EAGER: qRNA-PAINT as a method for high-throughput, quantitative, single molecule analysis of cellular RNAs and their networks

RNA分子被细胞用来传递和解码存储在DNA中的信息。这包括为制造蛋白质编码数据的信使rna和发挥重要调节作用的小rna。植物生物学家不断发现小rna在植物生长、发育和对环境的反应中令人兴奋的新作用。就像细胞中几乎所有的分子一样，小rna执行功能的能力取决于它们的位置和数量。然而，由于它们的体积小，检测和定位小rna特别具有挑战性。在植物中，目前的方法一次只能检测到一个或最多两个小rna。该项目开发了一种名为qRNA-PAINT的新技术，可以同时在单个样品中检测、量化和定位数十到数百个小rna。qRNA-PAINT使得以超分辨率精度检查植物细胞内小rna的复杂关系成为可能。由于小rna的广泛作用，这项技术将对科学家寻求回答植物和动物的基本生物学问题产生广泛的影响。了解小rna在植物信号通路中的作用可能有助于提高作物的生产力和对环境胁迫和病原体的抗性。在当前的基因组学时代，有丰富的小rna表达数据。然而，检测小rna的亚细胞、细胞甚至组织水平定位的方法是有限的。这使得研究不同rna（如miRNA、sirna、mrna和miRNA靶点）之间的关系变得具有挑战性。该提案将充分发展qRNA-PAINT方法，以检查数十到数百种不同的小rna之间的关系。qRNA-PAINT是一种改进的交换点积累成像的纳米级形貌（exchange- paint）方法，该方法已被用于检测多达十种不同的蛋白质目标。它通过将染料标记的成像仪寡核苷酸随机结合到附着在抗体上的对接链上来实现这一点。qRNA-PAINT方法不使用抗体，而只是将锁定的核酸（LNA）探针连接到对接链上进行交换- paint。因此，它有可能对许多小rna进行大规模多路复用，以检测单细胞和组织类型中的信号网络。玉米花药将被用作模型系统，qRNA-PAINT数据将补充现有的大量测序数据。此外，qRNA-PAINT将结合exchange-PAINT（蛋白质）检测蛋白质机制的相对定位，以研究不同类型小rna的生物发生。最终，它将被用来回答关于小RNA生物发生和信号网络的时空调节的悬而未决的问题。该奖项反映了美国国家科学基金会的法定使命，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。

RNA molecules are used by cells to transfer and decode information stored in DNA. This includes messenger RNAs that encode data for making proteins and small RNAs that play important regulatory roles. Plant biologists continue to discover exciting, novel roles for small RNAs during plant growth, development, and responses to the environment. Like nearly all molecules in cells, the ability of small RNAs to carry out their functions depends on their location and quantity. However, due to their small size, it has been particularly challenging to detect and locate small RNAs. In plants, current methods can only detect one, or at most, two small RNAs at a time. This project develops a new technique called qRNA-PAINT to detect, quantify, and localize tens to hundreds of small RNAs in a single sample, simultaneously. qRNA-PAINT makes it possible to examine the complex relationships of small RNAs inside of a plant cell with super-resolution precision. Due to the wide-ranging role of small RNAs, this technique will have broad impacts on scientists seeking to answer fundamental biological questions in both plants and animals. Insights into the roles of small RNAs in plant signalling pathways may be translatable to increasing crop plant productivity and resistance to environmental stress and pathogens.In the current age of genomics, there is a wealth of expression data for small RNAs. However, methods to examine the subcellular, cellular, and even tissue level localization of small RNAs are limited. This has made it challenging to examine the relationship of different RNAs, such as miRNAs, siRNAs, mRNAs, and miRNA targets. This proposal will fully develop the qRNA-PAINT method to examine the relationship of tens to hundreds of different small RNAs. qRNA-PAINT is a modification of the exchange points accumulation for imaging in nanoscale topography (exchange-PAINT) method, which has been used to detect up to ten different protein targets. It accomplishes this by using the stochastic binding of dye labeled imager oligonucleotides to docking strands attached to antibodies. The qRNA-PAINT method does not use antibodies, but rather just locked nucleic acid (LNA) probes connected to the docking strands for exchange-PAINT. Consequently, it potentially can be massively multiplexed for numerous small RNAs, to examine signaling networks in single cells and tissue types. The maize anther will be used as the model system and qRNA-PAINT data will complement the already available extensive set of sequencing data. Furthermore, qRNA-PAINT will be combined with exchange-PAINT (for proteins) to examine the relative localization of protein machinery to study the biogenesis of different types of small RNAs. Ultimately, it will be used to answer outstanding questions on the spatio-temporal regulation of small RNA biogenesis and signaling networks.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.