Collaborative Research: EAGER/Tools4Cells: Translating single cell data into an ultra-high resolution spatial map using fluorescent marker genes

合作研究：EAGER/Tools4Cells：使用荧光标记基因将单细胞数据转化为超高分辨率空间图

• 批准号: 2218236
• 负责人: Mao Li
• 金额: $ 8.96万
• 依托单位: Donald Danforth Plant Science Center
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2218236&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; Tools4Cells; Translating

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.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.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.

分子生物学的最新技术进步使确定多细胞生物体中每个细胞内信使RNA转录本的整个群体成为可能。 这项技术被称为单细胞RNA测序（scRNA-seq），有可能广泛应用于植物，以更好地了解它们的发育，进化和胁迫反应。 在该项目中，植物根将被用作模型器官，以构建一个超高分辨率的3D模型，显示嵌入该模型中的单个细胞的基因表达数据。使用这种方法，用户可以在植物器官中定位由荧光标记物标记的细胞，并确定标记和未标记细胞中数千个基因的表达水平。该工具还可用于组合来自不同报告基因的联合收割机荧光图像，以了解标记基因和在同一样品中表达的其它基因两者的基因表达的相似性和差异。 这项工作的更广泛影响包括研究成果的内在价值，这些成果将通过植物细胞图谱（PCA）传播给广大的研究界。这些结果将包括用于收集图像数据的协议，用于构建3D图像的计算管道，以及在植物根的概念模型中注释和分配细胞类型的方法。用于图像分析和机器学习的计算管道将存放在公共存储库中，并附有详细的文档、用户手册和同行评审的出版物。 将为研究生和博士后提供研究培训，并通过与弗吉尼亚州立大学合作，为VSU学生开发高级基因组数据分析培训讲习班。将单个细胞的空间位置与每个细胞内的基因表达模式联系起来是植物细胞生物学研究的前沿。目前可用的scRNA-seq方案不保留每个细胞的空间位置，而使用包埋组织的物理切片的空间转录组方法具有有限的分辨率。EAGER项目的目标是建立一种新的植物空间转录组分析方法。植物研究界的一个主要资源是过去几十年来积累的大量转基因报告基因系（例如启动子-GFP系）。该项目将利用这一大型报告基因资源，使用相同的GFP标记系进行成像和scRNA-seq实验的原理证明研究。使用植物根的分生组织区域作为我们的模型系统，将生成所选启动子-GFP标记系的scRNA-seq数据，并将应用机器学习模型来准确预测GFP+和GFP-细胞。荧光成像和语义标记将用于合并和建模3D根部图像和GFP表达。最后，将开发一种机器学习方法来将scRNA-seq数据映射到3D根模型。这种新方法的结果将与现有数据进行比较，并将在植物中进行验证。总之，这项工作将为开发任何植物物种的3D表达模型提供一种强大的新方法。该方法的结果可用于解决与根以及植物其他组织或器官中发育和胁迫反应中的不对称基因表达相关的问题。 该项目由生物科学理事会的分子和细胞科学部门（细胞动力学和功能计划）以及综合有机体系统（生理机制和生物力学计划）共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。