Development of Next-Generation Mass Spectrometry-based de novo RNA Sequencing for all Modifications

开发适用于所有修饰的下一代基于质谱的从头 RNA 测序

基本信息

批准号：
10581994
负责人：
Shenglong Zhang
金额：
$ 67.69万
依托单位：
NEW YORK INST OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-03-01 至 2026-12-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10581994
关键词：
2019-nCoV Adopted Affect Algorithms American Automation Base Sequence COVID-19 Chemicals Complementary DNA Complex Mixtures Defect Development Disease Disease model Goals Health High Fat Diet High-Throughput Nucleotide Sequencing Human Length Location Maps Mass Spectrum Analysis Metabolic Diseases Methods Modification Mus Nature Non-Insulin-Dependent Diabetes Mellitus Nucleotides Obesity Phenylalanine-Specific tRNA Preparation Proteomics RNA RNA Sequences Research Resolution Ribosomal RNA Running Sampling Series Site Small RNA Techniques Technology Time Tissues Transfer RNA Variant Yeasts base empowerment genome-wide human disease insight instrumentation malignant breast neoplasm mouse model next generation next generation sequencing novel pandemic disease posttranscriptional prevent scale up stoichiometry tool transcriptome transcriptome sequencing transcriptomics

项目摘要

PROJECT SUMMARY An RNA sequence with all its diverse modifications constitutes ‘true’ information content of the RNA. Defects in RNA modifications account for >100 human diseases, such as breast cancer, type-2 diabetes and obesity, affecting millions of Americans. Despite its significance, the true sequence of a RNA, i.e., identity and location of each and every nucleotide building block (modified or not) within a full-length RNA, remains a mystery, mainly because of the lack of a general method to directly sequence any nucleotide, especially modified nucleotides (including unknown ones) at single-nucleotide resolution. No existing technology can sequence all modifications simultaneously to unfold the true RNA sequences at a large scale or the transcriptomic level. What complicates RNA modification studies is that >170 modification types have been discovered, and not all of nucleotide modifications are modified completely to 100% at their RNA sites. They are even undetectable by NGS-based technologies, which require the conversion of RNA to cDNAs that do not have any modification information. Tools to map RNA modifications are limited only to a few popular modifications, and can usually analyze only one modification type at a time. Mass spectrometry (MS) is currently the only technique that can characterize all RNA modifications; however, conventional MS methods lose information regarding the location and co-occurrence of modified nucleotides. To resolve these outstanding issues, we have recently developed a series of novel next generation mass spectrometry-based sequencing (NextGen MassSpec-Seq) approaches that can de novo directly sequence tRNAs without a cDNA and can sequence and quantify all nucleotide modifications simultaneously. For the duration of this proposal, we will further develop NextGen MassSpec-Seq to sequence tRNAs efficiently in different cellular and even disease conditions, make it scalable toward high throughput, and expand its application to simultaneously sequence and map all modifications quantitatively on any RNA type and at the transcriptomic level. Specifically, we propose to develop MS for large-scale de novo sequencing of full-length tRNAs, together with all diverse nucleotide modifications (Aim 1), empower MS to simultaneously sequence and quantify multiple RNA modifications, allowing quantitative mapping at single nucleotide and stoichiometric precision (Aim 2), scale up NextGen MassSpec-Seq and combine it with high-throughput NGS sequencing for direct sequencing of diverse RNA modifications at the transcriptomic level (Aim 3). Our tool will address a long- standing issue of how to reveal the ‘true” RNA sequences and provide a transformative tool for studying RNA modifications, which will promote better understanding of functions of post-transcriptional modifications and their correlations to RNA-related diseases and pandemics.

项目概要具有各种修饰的 RNA 序列构成了 RNA 缺陷的“真实”信息内容。 RNA 修饰导致超过 100 种人类疾病，例如乳腺癌、2 型糖尿病和肥胖症，尽管 RNA 的真实序列（即身份和位置）影响着数百万美国人。全长 RNA 中的每个核苷酸构建模块（修饰或未修饰）仍然是一个谜，主要是因为缺乏直接对任何核苷酸（尤其是修饰的核苷酸）进行测序的通用方法现有技术无法以单核苷酸分辨率对所有修饰进行测序（包括未知修饰）。同时在大规模或转录组水平上展开真实的RNA序列。使 RNA 修饰研究变得复杂的是，已经发现了超过 170 种修饰类型，并且并非所有的核苷酸修饰都在其 RNA 位点上被完全修饰为 100%。基于 NGS 的技术无法检测到，该技术需要将 RNA 转化为不含任何修饰信息。绘制 RNA 修饰图的工具仅限于一些流行的修饰，并且通常一次只能分析一种修饰类型，质谱 (MS) 是目前唯一的技术。可以表征所有 RNA 修饰；然而，传统 MS 方法会丢失有关 RNA 修饰的信息；修饰核苷酸的位置和共现。为了解决这些突出问题，我们最近开发了一系列新颖的下一代大众基于光谱测定的测序 (NextGen MassSpec-Seq) 方法可以从头直接测序没有 cDNA 的 tRNA，可以同时对所有核苷酸修饰进行测序和定量。在此提案期间，我们将进一步开发 NextGen MassSpec-Seq，以在不同的细胞甚至疾病条件，使其可扩展到高通量，并扩展其应用程序可同时对任何 RNA 类型和位置上的所有修饰进行定量测序和作图具体来说，我们建议开发用于大规模全长从头测序的 MS。 tRNA 与所有不同的核苷酸修饰（目标 1）一起，使 MS 能够同时测序和量化多个 RNA 修饰，允许单核苷酸和化学计量的定量作图精度（目标 2），扩大 NextGen MassSpec-Seq 规模，并将其与高通量 NGS 测序相结合，以实现在转录组水平上直接测序不同的 RNA 修饰（目标 3）。如何揭示“真实”RNA 序列并为研究 RNA 提供变革性工具是一个长期存在的问题修饰，这将促进更好地理解转录后修饰的功能及其与 RNA 相关疾病和流行病的相关性。