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.
Quantitatively understanding how RNA molecules dynamically coordinate their interactions with multiple proteins is essential to understand fundamental molecular mechanisms of biological regulation in health and disease. We will develop proof-of-concept for a single-molecule fluorescence technology that quantitates these dynamics in real time across eukaryotic transcriptomes. This technology will advance the state of the art by enabling molecular-mechanistic hypotheses accessible only from single-molecule dynamics to be tested at transcriptome- scale breadth.
