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Mapping RNA–chromatin interactions by sequencing with iMARGI

Abstract

RNA–chromatin interactions represent an important aspect of the transcriptional regulation of genes and transposable elements. However, analyses of chromatin-associated RNAs (caRNAs) are often limited to one caRNA at a time. Here, we describe the iMARGI (in situ mapping of RNA–genome interactome) technique, which is used to discover caRNAs and reveal their respective genomic interaction loci. iMARGI starts with in situ crosslinking and genome fragmentation, followed by converting each proximal RNA–DNA pair into an RNA–linker–DNA chimeric sequence. These chimeric sequences are subsequently converted into a sequencing library suitable for paired-end sequencing. A standardized bioinformatic software package, iMARGI-Docker, is provided to decode the paired-end sequencing data into caRNA–DNA interactions. Compared to its predecessor MARGI (mapping RNA–genome interactions), the number of input cells for iMARGI is 3–5 million (a 100-fold reduction), experimental time is reduced, and clear checkpoints have been established. It takes a few hours a day and a total of 8 d to complete the construction of an iMARGI sequencing library and 1 d to carry out data processing with iMARGI-Docker.

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Fig. 1: iMARGI protocol and linker design.
Fig. 2: Computational workflow of iMARGI data analysis.
Fig. 3: Checkpoints for nuclear integrity.
Fig. 4: Size distributions of intermediate and final products from HEK293T cells.
Fig. 5: Checkpoints and troubleshooting.
Fig. 6: Visualization of iMARGI data in GIVE.

Data availability

An iMARGI dataset has been deposited into the NCBI Sequence Read Archive (SRA) under accession no. SRR8206679.

Code availability

The iMARGI-Docker software and its documentation are available at: https://sysbio.ucsd.edu/imargi_pipeline. The software is completely open source, under the BSD 2 license. The source code is available at https://github.com/Zhong-Lab-UCSD/iMARGI-Docker. The pre-built Docker image can be pulled from the Docker Hub. The version used in this paper is v.1.1.1.

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Acknowledgements

We thank X. Wen for help with graphics; 4DN DCIC (http://dcic.4dnucleome.org) for discussions on the data processing pipeline; A. Zheng, A. Kaul, K. Faizi, N. Moshiri, R. Zhang, J. Ma, J. Chen, X. Huang and Z. Zhang for testing the iMARGI-Docker software; and K. Sriram and A. Chen for proofreading. This work was funded by DP1HD087990 (to S.Z.), NIH 4D Nucleome U01CA200147 (to S.C. and S.Z.), and NIH R00HL122368 and R01HL145170 (to Z.C.).

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Authors

Contributions

W.W. and S.Z. designed the research; W.W., T.C.N. and S.Z. developed the experimental method and protocol; Z.Y. and S.Z. developed the computational method and data analysis tools; W.W., Z.Y. and S.Z. wrote the manuscript; Z.C. and S.C. revised the manuscript.

Corresponding author

Correspondence to Sheng Zhong.

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Competing interests

S.Z. is a cofounder of Genemo Inc.

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Peer review information Nature Protocols thanks Michiel de Hoon and other anonymous reviewer(s) for their contribution to the peer review of this work.

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Key references using this protocol

Sridhar, B. et al. Curr. Biol. 27, 602–609 (2017) https://doi.org/10.1016/j.cub.2017.01.011

Yan, Z. et al. Proc. Natl Acad. Sci. USA 116, 3328–3337 (2019) https://doi.org/10.1073/pnas.1819788116

Chen, W. et al. iScience 4, 204–215 (2018) https://doi.org/10.1016/j.isci.2018.06.005

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Wu, W., Yan, Z., Nguyen, T.C. et al. Mapping RNA–chromatin interactions by sequencing with iMARGI. Nat Protoc 14, 3243–3272 (2019). https://doi.org/10.1038/s41596-019-0229-4

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