\u201cWe\u2019ve come very far in our understanding of plant biology, but until recently, there has been a technological bottleneck preventing us from comprehensively cataloguing cell types and the genes they express uniformly, across developmental stages,\u201d says senior author Joseph Ecker, professor, Salk International Council Chair in Genetics, and Howard Hughes Medical Institute investigator. \u201cOur study changes that. We created a foundational gene expression dataset of most cell types, tissues, and organs, across the spectrum of the Arabidopsis life cycle.\u201d<\/p>\n
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From left: Tatsuya Nobori, Natanella Illouz Eliaz, and Travis Lee. Photo: Salk Institute<\/p>\n
How to Map a Plant<\/strong><\/p>\n Over decades of research, Arabidopsis thaliana has been the subject of countless experiments. Scientists have worked to decode its genome, mapping which genes are active in each cell type across different tissues and organs. These incremental maps help researchers understand which genes govern the identity and function of various parts of the plant.<\/p>\n One powerful tool for creating these maps is single-cell RNA sequencing. Unlike DNA sequencing, which reads the genome itself, this technique examines the RNA transcripts a cell produces. This allows scientists to see which genes are actually being used, and in what quantity. By analyzing these gene expression patterns, researchers can distinguish between different cell types, even though every cell contains the same underlying genetic code.<\/p>\n While single-cell RNA sequencing has produced detailed maps, they are often limited to specific organs or tissues \u2014 such as roots, leaving stems, leaves, and flowers uncharted. To build a more complete atlas, Salk researchers combined single-cell RNA sequencing with spatial transcriptomics.<\/p>\n Better Technology, Better Maps<\/strong><\/p>\n Single-cell RNA sequencing requires tissues to be separated and processed in isolation. In contrast, spatial transcriptomics allows researchers to map gene activity while preserving the plant\u2019s natural tissue structure. This method keeps the cells in place, maintaining their location and context within stems, leaves, flowers, and roots. The result is a comprehensive view of cell identity across multiple tissues and organs, providing unprecedented insight into plant development.<\/p>\n \u201cWhat excites me most about this work is that we can now see things we simply couldn\u2019t see before,\u201d says co-first author Natanella Illouz-Eliaz, a postdoctoral researcher in Ecker\u2019s lab. \u201cImagine being able to watch where up to a thousand genes are active all at once, in the real tissue and cell context of the plant. It\u2019s not only fascinating on its own, but it\u2019s already led us to discoveries, like finding genes involved in seedpod development that no one knew about before. There\u2019s so much more waiting to be uncovered in this data, and that sense of possibility is what I am truly enthusiastic about.\u201d<\/p>\n The single-cell and spatial transcriptomic atlas covers 10 developmental stages of Arabidopsis, from a seed in the soil to a flowering adult. Over 400,000 cells were analyzed, revealing the remarkable diversity of cell types that exist within a single plant.<\/p>\n Where the New Map Leads<\/strong><\/p>\n By examining the entire life cycle rather than a single moment in time, researchers have uncovered a dynamic and complex network of cells driving plant development. The study also identified numerous new genes whose activity and roles in specific cell types can now be investigated in greater detail, opening fresh avenues for understanding plant biology.<\/p>\n \u201cThis study will be a powerful tool for hypothesis generation across the entire plant biology field,\u201d says co-first author Travis Lee, a postdoctoral researcher in Ecker\u2019s lab. \u201cOur easy-to-use web application makes this life cycle atlas easily accessible to the plant science community through simply navigating to our website, and we can\u2019t wait to learn from the many single-cell genomic studies it will now enable.\u201d<\/p>\n