Programmable RNA-Based Sensors for In Situ Cell Type Detection and Response - Project summary There is a technology gap in currently developed tools that simultaneously monitor, compute, and respond to both coding and non-coding RNA in real-time within living cells or patients. The continued existence of this gap represents an urgent unmet need because, until it is filled, the accuracy of RNA-based therapeutics remains limited in complex and evolving biological systems like differentiation or cancer. The long-term goal of this proposal is to develop safe, universal, and programmable synthetic biology tools that using both coding and non- coding RNAs as disease marker inputs and program outputs to trigger therapeutic responses in patients. The objective of this particular application is to develop an RNA-based sensor (using mRNA as the delivery modality) that detects integrated changes in both mRNA and miRNA for in situ therapeutic responses within living cells and mouse models, given the crucial role of ncRNAs, especially microRNAs (miRNAs), as key regulators of post- transcriptional gene regulation, which allow only the correct set of genes to be active in each cell type. The central hypothesis is that an RNA-based sensor integrating both mRNA and miRNA inputs, using Boolean logic gate computation, can improve the specificity of cell type identification in complex biological systems. This proposed work builds on our and other’s recent works on sensing individual RNA species like mRNA in live cells. The rationale for the proposed research is that a deeper understanding of disease progression, derived from the vast RNA sequencing resources now available in user-friendly databases, creates a timely and unique opportunity for synthetic biologists to develop tools that can precisely identify diseased cells based on their RNA species and levels in living cells or even in patients. This allows for the development of treatments that specifically target diseased cells while minimizing off-target effects on healthy cells. Additionally, the success of COVID-19 mRNA vaccines using lipid nanoparticle delivery systems highlights the potential to translate RNA-based genetic circuits into practical medical applications. Given these advances, we plan to develop two independent and complementary aims for in situ cell state sensing using endogenous mRNA and miRNA as inputs: AND logic gates (requiring both inputs for an output) in Aim 1 and NOR logic gates (requiring neither input for an output) in Aim 2. This platform has broad biomedical potentials. As a proof of concept, we will demonstrate its ability to distinguish breast cancer cells from normal breast epithelial cells, evaluating its translational potential using a syngeneic mouse model of triple-negative breast cancer, which lacks key cell surface targets in current therapies. The proposed platform is innovative because it develops new platform by integration of existing miRNA sensing and RNA detecting approaches in a previously unproven combinatorial logic computation format to address a significant unmet need for accurate cell type identification for basic and translational applications. The proposed research is significant, because in situ monitoring and intervening based on endogenous RNAs will be key to addressing this unmet need, transforming disease detection and treatment.
