This directory contains scripts and files supporting the publication:
Heigwer et al., 2023, A global genetic interaction network by single-cell imaging and machine learning, Cell Systems
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.
Should you encounter any issues or have any questions please contact Florian Heigwer f.heigwer@dkfz.de or Michael Boutros m.boutros@dkfz.de.
Raw sequencing read data are deposit at ENA under the Study ID: PRJEB48815
Raw & processeed scRNA-seq matrices as well as Raw & processed well-average interaction feature matrices are availanble at : 10.6084/m9.figshare.14192783