A global genetic interaction network by single-cell imaging and machine learning

Cellular and organismal phenotypes are controlled by complex gene regulatory networks. However, reference maps of gene function are still scarce across different organisms. Here, we generated synthetic genetic interaction and cell morphology profiles of more than 6,800 genes in cultured Drosophila cells. The resulting map of genetic interactions was used for machine learning-based gene function discovery, assigning functions to genes in 47 modules. Furthermore, we devised Cytoclass as a method to dissect genetic interactions for discrete cell states at the single-cell resolution. This approach identified an interaction of Cdk2 and the Cop9 signalosome complex, triggering senescence-associated secretory phenotypes and immunogenic conversion in hemocytic cells. Together, our data constitute a genome-scale resource of functional gene profiles to uncover the mechanisms underlying genetic interactions and their plasticity at the single-cell level. •We build a genome-scale map encompassing genetic and morphology profiles•The multi-modal map reveals gene functional relationships•Cytoclass allows the linkage of cell-to-cell heterogeneity and genetic interactions•A genetic interaction induces immunogenic conversion and SASP Using image-based single-cell data, the authors map genetic interactions and cell morphologies of more than 6,800 genes in Drosophila cells and use machine learning to predict gene functions and modules. These data reveal how a Cdk2 and Cop9 signalosome interaction affects hemocytic cell immunogenicity by triggering senescence-associated phenotypes.