NSF Award
2218236
Recent technical advances in molecular biology now make it possible to determine the entire population of messenger RNA transcripts within each individual cell of a multicellular organism. This technology, known as single-cell RNA sequencing (scRNA-seq), has the potential to be broadly applied in plants to better understand their development, evolution, and stress responses. In this project, the plant root will be used as a model organ to construct an ultra-high-resolution 3D-model displaying gene expression data from individual cells embedded in this model. Using this method, a user can locate cells labeled by a fluorescent marker in a plant organ and determine the expression levels of thousands of genes in both labeled and unlabeled cells. This tool can also be used to combine fluorescent images from different reporter genes to understand the similarity and differences of gene expression for both the marker gene and other genes expressed in the same sample. The Broader Impacts of the work include the intrinsic merit of the research results, which will be disseminated to the broad research community via the Plant Cell Atlas (PCA). These results will include protocols for collecting image data, a computational pipeline for constructing 3D images, and a method to annotate and assign cell types in a conceptual model of plant roots. The computational pipeline for image analysis and machine learning will be deposited to public repository with detailed documentation and user manuals and peer-reviewed publications. Research training will be provided to graduate students and a post-doc and, through a collaboration with Virginia State University, training workshops will be developed for advanced genomic data analysis for VSU students.<br/><br/>Connecting spatial location of individual cells and gene expression patterns within each cell is the frontier of plant cell biology research. Currently available scRNA-seq protocols do not preserve spatial locations of each cell, whereas spatial transcriptome approaches using physical slices of embedded tissues have limited resolution. The goal of this EAGER project is to establish a new approach for spatial transcriptome analysis in plants. One major resource from the plant research community is a large number of transgenic reporter gene lines (e.g. promoter-GFP lines) that have been accumulated for the past several decades. This project will leverage this large reporter gene resource to perform a proof of principle study using the same GFP marker lines for both imaging and scRNA-seq experiments. Using the meristematic region of plant roots as our model system, scRNA-seq data for selected promoter-GFP marker lines will be generated and machine learning models will be applied to accurately predict GFP+ and GFP- cells. Fluorescent imaging and sematic labeling will be used to merge and model 3D root images and GFP expression. Finally, a machine learning method will be developed to map the scRNA-seq data to the 3D root model. Results from this new approach will be compared with existing data and will be validated in planta. Together, this work will provide a powerful new approach to develop 3D expression models for any plant species. Results from this method can be used to address questions related to asymmetrical gene expression in development and stress responses in roots, as well as in other tissues or organs in plants. This project is jointly funded by the Divisions of Molecular and Cellular Sciences (Cellular Dynamics and Function program) together with Integrative Organismal Systems (Physiological Mechanisms and Biomechanics program) , both in the Biological Sciences Directorate.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
