Transcriptome-wide, single-molecule dynamics of RNA-protein interaction.

RNA-蛋白质相互作用是细胞功能的重要组成部分。动态和协调的绑定和 由多种蛋白质释放的RNA支持整个基因表达的调控。然而，我们的 在剪接、翻译等过程的时间尺度上可视化这些动态的技术能力 或信使核糖核酸的衰变，仍然是有限的。检测RNA-蛋白质相互作用的转录组范围的方法-来自 微阵列到RIP-/Clip-Seq-提供静态、单时间点或平衡快照。相反，实时 单分子方法以精确的分子精度探测单个RNA的实时动力学，但 在转录组规模上部署是具有挑战性的。为弥补这一差距而开发的单分子方法已经 合成RNA大文库上蛋白质-RNA平衡亲和力和解离速率的测量 高达~300新台币。虽然这些突出了由于局部RNA序列和结构而导致的动力学多样性，但它们仍然 缺乏通过体内化学修饰来探测全长转录本的动力学的能力，他们不直接 测量结合率，重要的是，他们没有解决如何多个同时蛋白质-RNA 相互作用相互协调。在这里，我们建议开发一种绕过这些限制的技术， 专注于信使核糖核酸与蛋白质的相互作用。我们的方法利用荧光标记的直接观察 蛋白质结合和释放通过一系列零模式固定的数以万计的单个mRNA 以毫秒为单位的波导(ZMW)。基于ZMW的平台提供关键的吞吐量， 多色荧光检测，以及提高技术水平所需的信噪比度量。钥匙 必要的技术突破将通过两个具体目标实现。在目标1中，我们将制定一个 定量研究一种和两种蛋白质与表面固定化酵母菌相互作用动力学的工作流程 酿酒酵母转录组。我们将在重复性和完备性方面验证该协议 转录组捕获，以及动力学数据的重复性。在目标2中，我们将开发和优化 也可以确定实验中的每一个信使核糖核酸，允许指定(多个)蛋白质结合动力学 核糖核酸同一性。我们将采用一种合成测序的方法，对比酶的策略来稳健地 就地读出RNA序列。我们将通过比较ZMW中识别的序列来验证该方法 以及针对该信使核糖核酸群体的批量RNA-SEQ数据。这些目标的综合结果将是一个原型 用于分析(多)蛋白质相互作用动力学的技术和概念验证 转录组。这项技术将补充静态转录组范围的方法，深化 可以在整个RNA生物学中提出和回答的机械性问题。

RNA-protein interactions are a critical component of cellular function. Dynamic and coordinated binding and release of RNA by multiple proteins underpins regulation throughout gene expression. However, our technological capacity to visualize these dynamics on the timescales of processes such as splicing, translation, or mRNA decay, remains limited. Transcriptome-wide methods that probe RNA-protein interactions – from microarrays to RIP-/CLIP-seq – provide static, single-timepoint, or equilibrium snapshots. Conversely, real-time single-molecule methods probe real-time dynamics on individual RNAs with exquisite molecular precision, but are challenging to deploy at transcriptome scale. Single-molecule methods developed to bridge this gap have measured protein-RNA equilibrium affinities and dissociation rates on large libraries of synthetic RNA sequences up to ~300 nt. While these have highlighted kinetic diversity due to local RNA sequence and structure, they still lack the ability to probe dynamics on full-length transcripts with in vivo chemical modifications, they do not directly measure binding rates, and, importantly they have not addressed how multiple simultaneous protein-RNA interactions coordinate. Here we propose development of a technology that circumvents these limitations, focusing on mRNA-protein interactions. Our approach leverages direct observation of fluorescently-labeled proteins binding and releasing tens of thousands of single mRNAs immobilized across an array of zero-mode waveguides (ZMWs), on millisecond timescales. The ZMW-based platform offers the critical throughput, multicolor fluorescence detection, and signal-to-noise metrics needed to advance the state of the art. The key requisite technological breakthroughs will be made through two specific aims. In Aim 1, we will develop a workflow to quantify the interaction dynamics of one and two proteins with a surface-immobilized Saccharomyces cerevisiae transcriptome. We will validate this protocol in terms of reproducibility and completeness of transcriptome capture, and the reproducibility of the kinetic data. In Aim 2 we will develop and optimize an approach to also identify each mRNA in the experiment, allowing (multi)protein-binding dynamics to be assigned to RNA identity. We will adopt a sequencing-by-synthesis approach, contrasting enzymatic strategies to robustly read out RNA sequence in place. We will validate this approach by comparing the in-ZMW identified sequences with bulk RNA-seq data for the mRNA population. The combined outcome of these Aims will be a prototype technology and proof-of-concept for profiling (multi)protein interaction dynamics on each mRNA in the transcriptome. This technology will complement static transcriptome-wide approaches, deepening the range of mechanistic questions that can be asked and answered across RNA biology.