StoichRNA: A Scalable Framework to Map and Optimize Combinatorial RNA Systems at Single-Cell Resolution

Abstract

RNA-based genetic cargos have emerged as versatile tools in both fundamental research and translational medicine, with growing applications in regenerative medicine, gene therapy, cell therapy, and vaccination. As the field advances beyond single-RNA delivery, coordinated engineering of multiple RNA species is enabling increasingly sophisticated and combinatorial biological functions. However, this shift raises a fundamental challenge: what combination and stoichiometry of RNA species should be delivered together to achieve a desired cellular function? As the number of RNA components increases, the design space expands combinatorially, making conventional optimization strategies labor-intensive and poorly scalable. Here, we introduce StoichRNA, a framework for scalable exploration of multi-RNA stoichiometry in a single experiment. Individual RNA species are delivered independently, each coupled to a distinct fluorescent marker that reports its intracellular abundance. Because uptake is stochastic, each cell acquires a unique combination and copy number of RNA components. Across a sufficiently large population, cells collectively sample a broad stoichiometric landscape, allowing single-cell readouts such as flow cytometry to quantitatively map system output as a function of intracellular RNA composition. We first establish the conceptual validity of this approach by showing that StoichRNA-derived single-cell data can recover and predict the behavior of fixed-ratio RNA formulations. We then apply this framework to representative RNA circuits, demonstrating that StoichRNA can reveal design principles and identify improved stoichiometries that would be difficult to uncover through conventional workflows. Finally, we extend StoichRNA optimization to translationally relevant settings, including control of cell-state engineering in human induced pluripotent stem cells and tuning of RNA-based logic circuits for precision medicine applications. Together, these results establish StoichRNA as a generalizable strategy for engineering combinatorial RNA systems and provide a scalable solution to a central bottleneck in RNA circuit design.