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CRISPR-assisted detection of RNA–protein interactions in living cells

Nature Methods volume 17, pages 685–688 (2020)Cite this article

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Abstract

We have developed CRISPR-assisted RNA–protein interaction detection method (CARPID), which leverages CRISPR–CasRx-based RNA targeting and proximity labeling to identify binding proteins of specific long non-coding RNAs (lncRNAs) in the native cellular context. We applied CARPID to the nuclear lncRNA XIST, and it captured a list of known interacting proteins and multiple previously uncharacterized binding proteins. We generalized CARPID to explore binders of the lncRNAs DANCR and MALAT1, revealing the method’s wide applicability in identifying RNA-binding proteins.

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Fig. 1: CARPID identifies lncRNA XIST-associated proteins in living cells.
Fig. 2: Identification of lncRNA DANCR- and MALAT1-associated proteins in living cells.

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Data availability

Sequencing data have been deposited in the GEO under the accession number GSE137556. The secondary structure prediction data of XIST lncRNA from PARIS analysis used in Extended Data Fig. 2 were from a previous study (GEO accession: GSE74353)10 and are available in Supplementary Data 1 after additional processing. The raw sequence reads for TAF15 HTR-SELEX are available in Supplementary Data 2. Mass spectrometry raw data have been deposited to iProX database under the project ID number IPX0001797000. The TAF15 CLIP-seq data used in Extended Data Fig. 4c,d were from a previous study (GEO accession: GSE77700)12. Source data are provided with this paper.

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References

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

    Article  CAS  Google Scholar 

  2. Licatalosi, D. D., Ye, X. & Jankowsky, E. Approaches for measuring the dynamics of RNA-protein interactions. Wiley Interdiscip Rev. RNA 11, e1565 (2020).

    Article  CAS  Google Scholar 

  3. Ramanathan, M., Porter, D. F. & Khavari, P. A. Methods to study RNA–protein interactions. Nat. Methods 16, 225–234 (2019).

    Article  CAS  Google Scholar 

  4. Baranello, L., Kouzine, F., Sanford, S. & Levens, D. ChIP bias as a function of cross-linking time. Chromosome Res. 24, 175–181 (2016).

    Article  CAS  Google Scholar 

  5. Ramanathan, M. et al. RNA–protein interaction detection in living cells. Nat. Methods 15, 207–212 (2018).

    Article  CAS  Google Scholar 

  6. Myers, S. A. et al. Discovery of proteins associated with a predefined genomic locus via dCas9–APEX-mediated proximity labeling. Nat. Methods 15, 437–439 (2018).

    Article  CAS  Google Scholar 

  7. Konermann, S. et al. Transcriptome engineering with RNA-targeting type VI-D CRISPR effectors. Cel 173, 665–676 e614 (2018).

    Article  CAS  Google Scholar 

  8. Minajigi, A. et al. Chromosomes. A comprehensive Xist interactome reveals cohesin repulsion and an RNA-directed chromosome conformation. Science 349, aab2276 (2015).

  9. Lee, J. T. Epigenetic regulation by long noncoding RNAs. Science 338, 1435–1439 (2012).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  11. Mira-Bontenbal, H. & Gribnau, J. New Xist-interacting proteins in X-chromosome inactivation. Curr. Biol. 26, 1383 (2016).

    Article  CAS  Google Scholar 

  12. Kapeli, K. et al. Distinct and shared functions of ALS-associated proteins TDP-43, FUS and TAF15 revealed by multisystem analyses. Nat. Commun. 7, 12143 (2016).

    Article  CAS  Google Scholar 

  13. Hakimi, M. A. et al. A chromatin remodelling complex that loads cohesin onto human chromosomes. Nature 418, 994–998 (2002).

    Article  CAS  Google Scholar 

  14. Chan, K. M., Zhang, H., Malureanu, L., van Deursen, J. & Zhang, Z. Diverse factors are involved in maintaining X chromosome inactivation. Proc. Natl Acad. Sci. USA 108, 16699–16704 (2011).

    Article  CAS  Google Scholar 

  15. Yildirim, E., Sadreyev, R. I., Pinter, S. F. & Lee, J. T. X-chromosome hyperactivation in mammals via nonlinear relationships between chromatin states and transcription. Nat. Struct. Mol. Biol. 19, 56–61 (2011).

    Article  Google Scholar 

  16. Jegu, T. et al. Xist RNA antagonizes the SWI/SNF chromatin remodeler BRG1 on the inactive X chromosome. Nat. Struct. Mol. Biol. 26, 96–109 (2019).

    Article  CAS  Google Scholar 

  17. Kretz, M. et al. Suppression of progenitor differentiation requires the long noncoding RNA ANCR. Genes Dev. 26, 338–343 (2012).

    Article  CAS  Google Scholar 

  18. Hutchinson, J. N. et al. A screen for nuclear transcripts identifies two linked noncoding RNAs associated with SC35 splicing domains. BMC Genomics 8, 39 (2007).

    Article  Google Scholar 

  19. Li, J. et al. Prognostic value of ezrin in various cancers: a systematic review and updated meta-analysis. Sci. Rep. 5, 17903 (2015).

    Article  CAS  Google Scholar 

  20. Wilusz, J. E., Freier, S. M. & Spector, D. L. 3′ end processing of a long nuclear-retained noncoding RNA yields a tRNA-like cytoplasmic RNA. Cell 135, 919–932 (2008).

    Article  CAS  Google Scholar 

  21. Trendel, J. et al. The human RNA-binding proteome and its dynamics during translational arrest. Cell 176, 391–403 e319 (2019).

    Article  CAS  Google Scholar 

  22. Queiroz, R. M. L. et al. Comprehensive identification of RNA–protein interactions in any organism using orthogonal organic phase separation (OOPS). Nat. Biotechnol. 37, 169–178 (2019).

    Article  CAS  Google Scholar 

  23. Urdaneta, E. C. et al. Purification of cross-linked RNA–protein complexes by phenol-toluol extraction. Nat. Commun. 10, 990 (2019).

    Article  Google Scholar 

  24. Yi, W. et al. CRISPR-assisted detection of RNA–protein interactions in living cells. Protoc. Exch. https://doi.org/10.21203/rs.3.pex-904/v1 (2020).

  25. Lorenz, R. et al. ViennaRNA Package 2.0. Algorithms Mol. Biol. 6, 26 (2011).

    Article  Google Scholar 

  26. Breitling, R., Armengaud, P., Amtmann, A. & Herzyk, P. Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett. 573, 83–92 (2004).

    Article  CAS  Google Scholar 

  27. Yue, M., Charles Richard, J. L., Yamada, N., Ogawa, A. & Ogawa, Y. Quick fluorescent in situ hybridization protocol for Xist RNA combined with immunofluorescence of histone modification in X-chromosome inactivation. J. Vis. Exp., e52053 (2014).

  28. Arttu Jolma, J. Z. et al. Binding specificities of human RNA binding proteins towards structured and linear RNA sequences. Preprint at bioRxiv https://doi.org/10.1101/317909 (2019).

  29. Ghandi, M., Lee, D., Mohammad-Noori, M. & Beer, M. A. Enhanced regulatory sequence prediction using gapped k-mer features. PLoS Comput. Biol. 10, e1003711 (2014).

    Article  Google Scholar 

  30. Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).

    Article  CAS  Google Scholar 

  31. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  Google Scholar 

  32. Huang da, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    Article  Google Scholar 

  33. Huang da, W., Sherman, B. T. & Lempicki, R. A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13 (2009).

    Article  Google Scholar 

  34. Hou, Z. et al. A cost-effective RNA sequencing protocol for large-scale gene expression studies. Sci. Rep. 5, 9570 (2015).

    Article  Google Scholar 

  35. Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

    Article  CAS  Google Scholar 

  36. Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).

    Article  CAS  Google Scholar 

  37. Feng, J. et al. GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data. Bioinformatics 28, 2782–2788 (2012).

    Article  CAS  Google Scholar 

  38. Rozowsky, J. et al. AlleleSeq: analysis of allele-specific expression and binding in a network framework. Mol. Syst. Biol. 7, 522 (2011).

    Article  Google Scholar 

  39. Hou, J. et al. Extensive allele-specific translational regulation in hybrid mice. Mol. Syst. Biol. 11, 825 (2015).

    Article  Google Scholar 

  40. Luga, V. et al. Exosomes mediate stromal mobilization of autocrine Wnt–PCP signaling in breast cancer cell migration. Cell. 151, 1542–1556 (2012).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We are grateful to B. Ren (UC San Diego) and D. Leung (HKUST) for insightful comments during manuscript preparation. This work was supported by the City University of Hong Kong (7200595, 7005314, 9667188 and 9610424 to J. Yan), the National Natural Science Foundation of China (81873642 to J. Yan and 31900443 to W.S.), Research Grants Council of Hong Kong (21100615, 11102118, 11101919, C7007-17GF to K.M.C., and 21101917, 11103318 to L.Z.), the Shenzhen Science and Technology Fund Program (JCYJ20170818104203065, JCYJ20180307124019360 to K.M.C., JCYJ20170413141047772, JCYJ20180507181659781 to L.Z.); Opening Foundation of Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), the Chinese Ministry of Education (J. Ye); and the Hong Kong Epigenomics Project of the EpiHK consortium (Lo Ka Chung Charitable Foundation) (J. Yan and K.M.C.).

Author information

Author notes

  1. These authors contributed equally: Wenkai Yi, Jingyu Li, Xiaoxuan Zhu, Xi Wang.

Authors and Affiliations

  1. School of Medicine, Northwest University, Xi’an, China

    Wenkai Yi, Xi Wang, Ligang Fan, Wenju Sun, Linbu Liao, Xiaoyu Li, Fulin Chen & Jian Yan

  2. Department of Biomedical Sciences, City University of Hong Kong, Hong Kong SAR, China

    Wenkai Yi, Jingyu Li, Xiaoxuan Zhu, Ligang Fan, Xiaoyu Li, Kui Ming Chan, Liang Zhang & Jian Yan

  3. Key Laboratory of Biochip Technology, Biotech and Health Centre, Shenzhen Research Institute of City University of Hong Kong, Shenzhen, China

    Jingyu Li, Xiaoxuan Zhu, Kui Ming Chan & Liang Zhang

  4. Division of Theoretical Systems Biology, German Cancer Research Center, Heidelberg, Germany

    Xi Wang

  5. Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden

    Jilin Zhang & Jussi Taipale

  6. Department of Pathology, The Fourth Military Medical University, Xi’an, China

    Jing Ye

  7. Department of Biochemistry, University of Cambridge, Cambridge, UK

    Jussi Taipale

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  1. Wenkai Yi
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Contributions

W.Y., J.L., X.Z., K.M.C., L.Z. and J. Yan conceived the project. W.Y., J.L., X.Z., L.F., X.L. and L.Z. carried out experiments. X.W., W.S., L.L., J.Z., J.T., F.C., K.M.C., L.Z. and J. Ye performed data analysis. J. Yan, K.M.C. and L.Z. wrote the manuscript.

Corresponding authors

Correspondence to Kui Ming Chan, Liang Zhang or Jian Yan.

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Peer review information Lei Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Optimization of CARPID.

a, The location of the three sets of gRNAs on XIST. b, Scheme of BASU-dCasRx construct. c, Test of BASU efficiency at different length of reaction time in HEK293T cells. Three independent experiments were carried out with similar results and a representative result is shown. d, Scatter plots show comparison of gene expression level in HEK293T cells (WT) or cells transfected with BASU-dCasRx plus different gRNA sets (XIST-L1/XIST-L2/XIST-L3). The axes in each figure represent the log2 transformed gene expression. The expression level of XIST gene in each figure is highlighted (n = 3 independent experiments, two-sided Pearson correlation test).

Source data

Extended Data Fig. 2 The specificity of XIST gRNA sets.

a, RT-qPCR shows the specificity of the CRISPR/CasRx system in XIST normalized with GADPH. Data are represented as mean ± SEM, n = 3 independent experiments, one-sided ANOVA. b, The genome browser view of XIST-L1/L2/L3 over the XIST hairpin structure information resulted from Lu et al.10, using black strings to represent the region with complementary pairing. Vertical lines in different colors highlight the locations of different gRNA sets targeting loci on XIST lncRNA. The locations of known structural domains (A-H) are also indicated.

Extended Data Fig. 3 CARPID results comparison among different gRNA sets.

a, The Venn diagram illustrates the overlap of MS-detected proteins with ≥ 2 peptides in CARPID assays among the three different sets of gRNAs (XIST-L1/XIST-L2/XIST-L3). b, The Venn diagrams illustrate the overlap of MS-detected proteins with ≥ 2 peptides among the three independent experiments using the same gRNA set (Replicate#1/Replicate#2/Replicate#3). c, The Venn diagram illustrates the overlap of significantly enriched RBPs of XIST identified by CARPID using different sets of gRNAs (XIST-L1/XIST-L2/XIST-L3). d, The volcano plots show the enrichment of XIST-associated proteins in HEK293T cells. Significantly enriched proteins are labelled as orange dots. Proteins previously known to interact with XIST are labelled in orange font. SNF2L and TAF15, two novel XIST-associated proteins, are in blue font (n = 3 independent experiments for control and each gRNA group, respectively).

Extended Data Fig. 4 XIST-associated protein network.

a, White nodes indicate proteins identified specifically by one set of gRNAs. Pink nodes represent proteins identified by two sets of gRNAs. Red nodes show proteins identified by all three sets of gRNAs. Edges are drawn between two proteins with annotated interaction (STRING interaction score ≥ 0.40). The width of the edge is adjusted in proportion to the STRING interaction score. Nodes with purple edges highlight the proteins involved in chromatin remodeling. Previously reported XIST-associated proteins are highlighted in bold orange font. SNF2L and TAF15 are indicated by blue font. b, The top five significant GO terms for the XIST-associated proteins (n = 35 genes were included, Fisher’s exact test). c, Genome browser shows the binding of TAF15 to XIST using CLIP-seq data from mouse brain tissues12. Non-redundant reads are separately shown for both strands. d, Bar-plot shows the number of TAF15 peaks on XIST compared to the expected peak number (mean ± SD). The peak loci are from Ref. 12. One-sided Poisson test (p-value).

Extended Data Fig. 5 TAF15 HTR-SELEX.

a, The scheme of oligo design for HTR-SELEX. These oligos contain T7 promoter, Illumina adaptor (P5/P7), and 40-nt random sequence. b, Schematic representation of the HTR-SELEX experiment. c, RNA binding motif of TAF15 enriched from HTR-SELEX analysis. d, The scheme of machine learning.

Extended Data Fig. 6 Validation of the interaction between XIST and SNF2L.

a, Top: Western blot detection of SNF2L in input and streptavidin IP samples of control (Control) and three XIST gRNA sets (XIST-L1, XIST-L2 and XIST-L3). Bottom: immunoFISH images of XIST and SNF2L in HEK293T cells. The white boxed region on the left is magnified and shown on the right. Three independent experiments were carried out with similar results and a representative result is shown. b, Validation of XIST-SNF2L interaction using formaldehyde-assisted RIP assay (mean ± SEM, n = 3 independent experiments, two-sided paired Student’s t-test).

Source data

Extended Data Fig. 7 Function of TAF15 and SNF2L in XCI.

a, Representative microscopy images of GFP. Conditions are the same as in Fig. 1f. Three independent experiments were carried out and a representative result is shown. b, Validation of shRNA knockdown efficiency. β-actin was used to normalize the RNA expression (mean ± SD, n = 3 independent experiments, one-sided ANOVA test). c, Rescue efficiency of TAF15 knockdown. TAF15 expression was measured with RT-qPCR, and β-actin was used as an internal control (mean ± SD, n = 3 independent experiments, two-sided unpaired Student’s t-test). d, GFP expression upon TAF15 knockdown and rescue experiments. GFP expression under the same experimental conditions as in panel c was determined by RT-qPCR (mean ± SD, n = 3 independent experiments, two-sided unpaired Student’s t-test). e, Autosomal genes transcription under depletion of TAF15 in iMEF. Knockdown of TAF15 were conducted using two different shRNAs in female iMEF cells (E2C4) the same as in Fig. 1f. The expression levels were determined by RT-qPCR and normalized to β-actin (mean ± SEM, n = 3 independent experiments, one-sided ANOVA test with NT+5-aza as controls). f, Allelic RNA-seq confirms the role of TAF15 on XCI. Genes exhibiting significant allelic expression changes upon 5-aza treatment (NT+5-aza vs. NT) are grouped into X-chromosomal (chrX) genes and autosomal genes. By scaling the expression ratio between minor and major alleles before treatment as 0 (unbalanced) and after treatment as 1 (balanced), the allelic ratio of Taf15 knockdown is summarized separately for X-chromosomal (blue) and autosomal genes (grey). Data are represented as mean ± SEM, two independent experiments, two-sided unpaired Student’s t-test. g, A working model for the dual role of XIST lncRNA in mediating XCI.

Extended Data Fig. 8 CARPID Result of lncRNA DANCR.

a, Scatter plots show comparison of gene expression level in HEK293T cells (WT) or cells transfected with pre-gRNA sets (control). The axes in each figure represent the log2 transformed gene expression. The expression levels of XIST/MALAT1/DANCR are highlighted (n = 3 independent experiments, two-sided Pearson correlation test). b, The location of gRNA sets (Upper: DANCR-L1/2; Lower: MALAT1-L1/L2) and qPCR primers (Upper: DANCR: P1/P2; Lower: MALAT1: P1/P2) for DANCR used in CARPID. F, forward strand primer; R, reverse strand primer. The numbers within parentheses indicate the location of the gRNA sets in the corresponding RNA transcript, starting from 1 nt. c, The volcano plots show the enrichment of DANCR-associated proteins in HEK293T cells. Significantly enriched proteins are labelled as orange dots (n = 3 independent experiments for control and each gRNA group, respectively). d, DANCR-protein interaction network. White nodes indicate proteins identified with one set of gRNAs. Pink nodes represent proteins identified with two sets of gRNAs. Edges are drawn between two proteins with annotated interaction (STRING interaction score ≥ 0.40). The width of the edge is adjusted in proportion to the STRING interaction score.

Extended Data Fig. 9 The presence of DANCR in exosome.

a, Schematic representation of exosome isolation from cultured human cells. Exosome used for DANCR detection is highlighted in red font. b, Immunoblotting to examine the purification of exosome. 5 µg or 10 µg of cell lysates and exosomes fractions were resolved on the SDS-PAGE gel and subjected to Western blots of the indicated proteins. Note that CD81 was highly enriched in purified exosomes. Three independent experiments were carried out with similar results and a representative result is shown. c, Comparison of levels of XIST in HEK293T whole cell lysates and exosomes. XIST: P1/P2 represent two different sets of qPCR primers for XIST detection (as indicated in Extended Data Fig. 1a). Data are represented as mean ± SD, n = 3 independent experiments with three technical replicates. p values are calculated using two-sided unpaired Student’s t-test.

Source data

Extended Data Fig. 10 CARPID result of MALAT1.

a, MALAT1-protein interaction network. White nodes indicate proteins identified with one set of gRNAs. Pink nodes represent proteins identified with two sets of gRNAs. Edges are drawn between two proteins with annotated interaction (STRING interaction score ≥ 0.40). The width of the edge is adjusted in proportion to the STRING interaction score. b, The volcano plots show the enrichment of MALAT1-associated proteins in HEK293T cells. Significantly enriched proteins are labelled as orange dots (n = 3 independent experiments for control and each gRNA group, respectively). c, Comparison of CARPID results among different lncRNAs.

Supplementary information

Supplementary Information

Supplementary Data 1, Note and Protocol.

Reporting Summary

Supplementary Tables 1–9.

Supplementary Data 2

The raw sequence reads of TAF15 HTR-SELEX.

Source data

Source Data Fig. 1

Full blots for western blot in Fig. 1.

Source Data Fig. 2

Full blots for western blot in Fig. 2.

Source Data Extended Data Fig. 1

Full blots for western blot in Extended Data Fig. 1.

Source Data Extended Data Fig. 6

Full blots for western blot in Extended Data Fig. 6.

Source Data Extended Data Fig. 9

Full blots for western blot in Extended Data Fig. 9.

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Yi, W., Li, J., Zhu, X. et al. CRISPR-assisted detection of RNA–protein interactions in living cells. Nat Methods 17, 685–688 (2020). https://doi.org/10.1038/s41592-020-0866-0

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