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Science. 2019 Aug 23;365(6455):786-793. doi: 10.1126/science.aax4438. Epub 2019 Aug 8.

Exploring genetic interaction manifolds constructed from rich single-cell phenotypes.

Norman TM#1,2,3, Horlbeck MA#4,2,3, Replogle JM4,2,3, Ge AY5,6, Xu A4,2,3, Jost M4,2,3, Gilbert LA7,6, Weissman JS1,2,3.

Author information

1
Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA. thomas.norman@ucsf.edu luke.gilbert@ucsf.edu jonathan.weissman@ucsf.edu.
2
Howard Hughes Medical Institute, University of California, San Francisco, CA 94158, USA.
3
California Institute for Quantitative Biomedical Research, University of California, San Francisco, CA 94158, USA.
4
Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA.
5
Department of Urology, University of California, San Francisco, CA 94158, USA.
6
Helen Diller Family Comprehensive Cancer Center, San Francisco, CA 94158, USA.
7
Department of Urology, University of California, San Francisco, CA 94158, USA. thomas.norman@ucsf.edu luke.gilbert@ucsf.edu jonathan.weissman@ucsf.edu.
#
Contributed equally

Abstract

How cellular and organismal complexity emerges from combinatorial expression of genes is a central question in biology. High-content phenotyping approaches such as Perturb-seq (single-cell RNA-sequencing pooled CRISPR screens) present an opportunity for exploring such genetic interactions (GIs) at scale. Here, we present an analytical framework for interpreting high-dimensional landscapes of cell states (manifolds) constructed from transcriptional phenotypes. We applied this approach to Perturb-seq profiling of strong GIs mined from a growth-based, gain-of-function GI map. Exploration of this manifold enabled ordering of regulatory pathways, principled classification of GIs (e.g., identifying suppressors), and mechanistic elucidation of synergistic interactions, including an unexpected synergy between CBL and CNN1 driving erythroid differentiation. Finally, we applied recommender system machine learning to predict interactions, facilitating exploration of vastly larger GI manifolds.

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