Key Points
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There are a number of different methods and techniques for genetic perturbation screens.
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The phenomenon of cell-to-cell variability in mammalian cells has implications for the interpretation of gene function.
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We now have the ability to quantify, at a large scale, multiple parameters of genetic perturbation effects in thousands of single cells.
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Functional genetic interactions can be inferred from multivariate quantitative readouts.
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We present an outlook on the opportunities that the single-cell paradigm will bring to unravel the biological complexity of mammalian cells.
Abstract
Large-scale genetic perturbation screens are a classical approach in biology and have been crucial for many discoveries. New technologies can now provide unbiased quantification of multiple molecular and phenotypic changes across tens of thousands of individual cells from large numbers of perturbed cell populations simultaneously. In this Review, we describe how these developments have enabled the discovery of new principles of intracellular and intercellular organization, novel interpretations of genetic perturbation effects and the inference of novel functional genetic interactions. These advances now allow more accurate and comprehensive analyses of gene function in cells using genetic perturbation screens.
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Acknowledgements
The authors thank M. Muellner and S. Nijman for the images in the Box 1, and all members of the Pelkmans laboratory for discussions. P.L. is supported by a FEBS postdoctoral fellowship. B.S. is supported by an advanced postdoc fellowship of SNSF. Research in the Pelkmans laboratory on these topics is funded by the University of Zürich Research Priority Program (URPP) in Functional Genomics and Systems Biology, and the Swiss initiative in Systems Biology: Systemsx.ch.
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Supplementary information S1 (table)
Key papers in respect to multivariate dimensions (PDF 220 kb)
Glossary
- Cell-to-cell variability
-
The phenomenon that individual cells in a population of genetically identical cells display variable activities and behaviours.
- Cellular heterogeneity
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Similar to cell-to-cell variability. Sometimes, 'heterogeneity' is used to indicate multiple discrete phenotypes within a population, while 'variability' is used to indicate variation around a single phenotype. There is no consensus on which term to use in which occasion, and both terms are interchangeable.
- Multivariate readouts
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Phenotypic readouts consisting of multiple features of the cellular activity, state and microenvironment.
- Functional interactions
-
A general term that incorporates protein–protein interactions, classical genetic interactions, regulatory interactions (such as kinase–substrate interactions) and phenotypic interactions.
- Synthetic interaction screens
-
Genetic screens in which two perturbations are combined to assess the possible synergistic and epistatic effects between the two genes perturbed.
- Mammalian haploid cells
-
Mammalian cells that harbour only one copy of the genome.
- Computer vision
-
A field that processes, analyses and interprets images in order to produce numerical information.
- Microenvironment
-
The local environment of a single cell within a population and their relative positioning to each other, such as the local crowding of cells, the amount of neighbours, whether cells face empty space on one site and cells on another site, and whether cells are solitary.
- Cell segmentation
-
Automated detection and delineation of the outside of single cells and nuclei in microscope images.
- Cellular states
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A quantitative description of the physiological states of single cells reflected in, for instance, their sizes, shapes, cell cycle phases, senescence or other detectable readouts such as metabolic states.
- Gaussian mixture models
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(GMMs). Parametric probability density functions that fit the Gaussian distribution to the data set to model the presence of subpopulations within an overall population; they are represented as weighted sums of Gaussian component densities.
- Support vector machine
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(SVM). Supervised learning models that recognize patterns in data sets and that are used for classification and regression analyses.
- Kolmogorov–Smirnov test
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A statistical non-parametric test for the comparison of continuous, one-dimensional probability distributions.
- Population context
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A collective term for the context in which a single cell displays its activities and behaviours, which can be determined by both local and global effects from the population to which the cell belongs. The context is determined not only by the microenvironment of a single cell but also by its physiological state that is a consequence of population effects, such as the cell size.
- Parallel phenotypic screens
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Screens performed in parallel in the same cell line using the same perturbations but different phenotypic readouts.
- Nesting
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The phenomenon whereby the effects of a perturbation are a subset of the effects of another perturbation.
- Hierarchical Interaction Score
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(HIS). A statistical method that infers functional interactions between genes if they display perturbation effects in a consistent subset of readouts, or environmental or genetic backgrounds. It also infers statistical hierarchy, in which the perturbation with a broader set of effects is placed upstream of a perturbation with a narrower subset of these effects.
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Liberali, P., Snijder, B. & Pelkmans, L. Single-cell and multivariate approaches in genetic perturbation screens. Nat Rev Genet 16, 18–32 (2015). https://doi.org/10.1038/nrg3768
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DOI: https://doi.org/10.1038/nrg3768
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