Project Summary/Abstract: This proposal seeks to investigate the mechanisms that mediate nutrient control of lateral root (LR) development by exploiting new single-cell expression data. The ultimate goal is to develop plants with enhanced nitrogen (N)-uptake and N-use-efficiency (NUE). This would help ameliorate the application of excess N-fertilizer - a main cause of water and air pollution that negatively impacts human health. Plant roots can sense levels of nitrate in the soil, which induces LR foraging for nutrients. LRs initiate post-embryonically from differentiated cells in the pericycle layer of the root, which de-differentiate to form “founder cells” that give rise to the post-embryonic LRs meristems. Thus, LRs are vital for root developmental plasticity and nutrient acquisition. Previous studies using GFP-marked cell lines, found that N-responses in roots are cell-type specific. However, those studies lacked the single-cell resolution and time-component necessary to identify the N- regulation of transitional states of cell-types (i.e. pericycle-to-founder cell). This proposal aims to: i) use single- cell N-response time-series data to create developmental trajectories that model the transition from pericycle-to- founder cells and ii) identify/validate TFs that regulate LR initiation in response to N-sensing. To do this, single- cell N-response transcriptome data will be analyzed from N-treated Arabidopsis thaliana roots (Aim 1). To determine the TFàtarget gene relationships in this dataset, cell-specific N-response data will be used to learn gene regulatory networks (GRNs) using a time-based machine learning algorithm called OutPredict. Next, the predicted TFàtarget gene interactions in the GRN will be validated using TARGET, a root cell-based TF perturbation assay (Aim 2). Finally, the function of candidate TFs in N-regulation of LR development and N- uptake will be validated in planta using TF mutants in phenotyping assays (Aim 3). In a preliminary in silico analysis of the outlined approach applied to existing single-cell data from Arabidopsis roots, intersected with N- responsive transcriptome data from whole roots identified: 1. A founder cell pseudotime trajectory and N- responsive TFs (Aim 1), 2. A founder cell N-response GRN that predicts TFàtarget gene interactions, and 3. A preliminary list of N-response TFs from founder cell trajectories (Table 2). These preliminary TFs will be phenotyped for their role in N-responsive LR development and 15N-uptake (Aim 3). This proposal will be the first to collect single-cell N-response data in planta and model LR development using developmental trajectory approaches. This proposal also provides new computational training in single cell-data analysis and machine learning methods for GRN to the PI, which will complement her experimental skills-set in plant molecular biology, and provide her with a multi-faceted research foundation for a career as an independent researcher. This project will ...