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  • Review Article
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Single-cell and multivariate approaches in genetic perturbation screens

Nature Reviews Genetics volume 16, pages 18–32 (2015)Cite this article

Subjects

Key Points

  • There are a number of different methods and techniques for genetic perturbation screens.

  • The phenomenon of cell-to-cell variability in mammalian cells has implications for the interpretation of gene function.

  • We now have the ability to quantify, at a large scale, multiple parameters of genetic perturbation effects in thousands of single cells.

  • Functional genetic interactions can be inferred from multivariate quantitative readouts.

  • 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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Figure 1: Multidimensional genetic perturbation screens.
Figure 2: Single-cell measurements and phenotypic scoring.
Figure 3: Accounting for population context in the interpretation of genetic perturbation screens.
Figure 4: Inferring different types of genetic interactions.

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References

  1. Grunenfelder, B. & Winzeler, E. A. Treasures and traps in genome-wide data sets: case examples from yeast. Nature Rev. Genet. 3, 653–661 (2002).

    PubMed  Google Scholar 

  2. Adams, M. D. & Sekelsky, J. J. From sequence to phenotype: reverse genetics in Drosophila melanogaster. Nature Rev. Genet. 3, 189–198 (2002).

    CAS  PubMed  Google Scholar 

  3. Carpenter, A. E. & Sabatini, D. M. Systematic genome-wide screens of gene function. Nature Rev. Genet. 5, 11–22 (2004).

    CAS  PubMed  Google Scholar 

  4. Loo, L.-H., Wu, L. F. & Altschuler, S. J. Image-based multivariate profiling of drug responses from single cells. Nature Methods 4, 445–453 (2007). This paper identifies drug targets using a SVM-based method that takes into account the multivariate feature set of single cells in a population.

    CAS  PubMed  Google Scholar 

  5. Boutros, M., Bras, L. P. & Huber, W. Analysis of cell-based RNAi screens. Genome Biol. 7, R66 (2006).

    PubMed  PubMed Central  Google Scholar 

  6. Boutros, M. & Ahringer, J. The art and design of genetic screens: RNA interference. Nature Rev. Genet. 9, 554–566 (2008).

    CAS  PubMed  Google Scholar 

  7. Birmingham, A. et al. Statistical methods for analysis of high-throughput RNA interference screens. Nature Methods 6, 569–575 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Schadt, E. E., Linderman, M. D., Sorenson, J., Lee, L. & Nolan, G. P. Computational solutions to large-scale data management and analysis. Nature Rev. Genet. 11, 647–657 (2010).

    CAS  PubMed  Google Scholar 

  9. Friedman, A. & Perrimon, N. Genome-wide high-throughput screens in functional genomics. Curr. Opin. Genet. Dev. 14, 470–476 (2004).

    CAS  PubMed  Google Scholar 

  10. Mohr, S., Bakal, C. & Perrimon, N. Genomic screening with RNAi: results and challenges. Annu. Rev. Biochem. 79, 37–64 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Perlman, Z. E. et al. Multidimensional drug profiling by automated microscopy. Science 306, 1194–1198 (2004). This pioneering paper identifies drug targets by taking into account the full distribution of single cells in a population using the Kolmogorov–Smirnov test in image-based small-compound screens.

    CAS  PubMed  Google Scholar 

  12. Bakal, C., Aach, J., Church, G. & Perrimon, N. Quantitative morphological signatures define local signaling networks regulating cell morphology. Science 316, 1753–1756 (2007). This study uses quantitative morphological features of single cells as profiles to identify genes involved in cellular morphology.

    CAS  PubMed  Google Scholar 

  13. Loo, L.-H. et al. An approach for extensibly profiling the molecular states of cellular subpopulations. Nature Methods 6, 759–765 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Snijder, B. et al. Single-cell analysis of population context advances RNAi screening at multiple levels. Mol. Systems Biol. 8, 579 (2012). This paper shows that modelling single-cell behaviour and taking into account cell-to-cell variability strongly improve the data and comparability in siRNA screens.

    Google Scholar 

  15. Yin, Z. et al. A screen for morphological complexity identifies regulators of switch-like transitions between discrete cell shapes. Nature Cell Biol. 15, 860–871 (2013).

    CAS  PubMed  Google Scholar 

  16. Liberali, P., Snijder, B. & Pelkmans, L. A hierarchical map of regulatory genetic interactions in membrane trafficking. Cell 157, 1473–1487 (2014). This paper infers regulatory genetic interactions from parallel siRNA screens in human cells and from double-knockout synthetic screens in yeast using the HIS.

    CAS  PubMed  Google Scholar 

  17. Novick, P., Field, C. & Schekman, R. Identification of 23 complementation groups required for post-translational events in the yeast secretory pathway. Cell 21, 205–215 (1980).

    CAS  PubMed  Google Scholar 

  18. Gonczy, P. et al. Functional genomic analysis of cell division in C. elegans using RNAi of genes on chromosome III. Nature 408, 331–336 (2000).

    CAS  PubMed  Google Scholar 

  19. Fraser, A. G. et al. Functional genomic analysis of C. elegans chromosome I by systematic RNA interference. Nature 408, 325–330 (2000).

    CAS  PubMed  Google Scholar 

  20. Lum, L. et al. Identification of Hedgehog pathway components by RNAi in Drosophila cultured cells. Science 299, 2039–2045 (2003).

    CAS  PubMed  Google Scholar 

  21. Aza-Blanc, P. et al. Identification of modulators of TRAIL-induced apoptosis via RNAi-based phenotypic screening. Mol. Cell 12, 627–637 (2003).

    CAS  PubMed  Google Scholar 

  22. Brummelkamp, T. R., Nijman, S. M., Dirac, A. M. & Bernards, R. Loss of the cylindromatosis tumour suppressor inhibits apoptosis by activating NF-κB. Nature 424, 797–801 (2003).

    CAS  PubMed  Google Scholar 

  23. Heo, W. D. & Meyer, T. Switch-of-function mutants based on morphology classification of Ras superfamily small GTPases. Cell 113, 315–328 (2003).

    CAS  PubMed  Google Scholar 

  24. Boutros, M. et al. Genome-wide RNAi analysis of growth and viability in Drosophila cells. Science 303, 832–835 (2004).

    CAS  PubMed  Google Scholar 

  25. Paddison, P. J. et al. A resource for large-scale RNA-interference-based screens in mammals. Nature 428, 427–431 (2004).

    CAS  PubMed  Google Scholar 

  26. Neumann, B. et al. Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes. Nature 464, 721–727 (2010). This is the first genome-wide siRNA screen using time-lapse imaging of living cells, which extracted features from dynamic data to identify genes involved in mitosis.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Simpson, J. C. et al. Genome-wide RNAi screening identifies human proteins with a regulatory function in the early secretory pathway. Nature Cell Biol. 14, 764–774 (2012).

    CAS  PubMed  Google Scholar 

  28. Stevenson, L. F., Kennedy, B. K. & Harlow, E. A large-scale overexpression screen in Saccharomyces cerevisiae identifies previously uncharacterized cell cycle genes. Proc. Natl Acad. Sci. USA 98, 3946–3951 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Pritsker, M., Ford, N. R., Jenq, H. T. & Lemischka, I. R. Genomewide gain-of-function genetic screen identifies functionally active genes in mouse embryonic stem cells. Proc. Natl Acad. Sci. USA 103, 6946–6951 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Stockwell, B. R. Chemical genetics: ligand-based discovery of gene function. Nature Rev. Genet. 1, 116–125 (2000).

    CAS  PubMed  Google Scholar 

  31. Durai, S. et al. Zinc finger nucleases: custom-designed molecular scissors for genome engineering of plant and mammalian cells. Nucleic Acids Res. 33, 5978–5990 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Miller, J. C. et al. A TALE nuclease architecture for efficient genome editing. Nature Biotech. 29, 143–148 (2011).

    CAS  Google Scholar 

  33. Urnov, F. D., Rebar, E. J., Holmes, M. C., Zhang, H. S. & Gregory, P. D. Genome editing with engineered zinc finger nucleases. Nature Rev. Genet. 11, 636–646 (2010).

    CAS  PubMed  Google Scholar 

  34. Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013). This paper shows the first proof of principle of the CRISPR–Cas9 system in human cells.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Shalem, O. et al. Genome-scale CRISPR–Cas9 knockout screening in human cells. Science 343, 84–87 (2014). Using gene editing with the CRISPR–Cas9 system, this paper establishes genome-scale gene perturbation screening in a pooled format in human cancer and pluripotent stem cells.

    CAS  PubMed  Google Scholar 

  37. Wang, T., Wei, J. J., Sabatini, D. M. & Lander, E. S. Genetic screens in human cells using the CRISPR–Cas9 system. Science 343, 80–84 (2014). Using gene editing with the CRISPR–Cas9 system, this paper establishes genome-scale gene perturbation screening in a pooled format in haploid and diploid cell lines.

    CAS  PubMed  Google Scholar 

  38. Hsu, P. D., Lander, E. S. & Zhang, F. Development and applications of CRISPR–Cas9 for Genome engineering. Cell 157, 1262–1278 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Qi, L. S. et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173–1183 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014). In references 39 and 40, the researchers repurposed the CRISPR system to induce sequence-specific repression (CRISPRi) or activation (CRISPRa) of gene expression at a genome scale.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Eggert, U. S., Field, C. M. & Mitchison, T. J. Small molecules in an RNAi world. Mol. BioSystems 2, 93 (2006).

    CAS  Google Scholar 

  42. Weiss, W. A., Taylor, S. S. & Shokat, K. M. Recognizing and exploiting differences between RNAi and small-molecule inhibitors. Nature Chem. Biol. 3, 739–744 (2007).

    CAS  Google Scholar 

  43. Bakal, C. et al. Phosphorylation networks regulating JNK activity in diverse genetic backgrounds. Science 322, 453–456 (2008). This paper reports a high-throughput screen that uses RNAi to systematically inhibit two genes simultaneously in 17,724 combinations to study kinase regulation.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Pelkmans, L. et al. Genome-wide analysis of human kinases in clathrin- and caveolae/raft-mediated endocytosis. Nature 436, 78–86 (2005). This paper is the first to report parallel comparative siRNA screens.

    CAS  PubMed  Google Scholar 

  45. Collinet, C. et al. Systems survey of endocytosis by multiparametric image analysis. Nature 464, 243–249 (2010). This paper reports the first multivariate genome-wide screen of endocytosis.

    CAS  PubMed  Google Scholar 

  46. Chia, J. et al. RNAi screening reveals a large signaling network controlling the Golgi apparatus in human cells. Mol. Systems Biol. 8, 1–33 (2012).

    Google Scholar 

  47. Silva, J. M. et al. Profiling essential genes in human mammary cells by multiplex RNAi screening. Science 319, 617–620 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Bassik, M. C. et al. A systematic mammalian genetic interaction map reveals pathways underlying ricin susceptibility. Cell 152, 909–922 (2013). In this study, the researchers construct a double-shRNA library for pooled screens in human cells to identify genetic interaction between genes involved in ricin toxin susceptibility.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Carette, J. E. et al. Haploid genetic screens in human cells identify host factors used by pathogens. Science 326, 1231–1235 (2009). This is the first pooled screen in mammalian haploid cells using random mutational insertions.

    CAS  PubMed  Google Scholar 

  50. Winzeler, E. A. et al. Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285, 901–906 (1999).

    CAS  PubMed  Google Scholar 

  51. Giaever, G. et al. Functional profiling of the Saccharomyces cerevisiae genome. Nature 418, 387–391 (2002). This paper reports the construction of a collection of all viable single-gene deletion mutants of S. cerevisiae.

    CAS  PubMed  Google Scholar 

  52. Kim, D. U. et al. Analysis of a genome-wide set of gene deletions in the fission yeast Schizosaccharomyces pombe. Nature Biotech. 28, 617–623 (2010).

    CAS  Google Scholar 

  53. Tang, T. et al. A mouse knockout library for secreted and transmembrane proteins. Nature Biotech. 28, 749–755 (2010).

    CAS  Google Scholar 

  54. Dolgin, E. Mouse library set to be knockout. Nature 474, 262–263 (2011).

    CAS  PubMed  Google Scholar 

  55. Skarnes, W. C. et al. A conditional knockout resource for the genome-wide study of mouse gene function. Nature 474, 337–342 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Kotecki, M., Reddy, P. S. & Cochran, B. H. Isolation and characterization of a near-haploid human cell line. Exp. Cell Res. 252, 273–280 (1999).

    CAS  PubMed  Google Scholar 

  57. Leeb, M. & Wutz, A. Derivation of haploid embryonic stem cells from mouse embryos. Nature 479, 131–134 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Burckstummer, T. et al. A reversible gene trap collection empowers haploid genetics in human cells. Nature Methods 10, 965–971 (2013).

    PubMed  PubMed Central  Google Scholar 

  59. Tong, A. H. et al. Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 294, 2364–2368 (2001). In this study, the researchers develop a method for the systematic construction of a double-gene deletion library for synthetic screens in S. cerevisiae.

    CAS  PubMed  Google Scholar 

  60. Schuldiner, M. et al. Exploration of the function and organization of the yeast early secretory pathway through an epistatic miniarray profile. Cell 123, 507–519 (2005). This paper introduces the use of pairwise correlations between two genes across a large set of epistatic effects to infer functional genetic interactions in S. cerevisiae.

    CAS  PubMed  Google Scholar 

  61. Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425–431 (2010). In this study, researchers create a genome-scale genetic interaction map by examining 5.4 million gene–gene pairs for synthetic genetic interactions in S. cerevisiae.

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Eggert, U. S. et al. Parallel chemical genetic and genome-wide RNAi screens identify cytokinesis inhibitors and targets. PLoS Biol. 2, e379 (2004).

    PubMed  PubMed Central  Google Scholar 

  63. Jiang, H., Pritchard, J. R., Williams, R. T., Lauffenburger, D. A. & Hemann, M. T. A mammalian functional-genetic approach to characterizing cancer therapeutics. Nature Chem. Biol. 7, 92–100 (2011).

    CAS  Google Scholar 

  64. Laufer, C., Fischer, B., Billmann, M., Huber, W. & Boutros, M. Mapping genetic interactions in human cancer cells with RNAi and multiparametric phenotyping. Nature Methods 10, 427–431 (2013). This paper shows an arrayed synthetic screen in mammalian cells based on double-gene perturbation with RNAi and uses multiple readouts from single cells to infer genetic interactions.

    CAS  PubMed  Google Scholar 

  65. Roguev, A. et al. Quantitative genetic-interaction mapping in mammalian cells. Nature Methods 10, 432–437 (2013). This paper shows an arrayed synthetic screen in mammalian cells based on double-gene perturbation with RNAi to infer genetic interactions, which are compared to protein–protein interaction data.

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Reiling, J. H. et al. A haploid genetic screen identifies the major facilitator domain containing 2A (MFSD2A) transporter as a key mediator in the response to tunicamycin. Proc. Natl Acad. Sci. USA 108, 11756–11765 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Barbie, D. A. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462, 108–112 (2009). This study uses siRNA screens to detect synthetic lethal partners of oncogenic KRAS.

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Ashworth, A., Lord, C. J. & Reis, J. S. Genetic interactions in cancer progression and treatment. Cell 145, 30–38 (2011).

    CAS  PubMed  Google Scholar 

  69. Bandura, D. R. et al. Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal. Chem. 81, 6813–6822 (2009).

    CAS  PubMed  Google Scholar 

  70. Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Bodenmiller, B. et al. Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nature Biotech. 30, 858–867 (2012).

    CAS  Google Scholar 

  72. Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).

    PubMed  PubMed Central  Google Scholar 

  73. Eliceiri, K. W. et al. Biological imaging software tools. Nature Methods 9, 697–710 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Schubert, W. et al. Analyzing proteome topology and function by automated multidimensional fluorescence microscopy. Nature Biotech. 24, 1270–1278 (2006).

    CAS  Google Scholar 

  75. Zrazhevskiy, P. & Gao, X. Quantum dot imaging platform for single-cell molecular profiling. Nature Commun. 4, 1619 (2013).

    Google Scholar 

  76. Valm, A. M. et al. Systems-level analysis of microbial community organization through combinatorial labeling and spectral imaging. Proc. Natl Acad. Sci. USA 108, 4152–4157 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Dehmelt, L. & Bastiaens, P. I. Spatial organization of intracellular communication: insights from imaging. Nature Rev. Mol. Cell Biol. 11, 440–452 (2010).

    CAS  Google Scholar 

  78. Welch, C. M., Elliott, H., Danuser, G. & Hahn, K. M. Imaging the coordination of multiple signalling activities in living cells. Nature Rev. Mol. Cell Biol. 12, 749–756 (2011).

    CAS  Google Scholar 

  79. Snijder, B. et al. Population context determines cell-to-cell variability in endocytosis and virus infection. Nature 461, 520–523 (2009). This paper shows that cell-to-cell variability in a population of monoclonal cells is not stochastic but can be predicted at the single-cell level by features of the cellular state and microenvironment.

    CAS  PubMed  Google Scholar 

  80. Ramo, P., Sacher, R., Snijder, B., Begemann, B. & Pelkmans, L. CellClassifier: supervised learning of cellular phenotypes. Bioinformatics 25, 3028–3030 (2009).

    CAS  PubMed  Google Scholar 

  81. Jones, T. R. et al. CellProfiler Analyst: data exploration and analysis software for complex image-based screens. BMC Bioinformatics 9, 482 (2008).

    PubMed  PubMed Central  Google Scholar 

  82. Bendall, S. C. et al. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157, 714–725 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Hinton, G. & van der Maaten, L. Visualizing data using t-SNE. J. Machine Learning Research 9, 2579–2605 (2008).

    Google Scholar 

  84. Li, L. Dimension reduction for high-dimensional data. Methods Mol. Biol. 620, 417–434 (2010).

    CAS  PubMed  Google Scholar 

  85. Duda, R. O., Hart, P. E. & Stork, D. G. Pattern Classification (John Wiley, 2001).

    Google Scholar 

  86. Jones, T. R. et al. Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning. Proc. Natl Acad. Sci. USA 106, 1826–1831 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Slack, M. D., Martinez, E. D., Wu, L. F. & Altschuler, S. J. Characterizing heterogeneous cellular responses to perturbations. Proc. Natl Acad. Sci. USA 105, 19306–193011 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Singh, D. K. et al. Patterns of basal signaling heterogeneity can distinguish cellular populations with different drug sensitivities. Mol. Syst. Biol. 6, 639 (2010).

    Google Scholar 

  89. Zhong, Q., Busetto, A. G., Fededa, J. P., Buhmann, J. M. & Gerlich, D. W. Unsupervised modeling of cell morphology dynamics for time-lapse microscopy. Nature Methods 9, 711–713 (2012).

    CAS  PubMed  Google Scholar 

  90. Qiu, P. et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nature Biotech. 29, 886–891 (2011).

    CAS  Google Scholar 

  91. Battich, N., Stoeger, T. & Pelkmans, L. Image-based transcriptomics in thousands of single human cells at single-molecule resolution. Nature Methods 10, 1127–1133 (2013).

    CAS  PubMed  Google Scholar 

  92. Waddington, C. H. Canalization of development and the inheritance of acquired characters. Nature 150, 563–565 (1942).

    Google Scholar 

  93. Kafri, R. et al. Dynamics extracted from fixed cells reveal feedback linking cell growth to cell cycle. Nature 494, 480–483 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Spencer, S. L., Gaudet, S., Albeck, J. G., Burke, J. M. & Sorger, P. K. Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis. Nature 459, 428–432 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Altschuler, S. J. & Wu, L. F. Cellular heterogeneity: do differences make a difference? Cell 141, 559–563 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Snijder, B. & Pelkmans, L. Origins of regulated cell-to-cell variability. Nature Rev. Mol. Cell Biol. 12, 119–125 (2011).

    CAS  Google Scholar 

  97. Yuan, T. L., Wulf, G., Burga, L. & Cantley, L. C. Cell-to-cell variability in PI3K protein level regulates PI3K–AKT pathway activity in cell populations. Curr. Biol. 21, 173–183 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Keren, K. et al. Mechanism of shape determination in motile cells. Nature 453, 475–480 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Colman-Lerner, A. et al. Regulated cell-to-cell variation in a cell-fate decision system. Nature 437, 699–706 (2005).

    CAS  PubMed  Google Scholar 

  100. Castor, L. N. Flattening, movement and control of division of epithelial-like cells. J. Cell. Physiol. 75, 57–64 (1970).

    CAS  PubMed  Google Scholar 

  101. Eagle, H., Levine, E. M. & Koprowski, H. Species specificity in growth regulatory effects of cellular interaction. Nature 220, 266–269 (1968).

    CAS  PubMed  Google Scholar 

  102. Eagle, H. & Levine, E. M. Growth regulatory effects of cellular interaction. Nature 213, 1102–1106 (1967).

    CAS  PubMed  Google Scholar 

  103. Zeng, L. et al. Decision making at a subcellular level determines the outcome of bacteriophage infection. Cell 141, 682–691 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. St-Pierre, F. & Endy, D. Determination of cell fate selection during phage λ infection. Proc. Natl Acad. Sci. USA 105, 20705–20710 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  105. Robert, L. et al. Pre-dispositions and epigenetic inheritance in the Escherichia coli lactose operon bistable switch. Mol. Syst. Biol. 6, 357 (2010).

    PubMed  PubMed Central  Google Scholar 

  106. Pelkmans, L. Cell Biology. Using cell-to-cell variability — a new era in molecular biology. Science 336, 425–426 (2012).

    CAS  PubMed  Google Scholar 

  107. Halme, A., Bumgarner, S., Styles, C. & Fink, G. R. Genetic and epigenetic regulation of the FLO gene family generates cell-surface variation in yeast. Cell 116, 405–415 (2004).

    CAS  PubMed  Google Scholar 

  108. Avery, S. V. Microbial cell individuality and the underlying sources of heterogeneity. Nature Rev. Microbiol. 4, 577–587 (2006).

    CAS  Google Scholar 

  109. Vlamakis, H., Aguilar, C., Losick, R. & Kolter, R. Control of cell fate by the formation of an architecturally complex bacterial community. Genes Dev. 22, 945–953 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  110. Parsons, B. D., Schindler, A., Evans, D. H. & Foley, E. A. Direct phenotypic comparison of siRNA pools and multiple individual duplexes in a functional assay. PLoS ONE 4, e8471 (2009).

    PubMed  PubMed Central  Google Scholar 

  111. Cap, M., Stepanek, L., Harant, K., Vachova, L. & Palkova, Z. Cell differentiation within a yeast colony: metabolic and regulatory parallels with a tumor-affected organism. Mol. Cell 46, 436–448 (2012).

    CAS  PubMed  Google Scholar 

  112. Dixon, S. J., Costanzo, M., Baryshnikova, A., Andrews, B. & Boone, C. Systematic mapping of genetic interaction networks. Annu. Rev. Genet. 43, 601–625 (2009).

    CAS  PubMed  Google Scholar 

  113. Collins, S. R., Roguev, A. & Krogan, N. J. Quantitative Genetic Interaction Mapping Using the E-MAP Approach Ch. 9 (Elsevier, 2010).

    Google Scholar 

  114. Tong, A. H. Y. et al. Global mapping of the yeast genetic interaction network. Science 303, 808–813 (2004).

    CAS  PubMed  Google Scholar 

  115. Nichols, R. J. et al. Phenotypic landscape of a bacterial cell. Cell 144, 143–156 (2011). This study combines large-scale chemical genomics with quantitative fitness measurements in hundreds of parallel conditions in Escherichia coli.

    CAS  PubMed  Google Scholar 

  116. Horn, T. et al. Mapping of signaling networks through synthetic genetic interaction analysis by RNAi. Nature Methods 8, 341–U391 (2011).

    CAS  PubMed  Google Scholar 

  117. Guénolé, A. et al. Dissection of DNA damage responses using multiconditional genetic interaction maps. Mol. Cell 49, 346–358 (2013).

    PubMed  Google Scholar 

  118. Bandyopadhyay, S. et al. Rewiring of genetic networks in response to DNA damage. Science 330, 1385–1389 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. Ideker, T. & Krogan, N. J. Differential network biology. Mol. Systems Biol. 8, 565 (2012).

    Google Scholar 

  120. Santos, S. D. M., Verveer, P. J. & Bastiaens, P. I. H. Growth factor-induced MAPK network topology shapes Erk response determining PC-12 cell fate. Nature Cell Biol. 9, 324–U139 (2007).

    CAS  PubMed  Google Scholar 

  121. Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protoc. 4, 44–57 (2009).

    CAS  Google Scholar 

  122. Szklarczyk, D. et al. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 39, D561–D568 (2011).

    CAS  PubMed  Google Scholar 

  123. Cerami, E. G. et al. Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res. 39, D685–D690 (2011).

    CAS  PubMed  Google Scholar 

  124. Barabasi, A. L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nature Rev. Genet. 12, 56–68 (2011).

    CAS  PubMed  Google Scholar 

  125. Markowetz, F. How to understand the cell by breaking it: network analysis of gene perturbation screens. PLoS Comput. Biol. 6, e1000655 (2010).

    PubMed  PubMed Central  Google Scholar 

  126. Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. J. Machine Learn. Res. 3, 1157–1182 (2003).

    Google Scholar 

  127. Wang, L., Wang, X., Arkin, A. P. & Samoilov, M. S. Inference of gene regulatory networks from genome-wide knockout fitness data. Bioinformatics 29, 338–346 (2013).

    PubMed  Google Scholar 

  128. Boone, C., Bussey, H. & Andrews, B. J. Exploring genetic interactions and networks with yeast. Nature Rev. Genet. 8, 437–449 (2007).

    CAS  PubMed  Google Scholar 

  129. Battle, A., Jonikas, M. C., Walter, P., Weissman, J. S. & Koller, D. Automated identification of pathways from quantitative genetic interaction data. Mol. Syst. Biol. 6, 379 (2010).

    PubMed  PubMed Central  Google Scholar 

  130. Wang, L. M., Wang, X. D., Arkin, A. P. & Samoilov, M. S. Inference of gene regulatory networks from genome-wide knockout fitness data. Bioinformatics 29, 338–346 (2013).

    PubMed  Google Scholar 

  131. Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 95, 14863–14868 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  132. Hughes, T. R. et al. Functional discovery via a compendium of expression profiles. Cell 102, 109–126 (2000).

    CAS  PubMed  Google Scholar 

  133. Gunsalus, K. C. et al. Predictive models of molecular machines involved in Caenorhabditis elegans early embryogenesis. Nature 436, 861–865 (2005).

    CAS  PubMed  Google Scholar 

  134. Fiedler, D. et al. Functional organization of the S. cerevisiae phosphorylation network. Cell 136, 952–963 (2009). This paper analyses synthetic interactions between gene knockouts of kinases, phosphatases and their substrates in S. cerevisiae . It shows that kinases, phosphatases and their substrates have positive epistatic interactions between each other but no significant correlation between their epistatic effect profiles.

    CAS  PubMed  PubMed Central  Google Scholar 

  135. Boutros, M., Agaisse, H. & Perrimon, N. Sequential activation of signaling pathways during innate immune responses in Drosophila. Dev. Cell 3, 711–722 (2002).

    CAS  PubMed  Google Scholar 

  136. Markowetz, F., Bloch, J. & Spang, R. Non-transcriptional pathway features reconstructed from secondary effects of RNA interference. Bioinformatics 21, 4026–4032 (2005).

    CAS  PubMed  Google Scholar 

  137. Markowetz, F., Kostka, D., Troyanskaya, O. G. & Spang, R. Nested effects models for high-dimensional phenotyping screens. Bioinformatics 23, i305–i312 (2007).

    CAS  PubMed  Google Scholar 

  138. Snijder, B., Liberali, P., Frechin, M., Stoeger, T. & Pelkmans, L. Predicting functional gene interactions with the hierarchical interaction score. Nature Methods 10, 1089–1092 (2013).

    CAS  PubMed  Google Scholar 

  139. Young, D. W. et al. Integrating high-content screening and ligand–target prediction to identify mechanism of action. Nature Chem. Biol. 4, 59–68 (2007).

    Google Scholar 

  140. Irish, J. M. et al. Single cell profiling of potentiated phospho-protein networks in cancer cells. Cell 118, 217–228 (2004).

    CAS  PubMed  Google Scholar 

  141. Elowitz, M. B. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002).

    CAS  PubMed  Google Scholar 

  142. Eldar, A. & Elowitz, M. B. Functional roles for noise in genetic circuits. Nature 467, 167–173 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  143. Raj, A., Rifkin, S. A., Andersen, E. & van Oudenaarden, A. Variability in gene expression underlies incomplete penetrance. Nature 463, 913–918 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  144. Munsky, B., Neuert, G. & van Oudenaarden, A. Using gene expression noise to understand gene regulation. Science 336, 183–187 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  145. Stelling, J. et al. Robustness of cellular functions. Cell 118, 675–685 (2004).

    CAS  PubMed  Google Scholar 

  146. Macarthur, B. D., Ma'ayan, A. & Lemischka, I. R. Systems biology of stem cell fate and cellular reprogramming. Nature Rev. Mol. Cell Biol. 10, 672–681 (2009).

    CAS  Google Scholar 

  147. Barad, O. et al. Robust selection of sensory organ precursors by the Notch-δ pathway. Curr. Opin. Cell Biol. 23, 663–667 (2011).

    CAS  PubMed  Google Scholar 

  148. Ribrault, C., Sekimoto, K. & Triller, A. From the stochasticity of molecular processes to the variability of synaptic transmission. Nature Rev. Neurosci. 12, 375–387 (2011).

    CAS  Google Scholar 

  149. Brandman, O. & Meyer, T. Feedback loops shape cellular signals in space and time. Science 322, 390–395 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  150. Connelly, J. T. et al. Actin and serum response factor transduce physical cues from the microenvironment to regulate epidermal stem cell fate decisions. Nature Cell Biol. 12, 711–718 (2010).

    CAS  PubMed  Google Scholar 

  151. Engler, A. J., Sen, S., Sweeney, H. L. & Discher, D. E. Matrix elasticity directs stem cell lineage specification. Cell 126, 677–689 (2006).

    CAS  PubMed  Google Scholar 

  152. Ullal, A. V. et al. Cancer cell profiling by barcoding allows multiplexed protein analysis in fine-needle aspirates. Sci. Transl Med. 6, 219ra9 (2014).

    PubMed  PubMed Central  Google Scholar 

  153. Jungmann, R. et al. Multiplexed 3D cellular super-resolution imaging with DNA-PAINT and Exchange-PAINT. Nature Methods 11, 313–318 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  154. Lubeck, E. & Cai, L. Single-cell systems biology by super-resolution imaging and combinatorial labeling. Nature Methods 9, 743–748 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  155. Gerdes, M. J. et al. Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue. Proc. Natl Acad. Sci. USA 110, 11982–11987 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

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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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  1. Berend Snijder

    Present address: Present address: CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, A-1090 Vienna, Austria.,

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  1. Institute of Molecular Life Sciences, University of Zürich, Winterthurerstrasse 190, Zürich, CH-8057, Switzerland

    Prisca Liberali, Berend Snijder & Lucas Pelkmans

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Supplementary information

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

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

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

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

(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

(SVM). Supervised learning models that recognize patterns in data sets and that are used for classification and regression analyses.

Kolmogorov–Smirnov test

A statistical non-parametric test for the comparison of continuous, one-dimensional probability distributions.

Population context

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

Screens performed in parallel in the same cell line using the same perturbations but different phenotypic readouts.

Nesting

The phenomenon whereby the effects of a perturbation are a subset of the effects of another perturbation.

Hierarchical Interaction Score

(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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