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EAGER: Single-cell, spatial transcriptomics of plant-fungal interactions using a maskless array technology

EAGER：使用无掩模阵列技术进行植物-真菌相互作用的单细胞空间转录组学

基本信息

批准号：
1945854
负责人：
Blake Meyers
金额：
$ 29.94万
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：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1945854&HistoricalAwards=false
关键词：
EAGER Single cell spatial transcriptomics

项目摘要

The goal of this proposal is to develop new technology to allow detection of RNA molecules produced by single, living cells. Cells contain thousands of RNAs, and the RNAs produced from one cell may differ from the RNAs in a neighboring cell. Detecting these differences on a cell-by-cell basis is not easy, especially for plant cells. This project will create a method that transcends past technical limitations by preserving spatial information, thereby making it possible to tell which RNAs come from which cells. The new method will be put to the test by identifying RNAs in single plant cells that are undergoing infection by a fungal pathogen. This is a high-risk project that involves a complicated experimental design that will be challenging to implement. If successful, the results will provide plant biologists with a revolutionary new tool for studying cell-specific gene-to-RNA-to-trait relationships. This could lead to new discoveries in understanding plant diseases, which in turn could advance efforts to breed disease resistant plants. The project will be led by a postdoctoral scientist, who will receive multidisciplinary training in plant biology, microscopy, genomics, and computational science. Several methods have been established in plants for transcriptional analysis in tissue sections, cell types or even single cells. These methods typically require cell sorting, transgenic plants, protoplast production, or other damaging or laborious processes. Most of these technologies lose most or all spatial resolution during implementation, while those that offer high spatial resolution for RNA localization lack breadth in the number of transcripts characterized, particularly for more challenging RNAs like small RNAs. Plant small RNAs function in many more pathways than other organisms, yet there are no techniques for their single- cell analysis other than fluorescent in situ hybridization microscopy typically performed with just one or a few small RNAs (or mRNAs) at a time. A new approach, spatial transcriptomics, has been described that overcomes many of these limitations for mRNA analysis; RNA-seq libraries are made using microarrays with high positional resolution within sectioned materials. Yet, spatial transcriptomics has not been developed for small RNAs or cleaved mRNAs, and the implementations described to date still suffer from various shortcomings. This project will substantially advance this method, using high-resolution arrays (14 micron grids with 1 micron linear gaps) built on a maskless array synthesis instrument, to enable near single-cell analysis of diverse types of RNAs in plants and potentially other organisms. The test bed for this work is a system in which such high-resolution analyses would be highly informative, namely plant-pathogen interactions, for which localized and systemic transcriptional responses will be measured. This award is jointly funded by the Genetic Mechanisms and the Cellular Dynamics and Function programs in the Division of Molecular and Cellular Biosciences 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.

该提案的目标是开发新技术来检测单个活细胞产生的 RNA 分子。细胞含有数千种 RNA，并且一个细胞产生的 RNA 可能与邻近细胞中的 RNA 不同。逐个细胞地检测这些差异并不容易，尤其是对于植物细胞。该项目将创建一种通过保留空间信息来超越过去技术限制的方法，从而能够区分哪些 RNA 来自哪些细胞。新方法将通过鉴定正在遭受真菌病原体感染的单个植物细胞中的 RNA 来进行测试。这是一个高风险的项目，涉及复杂的实验设计，实施起来具有挑战性。如果成功，这些结果将为植物生物学家提供一种革命性的新工具，用于研究细胞特异性基因与 RNA 与性状的关系。这可能会带来了解植物病害的新发现，进而推动培育抗病植物的努力。该项目将由一名博士后科学家领导，他将接受植物生物学、显微镜学、基因组学和计算科学方面的多学科培训。植物中已经建立了几种用于组织切片、细胞类型甚至单细胞转录分析的方法。这些方法通常需要细胞分选、转基因植物、原生质体生产或其他破坏性或费力的过程。这些技术中的大多数在实施过程中会损失大部分或全部空间分辨率，而那些为 RNA 定位提供高空间分辨率的技术缺乏表征转录本数量的广度，特别是对于更具挑战性的 RNA（如小 RNA）。植物小RNA比其他生物体在更多的途径中发挥作用，但除了通常一次仅使用一个或几个小RNA（或mRNA）进行的荧光原位杂交显微镜之外，没有其他技术可用于其单细胞分析。一种新的方法——空间转录组学——已经被描述，它克服了 mRNA 分析的许多局限性； RNA-seq 文库是使用切片材料中具有高位置分辨率的微阵列制成的。然而，空间转录组学尚未开发用于小 RNA 或切割的 mRNA，并且迄今为止描述的实施仍然存在各种缺点。该项目将极大地推进这一方法，使用在无掩模阵列合成仪器上构建的高分辨率阵列（14微米网格，具有1微米线性间隙），以实现植物和潜在其他生物体中不同类型RNA的近单细胞分析。这项工作的测试平台是一个系统，在该系统中，这种高分辨率分析将提供大量信息，即植物与病原体的相互作用，将测量局部和系统的转录反应。该奖项由生物科学理事会分子和细胞生物科学部的遗传机制和细胞动力学和功能项目共同资助。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。