A single cell view of the spatiotemporal nitrogen response in roots

根时空氮响应的单细胞视图

• 批准号: 10458510
• 负责人: Carly M Shanks
• 金额: $ 7.42万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-16 至 2023-09-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10458510
• 关键词: Air Pollution; Arabidopsis; Architecture; Auxins; Biological Assay; Cell Differentiation process; Cell Line; Cell division; Cells; Complement; Data; Data Analyses; Data Set; Development; Embryo; Environment; Fertilizers; Foundations; Generations; Genes; Goals; Growth; Hazardous Chemicals; Health; Human; Knowledge; Laboratories; Lateral; Learning; Length; Mediating; Meristem; Methods; Modeling; Molecular Biology; Mouse-ear Cress; Nitrates; Nitrogen; Nutrient; Pathway Analysis; Phenotype; Plant Growth Regulators; Plant Roots; Plants; Reaction Time; Regulation; Research; Research Personnel; Resolution; Role; Series; Soil; Time; Training; Water; Water Pollution; base; career; cell type; comparative; developmental plasticity; experimental study; gene interaction; gene network; gene regulatory network; improved; in silico; insight; machine learning algorithm; machine learning method; mutant; network models; overexpression; overtreatment; response; single cell analysis; single-cell RNA sequencing; skills; spatiotemporal; success; time use; transcriptome; uptake

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 also benefit from a sponsor environment with proven success for providing new training to the PI, as well as, the ideal laboratory and collaborator environment for the for the proposed single-cell and network modeling experiments.

项目概要/摘要：该提案旨在研究介导养分控制的机制 侧根（LR）的发展，通过利用新的单细胞表达数据。最终的目标是开发 提高植物的氮素吸收和氮素利用效率。这将有助于改善应用程序 过量的氮肥-水和空气污染的主要原因，对人类健康产生负面影响。植物根系 可以感知土壤中的硝酸盐水平，从而诱导LR搜寻营养物质。LR在胚胎后开始 来自根的周皮层中的分化细胞，其去分化形成“创始细胞”， 上升到胚后LRs分生组织。因此，LRs对根系发育的可塑性和养分的吸收至关重要。 采集先前的研究使用GFP标记的细胞系，发现根中的N-反应是细胞类型特异性的。 然而，这些研究缺乏识别N- 调节细胞类型的过渡状态（即周环到创始细胞）。该建议旨在：（一）使用单一的- 细胞N-反应时间序列数据，以创建发育轨迹，模拟从周周期到 建立者细胞和ii）鉴定/验证响应于N感测调节LR起始的TF。为了做到这一点，单- 将从N-处理的拟南芥根分析细胞N-应答转录组数据（Aim 1）。到 为了确定该数据集中的TFà靶基因关系，将使用细胞特异性N响应数据来学习 基因调控网络（GRNs）使用基于时间的机器学习算法OutPredict。接下来 GRN中预测的TFàtarget基因相互作用将使用TARGET进行验证，TARGET是一种基于根细胞的TF 干扰测定（目的2）。最后，对候选转录因子在氮调节LR发育和氮调节中的作用进行了初步探讨。 将在表型分析中使用TF突变体在植物中验证摄取（Aim 3）。在计算机模拟的初步研究中， 分析概述的方法适用于现有的单细胞数据，从拟南芥根，N- 鉴定了来自全根的响应性转录组数据：1.创始细胞伪时间轨迹和N- 响应性TF（目标1），2.一个创始细胞N-反应GRN，预测TFàtarget基因的相互作用，和3。一 来自创始细胞轨迹的N-响应TF的初步列表（表2）。这些初步的TF将 针对它们在N-响应LR发育和15 N-摄取中的作用进行表型分析（Aim 3）。这一提议将是第一个 收集植物单细胞N-反应数据，并使用发育轨迹模型LR发育 接近。这一建议也提供了新的计算训练单细胞-数据分析和机器 向PI学习GRN的方法，这将补充她在植物分子生物学方面的实验技能， 并为她作为独立研究人员的职业生涯提供多方面的研究基础。这个项目 还将受益于申办者环境，该环境已被证明成功为PI提供了新的培训，以及， 理想的实验室和协作者环境，用于拟议的单细胞和网络建模 实验