Deciphering cellular context for efficient and cell type-specific CRISPR-Cas13d gRNA design using in vivo RNA structure and deep learning

The efficacy and tissue specificity of RNA therapeutics are critical for clinical translation. Here, by large-scale profiling of the dynamic RNA structurome across four cell lines, we systematically characterized the impact of in vivo target RNA structure and RNA-protein interactions on CRISPR/Cas13d gRNA activity. We identified the structural patterns of high-efficacy gRNA targets and observed that structural differences can lead to variations in efficacy across different cellular contexts. By stabilizing single-stranded structure, RNA-binding proteins also enhanced gRNA efficacy. Leveraging this cell context information, along with approximately 290,000 RfxCas13d screening data, we developed SCALPEL, a deep learning model that predicts gRNA performance across various cellular environments. SCALPEL outperforms existing state-of-the-art models, and most importantly, it enables cell type-specific predictions of gRNA activity. Validation screens across multiple cell lines demonstrate that cellular context significantly influences gRNA performance, even for identical targeting sequences, underscoring the feasibility of cell type-specific knockdown by targeting structural dynamic regions. SCALPEL can also facilitate designing highly efficient virus-targeting gRNAs and gRNAs that robustly knockdown maternal transcripts essential for early zebrafish development. Our method offers a novel approach to develop context-specific gRNAs, with potential to advance tissue- or organ-specific RNA therapies.