Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Global in situ profiling of RNA-RNA spatial interactions with RIC-seq


Emerging evidence has demonstrated that RNA-RNA interactions are vital in controlling diverse biological processes, including transcription, RNA splicing and protein translation. RNA in situ conformation sequencing (RIC-seq) is a technique for capturing protein-mediated RNA-RNA proximal interactions globally in living cells at single-base resolution. Cells are first treated with formaldehyde to fix all the protein-mediated RNA-RNA interactions in situ. After cell permeabilization and micrococcal nuclease digestion, the proximally interacting RNAs are 3′ end-labeled with pCp-biotin and subsequently ligated using T4 RNA ligase. The chimeric RNAs are then enriched and converted into libraries for paired-end sequencing. After deep sequencing, computational analysis yields interaction strength scores for every base on proximally interacting RNAs in the starting populations. The whole experimental procedure is designed to be completed within 6 d, followed by an additional 8 d for computational analysis. RIC-seq technology can unbiasedly detect intra- and intermolecular RNA-RNA interactions, thereby rendering it useful for reconstructing RNA higher-order structures and identifying direct noncoding RNA targets.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Overview of RIC-seq technology.
Fig. 2: Evaluation of RIC-seq library.
Fig. 3: Inter- and intramolecular RNA-RNA interactions revealed by RIC-seq.

Data availability

RIC-seq data for HeLa cells (originally published in ref. 36) are available in the Gene Expression Omnibus under accession number GSE127188. An example dataset is deposited at cKCJIyB-?usp=sharing.

Code availability

The scripts for RIC-seq data analysis can be freely downloaded from GitHub at


  1. 1.

    Djebali, S. et al. Landscape of transcription in human cells. Nature 489, 101–108 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Chen, J. & Xue, Y. Emerging roles of non-coding RNAs in epigenetic regulation. Sci. China Life Sci. 59, 227–235 (2016).

    CAS  PubMed  Google Scholar 

  3. 3.

    Palazzo, A. F. & Lee, E. S. Non-coding RNA: what is functional and what is junk? Front. Genet. 6, 2 (2015).

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Butcher, S. E. & Pyle, A. M. The molecular interactions that stabilize RNA tertiary structure: RNA motifs, patterns, and networks. Acc. Chem. Res. 44, 1302–1311 (2011).

    CAS  PubMed  Google Scholar 

  5. 5.

    Pyle, A. M. Looking at LncRNAs with the ribozyme toolkit. Mol. Cell 56, 13–17 (2014).

    CAS  PubMed  Google Scholar 

  6. 6.

    Guil, S. & Esteller, M. RNA-RNA interactions in gene regulation: the coding and noncoding players. Trends Biochem. Sci. 40, 248–256 (2015).

    CAS  PubMed  Google Scholar 

  7. 7.

    Steitz, J. RNA-RNA base-pairing: theme and variations. RNA 21, 476–477 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Wan, Y., et al. Understanding the transcriptome through RNA structure. Nat. Rev. Genet. 12, 641–655 (2011).

    CAS  PubMed  Google Scholar 

  9. 9.

    Aw, J. G. et al. In vivo mapping of eukaryotic RNA interactomes reveals principles of higher-order organization and regulation. Mol. Cell 62, 603–617 (2016).

    CAS  PubMed  Google Scholar 

  10. 10.

    Ding, Y. et al. In vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features. Nature 505, 696–700 (2014).

    CAS  PubMed  Google Scholar 

  11. 11.

    Kertesz, M. et al. Genome-wide measurement of RNA secondary structure in yeast. Nature 467, 103–107 (2010).

    CAS  PubMed  Google Scholar 

  12. 12.

    Lu, Z. et al. RNA duplex map in living cells reveals higher-order transcriptome structure. Cell 165, 1267–1279 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Lucks, J. B. et al. Multiplexed RNA structure characterization with selective 2′-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq). Proc. Natl Acad. Sci. USA 108, 11063–11068 (2011).

    CAS  PubMed  Google Scholar 

  14. 14.

    Rouskin, S., et al. Genome-wide probing of RNA structure reveals active unfolding of mRNA structures in vivo. Nature 505, 701–705 (2014).

    CAS  PubMed  Google Scholar 

  15. 15.

    Sharma, E., Sterne-Weiler, T., O’Hanlon, D. & Blencowe, B. J. Global mapping of human RNA-RNA interactions. Mol. Cell 62, 618–626 (2016).

    CAS  PubMed  Google Scholar 

  16. 16.

    Smola, M. J., et al. Selective 2′-hydroxyl acylation analyzed by primer extension and mutational profiling (SHAPE-MaP) for direct, versatile and accurate RNA structure analysis. Nat. Protoc. 10, 1643–1669 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Spitale, R. C. et al. Structural imprints in vivo decode RNA regulatory mechanisms. Nature 519, 486–490 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Talkish, J., et al. Mod-seq: high-throughput sequencing for chemical probing of RNA structure. RNA 20, 713–720 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Underwood, J. G. et al. FragSeq: transcriptome-wide RNA structure probing using high-throughput sequencing. Nat. Methods 7, 995–1001 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Gerstberger, S., Hafner, M. & Tuschl, T. A census of human RNA-binding proteins. Nat. Rev. Genet. 15, 829–845 (2014).

    CAS  PubMed  Google Scholar 

  21. 21.

    Castello, A. et al. Insights into RNA biology from an atlas of mammalian mRNA-binding proteins. Cell 149, 1393–1406 (2012).

    CAS  PubMed  Google Scholar 

  22. 22.

    Castello, A. et al. Comprehensive identification of RNA-binding domains in human cells. Mol. Cell 63, 696–710 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Lee, F. C. Y. & Ule, J. Advances in CLIP technologies for studies of protein-RNA interactions. Mol. Cell 69, 354–369 (2018).

    CAS  PubMed  Google Scholar 

  24. 24.

    Xue, Y. et al. Direct conversion of fibroblasts to neurons by reprogramming PTB-regulated microRNA circuits. Cell 152, 82–96 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Xue, Y. et al. Genome-wide analysis of PTB-RNA interactions reveals a strategy used by the general splicing repressor to modulate exon inclusion or skipping. Mol. Cell 36, 996–1006 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Helwak, A., Kudla, G., Dudnakova, T. & Tollervey, D. Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell 153, 654–665 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Shen, E. Z. et al. Identification of piRNA binding sites reveals the Argonaute regulatory landscape of the C. elegans germline. Cell 172, 937–951.e18 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Kudla, G. et al. Cross-linking, ligation, and sequencing of hybrids reveals RNA-RNA interactions in yeast. Proc. Natl Acad. Sci. USA 108, 10010–10015 (2011).

    CAS  PubMed  Google Scholar 

  29. 29.

    Sugimoto, Y. et al. hiCLIP reveals the in vivo atlas of mRNA secondary structures recognized by Staufen 1. Nature 519, 491–494 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Song, Y. et al. irCLASH reveals RNA substrates recognized by human ADARs. Nat. Struct. Mol. Biol. 27, 351–362 (2020).

    CAS  PubMed  Google Scholar 

  31. 31.

    Ramani, V., Qiu, R. & Shendure, J. High-throughput determination of RNA structure by proximity ligation. Nat. Biotechnol. 33, 980–984 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Nguyen, T. C. et al. Mapping RNA-RNA interactome and RNA structure in vivo by MARIO. Nat. Commun. 7, 12023 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Nagano, T. et al. Comparison of Hi-C results using in-solution versus in-nucleus ligation. Genome Biol. 16, 175 (2015).

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Quinodoz, S. A. et al. Higher-order inter-chromosomal hubs shape 3D genome organization in the nucleus. Cell 174, 744–757.e24 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Li, P. et al. Integrative analysis of Zika virus genome RNA structure reveals critical determinants of viral infectivity. Cell Host Microbe 24, 875–886.e5 (2018).

    CAS  PubMed  Google Scholar 

  36. 36.

    Cai, Z. et al. RIC-seq for global in situ profiling of RNA-RNA spatial interactions. Nature 582, 432–437 (2020).

    CAS  Google Scholar 

  37. 37.

    Preker, P. et al. RNA exosome depletion reveals transcription upstream of active human promoters. Science 322, 1851–1854 (2008).

    CAS  PubMed  Google Scholar 

  38. 38.

    Core, L. J. et al. Analysis of nascent RNA identifies a unified architecture of initiation regions at mammalian promoters and enhancers. Nat. Genet. 46, 1311–1320 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Metkar, M. et al. Higher-order organization principles of pre-translational mRNPs. Mol. Cell 72, 715–726.e3 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Parkhomchuk, D. et al. Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic Acids Res. 37, e123 (2009).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Ule, J. et al. CLIP identifies Nova-regulated RNA networks in the brain. Science 302, 1212–1215 (2003).

    CAS  Google Scholar 

  42. 42.

    Ingolia, N. T., et al. The ribosome profiling strategy for monitoring translation in vivo by deep sequencing of ribosome-protected mRNA fragments. Nat. Protoc. 7, 1534–1550 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Google Scholar 

  48. 48.

    Adiconis, X. et al. Comparative analysis of RNA sequencing methods for degraded or low-input samples. Nat. Methods 10, 623–629 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Morf, J. et al. RNA proximity sequencing reveals the spatial organization of the transcriptome in the nucleus. Nat. Biotechnol. 37, 793–802 (2019).

    CAS  PubMed  Google Scholar 

  50. 50.

    Anger, A. M. et al. Structures of the human and Drosophila 80S ribosome. Nature 497, 80–85 (2013).

    CAS  PubMed  Google Scholar 

  51. 51.

    Thorvaldsdottir, H., Robinson, J. T. & Mesirov, J. P. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief. Bioinform. 14, 178–192 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references


We thank Jing Hu for the critical review of this manuscript. This work was supported by the Ministry of Science and Technology of China (2017YFA0504400), the National Natural Science Foundation of China (32025008, 91740201, 91940306, 31522015 and 81921003), the Strategic Priority Program of CAS (XDB37000000; to Y.X.), the Beijing Municipal Natural Science Foundation (5182024) and the National Natural Science Foundation of China (31900465; to C.C).

Author information




Z.C. and Y.X. designed, implemented and optimized the original RIC-seq experimental protocol with the help of R.Y. and R.S. C.C. designed the data-processing pipeline. N.H. and H.Z. tested the computational pipeline. Y.X., C.C. and Z.C. prepared the manuscript.

Corresponding author

Correspondence to Yuanchao Xue.

Ethics declarations

Competing interests

C.C., Z.C. and Y.X. have filed a patent application for RIC-seq technology with the application number 201910384194.2.

Additional information

Peer review information Nature Protocols thanks Rory Johnson, John Rinn and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Key reference using this protocol

Cai, Z. et al. Nature 582, 432–437 (2020):

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Cao, C., Cai, Z., Ye, R. et al. Global in situ profiling of RNA-RNA spatial interactions with RIC-seq. Nat Protoc 16, 2916–2946 (2021).

Download citation


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing