Morphological profiles of RNAi-induced gene knockdown are highly reproducible but dominated by seed effects

PLoS ONE

Volumn 10, Issue 7, 2015, Pages

  Singh, Shantanu ^a   Wu, Xiaoyun ^a   Ljosa, Vebjorn ^a   Bray, Mark Anthony ^a   Piccioni, Federica ^a   Root, David E ^a   Doench, John G ^a   Boehm, Jesse S ^a   Carpenter, Anne E ^a  

^a BROAD INSTITUTE OF MIT AND HARVARD   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MICRORNA; SHORT HAIRPIN RNA; SMALL INTERFERING RNA;

ARTICLE; CELL CULTURE; CELL STRUCTURE; CHROMOSOME PAINTING; CONTROLLED STUDY; GENE EXPRESSION PROFILING; GENE SEQUENCE; GENE SILENCING; GENE TARGETING; HIGH THROUGHPUT SCREENING; IMAGE ANALYSIS; NONHUMAN; PHENOTYPE; PLANT SEED; POLYMERASE CHAIN REACTION; QUANTITATIVE ANALYSIS; RNA INTERFERENCE; CELL LINE; GENETICS; HUMAN; METABOLISM; PROCEDURES;

CELL LINE; GENE EXPRESSION PROFILING; GENE KNOCKDOWN TECHNIQUES; HUMANS; RNA INTERFERENCE; RNA, SMALL INTERFERING;

EID: 84941309166     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0131370     Document Type: Article

References (60)

1. Mohr S, Bakal C, Perrimon N. Genomic screening with RNAi: Results and challenges. Annu Rev Biochem. 2010; 79: 37-64. doi:10.1146/annurev-biochem-060408-092949 PMID: 20367032.
2. Panda D, Cherry S. Cell-based genomic screening: Elucidating virus-host interactions. Curr Opin Virol. 2012; 2: 784-792. doi:10.1016/j.coviro.2012.10.007 PMID: 23122855.
3. Mohr SE, Smith JA, Shamu CE, Neumüller RA, Perrimon N. RNAi screening comes of age: improved techniques and complementary approaches. Nature Publishing Group; 2014; 15: 591-600. doi:10.1038/nrm3860.
4. Sigoillot FD, King RW. Vigilance and validation: Keys to success in RNAi screening. ACS Chem Biol. 2011; 6: 47-60. doi:10.1021/cb100358f PMID: 21142076.
5. Echeverri CJ, Beachy PA, Baum B, Boutros M, Buchholz F, Chanda SK, et al. Minimizing the risk of reporting false positives in large-scale RNAi screens. Nat Meth. 2006; 3: 777-779. doi:10.1038/nmeth1006-777.
6. Franceschini A, Meier R, Casanova A, Kreibich S, Daga N, Andritschke D, et al. Specific inhibition of diverse pathogens in human cells by synthetic microRNA-like oligonucleotides inferred from RNAi screens. Proc Natl Acad Sci USA. 2014; 111: 4548-4553. doi:10.1073/pnas.1402353111 PMID: 24616511.
7. Conrad C, Gerlich DW. Automated microscopy for high-content RNAi screening. The Journal of Cell Biology. Rockefeller Univ Press; 2010; 188: 453-461. doi:10.1083/jcb.200910105 PMID: 20176920.
8. Feng Y, Mitchison TJ, Bender A, Young DW, Tallarico JA. Multi-parameter phenotypic profiling: using cellular effects to characterize small-molecule compounds. Nat Rev Drug Discov. 2009; 8: 567-578. doi:10.1038/nrd2876 PMID: 19568283.
9. Kepp O, Galluzzi L, Lipinski M, Yuan J, Kroemer G. Cell death assays for drug discovery. Nat Rev Drug Discov. 2011; 10: 221-237. doi:10.1038/nrd3373 PMID: 21358741.
10. Singh S, Carpenter AE, Genovesio A. Increasing the Content of High-Content Screening: An Overview. J Biomol Screen. 2014; 19: 640-650. doi:10.1177/1087057114528537 PMID: 24710339.
11. Snijder B, Liberali P, Frechin M, Stoeger T, Pelkmans L. Predicting functional gene interactions with the hierarchical interaction score. Nat Meth. 2013; 10: 1089-1092. doi:10.1038/nmeth.2655.
12. Slack MD, Martinez ED, Wu LF, Altschuler SJ. Characterizing heterogeneous cellular responses to perturbations. Proc Natl Acad Sci USA. National Acad Sciences; 2008; 105: 19306-19311. doi:10.1073/pnas.0807038105.
13. Adams CL, Kutsyy V, Coleman DA, Cong G, Crompton AM, Elias KA, et al. Compound classification using image-based cellular phenotypes. Meth Enzymol. Elsevier; 2006; 414: 440-468doi:10.1016/S0076-6879(06)14024-0 PMID: 17110206.
14. Loo L-H, Wu LF, Altschuler SJ. Image-based multivariate profiling of drug responses from single cells. Nat Meth. 2007; 4: 445-453. doi:10.1038/nmeth1032.
15. Loo L-H, Lin H-J, Steininger RJ, Wang Y, Wu LF, Altschuler SJ. An approach for extensibly profiling the molecular states of cellular subpopulations. Nat Meth. 2009; 6: 759-765. doi:10.1038/nmeth.1375.
16. Perlman ZE, Slack MD, Feng Y, Mitchison TJ, Wu LF, Altschuler SJ. Multidimensional drug profiling by automated microscopy. Science. American Association for the Advancement of Science; 2004; 306: 1194-1198. doi:10.1126/science.1100709.
17. Sundaramurthy V, Barsacchi R, Chernykh M, Stöter M, Tomschke N, Bickle M, et al. Deducing the mechanism of action of compounds identified in phenotypic screens by integrating their multiparametric profiles with a reference genetic screen. Nat Protoc. 2014; 9: 474-490. doi:10.1038/nprot.2014.027 PMID: 24481274.
18. Young DW, Bender A, Hoyt J, McWhinnie E, Chirn G-W, Tao CY, et al. Integrating high-content screening and ligand-target prediction to identify mechanism of action. Nat Chem Biol. 2008; 4: 59-68. doi:10.1038/nchembio.2007.53 PMID: 18066055.
19. Fuchs F, Pau G, Kranz D, Sklyar O, Budjan C, Steinbrink S, et al. Clustering phenotype populations by genome-wide RNAi and multiparametric imaging. Mol Syst Biol. 2010; 6: 370. doi:10.1038/msb.2010.25 PMID: 20531400.
20. Laufer C, Fischer B, Billmann M, Huber W, Boutros M. Mapping genetic interactions in human cancer cells with RNAi and multiparametric phenotyping. Nat Meth. 2013; 10: 427-431. doi:10.1038/nmeth.2436.
21. Collinet C, Stöter M, Bradshaw CR, Samusik N, Rink JC, Kenski D, et al. Systems survey of endocytosis by multiparametric image analysis. Nature. 2010; 464: 243-249. doi:10.1038/nature08779 PMID: 20190736.
22. Liberali P, Snijder B, Pelkmans L. A Hierarchical Map of Regulatory Genetic Interactions in Membrane Trafficking. Cell. 2014; 157: 1473-1487doi:10.1016/j.cell.2014.04.029 PMID: 24906158.
23. Simeone A, Marsico G, Collinet C, Galvez T, Kalaidzidis Y, Zerial M, et al. Revealing Molecular Mechanisms by Integrating High-Dimensional Functional Screens with Protein Interaction Data. Morris Q, editor. PLoS Comput Biol. Public Library of Science; 2014; 10: E1003801. doi:10.1371/journal.pcbi.1003801.
24. Snijder B, Sacher R, Rämö P, Liberali P, Mench K, Wolfrum N, et al. Single-cell analysis of population context advances RNAi screening at multiple levels. 2012; 8. doi:10.1038/msb.2012.9.
25. Futamura Y, Kawatani M, Kazami S, Tanaka K, Muroi M, Shimizu T, et al. Morphobase, an encyclopedic cell morphology database, and its use for drug target identification. Chem Biol. 2012; 19: 1620-1630. doi:10.1016/j.chembiol.2012.10.014 PMID: 23261605.
26. Held M, Schmitz MHA, Fischer B, Walter T, Neumann B, Olma MH, et al. CellCognition: Time-resolved phenotype annotation in high-throughput live cell imaging. Nat Meth. 2010; 7: 747-754. doi:10.1038/nmeth.1486.
27. Rämö P, Sacher R, Snijder B, Begemann B, Pelkmans L. CellClassifier: supervised learning of cellular phenotypes. Bioinformatics. Oxford University Press; 2009; 25: 3028-3030. doi:10.1093/bioinformatics/btp524 PMID: 19729371.
28. Jones TR, Carpenter AE, Lamprecht MR, Moffat J, Silver SJ, Grenier JK, et al. Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning. Proc Natl Acad Sci USA. National Acad Sciences; 2009; 106: 1826-1831. doi:10.1073/pnas.0808843106.
29. Horvath P, Wild T, Kutay U, Csucs G. Machine learning improves the precision and robustness of highcontent screens: using nonlinear multiparametric methods to analyze screening results. J Biomol Screen. 2011; 16: 1059-1067. doi:10.1177/1087057111414878 PMID: 21807964.
30. Misselwitz B, Strittmatter G, Periaswamy B, Schlumberger MC, Rout S, Horvath P, et al. Enhanced CellClassifier: A multi-class classification tool for microscopy images. BMC Bioinformatics. BioMed Central Ltd; 2010; 11: 30. doi:10.1186/1471-2105-11-30.
31. Sommer C, Gerlich DW. Machine learning in cell biology-teaching computers to recognize phenotypes. J Cell Sci. 2013; 126: 5529-5539. doi:10.1242/jcs.123604 PMID: 24259662.
32. Sundaramurthy V, Barsacchi R, Samusik N, Marsico G, Gilleron J, Kalaidzidis I, et al. Integration of chemical and RNAi multiparametric profiles identifies triggers of intracellular mycobacterial killing. Cell Host Microbe. 2013; 13: 129-142. doi:10.1016/j.chom.2013.01.008 PMID: 23414754.
33. Ljosa V, Caie PD, Horst Ter R, Sokolnicki KL, Jenkins EL, Daya S, et al. Comparison of methods for image-based profiling of cellular morphological responses to small-molecule treatment. J Biomol Screen. 2013; 18: 1321-1329. doi:10.1177/1087057113503553 PMID: 24045582.
34. Gustafsdottir SM, Ljosa V, Sokolnicki KL, Anthony Wilson J, Walpita D, Kemp MM, et al. Multiplex cytological profiling assay to measure diverse cellular states. PLoS ONE. 2013; 8: E80999. doi:10.1371/journal.pone.0080999 PMID: 24312513.
35. Wawer MJ, Li K, Gustafsdottir SM, Ljosa V, Bodycombe NE, Marton MA, et al. Toward performancediverse small-molecule libraries for cell-based phenotypic screening using multiplexed highdimensional profiling. Proc Natl Acad Sci USA. National Acad Sciences; 2014; 111: 10911-10916. doi:10.1073/pnas.1410933111.
36. Buehler E, Chen Y-C, Martin S. C911: A bench-level control for sequence specific siRNA off-target effects. Semsey S, editor. PLoS ONE. 2012; 7: E51942. doi:10.1371/journal.pone.0051942 PMID: 23251657.
37. Buehler E, Khan AA, Marine S, Rajaram M, Bahl A, Burchard J, et al. siRNA off-target effects in genome-wide screens identify signaling pathway members. Sci Rep. 2012; 2: 428doi:10.1038/srep00428 PMID: 22645644.
38. Schultz N, Marenstein DR, De Angelis DA, Wang W-Q, Nelander S, Jacobsen A, et al. Off-target effects dominate a large-scale RNAi screen for modulators of the TGF-β pathway and reveal microRNA regulation of TGFBR2. Silence. BioMed Central Ltd; 2011; 2: 3. doi:10.1186/1758-907X-2-3.
39. Sigoillot FD, Lyman S, Huckins JF, Adamson B, Chung E, Quattrochi B, et al. A bioinformatics method identifies prominent off-targeted transcripts in RNAi screens. Nat Meth. 2012; 9: 363-366. doi:10.1038/nmeth.1898.
40. Zhong R, Kim J, Kim HS, Kim M, Lum L, Levine B, et al. Computational detection and suppression of sequence-specific off-target phenotypes from whole genome RNAi screens. Nucleic Acids Research. Oxford University Press; 2014; 42: 8214-8222. doi:10.1093/nar/gku306 PMID: 24972830.
41. Jackson AL, Burchard J, Schelter J, Chau BN, Cleary M, Lim L, et al. Widespread siRNA "off-target" transcript silencing mediated by seed region sequence complementarity. RNA. Cold Spring Harbor Lab; 2006; 12: 1179-1187. doi:10.1261/rna.25706.
42. Fischer B, Sandmann T, Horn T, Billmann M, Chaudhary V, Huber W, et al. A map of directional genetic interactions in a metazoan cell. Elife. 2015; 4. doi:10.7554/eLife.05464.
43. Kulkarni MM, Booker M, Silver SJ, Friedman A, Hong P, Perrimon N, et al. Evidence of off-target effects associated with long dsRNAs in Drosophila melanogaster cell-based assays. Nat Meth. Nature Publishing Group; 2006; 3: 833-838. doi:10.1038/nmeth935.
44. Ma Y, Creanga A, Lum L, Beachy PA. Prevalence of off-target effects in Drosophila RNA interference screens. Nature. Nature Publishing Group; 2006; 443: 359-363. doi:10.1038/nature05179.
45. Moffat J, Reiling JH, Sabatini DM. Off-target effects associated with long dsRNAs in Drosophila RNAi screens. Trends Pharmacol Sci. 2007; 28: 149-151. doi:10.1016/j.tips.2007.02.009 PMID: 17350110.
46. Kamentsky L, Jones TR, Fraser A, Bray M-A, Logan DJ, Madden KL, et al. Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software. Bioinformatics. Oxford University Press; 2011; 27: 1179-1180. doi:10.1093/bioinformatics/btr095 PMID: 21349861.
47. Doench JG, Petersen CP, Sharp PA. siRNAs can function as miRNAs. Genes Dev. Cold Spring Harbor Lab; 2003; 17: 438-442. doi:10.1101/gad.1064703.
48. Jackson AL, Linsley PS. Recognizing and avoiding siRNA off-target effects for target identification and therapeutic application. Nat Rev Drug Discov. 2010; 9: 57-67. doi:10.1038/nrd3010 PMID: 20043028.
49. Jackson AL, Bartz SR, Schelter J, Kobayashi SV, Burchard J, Mao M, et al. Expression profiling reveals off-target gene regulation by RNAi. Nat Biotechnol. 2003; 21: 635-637. doi:10.1038/nbt831 PMID: 12754523.
50. Bushman FD, Malani N, Fernandes J, D'Orso I, Cagney G, Diamond TL, et al. Host cell factors in HIV replication: meta-analysis of genome-wide studies. Rall GF, editor. PLoS Pathog. Public Library of Science; 2009; 5: E1000437. doi:10.1371/journal.ppat.1000437.
51. Birmingham A, Selfors LM, Forster T, Wrobel D, Kennedy CJ, Shanks E, et al. Statistical methods for analysis of high-throughput RNA interference screens. Nat Meth. 2009; 6: 569-575. doi:10.1038/nmeth.1351.
52. Boutros M, Ahringer J. The art and design of genetic screens: RNA interference. Nat Rev Genet. 2008; 9: 554-566. doi:10.1038/nrg2364 PMID: 18521077.
53. Shao DD, Tsherniak A, Gopal S, Weir BA, Tamayo P, Stransky N, et al. ATARiS: computational quantification of gene suppression phenotypes from multisample RNAi screens. Genome Res. 2013; 23: 665-678. doi:10.1101/gr.143586.112 PMID: 23269662.
54. Jackson AL, Burchard J, Leake D, Reynolds A, Schelter J, Guo J, et al. Position-specific chemical modification of siRNAs reduces "off-target" transcript silencing. RNA. Cold Spring Harbor Lab; 2006; 12: 1197-1205. doi:10.1261/rna.30706.
55. Kittler R, Surendranath V, Heninger A-K, Slabicki M, Theis M, Putz G, et al. Genome-wide resources of endoribonuclease-prepared short interfering RNAs for specific loss-of-function studies. Nat Meth. 2007; 4: 337-344. doi:10.1038/nmeth1025.
56. Hannus M, Beitzinger M, Engelmann JC, Weickert M-T, Spang R, Hannus S, et al. siPools: highly complex but accurately defined siRNA pools eliminate off-target effects. Nucleic Acids Research. Oxford University Press; 2014; 42: 8049-8061. doi:10.1093/nar/gku480 PMID: 24875475.
57. Yang X, Boehm JS, Yang X, Salehi-Ashtiani K, Hao T, Shen Y, et al. A public genome-scale lentiviral expression library of human ORFs. Nat Meth. 2011; 8: 659-661. doi:10.1038/nmeth.1638.
58. Cong L, Ran FA, Cox D, Lin S, Barretto R, Habib N, et al. Multiplex genome engineering using CRISPR/Cas systems. Science. American Association for the Advancement of Science; 2013; 339: 819-823. doi:10.1126/science.1231143.
59. Bookout AL, Cummins CL, Mangelsdorf DJ, Pesola JM, Kramer MF. High-throughput real-time quantitative reverse transcription PCR. Curr Protoc Mol Biol. Hoboken, NJ, USA: John Wiley & Sons, Inc; 2006;Chapter 15: Unit 15.8-15.8.28. doi:10.1002/0471142727.mb1508s73.
60. Chung N, Zhang XD, Kreamer A, Locco L, Kuan P-F, Bartz S, et al. Median absolute deviation to improve hit selection for genome-scale RNAi screens. J Biomol Screen. 2008; 13: 149-158. doi:10.1177/1087057107312035 PMID: 18216396.