Systematic epistatic mapping of cellular processes
© The Author(s) 2017
Received: 19 September 2016
Accepted: 26 December 2016
Published: 6 January 2017
Genetic screens have identified many novel components of various biological processes, such as components required for cell cycle and cell division. While forward genetic screens typically generate unstructured ‘hit’ lists, genetic interaction mapping approaches can identify functional relations in a systematic fashion. Here, we discuss a recent study by our group demonstrating a two-step approach to first screen for regulators of the mitotic cell cycle, and subsequently guide hypothesis generation by using genetic interaction analysis. The screen used a high-content microscopy assay and automated image analysis to capture defects during mitotic progression and cytokinesis. Genetic interaction networks derived from process-specific features generate a snapshot of functional gene relations in those processes, which follow a temporal order during the cell cycle. This complements a recently published approach, which inferred directional genetic interactions reconstructing hierarchical relationships between genes across different phases during mitotic progression. In conclusion, this strategy leverages unbiased, genome-wide, yet highly sensitive and process-focused functional screening in cells.
KeywordsGenetic interactions Image analysis RNAi Cell cycle
To identify regulators of the cell cycle in a systematic fashion, model systems such as budding yeast, cultured Drosophila or human cells have been exploited in genome-scale functional screens [8–10]. Advanced automated image analysis have enabled screening for modulators of diverse biological processes [11–13]. For instance, an RNAi screen with live imaging of human HeLa cells has exploited a stably expressed GFP-labeled histone H2B to identify genes required for proper chromosome segregation and cell cycle propagation . While such studies have identified cell cycle regulators with high sensitivity, genetic interaction analysis approaches have been able to define functional and epistatic relations between genes . Genetic interaction analysis systematically exploits genetic buffering by genetic variants, which completely or partially overlap in function [14–17]. Genetic interaction studies have been performed in yeast and assayed cell fitness as a composite phenotype [14, 16] capturing a broad spectrum of biological processes such as sister chromatid segregation, cytokinesis or the mitotic exit [18–20]. Such genetic interaction analyses have successfully been applied to further characterize hits from single gene screens [19, 21]. Recently, genetic interaction analyses approach in yeast have increased the throughput to the genome-scale  and reported a close-to-complete coverage of all gene pairs by measuring ~23 × 106 combinatorial knockouts . To score genetic interactions in a metazoan model system, we developed an approach that uses systematic combinatorial RNAi in cultured Drosophila cells [24, 25].
Here, we discuss a recent study by our laboratory, which focuses on cell cycle-relevant phenotypic features in Drosophila cells and uses genetic interaction mapping to visualize functional networks underlying mitotic progression and cytokinesis . This study characterized novel modulators by genome-wide high-content imaging RNAi screening, and structured the resulting ‘hit’ list using mitotic index- and nuclear area-focused genetic interaction analysis.
Distinct phenotypic features guide the detection of specific genetic interactions
Multi-phenotype interactions can reconstruct directed hierarchies
For instance, this reconstructed an epistasis network between the components of functional modules of the mitotic cell cycle comprising structural modules such as the γ-tubulin ring complex, Condensin or Cohesin, regulatory modules such as the anaphase-promoting complex/cyclosome (APC/C) or the spindle assembly checkpoint (SAC), motor proteins (Dynein, Dynactin) and regulatory genes such as polo (Drosophila PLK1) . This approach demonstrated that multi-parametric genetic interaction-based networks associate gene function and, in addition, provide epistatic relationships, thereby systematically visualizing functional relations between genes. Finally, features derived from the mitosis marker pH3 were highly informative for functional modules regulating mitosis, suggesting that feature-specific genetic interaction networks provide a snapshot of functional relations in the specific biological process.
Phenotype-specific genetic interactions visualize process-specific networks
Those networks functionally assigned many potentially novel cell cycle regulators, which had often been described in processes not directly connected to cell cycle regulation. For those genes, their phenotypic strength alone insufficiently guided hypothesis generation. The second-line genetic interaction mapping approach deprioritized many of those hits, while suggesting hypothesis for others such as Golgi-resident components during mitotic progression .
Multi-feature imaging enables the visualization of epistatic relationships between genes by considering genetic interactions along the vector of phenotypic features such as cell count, mitotic index and nuclear area [5, 33]. Moreover, genetic interactions affecting one process-specific feature capture a network of functional relations, zooming into a step of the causal chain in biological processes (Fig. 3).
Methodological rapid advances in CRISPR-based screens in mammalian systems and small molecule screens will require robust experimental and computational strategies to guide testable hypothesis. For example, a recent study in yeast generated various distinct phenotypic reporters by endogenously tagging various proteins with a GFP. The authors subsequently applied deep learning algorithms to the images to define cellular compartments and assess the response to genetic perturbations at multiple phenotypic levels . Recently, a method integrated this high-content approach with a technique for systematic genetic interaction analysis in yeast , which will enable building networks illustrating functional relations in various biological processes.
Recent studies have also shown how to use image-based screening for cellular phenotypes after treatment with small molecules in different genetic backgrounds to functionally group ~1300 pharmacologically active compounds . Two-step screening approaches would allow to extent the number of screened small molecules by several orders of magnitude, while sensitively mapping the mode of action for pre-selected compounds.
Mapping gene function using genetic interactions has also been performed in mammalian cells [40–42]. Due to the larger genome size, two-step genetic interaction screening approaches provide an attractive strategy. While combinational RNAi face several challenges , more recent gene editing CRISPR/Cas9-based technologies enabled efficient and reliable gene perturbation across human cells . In combination with a scRNA-seq phenotypic readout, pooled CRISPR screens can be exploited to build process-focused genetic interaction networks in higher organisms . Eventually, multi-step combinatorial gene depletion approaches will help building a systems view of biological processes such as the cell cycle  across genetic model systems.
phosphorylated histone H3
spindle assembly checkpoint
green fluorescent protein
clustered regularly interspaced short palindromic repeats
single cell RNA sequencing
MBi and MB wrote the manuscript. Both authors have read and approved the final manuscript.
We would like to thank Thomas Horn and Bernd Fischer for critical discussions and comments on the manuscript.
The authors declare that they have no competing interests.
Work in the laboratory of M.B. is supported in part by an ERC Advanced Grant of the European Commission.
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