Application of RIP-Chip for the Identification of miRNA Targets

  • Lu Ping Tan
  • Anke van den Berg
  • Joost L. KluiverEmail author
Part of the Neuromethods book series (NM, volume 58)


MicroRNAs (miRNAs) are a class of noncoding small RNAs that can regulate gene expression at the posttranscriptional level. To understand how miRNAs function, it is crucial to determine the mRNA targets that are regulated by specific miRNAs. Based on known miRNA:mRNA interactions, miRNA target gene prediction programs have been developed that provide users with long lists of potential target genes. However, due to the use of different thresholds and/or criteria used, there is limited overlap between the putative miRNA targets as obtained by different miRNA target gene prediction programs. Moreover, it has been shown that there are many exceptions to the general rules of miRNA targeting, and cell-type specific miRNA and target gene expression patterns are not considered. Therefore, predicted targets need to be validated using, for instance, reporter assays that are labor intensive and may not always mimic endogenous miRNA:mRNA interactions. For these reasons, there is a clear need for a high-throughput method that allows for the unbiased detection of miRNA:mRNA interactions in the cell type of interest without the need of target gene prediction programs. Here, we provide a protocol called Ribonucleoprotein ImmunoPrecipitation – gene Chip (RIP-Chip) that results in the identification of all miRNA targets (miRNA targetome) in a given cell population. This biochemical approach is based on the immunoprecipiation of the RNA-induced silencing complex (RISC) followed by the identification of the transcripts that are enriched in the immunoprecipitated fraction by microarray analysis.

Key words

MicroRNA RIP-Chip AGO2 miRNA targetome 



This study was funded by the Dutch Cancer Society (# RUG 2009-4279).


  1. 1.
    Lai EC. Micro RNAs are complementary to 3′ UTR sequence motifs that mediate negative post-transcriptional regulation. Nat Genet 2002;30:363–4.PubMedCrossRefGoogle Scholar
  2. 2.
    Stark A, Brennecke J, Russell RB, Cohen SM. Identification of Drosophila MicroRNA targets. PLoS Biol 2003;1:E60.PubMedCrossRefGoogle Scholar
  3. 3.
    John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS. Human MicroRNA targets. PLoS Biol 2004;2:e363.PubMedCrossRefGoogle Scholar
  4. 4.
    Lewis BP, Shih IH, Jones-Rhoades MW, Bartel DP, Burge CB. Prediction of mammalian microRNA targets. Cell 2003;115:787–98.PubMedCrossRefGoogle Scholar
  5. 5.
    Lal A, Navarro F, Maher CA, et al. miR-24 Inhibits cell proliferation by targeting E2F2, MYC, and other cell-cycle genes via binding to “seedless” 3′UTR microRNA recognition elements. Mol Cell 2009;35:610–25.PubMedCrossRefGoogle Scholar
  6. 6.
    Lee I, Ajay SS, Yook JI, et al. New class of microRNA targets containing simultaneous 5′-UTR and 3′-UTR interaction sites. Genome Res 2009.Google Scholar
  7. 7.
    Orom UA, Nielsen FC, Lund AH. MicroRNA-10a binds the 5′UTR of ribosomal protein mRNAs and enhances their translation. Mol Cell 2008;30:460–71.PubMedCrossRefGoogle Scholar
  8. 8.
    Steitz JA, Vasudevan S. miRNPs: versatile regulators of gene expression in vertebrate cells. Biochem Soc Trans 2009;37:931–5.PubMedCrossRefGoogle Scholar
  9. 9.
    Vasudevan S, Tong Y, Steitz JA. Switching from repression to activation: microRNAs can up-regulate translation. Science 2007;318:1931–4.PubMedCrossRefGoogle Scholar
  10. 10.
    Gao Y, He Y, Ding J, et al. An insertion/­deletion polymorphism at miRNA-122-­binding site in the interleukin-1alpha 3′ untranslated region confers risk for hepatocellular carcinoma. Carcinogenesis 2009;30:2064–9.PubMedCrossRefGoogle Scholar
  11. 11.
    Mishra PJ, Humeniuk R, Mishra PJ, Longo-Sorbello GS, Banerjee D, Bertino JR. A miR-24 microRNA binding-site polymorphism in dihydrofolate reductase gene leads to methotrexate resistance. Proc Natl Acad Sci U S A 2007;104:13513–8.PubMedCrossRefGoogle Scholar
  12. 12.
    Mayr C, Bartel DP. Widespread shortening of 3′UTRs by alternative cleavage and polyadenylation activates oncogenes in cancer cells. Cell 2009;138:673–84.PubMedCrossRefGoogle Scholar
  13. 13.
    Zhu S, Si ML, Wu H, Mo YY. MicroRNA-21 targets the tumor suppressor gene tropomyosin 1 (TPM1). J Biol Chem 2007;282:14328–36.PubMedCrossRefGoogle Scholar
  14. 14.
    Vinther J, Hedegaard MM, Gardner PP, Andersen JS, Arctander P. Identification of miRNA targets with stable isotope labeling by amino acids in cell culture. Nucleic Acids Res 2006;34:e107.PubMedCrossRefGoogle Scholar
  15. 15.
    Baek D, Villen J, Shin C, Camargo FD, Gygi SP, Bartel DP. The impact of microRNAs on protein output. Nature 2008;455:64–71.PubMedCrossRefGoogle Scholar
  16. 16.
    Taguchi A, Yanagisawa K, Tanaka M, et al. Identification of hypoxia-inducible factor-1 alpha as a novel target for miR-17-92 microRNA cluster. Cancer Res 2008;68:5540–5.PubMedCrossRefGoogle Scholar
  17. 17.
    Yang Y, Chaerkady R, Beer MA, Mendell JT, Pandey A. Identification of miR-21 targets in breast cancer cells using a quantitative proteomic approach. Proteomics 2009;9:1374–84.PubMedCrossRefGoogle Scholar
  18. 18.
    Lal A, Kim HH, Abdelmohsen K, et al. p16(INK4a) translation suppressed by miR-24. PLoS ONE 2008;3:e1864.PubMedCrossRefGoogle Scholar
  19. 19.
    Tan LP, Seinen E, Duns G, et al. A high throughput experimental approach to identify miRNA targets in human cells. Nucleic Acids Res 2009.Google Scholar
  20. 20.
    Keene JD, Komisarow JM, Friedersdorf MB. RIP-Chip: the isolation and identification of mRNAs, microRNAs and protein components of ribonucleoprotein complexes from cell extracts. Nat Protoc 2006;1:302–7.PubMedCrossRefGoogle Scholar
  21. 21.
    Zhang L, Ding L, Cheung TH, et al. Systematic identification of C. elegans miRISC proteins, miRNAs, and mRNA ­targets by their interactions with GW182 proteins AIN-1 and AIN-2. Mol Cell 2007;28:598–613.PubMedCrossRefGoogle Scholar
  22. 22.
    Easow G, Teleman AA, Cohen SM. Isolation of microRNA targets by miRNP immunopurification. RNA 2007;13:1198–204.PubMedCrossRefGoogle Scholar
  23. 23.
    Landthaler M, Gaidatzis D, Rothballer A, et al. Molecular characterization of human Argonaute-containing ribonucleoprotein complexes and their bound target mRNAs. RNA 2008;14:2580–96.PubMedCrossRefGoogle Scholar
  24. 24.
    Beitzinger M, Peters L, Zhu JY, Kremmer E, Meister G. Identification of human microRNA targets from isolated argonaute protein ­complexes. RNA Biol 2007;4:76-84.PubMedCrossRefGoogle Scholar
  25. 25.
    Chi SW, Zang JB, Mele A, Darnell RB. Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature 2009;460:479–86.PubMedGoogle Scholar
  26. 26.
    Licatalosi DD, Mele A, Fak JJ, et al. HITS-CLIP yields genome-wide insights into brain alternative RNA processing. Nature 2008;456:464–9.PubMedCrossRefGoogle Scholar
  27. 27.
    Lewis BP, Burge CB, Bartel DP. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 2005;120:15–20.PubMedCrossRefGoogle Scholar
  28. 28.
    Griffiths-Jones S. miRBase: the microRNA sequence database. Methods Mol Biol 2006;342:129–29.PubMedGoogle Scholar
  29. 29.
    Krek A, Grun D, Poy MN, et al. Combinatorial microRNA target predictions. Nat Genet 2005;37:495–500.PubMedCrossRefGoogle Scholar
  30. 30.
    Rehmsmeier M, Steffen P, Hochsmann M, Giegerich R. Fast and effective prediction of microRNA/target duplexes. RNA 2004;10:1507–17.PubMedCrossRefGoogle Scholar
  31. 31.
    van Dongen S, Abreu-Goodger C, Enright AJ. Detecting microRNA binding and siRNA off-target effects from expression data. Nat Methods 2008;5:1023–5.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Lu Ping Tan
  • Anke van den Berg
  • Joost L. Kluiver
    • 1
    Email author
  1. 1.Department of PathologyUniversity Medical Center Groningen, University of GroningenGroningenThe Netherlands

Personalised recommendations