Exploring genetic interaction manifolds constructed from rich single-cell phenotypes

By Thomas M. Norman, Max A. Horlbeck, J. Replogle, A. Ge, Albert Xu, M. Jost, Luke A. Gilbert, Jonathan S. Weissman. 2019

390 citations recorded by the source corpus.

Manifold destiny Mapping of genetic interactions (GIs) is usually based on cell fitness as the phenotypic readout, which obscures the mechanistic origin of interactions. Norman et al. developed a framework for mapping and understanding GIs. This approach leverages high-dimensional single-cell RNA sequencing data gathered from CRISPR-mediated, pooled perturbation screens. Diverse transcriptomic phenotypes construct a “manifold” representing all possible states of the cell. Each perturbation and GI projects the cell state to a particular position on this manifold, enabling unbiased ordering of genes in pathways and systematic classifications of GIs. Science, this issue p. 786 Rich phenotyping with single-cell RNA sequencing reveals principles and mechanisms of genetic interactions in mammalian cells. 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.

Subfields: Molecular Biology

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Citations in the corpus

Methods and applications for single-cell and spatial multi-omics (2023, background): “Notably, a similar approach can be deployed to characterize gene interactions 95 .”
Predicting cellular responses to complex perturbations in high‐throughput screens (2023, methodology): “Headers indicate gene-wise regression coefficients, and interaction mode suggested by (Norman et al , 2019 ).”
Large Scale Foundation Model on Single-cell Transcriptomics (2023, neutral): “We used the robust regression with a Theil–Sen estimator following the same procedure used in previous study 72 .”