Genetic interactions can reveal gene function and identify cancer-relevant synthetic lethals, but systematic mapping in human cells is constrained by inefficient reagents, vast combinatorial search space, and diversity of cell types. Here, we leverage principles from yeast genetic networks to identify human gene modules enriched for genetic interactions. Using our Cas12a-based In4mer combinatorial knockout platform, we screen all pairwise interactions within receptor tyrosine kinase and DNA damage response modules across eight diverse cancer cell lines. We identify hundreds of unreported synthetic lethals, including a dense network within the protein glycosylation machinery, and confirm that interactions in 2D cell culture are maintained in more physiologically relevant models. Our targeted modules show up to 16-fold enrichment of interaction density, providing a scalable strategy for systematic interaction mapping.
Real-Time Segmentation and Classification of Birdsong Syllables for Learning Experiments
Songbirds are essential animal models for studying neuronal and behavioral mechanisms of learned vocalizations. Bengalese finch (Lonchura striata domestica) songs contain a limited number of