, title={High-throughput image segmentation and machine learning approaches in the plant sciences across multiple scales}, volume={5}, ISSN={["2397-8562"]}, url={https://doi.org/10.1042/ETLS20200273}, DOI={10.1042/ETLS20200273}, abstractNote={Agriculture has benefited greatly from the rise of big data and high-performance computing. The acquisition and analysis of data across biological scales have resulted in strategies modeling inter- actions between plant genotype and environment, models of root architecture that provide insight into resource utilization, and the elucidation of cell-to-cell communication mechanisms that are instrumental in plant development. Image segmentation and machine learning approaches for interpreting plant image data are among many of the computational methodologies that have evolved to address challenging agricultural and biological problems. These approaches have led to contributions such as the accelerated identification of gene that modulate stress responses in plants and automated high-throughput phenotyping for early detection of plant diseases. The continued acquisition of high throughput imaging across multiple biological scales provides opportunities to further push the boundaries of our understandings quicker than ever before. In this review, we explore the current state of the art methodologies in plant image segmentation and machine learning at the agricultural, organ, and cellular scales in plants. We show how the methodologies for segmentation and classification differ due to the diversity of physical characteristics found at these different scales. We also discuss the hardware technologies most commonly used at these different scales, the types of quantitative metrics that can be extracted from these images, and how the biological mechanisms by which plants respond to abiotic/biotic stresses or genotypic modifications can be extracted from these approaches.}, number={2}, journal={EMERGING TOPICS IN LIFE SCIENCES}, publisher={Portland Press Ltd.}, author={Buckner, Eli and Tong, Haonan and Ottley, Chanae and Williams, Cranos}, editor={Jez, Joseph M. and Topp, Christopher N.Editors}, year={2021}, pages={239–248} }
 @inbook{buckner_madison_melvin_long_sozzani_williams_2020, title={BioVision Tracker: A semi-automated image analysis software for spatiotemporal gene expression tracking in Arabidopsis thaliana}, volume={160}, ISBN={9780128215333}, ISSN={0091-679X}, url={http://dx.doi.org/10.1016/bs.mcb.2020.04.017}, DOI={10.1016/bs.mcb.2020.04.017}, abstractNote={Fluorescence microscopy can produce large quantities of data that reveal the spatiotemporal behavior of gene expression at the cellular level in plants. Automated or semi-automated image analysis methods are required to extract data from these images. These data are helpful in revealing spatial and/or temporal-dependent processes that influence development in the meristematic region of plant roots. Tracking spatiotemporal gene expression in the meristem requires the processing of multiple microscopy imaging channels (one channel used to image root geometry which serves as a reference for relating locations within the root, and one or more channels used to image fluorescent gene expression signals). Many automated image analysis methods rely on the staining of cell walls with fluorescent dyes to capture cellular geometry and overall root geometry. However, in long time-course imaging experiments, dyes may fade which hinders spatial assessment in image analysis. Here, we describe a procedure for analyzing 3D microscopy images to track spatiotemporal gene expression signals using the MATLAB-based BioVision Tracker software. This software requires either a fluorescence image or a brightfield image to analyze root geometry and a fluorescence image to capture and track temporal changes in gene expression.}, booktitle={Methods in Cell Biology}, publisher={Elsevier}, author={Buckner, Eli and Madison, Imani and Melvin, Charles and Long, Terri and Sozzani, Rosangela and Williams, Cranos}, year={2020}, pages={419–436} }
 @inbook{madison_melvin_buckner_williams_sozzani_long_2020, title={MAGIC: Live imaging of cellular division in plant seedlings using lightsheet microscopy}, volume={160}, ISBN={9780128215333}, ISSN={0091-679X}, url={http://dx.doi.org/10.1016/bs.mcb.2020.04.004}, DOI={10.1016/bs.mcb.2020.04.004}, abstractNote={Imaging technologies have been used to understand plant genetic and developmental processes, from the dynamics of gene expression to tissue and organ morphogenesis. Although the field has advanced incredibly in recent years, gaps remain in identifying fine and dynamic spatiotemporal intervals of target processes, such as changes to gene expression in response to abiotic stresses. Lightsheet microscopy is a valuable tool for such studies due to its ability to perform long-term imaging at fine intervals of time and at low photo-toxicity of live vertically oriented seedlings. In this chapter, we describe a detailed method for preparing and imaging Arabidopsis thaliana seedlings for lightsheet microscopy via a Multi-Sample Imaging Growth Chamber (MAGIC), which allows simultaneous imaging of at least four samples. This method opens new avenues for acquiring imaging data at a high temporal resolution, which can be eventually probed to identify key regulatory time points and any spatial dependencies of target developmental processes.}, booktitle={Methods in Cell Biology}, publisher={Elsevier}, author={Madison, Imani and Melvin, Charles and Buckner, Eli and Williams, Cranos and Sozzani, Rosangela and Long, Terri}, year={2020}, pages={405–418} }
 @article{clark_buckner_fisher_nelson_nguyen_simmons_balaguer_butler-smith_sheldon_bergmann_et al._2019, title={Stem-cell-ubiquitous genes spatiotemporally coordinate division through regulation of stem-cell-specific gene networks}, volume={10}, ISSN={["2041-1723"]}, DOI={10.1038/s41467-019-13132-2}, abstractNote={Abstract}, journal={NATURE COMMUNICATIONS}, author={Clark, Natalie M. and Buckner, Eli and Fisher, Adam P. and Nelson, Emily C. and Nguyen, Thomas T. and Simmons, Abigail R. and Balaguer, Maria A. de Luis and Butler-Smith, Tiara and Sheldon, Parnell J. and Bergmann, Dominique C. and et al.}, year={2019}, month={Dec} }