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

通过单细胞成像和机器学习构建全球遗传相互作用网络

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作者:Florian Heigwer, Christian Scheeder, Josephine Bageritz, Schayan Yousefian, Benedikt Rauscher, Christina Laufer, Sergi Beneyto-Calabuig, Maja Christina Funk, Vera Peters, Maria Boulougouri, Jana Bilanovic, Thilo Miersch, Barbara Schmitt, Claudia Blass, Fillip Port, Michael Boutros

Abstract

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.

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