Cell Perturbation Screens for Target Identification by RNAi

Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 910)

Abstract

Over the last decade, cell-based screening has become a powerful method in target identification and plays an important role both in basic research and drug discovery. The availability of whole genome sequences and improvements in cell-based screening techniques opened new avenues for high-throughput experiments. Large libraries of RNA interference reagents available for many organisms allow the dissection of broad spectrum of cellular processes. Here, we describe the current state of the large-scale phenotype screening with a focus on cell-based screens. We underline the importance and provide details of screen design, scalability, performance, data analysis, and hit prioritization. Similar to classical high-throughput in vitro screens with defined-target approaches in the past, cell-based screens depend on a successful establishment of robust phenotypic assays, the ability to quantitatively measure phenotypic changes and bioinformatics methods for data analysis, integration, and interpretation.

Key words

Functional genomics Drug and RNAi screen design Assay development RNAi libraries Homogeneous and high-content screen analysis Hit prioritization 

Notes

Acknowledgements

The authors would like to thank Julia Gross and Thomas Sandmann for critical comments on the manuscript. This work has been in part supported by NGFN-Plus NeuroNet.

References

  1. 1.
    An WF, Tolliday N (2010) Cell-based assays for high-throughput screening. Mol Biotechnol 45:180–186PubMedCrossRefGoogle Scholar
  2. 2.
    Wignall SM, Gray NS, Chang YT, Juarez L, Jacob R, Burlingame A, Schultz PG, Heald R (2004) Identification of a novel protein regulating microtubule stability through a chemical approach. Chem Biol 11:135–146PubMedGoogle Scholar
  3. 3.
    Bartscherer K, Pelte N, Ingelfinger D, Boutros M (2006) Secretion of Wnt ligands requires Evi, a conserved transmembrane protein. Cell 125:523–533PubMedCrossRefGoogle Scholar
  4. 4.
    Jacob LS, Wu X, Dodge ME, Fan CW, Kulak O, Chen B, Tang W, Wang B, Amatruda JF, Lum L (2011) Genome-wide RNAi screen reveals disease-associated genes that are common to Hedgehog and Wnt signaling. Sci Signal 4:ra4PubMedCrossRefGoogle Scholar
  5. 5.
    Gilbert DF, Erdmann G, Zhang X, Fritzsche A, Demir K, Jaedicke A, Muehlenberg K, Wanker EE, Boutros M (2011) A novel multiplex cell viability assay for high-throughput RNAi screening. PLoS One 6:e28338PubMedCrossRefGoogle Scholar
  6. 6.
    Ketteler R, Sun Z, Kovacs KF, He WW, Seed B (2008) A pathway sensor for genome-wide screens of intracellular proteolytic cleavage. Genome Biol 9:R64PubMedCrossRefGoogle Scholar
  7. 7.
    Badr CE, Wurdinger T, Tannous BA (2011) Functional drug screening assay reveals potential glioma therapeutics. Assay and Drug Development Technologies 9:281–289CrossRefGoogle Scholar
  8. 8.
    Beck V, Pfitscher A, Jungbauer A (2005) GFP-reporter for a high throughput assay to monitor estrogenic compounds. J Biochem Biophys Methods 64:19–37PubMedCrossRefGoogle Scholar
  9. 9.
    Zanella F, Rosado A, Garcia B, Carnero A, Link W (2009) Using multiplexed regulation of luciferase activity and GFP translocation to screen for FOXO modulators. BMC Cell Biol 10:14PubMedCrossRefGoogle Scholar
  10. 10.
    Giuliano KA, Johnston PA, Gough A, Taylor DL (2006) Systems cell biology based on high-content screening. Methods Enzymol 414:601–619Google Scholar
  11. 11.
    Korn K, Krausz E (2007) Cell-based high-content screening of small-molecule libraries. Curr Opin Chem Biol 11:503–510PubMedCrossRefGoogle Scholar
  12. 12.
    Lundholt BK, Linde V, Loechel F, Pedersen HC, Moller S, Praestegaard M, Mikkelsen I, Scudder K, Bjorn SP, Heide M, Arkhammar PO, Terry R, Nielsen SJ (2005) Identification of Akt pathway inhibitors using redistribution screening on the FLIPR and the IN Cell 3000 analyzer. J Biomol Screen 10:20–29PubMedCrossRefGoogle Scholar
  13. 13.
    Neumann B, Walter T, Heriche JK, Bulkescher J, Erfle H, Conrad C, Rogers P, Poser I, Held M, Liebel U, Cetin C, Sieckmann F, Pau G, Kabbe R, Wunsche A, Satagopam V, Schmitz MH, Chapuis C, Gerlich DW, Schneider R, Eils R, Huber W, Peters JM, Hyman AA, Durbin R, Pepperkok R, Ellenberg J (2010) Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes. Nature 464:721–727PubMedCrossRefGoogle Scholar
  14. 14.
    Pardo-Martin C, Chang TY, Koo BK, Gilleland CL, Wasserman SC, Yanik MF (2010) High-throughput in vivo vertebrate screening. Nat Methods 7:634–636PubMedCrossRefGoogle Scholar
  15. 15.
    Mayr LM, Bojanic D (2009) Novel trends in high-throughput screening. Curr Opin Pharmacol 9:580–588PubMedCrossRefGoogle Scholar
  16. 16.
    Boutros M, Ahringer J (2008) The art and design of genetic screens: RNA interference. Nat Rev Genet 9:554–566PubMedCrossRefGoogle Scholar
  17. 17.
    Falschlehner C, Steinbrink S, Erdmann G, Boutros M (2010) High-throughput RNAi screening to dissect cellular pathways: a how-to guide. Biotechnol J 5:368–376PubMedCrossRefGoogle Scholar
  18. 18.
    Mohr S, Bakal C, Perrimon N (2010) Genomic screening with RNAi: results and challenges. Annu Rev Biochem 79:37–64PubMedCrossRefGoogle Scholar
  19. 19.
    Fire A, Xu S, Montgomery MK, Kostas SA, Driver SE, Mello CC (1998) Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature 391:806–811PubMedCrossRefGoogle Scholar
  20. 20.
    Tabara H, Grishok A, Mello CC (1998) RNAi in C. elegans: soaking in the genome sequence. Science 282:430–431PubMedCrossRefGoogle Scholar
  21. 21.
    Timmons L, Fire A (1998) Specific interference by ingested dsRNA. Nature 395:854PubMedCrossRefGoogle Scholar
  22. 22.
    Clemens JC, Worby CA, Simonson-Leff N, Muda M, Maehama T, Hemmings BA, Dixon JE (2000) Use of double-stranded RNA interference in Drosophila cell lines to dissect signal transduction pathways. Proc Natl Acad Sci USA97:6499–6503PubMedCrossRefGoogle Scholar
  23. 23.
    Reynolds A, Anderson EM, Vermeulen A, Fedorov Y, Robinson K, Leake D, Karpilow J, Marshall WS, Khvorova A (2006) Induction of the interferon response by siRNA is cell type- and duplex length-dependent. RNA 12:988–993PubMedCrossRefGoogle Scholar
  24. 24.
    Mittal V (2004) Improving the efficiency of RNA interference in mammals. Nat Rev Genet 5:355–365PubMedCrossRefGoogle Scholar
  25. 25.
    Fraser AG, Kamath RS, Zipperlen P, Martinez-Campos M, Sohrmann M, Ahringer J (2000) Functional genomic analysis of C. elegans chromosome I by systematic RNA interference. Nature 408:325–330PubMedCrossRefGoogle Scholar
  26. 26.
    Kamath RS, Fraser AG, Dong Y, Poulin G, Durbin R, Gotta M, Kanapin A, Le Bot N, Moreno S, Sohrmann M, Welchman DP, Zipperlen P, Ahringer J (2003) Systematic functional analysis of the Caenorhabditis elegans genome using RNAi. Nature 421:231–237PubMedCrossRefGoogle Scholar
  27. 27.
    Malo N, Hanley JA, Cerquozzi S, Pelletier J, Nadon R (2006) Statistical practice in high-throughput screening data analysis. Nat Biotechnol 24:167–175PubMedCrossRefGoogle Scholar
  28. 28.
    Zhang JH, Chung TD, Oldenburg KR (1999) A simple statistical parameter for use in evaluation and validation of high throughput screening assays. J Biomol Screen 4:67–73PubMedCrossRefGoogle Scholar
  29. 29.
    Brideau C, Gunter B, Pikounis B, Liaw A (2003) Improved statistical methods for hit selection in high-throughput screening. J Biomol Screen 8:634–647PubMedCrossRefGoogle Scholar
  30. 30.
    Boutros M, Brás LP, Huber W (2006) Analysis of cell-based RNAi screens. Genome Biol 7:R66PubMedCrossRefGoogle Scholar
  31. 31.
    Bras L, Pau G, Hahne F, Boutros M, Huber W (2012) Analysis of cell-based screens—cellHTS2. Reference Manual. Bioconductor Release 2.9 Google Scholar
  32. 32.
    Pelz O, Gilsdorf M, Boutros M (2010) web cellHTS2: a web-application for the analysis of high-throughput screening data. BMC Bioinform 11:185CrossRefGoogle Scholar
  33. 33.
    Perlman ZE, Slack MD, Feng Y, Mitchison TJ, Wu LF, Altschuler SJ (2004) Multidimensional drug profiling by automated microscopy. Science 306:1194–1198PubMedCrossRefGoogle Scholar
  34. 34.
    Kiger AA, Baum B, Jones S, Jones MR, Coulson A, Echeverri C, Perrimon N (2003) A functional genomic analysis of cell morphology using RNA interference. J Biol 2:27PubMedCrossRefGoogle Scholar
  35. 35.
    Eggert US, Kiger AA, Richter C, Perlman ZE, Perrimon N, Mitchison TJ, Field CM (2004) Parallel chemical genetic and genome-wide RNAi screens identify cytokinesis inhibitors and targets. PLoS Biol 2:e379PubMedCrossRefGoogle Scholar
  36. 36.
    Shariff A, Kangas J, Coelho LP, Quinn S, Murphy RF (2010) Automated image analysis for high-content screening and analysis. J Biomol Screen 15:726–734PubMedCrossRefGoogle Scholar
  37. 37.
    Carpenter AE, Jones TR, Lamprecht MR, Clarke C, Kang IH, Friman O, Guertin DA, Chang JH, Lindquist RA, Moffat J, Golland P, Sabatini DM (2006) Cell profiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol 7:R100PubMedCrossRefGoogle Scholar
  38. 38.
    Abramoff MD, Magelhaes PJ, Ram S (2004) Image processing with ImageJ. Biophotonics Int 11:36–42Google Scholar
  39. 39.
    Pau G, Fuchs F, Sklyar O, Boutros M, Huber W (2010) EBImage—an R package for image processing with applications to cellular phenotypes. Bioinformatics 26:979–981PubMedCrossRefGoogle Scholar
  40. 40.
    Bakal C, Aach J, Church G, Perrimon N (2007) Quantitative morphological signatures define local signaling networks regulating cell morphology. Science 316:1753–1756PubMedCrossRefGoogle Scholar
  41. 41.
    Gunsalus KC, Yueh WC, MacMenamin P, Piano F (2004) RNAiDB and PhenoBlast: web tools for genome-wide phenotypic mapping projects. Nucleic Acids Res 32:D406–410PubMedCrossRefGoogle Scholar
  42. 42.
    Perez-Iratxeta C, Bork P, Andrade MA (2002) Association of genes to genetically inherited diseases using data mining. Nat Genet 31:316–319PubMedGoogle Scholar
  43. 43.
    Morrison JL, Breitling R, Higham DJ, Gilbert DR (2005) GeneRank: using search engine technology for the analysis of microarray experiments. BMC Bioinform 6:233CrossRefGoogle Scholar
  44. 44.
    Ma X, Lee H, Wang L, Sun F (2007) CGI: a new approach for prioritizing genes by combining gene expression and protein-protein interaction data. Bioinformatics 23:215–221PubMedCrossRefGoogle Scholar
  45. 45.
    Tranchevent LC, Capdevila FB, Nitsch D, De Moor B, De Causmaecker P, Moreau Y (2011) A guide to web tools to prioritize candidate genes. Brief Bioinform 12:22–32Google Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Division of Signaling and Functional Genomics, Department for Cell and Molecular Biology, German Cancer Research Center (DKFZ)Heidelberg UniversityHeidelbergGermany

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