AbstractRNA-binding proteins (RBPs) control varied processes, including RNA splicing, stability, transport and translation1,2,3. Dysfunctional RNA–RBP interactions contribute to the pathogenesis of human disease1,4,5; however, characterizing the nature and dynamics of multiprotein assemblies on RNA has been challenging. Here, to address this, non-isotopic ligation-based ultraviolet-light-induced cross-linking and immunoprecipitation6 was combined with mass spectrometry (irCLIP-RNP) to identify RNA-dependent associated proteins (RDAPs) co-bound to RNA with any RBP of interest. irCLIP-RNP defined landscapes of multimeric protein assemblies on RNA, revealing patterns of RBP–RNA associations, including cell-type-selective combinatorial relationships between RDAPs and primary RBPs. irCLIP-RNP also defined dynamic RDAP remodelling in response to epidermal growth factor (EGF), revealing that EGF-induced recruitment of UPF1 adjacent to HNRNPC promotes splicing surveillance of cell proliferation mRNAs. To identify the RNAs simultaneously co-bound by multiple studied RBPs, a sequential immunoprecipitation irCLIP (Re-CLIP) method was also developed. Re-CLIP confirmed binding relationships observed in irCLIP-RNP and identified HNRNPC and UPF1 RBP co-binding on RND3 and DDX3X mRNAs. irCLIP-RNP and Re-CLIP provide a framework to identify and characterize dynamic RNA–protein assemblies in living cells.
Access through your institution
Buy or subscribe
This is a preview of subscription content, access via your institution
Access options
Access through your institution
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Learn more
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Learn more
Buy this article
Purchase on SpringerLink
Instant access to full article PDF
Buy now
Prices may be subject to local taxes which are calculated during checkout
Additional access options:
Log in
Learn about institutional subscriptions
Read our FAQs
Contact customer support
Fig. 1: The complexity of protein assemblies on RNA.Fig. 2: irCLIP-RNP reveals cell-type-selective RDAP patterns and co-binding of RNAs.Fig. 3: irCLIP-RNP reveals dynamic remodelling of RDAPs.Fig. 4: UPF1 supports HNRNPC RNA splicing surveillance of EGF-regulated mRNAs.
Data availability
All of the MS raw and search files were deposited to the ProteomeXchange Consortium via the PRIDE70 partner repository under identifier PXD053494. All sequencing data files are available from the GEO under identifiers GSE271167 and GSE271168. The following databases were used in this study: BioGRID (https://thebiogrid.org/)16, MSGP (https://msgp.pt/)71, RNP granule (https://rnagranuledb.lunenfeld.ca/)72, human UniProt (https://www.uniprot.org/) and STRING (https://string-db.org/). Uncropped gel source data are provided in Supplementary Fig. 1. Source data are provided with this paper.
Code availability
All code and snakemake pipelines used in this study are available on Figshare73,74 (https://doi.org/10.6084/m9.figshare.26169718 and https://doi.org/10.6084/m9.figshare.26156764).
ReferencesGebauer, F., Schwarzl, T., Valcárcel, J. & Hentze, M. W. RNA-binding proteins in human genetic disease. Nat. Rev. Genet. 22, 185–198 (2021).CAS
PubMed
Google Scholar
Hentze, M. W., Castello, A., Schwarzl, T. & Preiss, T. A brave new world of RNA-binding proteins. Nat. Rev. Mol. Cell Biol. 19, 327–341 (2018).CAS
PubMed
Google Scholar
Buccitelli, C. & Selbach, M. mRNAs, proteins and the emerging principles of gene expression control. Nat. Rev. Genet. 21, 630–644 (2020).CAS
PubMed
MATH
Google Scholar
Mehta, M., Raguraman, R., Ramesh, R. & Munshi, A. RNA binding proteins (RBPs) and their role in DNA damage and radiation response in cancer. Adv. Drug Deliv. Rev. 191, 114569 (2022).CAS
PubMed
PubMed Central
Google Scholar
Heyn, L., Finset, A. & Ruland, C. M. Talking about feelings and worries in cancer consultations: the effects of an interactive tailored symptom assessment on source, explicitness, and timing of emotional cues and concerns. Cancer Nurs. 36, E20–E30 (2013).PubMed
Google Scholar
Zarnegar, B. J. et al. irCLIP platform for efficient characterization of protein-RNA interactions. Nat. Methods 13, 489–492 (2016).CAS
PubMed
PubMed Central
MATH
Google Scholar
Ramanathan, M., Porter, D. F. & Khavari, P. A. Methods to study RNA-protein interactions. Nat. Methods 16, 225–234 (2019).CAS
PubMed
PubMed Central
Google Scholar
Hafner, M. et al. CLIP and complementary methods. Nat. Rev. Methods Primer 1, 20 (2021).CAS
MATH
Google Scholar
Lee, F. C. Y. & Ule, J. Advances in CLIP technologies for studies of protein-RNA interactions. Mol. Cell 69, 354–369 (2018).CAS
PubMed
MATH
Google Scholar
Schell, B., Legrand, P. & Fribourg, S. Crystal structure of SFPQ-NONO heterodimer. Biochimie 198, 1–7 (2022).CAS
PubMed
MATH
Google Scholar
Wandrey, F. et al. The NF45/NF90 heterodimer contributes to the biogenesis of 60S ribosomal subunits and influences nucleolar morphology. Mol. Cell. Biol. 35, 3491–3503 (2015).CAS
PubMed
PubMed Central
MATH
Google Scholar
Damianov, A. et al. Rbfox proteins regulate splicing as part of a large multiprotein complex LASR. Cell 165, 606–619 (2016).CAS
PubMed
PubMed Central
MATH
Google Scholar
Choi, Y. D. & Dreyfuss, G. Isolation of the heterogeneous nuclear RNA-ribonucleoprotein complex (hnRNP): a unique supramolecular assembly. Proc. Natl Acad. Sci. USA 81, 7471–7475 (1984).ADS
CAS
PubMed
PubMed Central
MATH
Google Scholar
Pandolfo, M., Valentini, O., Biamonti, G., Rossi, P. & Riva, S. Large-scale purification of hnRNP proteins from HeLa cells by affinity chromatography on ssDNA-cellulose. Eur. J. Biochem. 162, 213–220 (1987).CAS
PubMed
Google Scholar
Swanson, M. S. & Dreyfuss, G. Classification and purification of proteins of heterogeneous nuclear ribonucleoprotein particles by RNA-binding specificities. Mol. Cell. Biol. 8, 2237–2241 (1988).CAS
PubMed
PubMed Central
MATH
Google Scholar
Stark, C. et al. BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34, D535–D539 (2006).CAS
PubMed
MATH
Google Scholar
Geiger, T., Wehner, A., Schaab, C., Cox, J. & Mann, M. Comparative proteomic analysis of eleven common cell lines reveals ubiquitous but varying expression of most proteins. Mol. Cell. Proteom. 11, M111.014050 (2012).
Google Scholar
Kristofich, J. & Nicchitta, C. V. High-throughput quantitation of protein-RNA UV-crosslinking efficiencies as a predictive tool for high-confidence identification of RNA-binding proteins. RNA 30, 644–661 (2024).CAS
PubMed
Google Scholar
Briata, P. et al. Diverse roles of the nucleic acid-binding protein KHSRP in cell differentiation and disease. Wiley Interdiscip. Rev. RNA 7, 227–240 (2016).CAS
PubMed
MATH
Google Scholar
Papadopoulou, C., Patrinou-Georgoula, M. & Guialis, A. Extensive association of HuR with hnRNP proteins within immunoselected hnRNP and mRNP complexes. Biochim. Biophys. Acta 1804, 692–703 (2010).CAS
PubMed
Google Scholar
Schwarzl, T. et al. Improved discovery of RNA-binding protein binding sites in eCLIP data using DEWSeq. Nucleic Acids Res. 52, e1 (2024).CAS
PubMed
MATH
Google Scholar
Porter, D. F. et al. easyCLIP analysis of RNA-protein interactions incorporating absolute quantification. Nat. Commun. 12, 1569 (2021).ADS
CAS
PubMed
PubMed Central
MATH
Google Scholar
Feng, H. et al. Modeling RNA-binding protein specificity in vivo by precisely registering protein-RNA crosslink sites. Mol. Cell 74, 1189–1204 (2019).CAS
PubMed
PubMed Central
MATH
Google Scholar
Cai, H. et al. Roles of embryonic lethal abnormal vision-like RNA binding proteins in cancer and beyond. Front. Cell Dev. Biol. 10, 847761 (2022).PubMed
PubMed Central
Google Scholar
Rothamel, K. et al. ELAVL1 primarily couples mRNA stability with the 3′ UTRs of interferon-stimulated genes. Cell Rep. 35, 109178 (2021).CAS
PubMed
PubMed Central
MATH
Google Scholar
Iadevaia, V. et al. Tandem RNA isolation reveals functional rearrangement of RNA-binding proteins on CDKN1B/p27Kip1 3′UTRs in cisplatin treated cells. RNA Biol. 17, 33–46 (2020).CAS
PubMed
Google Scholar
Yang, Y.-C. et al. The RNA-binding protein KSRP aggravates malignant progression of clear cell renal cell carcinoma through transcriptional inhibition and post-transcriptional destabilization of the NEDD4L ubiquitin ligase. J. Biomed. Sci. 30, 68 (2023).CAS
PubMed
PubMed Central
Google Scholar
Burgess, A. W. Regulation of signaling from the epidermal growth factor family. J. Phys. Chem. B 126, 7475–7485 (2022).CAS
PubMed
MATH
Google Scholar
Nava, M. et al. Transcriptomic and ChIP-sequence interrogation of EGFR signaling in HER2+ breast cancer cells reveals a dynamic chromatin landscape and S100 genes as targets. BMC Med. Genom. 12, 32 (2019).MATH
Google Scholar
Ma, H., Zhang, Z. & Tong, T. The effects of epidermal growth factor on gene expression in human fibroblasts. In Vitro Cell. Dev. Biol. Anim. 38, 481–486 (2002).CAS
PubMed
MATH
Google Scholar
Geuens, T., Bouhy, D. & Timmerman, V. The hnRNP family: insights into their role in health and disease. Hum. Genet. 135, 851–867 (2016).CAS
PubMed
PubMed Central
MATH
Google Scholar
Mo, L. et al. An analysis of the role of HnRNP C dysregulation in cancers. Biomark. Res. 10, 19 (2022).PubMed
PubMed Central
MATH
Google Scholar
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).CAS
PubMed
Google Scholar
Zhou, R. et al. Concerted effects of heterogeneous nuclear ribonucleoprotein C1/C2 to control vitamin D-directed gene transcription and RNA splicing in human bone cells. Nucleic Acids Res. 45, 606–618 (2017).CAS
PubMed
MATH
Google Scholar
Martino, F. et al. The mechanical regulation of RNA binding protein hnRNPC in the failing heart. Sci. Transl. Med. 14, eabo5715 (2022).CAS
PubMed
MATH
Google Scholar
Kim, J. H. et al. Heterogeneous nuclear ribonucleoprotein C modulates translation of c-myc mRNA in a cell cycle phase-dependent manner. Mol. Cell. Biol. 23, 708–720 (2003).CAS
PubMed
PubMed Central
MATH
Google Scholar
Hong, D., Park, T. & Jeong, S. Nuclear UPF1 Is associated with chromatin for transcription-coupled RNA surveillance. Mol. Cells 42, 523–529 (2019).CAS
PubMed
PubMed Central
MATH
Google Scholar
Singh, A. K. et al. The RNA helicase UPF1 associates with mRNAs co-transcriptionally and is required for the release of mRNAs from gene loci. eLife 8, e41444 (2019).PubMed
PubMed Central
Google Scholar
Kim, Y. K. & Maquat, L. E. UPFront and center in RNA decay: UPF1 in nonsense-mediated mRNA decay and beyond. RNA 25, 407–422 (2019).CAS
PubMed
PubMed Central
Google Scholar
Jacob, A. G. & Smith, C. W. J. Intron retention as a component of regulated gene expression programs. Hum. Genet. 136, 1043–1057 (2017).CAS
PubMed
PubMed Central
MATH
Google Scholar
Hurt, J. A., Robertson, A. D. & Burge, C. B. Global analyses of UPF1 binding and function reveal expanded scope of nonsense-mediated mRNA decay. Genome Res. 23, 1636–1650 (2013).CAS
PubMed
PubMed Central
Google Scholar
Xing, S. et al. Identification of HnRNPC as a novel Tau exon 10 splicing factor using RNA antisense purification mass spectrometry. RNA Biol. 19, 104–116 (2022).CAS
PubMed
MATH
Google Scholar
Almarán, B., Ramis, G., Fernández de Mattos, S. & Villalonga, P. Rnd3 Is a crucial mediator of the invasive phenotype of glioblastoma cells downstream of receptor tyrosine kinase signalling. Cells 11, 3716 (2022).PubMed
PubMed Central
Google Scholar
Nozaki, K. et al. DDX3X induces primary EGFR-TKI resistance based on intratumor heterogeneity in lung cancer cells harboring EGFR-activating mutations. PLoS ONE 9, e111019 (2014).ADS
PubMed
PubMed Central
MATH
Google Scholar
Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).ADS
CAS
PubMed
PubMed Central
MATH
Google Scholar
Nystrom, S. L. & McKay, D. J. Memes: a motif analysis environment in R using tools from the MEME Suite. PLoS Comput. Biol. 17, e1008991 (2021).ADS
CAS
PubMed
PubMed Central
MATH
Google Scholar
Miao, W. et al. Glucose dissociates DDX21 dimers to regulate mRNA splicing and tissue differentiation. Cell 186, 80–97 (2023).CAS
PubMed
PubMed Central
MATH
Google Scholar
Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).CAS
PubMed
Google Scholar
Chen, X., Cao, Z. & Wang, Y. Amyotrophic lateral sclerosis-associated mutants of SOD1 perturb mRNA splicing through aberrant interactions with SRSF2. Anal. Chem. 96, 9713–9720 (2024).CAS
PubMed
MATH
Google Scholar
Feng, Z., Fang, P., Zheng, H. & Zhang, X. DEP2: an upgraded comprehensive analysis toolkit for quantitative proteomics data. Bioinformatics 39, btad526 (2023).CAS
PubMed
PubMed Central
Google Scholar
Ou, J. & Zhu, L. J. trackViewer: a Bioconductor package for interactive and integrative visualization of multi-omics data. Nat. Methods 16, 453–454 (2019).CAS
PubMed
MATH
Google Scholar
Ge, X. et al. Clipper: p-value-free FDR control on high-throughput data from two conditions. Genome Biol. 22, 288 (2021).PubMed
PubMed Central
MATH
Google Scholar
Raudvere, U. et al. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 47, W191–W198 (2019).CAS
PubMed
PubMed Central
Google Scholar
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).PubMed
PubMed Central
MATH
Google Scholar
Kechin, A., Boyarskikh, U., Kel, A. & Filipenko, M. cutPrimers: a new tool for accurate cutting of primers from reads of targeted next generation sequencing. J. Comput. Biol. 24, 1138–1143 (2017).CAS
PubMed
Google Scholar
Smith, T., Heger, A. & Sudbery, I. UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Res. 27, 491–499 (2017).CAS
PubMed
PubMed Central
MATH
Google Scholar
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).CAS
PubMed
Google Scholar
Sahadevan, S. et al. htseq-clip: a toolset for the preprocessing of eCLIP/iCLIP datasets. Bioinformatics 39, btac747 (2023).CAS
PubMed
MATH
Google Scholar
Zhu, L. J. et al. ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinform. 11, 237 (2010).MATH
Google Scholar
Gu, Z., Eils, R., Schlesner, M. & Ishaque, N. EnrichedHeatmap: an R/Bioconductor package for comprehensive visualization of genomic signal associations. BMC Genom. 19, 234 (2018).
Google Scholar
Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).CAS
PubMed
PubMed Central
Google Scholar
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed
PubMed Central
MATH
Google Scholar
Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).CAS
PubMed
MATH
Google Scholar
Narykov, O., Srinivasan, S. & Korkin, D. Computational protein modeling and the next viral pandemic. Nat. Methods 18, 444–445 (2021).CAS
PubMed
PubMed Central
MATH
Google Scholar
Meng, E. C. et al. UCSF ChimeraX: tools for structure building and analysis. Protein Sci. 32, e4792 (2023).CAS
PubMed
PubMed Central
Google Scholar
Shen, S. et al. rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-seq data. Proc. Natl Acad. Sci. USA 111, E5593–E5601 (2014).CAS
PubMed
PubMed Central
Google Scholar
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).CAS
PubMed
PubMed Central
MATH
Google Scholar
Zhu, A., Srivastava, A., Ibrahim, J. G., Patro, R. & Love, M. I. Nonparametric expression analysis using inferential replicate counts. Nucleic Acids Res. 47, e105 (2019).CAS
PubMed
PubMed Central
Google Scholar
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).CAS
PubMed
MATH
Google Scholar
Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).CAS
PubMed
Google Scholar
Nunes, C. et al. MSGP: the first database of the protein components of the mammalian stress granules. Database 2019, baz031 (2019).PubMed
PubMed Central
MATH
Google Scholar
Millar, S. R. et al. A new phase of networking: the molecular composition and regulatory dynamics of mammalian stress granules. Chem. Rev. 123, 9036–9064 (2023).CAS
PubMed
PubMed Central
MATH
Google Scholar
Ducoli, L. et al. Codes for ‘irCLIP-RNP and Re-CLIP reveal patterns of dynamic protein assemblies on RNA’. Figshare https://doi.org/10.6084/m9.figshare.26169718 (2025).Ducoli, L. et al. snakemake pipeline for ‘irCLIP-RNP and Re-CLIP reveal patterns of dynamic protein assemblies on RNA’. Figshare https://doi.org/10.6084/m9.figshare.26156764 (2025).Download referencesAcknowledgementsWe thank A. E. Oro and H. Y. Chang for pre-submission review; M. Pilo, P. Bernstein and A. Dazey for expert administrative assistance; and the members of the Khavari laboratory for helpful discussions. This work was supported by the USVA Office of Research and Development, by USVA Merit Review grant BX001409 to P.A.K., and by NIAMS/NIH grants AR045192 and AR076965 to P.A.K.; R35 ES031707 to Y.W.; K01AR071481 to B.J.Z.; K99 through NIGMS K99GM147304 to N.M.R.; and the Swiss National Science Foundation Postdoc Mobility Fellowship P500BP-203019 to L.D.Author informationAuthor notesThese authors contributed equally: Luca Ducoli, Brian J. ZarnegarThese authors jointly supervised this work: Ryan A. Flynn, Paul A. KhavariAuthors and AffiliationsProgram in Epithelial Biology, Stanford University, Stanford, CA, USALuca Ducoli, Brian J. Zarnegar, Douglas F. Porter, Robin M. Meyers, Weili Miao, Suhas Srinivasan, Leandra V. Jackrazi & Paul A. KhavariDepartment of Chemistry and Sarafan ChEM-H, Stanford University, Stanford, CA, USANicholas M. Riley & Carolyn R. BertozziHoward Hughes Medical Institute, Stanford University, Stanford, CA, USANicholas M. Riley & Carolyn R. BertozziDepartment of Chemistry, University of California, Riverside, CA, USAYen-Yu Yang, Zhouxian Li & Yinsheng WangStem Cell Program and Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA, USARyan A. FlynnDepartment of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USARyan A. FlynnHarvard Stem Cell Institute, Harvard University, Cambridge, MA, USARyan A. FlynnProgram in Cancer Biology, Stanford University, Stanford, CA, USAPaul A. KhavariVeterans Affairs, Palo Alto Healthcare System, Palo Alto, CA, USAPaul A. KhavariAuthorsLuca DucoliView author publicationsYou can also search for this author inPubMed Google ScholarBrian J. ZarnegarView author publicationsYou can also search for this author inPubMed Google ScholarDouglas F. PorterView author publicationsYou can also search for this author inPubMed Google ScholarRobin M. MeyersView author publicationsYou can also search for this author inPubMed Google ScholarWeili MiaoView author publicationsYou can also search for this author inPubMed Google ScholarNicholas M. RileyView author publicationsYou can also search for this author inPubMed Google ScholarSuhas SrinivasanView author publicationsYou can also search for this author inPubMed Google ScholarLeandra V. JackraziView author publicationsYou can also search for this author inPubMed Google ScholarYen-Yu YangView author publicationsYou can also search for this author inPubMed Google ScholarZhouxian LiView author publicationsYou can also search for this author inPubMed Google ScholarYinsheng WangView author publicationsYou can also search for this author inPubMed Google ScholarCarolyn R. BertozziView author publicationsYou can also search for this author inPubMed Google ScholarRyan A. FlynnView author publicationsYou can also search for this author inPubMed Google ScholarPaul A. KhavariView author publicationsYou can also search for this author inPubMed Google ScholarContributionsL.D., B.J.Z., R.A.F. and P.A.K. conceived the project. L.D., B.J.Z., D.F.P., W.M., N.M.R. and L.V.J. performed experiments. L.D., B.J.Z., R.M.M., D.F.P., N.M.R. and S.S. performed data analysis. Z.L., Y.-Y.Y., Y.W. and C.R.B. provided support for spectrometry data generation. R.A.F. and P.A.K. guided experiments and data analysis. L.D. and P.A.K. wrote the manuscript with input from all of the authors.Corresponding authorCorrespondence to
Paul A. Khavari.Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature thanks Yuanchao Xue and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended data figures and tablesExtended Data Fig. 1 irCLIP-RNP reveals distinct protein associations after intermediate RNase digestion.(a) Nitrocellulose images indicate the infrared signal of irCLIP ligations subsequent to in-lysate digestion with six RNase A concentrations (from 1100 ng/mL to 8 ng/mL) for additional RBPs also involved in RNA processing. IP: immunoprecipitation; UVC: UV crosslinking; green signal: irCLIP ligation; red signal: immunoblotting (IB) for the tested RBP; asterisks in orange box: RBP monomeric zone. The reported kDa for each RBP represents the calculated molecular weight. (b) Immunoblot images indicate HNRNPC and TARDBP protein signal after in-lysate RNase A titration (1100 ng/mL to 8 ng/mL) followed by irCLIP ligation. L: ladder. (c) Bars indicate irCLIP ligations per cell calculated from infrared signal spanning from 60 to 350 kDa for HNRNPC, HNRNPA2B1, and FUS over six RNase A concentrations ranging from 1100 ng/mL to 8 ng/mL. (d) Representative immunoblot images indicate protein signal for SFPQ, NONO, ILF2/3, HNRNPM, and HNRNPC after immunoprecipitation following standard ENCODE CLIP purification conditions for SFPQ, NONO, HNRNPM, HNRNPC, ILF2, and ILF3 in HEK293T cells. IP and IB: immunoprecipitated and immunoblotted proteins, respectively. (e) Workflow of HNRNPC irCLIP-RNP of two gel sections ranging from 30–60 kDa and 60–350 kDa in HEK293T cells. (f) In-gel image indicates the infrared signal of HNRNPC irCLIP-RNP ligations of n = 4 UVC and no-UV biologically independent replicates. Blue box: gel section corresponding to free RNA ligations; yellow box: gel section corresponding to the RNP zone. L: ladder. (g) Heatmap indicates Pearson correlation coefficients between 4 no-UV and 4 UVC HNRNPC irCLIP-RNP biologically independent replicates. Blue box: free RNA ligations gel section; yellow box: RNP zone gel section. (h) Heatmap indicates imputed log2-transformed LFQ intensities of proteins detected after HNRNPC irCLIP-RNP. Green, blue, and purple boxes: significant proteins against no-UV 60–350 kDa, no-UV 30–60 kDa, and UVC 30–60 kDa gel sections. (i) Workflow of HNRNPC/HNRNPA2B1 irCLIP-RNP species-mixing experiment using a 1:1 mixing of human (HEK293T) and mouse (3T3) cells. (j) In-gel scan indicates the infrared signal of irCLIP-RNP ligations of n = 2 species-mixing biologically independent replicates. L: ladder. (k) Pie charts indicate the ratio of imputed LFQ intensities of cell-type-specific unique peptides between HNRNPC irCLIP-RNP (human UVC; mouse no-UV) and HNRNPA2B1 irCLIP-RNP (human no-UV; mouse UVC) across KHSRP/Khsrp, ELAVL1/Elavl1, RALY/Raly, and HNRNPM/Hnrnpm proteins. Data in a,b, and d are representative of n = 3 biologically independent experiments.Source DataExtended Data Fig. 2 irCLIP-RNP identifies proximal and distal cis-RNA-dependent protein associations (RDAPs).(a) Workflow of irCLIP-RNP to analyse the effects on RNA-dependent protein associations of 1U/µL and 0.02U/µL RNase I treatments in HEK293T. (b) Nitrocellulose images indicate the infrared signal of irCLIP-RNP ligations after 1U/µL and 0.02U/µL RNase I digestions in HEK293T for HNRNPC, HNRNPA2B1, and HNRNPU of n = 2 biologically independent replicates. L: ladder. (c) Rank plots indicate imputed log2-transformed LFQ intensities of proteins detected after HNRNPA2B1/HNRNPC/HNRNPU irCLIP-RNP using two doses of RNase I (1U/µL and 0.02U/µL). Orange dot: UVC-enriched proteins in at least one RNase I dose. (d) Bars indicate the intensity sum of mouse or human unique peptides detected in irCLIP-RNP species-mixing datasets mapping to HNRNPC and HNRNPA2B1 RDAPs determined in c. Orange and purple bars: intensities from unique peptides mapping to either human or mouse RDAP sequences. (e) Volcano plots indicate -log10(P-value) and log2FC values as determined by DEP250 analysis of differential RDAPs between 1U/µL and 0.02U/µL RNase I samples associated with HNRNPC, HNRNPA2B1, and HNRNPU. Black dots: significant proteins; orange circle: selected RDAPs with a significant reduction in 1U/µL RNase I treatment (n = 2 biologically independent experiments). (f) Heatmaps indicate Z scores of imputed log2-transformed LFQ intensities of 1U/µL and 0.02U/µL RNase I samples for the 27 significant RDAPs associating with HNRNPC, HNRNPA2B1, and HNRNPU. Green, orange, and purple boxes: significant RDAPs for HNRNPA2B1, HNRNPC, and HNRNPU, respectively; grey boxes: n.d. values. (g) Bars indicate the overlap between HNRNPC, HNRNPA2B1, and HNRNPU of the 27 significantly reduced RDAPs after 1U/µL RNase I in-lysate digestion. (h) RNA sizing through TBE-Urea gel of HNRNPC irCLIP-RNP isolated RNA in HEK293T from the three RNP subzones ranging from 60–120 kDa (blue), 120–225 kDa (light green), and 225–350 kDa (green). L: ladder. (i) Lines indicate the mean infrared signal distribution of RNA isolated from HNRNPC irCLIP-RNP ligations from three RNP subzones shown in h (n = 2 biologically independent experiments). Data are mean ± SD. Blue line: 60–120 kDa; light-green line: 120–225 kDa; green line: 225–350 kDa RNP subzones.Source DataExtended Data Fig. 3 RBPs show different RDAP landscapes between HEK293T and HepG2.(a) Workflow of the integrated analysis of label-free MS from the whole RNP zone and the TMT analysis from the three RNP subzones for 13 RBPs in HEK293T and HepG2. (b) Scatter plots indicate imputed log2-transformed LFQ intensities in HEK293T and HepG2 of irCLIP-RNP-detected proteins across the 13 RBPs. Orange and blue dots: UVC-enriched only in HEK293T or HepG2, respectively; red dots: UVC-enriched in HEK293T and HepG2. (c) Stacked bars indicate the distribution of 360 RDAPs across the 13 RBPs in HEK293T and HepG2. (d) Scatter plot indicates imputed log2-transformed iBAQ intensities taken from Geiger et al. 17 in HEK293T and HepG2 of 346 out of the 360 RDAPs. Red line and grey band: fitted linear regression with standard error. P-values determined by a two-sided t-test on regression coefficients. (e) Scatter plots indicate the log2 normalized crosslinking efficiency taken from18 and log2FC UVC vs. no-UV of the RDAPs identified in the irCLIP-RNP datasets for 13 RBPs in HEK293T (orange) and HepG2 (light blue). Every dot represents a bait-RDAP pair. (f) Bubble plot indicates -log10(P-values) as determined by gProfileR253 analysis of top GO terms for cellular components, biological processes, and molecular functions of the 360 RDAPs. (g) Reciprocal heatmap indicates imputed log2-transformed LFQ intensities between the 13 RBPs (dark fuchsia: bait; green: prey). LFQ intensities from no-UV samples are displayed as the mean of HEK293T and HepG2 samples. Orange boxes: HEK293T; light blue boxes: HepG2. (h) Representative nitrocellulose images of n = 2 biologically independent experiments indicate the infrared signal of TMT irCLIP-RNP ligations for the HNRNPA2B1, HNRNPC, and HNRNPM in HEK293T (orange) and HepG2 (blue). Blue, light-green, and green boxes highlight the gel sectioning of the three RNP subzones. L: ladder. (i) Reciprocal heatmap indicates the TMT intensity percentages across the three RNP subzones between the 13 RBPs (dark fuchsia: bait; green: prey) in HEK293T (orange) and HepG2 (blue). Blue, light-green, and green boxes: RNP subzones #1-3.Source DataExtended Data Fig. 4 Cell-context-dependent RDAP assemblies of 13 RBPs.(a) Scatter plots indicate log2FC values in HEK293T and HepG2 of differentially enriched RDAPs with the 13 RBPs after comparing RNP subzones #2 and #3 against #1 (left and right plots, respectively). Green and blue dots: low and high-molecular-mass (MM) RDAPs; dark red dots: ambivalent distributed RDAPs between HEK293T and HepG2; orange circle: RDAPs with significant differences in TMT intensity distribution across the RNP subzones between HEK293T and HepG2. (b) Lines indicate the cumulative fraction of MM of low and high-MM RDAPs and background proteins in HEK293T (left) and HepG2 (right). Black line: background proteins; green and blue lines: high and low-MM RDAPs in HEK293T and HepG2. P-values between high and low-MM RDAPs against the background in each cell line were determined by a two-sided Kolmogorov-Smirnov test. (c) Schematics of the RDAP categorization in the different RNP subzones, according to slope values calculated between the RNP subzone #1-3 intensity ratios. (d) Representative nitrocellulose image of n = 3 biologically independent experiments indicates the infrared signal of sequential irCLIP-RNP immunoprecipitation of NONO followed by the RDAPs HNRNPC, HNRNPM, HNRNPU, NONO, SFPQ, and ABCF1 as well as the negative control proteins EIF5B and THOC2. L: ladder.Source DataExtended Data Fig. 5 irCLIPv2 supports a multi-RBP model on the same RNA molecule.(a) Workflow of irCLIPv2 experiments of three RNP subzones for 3 RDAPs and 4 RBPs belonging to HNRNPs protein family in HEK293T. (b) Nitrocellulose images indicate the infrared signal of irCLIP-RNP ligations for the 3 RDAPs (green) and 4 RBPs (dark fuchsia) in HEK293T. Blue, light-green, and green boxes highlight the gel sectioning of the three RNP subzones (n = 2 biologically independent experiments). L: ladder. (c) Pie chart indicates the percentages of total significant binding regions of 3 RDAPs (green) and 4 RBPs (dark fuchsia). (d) Heatmap indicates the number of significant regions per bin along each chromosome for RDAPs (green) and RBPs (dark fuchsia). Genes distribution across chromosomes are displayed in shades of blue. (e) Venn diagram indicates the overlap between irCLIPv2-identified regions for HNRNPC and previously published binding regions identified with easyCLIP22. (f) Lines indicate the normalized coverage at significant binding regions in the three RNP subzones and no-UV samples (blue: #1; light-green: #2; green: #3; grey: no-UV) for RBPs (dark fuchsia) and RDAPs (green). Coverage on the negative strand was reversed to be represented in a 5’ to 3’ orientation. An extension of +/− 50 bp was applied from the highest RBP peaks in the RNP subzone #1. (g) Schematics of cDNA synthesis early truncation resulting in signal 3’-shift due to RDAPs co-binding. (h) Lines indicate the slope density distribution calculated from average normalized RT stop counts of significant regions for RDAPs (green) and RBPs (dark fuchsia). One standard deviation (SD, dotted lines) was used to classify the significant regions according to their occurrence in the three RNP subzones. Blue/light-green/green boxes: RNP subzones #1-3. (i) Stacked bars indicate the percentage of significant regions in transcriptomic landscape features for RBPs (green) and RDAPs (dark fuchsia). Blue/light-green/green boxes: RNP subzones #1-3. (j) Schematics of RDAP motif analysis across RNP subzones in the regions directly downstream of highest RBP peaks. 250 bp, 150 bp, and 75 bp fragments were used as input in the AME tool for RNP subzones #1 to #3, respectively. (k) Heatmap indicates the enriched motif -log10(P-values) as determined by AME analysis46 of high-MM proteins in the three RNP subzones of RDAPs (green) and RBPs (dark fuchsia). Blue/light-green/green boxes: RNP subzones #1-3. (l) Raincloud plots indicate the distribution of average log2FC against no-UV samples for HNRNPC regions categorized as low (blue), medium (light green), or high (green) distances in their corresponding RNP subzone. one-way ANOVA with one-sided Tukey’s multiple comparison tests. Only the significance of high-categorized regions against low and medium categories related to the same protein or against the ALYREF long-distance category is shown (n = number of regions specified in the plot). Boxes indicate the interquartile range (IQR) with 25th and 75th percentile box limits; whiskers denote the 1.5 × IQR from the 25th and 75th percentiles; center lines: medians; black dots: outliers; red dots: mean values. (m) Representative immunoblot image of n = 3 biologically independent experiments indicates the protein signal of HNRNPC and KHSRP after native RNA pull-down of biotinylated HNRNPC-KHSRP flanking regions in HEK293T cells. (n) Bars indicate the fold change against scramble control of HNRNPC and KHSRP protein signal after native RNA pull-down of biotinylated HNRNPC-KHSRP flanking regions in HEK293T cells. Data are mean ± SD; KHSRP-HNRNPC P-value = 5.3e-3**; KHSRP-PTBP2 P-value = 4.0e-4***; HNRNPC-HNRNPC P-value = 1e-3***; HNRNPC-PTBP2 P-value = 2.0e-4*** – unpaired two-tailed Student’s t-test against scramble control 1 and 2 (n = 3 biologically independent experiments).Source DataExtended Data Fig. 6 Re-CLIP reveals multiprotein assemblies at RBP-RDAPs co-bound regions.(a) Workflow of Re-CLIP experiments where sequential immunoprecipitations of HNRNPC followed by the RDAPs ELAVL1, KHSRP, and HNRNPM were performed. (b) Nitrocellulose image indicates the infrared signal of HNRNPC Re-CLIP ligations after sequential immunoprecipitation of itself and the RDAPs HNRNPM, ELAVL1, KHSRP, as well as the negative control IgG. The signal represents pooled libraries of n = 2 biologically independent experiments. (c) IGV tracks indicate normalized coverage of Re-CLIP samples at several binding sites across the HNRNPC transcript. Tracks show the signal sum of two replicates. (d) Surface plots indicate the binned HNRNPC and RDAP primary irCLIPv2 log2FC values vs. no-UV of the unified set of HNRNPC and its RDAP binding regions. Continuous colour code represents Re-CLIP log2FC value vs. no-UV for the indicated RDAPs. (e) Scatter plots indicate the observed score (Re-CLIP or IgG log2FC vs. no-UV) against the expected score (predicted values from linear regression of primary HNRNPC and RDAP irCLIPv2 log2FC vs. no-UV, modelled against Re-CLIP or IgG log2FC vs. no-UV) for the union set of HNRNPC and its RDAP binding regions. Blue and red lines represent the linear regression of observed and expected values for Re-CLIP (blue) and IgG (red). Significant regions were ranked according to their residuals. (f) Line plots indicate the residual distribution of the union set of HNRNPC and its RDAP regions, as determined in e, residing at the genomic features 5’ and 3’ UTRs (black and grey), exon (blue), and intron (red). (g) Cleveland plots indicate the -log10(e-value) of enriched motifs as determined by AME analysis46 of residual-ranked HNRNPC and its RDAP unified regions. Red dots: Re-CLIP; grey dots: IgG. (h) Lines indicate the cumulative fraction of irCLIP-RNP intensity of the HNRNPC and HNRNPA2B1 RDAPs with (red) and without (grey) enriched motifs, as determined in g. P-values between RDAPs with and without enriched motifs were calculated by a one-sided Kolmogorov-Smirnov test.Source DataExtended Data Fig. 7 Dynamic remodelling of the RDAP landscape after cellular stimulations.(a) Workflow of HNRNPC irCLIP-RNP-TMT during EGF stimulation in A431 cells. (b) Immunoblot images indicate the phosphorylated ERK and HNRNPC protein signal in n = 2 EGF-stimulated irCLIP-RNP-TMT biologically independent replicates. L: ladder. (c) Scatter plot indicates the RNP subzone #1-2 log2FC values of differentially enriched RDAPs during EGF stimulation followed by HNRNPC irCLIP-RNP. Orange dots: EGF-responsive RDAPs. (d) Bubble plot indicates -log10(P-values) as determined by gProfileR253 analysis of top GO terms for cellular components, biological processes, and molecular functions of the 19 EGF-responsive RDAPs. (e) STRING network of HNRNPC and the 19 EGF-responsive RDAPs. Red and blue nodes: EGF-induced or reduced, respectively. Triangle-shaped nodes: canonical interactors according to STRING. Yellow node border: RDAPs part of spliceosome according to KEGG and Ractome databases. Lines: interactions within each complex; line width: interaction confidence from text mining, databases, experiments, and co-expression. Dotted lines: edges between clusters. Protein-protein interaction (PPI) enrichment network P-value was determined by STRING. (f) Workflow of CAPRIN1 irCLIP-RNP of sodium-arsenite-treated HEK293T leading to stress-granule (SG) formation. (g) Nitrocellulose image indicates the infrared signal of CAPRIN1 irCLIP-RNP ligations in n = 2 sodium arsenite-treated HEK293T biologically independent replicates. L: ladder. (h) Venn diagram indicates the overlap between CAPRIN1 RDAPs and previously published proteins associated with SG formation (MSGP database71; RNP granule database72). (i) Scatter plot indicates the log2FC values against time point 0 of differentially enriched RDAPs during SG formation followed by CAPRIN1 irCLIP-RNP. Orange dots: SG-responsive RDAPs. (j) Heatmap indicates log2FC values against time point 0 calculated from imputed log2-transformed LFQ intensities for the 11 SG-responsive RDAPs. *: SG-responsive RDAP.Source DataExtended Data Fig. 8 Splicing analysis of HNRNPC and RDAP-depleted A431 cells.(a) Workflow of splicing analysis after the knockdown (KD) of HNRNPC and RDAPs (CSDE1 and UPF1) in EGF-stimulated (1 h) A431 cells. (b) Bars indicate knockdown (KD) efficiencies of HNRNPC and RDAPs in EGF-stimulated HNRNPC and RDAPs KD samples, as detected by qPCR. Data are mean ± SD (n = 2 biologically independent experiments). (c) Heatmap indicates Z score of normalized counts for CSDE1, UPF1, and HNRNPC in EGF-stimulated HNRNPC and RDAPs KD samples. (d) Stacked bars indicate the percentage of significant AS events compared to control siRNAs in EGF-stimulated HNRNPC and RDAPs KD samples. (e) Bars indicate the overlap of significant AS events between HNRNPC and RDAP knockdown samples after EGF stimulation. (f) Immunostaining images for HNRNPC (green) and UPF1 (red) in A431 cells after HNRNPC and UPF1 KDs. DAPI (blue signal) was used to outline the nuclear shape. Scale bars represent 20 µm. (g) Bars indicate the fold change against control siRNA of HNRNPC and UPF1 fluorescence intensity in HNRNPC and UPF1 knockdown A431 cells. Data are mean ± SD; HNRNPC-KD P-value = 6.0e-4***, UPF1-KD P-value = 0.013* – unpaired two-sided Student’s t-test against control siRNA cells (n = 3 biologically independent experiments). (h) Proximity ligation assay (PLA) images for HNRNPC and UPF1 in A431 cells after HNRNPC and UPF1 KDs. DAPI (blue signal) was used to outline the nuclear shape. Scale bars represent 20 µm. (i) Bars indicate the fold change against control siRNA calculated from the number of HNRNPC and UPF1 PLA foci quantified in HNRNPC and UPF1 knockdown A431 cells. Data are mean ± SD; HNRNPC-KD P-value = 1.2e-3**, UPF1-KD P-value = 1.8e-3** – one-way ANOVA with two-sided Dunnett’s multiple comparison tests against control siRNA cells (n = 3 biologically independent experiments). (j) Immunoblot images indicate phosphorylated ERK, HNRNPC, and UPF1 protein signals in EGF-stimulated (0, 15, 30, 60 min) A431 cells. (k) Immunoblot images indicate protein signal of HNRNPC and UPF1 as well as of the nuclear marker Lamin A/C and cytoplasmic marker Tubulin after subcellular fractionation of A431 cells. (l) Stacked bars indicate the percentage of HNRNPC and UPF1 protein signals in nuclear and cytoplasmic fractions. Data are mean ± SD (n = 3 biologically independent experiments). Data in f,h, j, and k are representative of n = 3 biologically independent experiments.Source DataExtended Data Fig. 9 UPF1 and HNRNPC regulate the stability of alternative-spliced RNA transcripts during EGF stimulation.(a) Bars indicate the overlap of significant AS events between EGF-stimulated HNRNPC-KD and UPF1-KD samples. 2568 common AS events (1721 genes) with congruent ∆PSI patterns after HNRNPC-KD and UPF1-KD were selected. (b) Bubble plots indicate -log10(P-value) as determined by gProfileR253 analysis of top GO terms for molecular functions, cellular components, and regulatory processes of 1721 common HNRNPC-KD/UPF-KD AS genes. (c) Workflow of differential transcript expression analysis of EGF-stimulated HNRNPC- and UPF1-KD data compared to control siRNAs. 844 AS transcripts were found to be commonly differentially expressed (DE) in EGF-treated HNRNPC and UPF1 KD samples. (d) Scatter plots indicate log2FC values against control siRNA of common DE transcripts in EGF-stimulated HNRNPC-KD and UPF1-KD samples. Purple, light blue, orange, steel blue, and red dots: 844 common DE AS transcripts divided for each AS category; grey dots: 3724 significant DE background transcripts. (e) Lines indicate the cumulative fraction of log2FC values against control siRNA of common DE transcripts in EGF-stimulated HNRNPC-KD and UPF1-KD samples. Purple, light blue, orange, steel blue, and red lines: 844 DE AS transcripts divided for each AS category; grey lines: 3,724 significant DE background transcripts in HNRNPC and UPF1 knockdown samples. (f) Workflow of mRNA stability analysis using actinomycin D after the knockdown of HNRNPC and UPF1 in EGF-stimulated (1 h) A431 cells. 665 AS transcripts were found to be commonly differentially stable (DS) after actinomycin D in EGF-treated HNRNPC and UPF1 KD samples. (g) Bars indicate knockdown efficiencies of HNRNPC and UPF1 in HNRNPC and UPF1 knockdown A431 cells treated with EGF for 1 h and then with actinomycin D for a maximum of 4 h, as detected by qPCR. Data are mean (n = 2 biologically independent experiments). (h) mRNA stability profiles for HNRNPC and UPF1 knocked down transcripts as a quality check of actinomycin D treatment, determined by qPCR, in HNRNPC and UPF1 knockdown samples with EGF for 1 h and then with actinomycin D for a maximum of 4 h. Data are mean (n = 2 biologically independent experiments). (i) Principal component analysis (PCA) plots of EGF-treated HNRNPC and UPF1 KD samples followed by actinomycin D timecourse. Green dots: 0 h; blue dots: 1 h; purple dots: 3 h; red dots: 4 h actinomycin D timepoints. (j) Scatter plots indicate log2FC values against control siRNA of common DS transcripts in EGF-stimulated HNRNPC-KD and UPF1-KD samples. Purple, light blue, orange, steel blue, and red dots: 665 common DS AS transcripts divided by each AS category; grey dots: 2,900 significant DS background transcripts. (k) Lines indicate the cumulative fraction of log2FC values against control siRNA of common DS transcripts in EGF-stimulated HNRNPC-KD and UPF1-KD samples. Purple, light blue, orange, steel blue, and red lines: 665 DS AS transcripts; grey lines: 2900 significant DS background transcripts in HNRNPC and UPF1 knockdown samples. (l) Venn diagram indicates the overlap between the 35 co-bound RI genes by HNRNPC and UPF1 and the genes harbouring transcripts that were differential expressed or stable (DE and DS) after HNRNPC and UPF1 KD in EGF-treated A431 cells. (m) Schematics of the minigene construct for RND3 and RPS9 intron retained regions. (n) Representative image of n = 3 minigene assay biologically independent experiments for RND3 (left) and RPS9 (right) intron-retained regions in EGF-treated (1 h) HNRNPC and UPF1 KD samples. Unspliced and spliced regions are indicated with blue-orange-blue and blue-blue boxes, respectively. (o) Stacked bars indicate the percentage of unspliced and spliced minigene fragments for RND3 and RPS9 intron-retained regions in EGF-treated (1 h) HNRNPC and UPF1 KD samples. Data are mean ± SD; RND3-HNRNPC-KD P-value = 2.2e-5***, RND3-UPF1-KD P-value = 1.9e-5***, RPS9-HNRNPC-KD P-value = 8.0e-4***, RPS9-UPF1-KD P-value = 1.1e-3** – one-way ANOVA with two-sided Dunnett’s multiple comparison tests against control siRNA cells (n = 3 biologically independent experiments). In d and j, Red and black lines and grey bands: fitted robust linear regression with standard error. Statistical comparison of regressions between common DE (d) and DS (j) HNRNPC-KD/UPF1-KD AS transcripts and significant background transcripts was made using a two-sided Chow’s test. In e and k, P-values between common DE (e) or DS (k) HNRNPC-KD/UPF1 AS transcripts and significant background transcripts were calculated by a one-sided Kolmogorov-Smirnov test.Source DataExtended Data Fig. 10 UPF1 supports HNRNPC RNA splicing surveillance of EGF-regulated proliferation mRNAs.(a) Workflow of HNRNPC and UPF1 irCLIPv2 and HNRNPC-UPF1 Re-CLIP in EGF-stimulated (0, 15, 30, and 60 min) A431 cells. (b-c) Nitrocellulose images indicate the infrared signal of HNRNPC and UPF1 irCLIPv2 ligations in EGF-stimulated (0, 15, 30, and 60 min) A431 cells in b and infrared signal of HNRNPC-UPF1 Re-CLIP ligations from EGF-stimulated (0, 30, and 60 min) A431 cells in c. The signal represents pooled libraries of n = 2 biologically independent experiments. (d) Pie chart indicates the percentage of 1721 common AS genes categorized as protein-coding (orange), lncRNA (yellow), or pseudogene (green). (e) Stacked bar graph indicates the percentage of the 2388 common AS regions residing in protein-coding genes categorized as 5’UTR (orange), 3’UTR (yellow), or CDS (green). (f) Heatmap indicates the percentage of RT stop location of HNRNPC and UPF1 irCLIPv2 and HNRNPC Re-CLIP followed by UPF1 in the genomic features 5’ and 3’ UTRs, exon, and intron of the AS transcripts. (g) Workflow of HNRNPC and UPF1 irCLIPv2 after UPF1 and HNRNPC KD in EGF-stimulated (0, 60 min) A431 cells. (h) Nitrocellulose images indicate the infrared signal of HNRNPC and UPF1 irCLIPv2 ligations after UPF1-KD or HNRNPC-KD, respectively (n = 2 biologically independent experiments). (i) Bars indicate KD efficiencies of HNRNPC and UPF1 in EGF-stimulated HNRNPC- and UPF1-KD irCLIPv2 samples, as detected by qPCR. Data are represented as mean (n = 2 biologically independent experiments). (j) Scatter plots indicate the log2 mean HNRNPC and UPF1 total signal across 3’UTR regions and the Z score estimated from log2FC upon UPF1 or HNRNPC-KD during EGF stimulation. Marginal box plots indicate the distribution of co-bound HNRNPC and UPF1 regions in AS transcripts or not (red and orange dots) or not co-bound regions in AS transcripts or not (blue and grey dots). Boxes indicate the interquartile range (IQR) with 25th and 75th percentile box limits; whiskers denote the 1.5 × IQR from the 25th and 75th percentiles; center lines: medians; black dots: outliers; red dots: mean values. P-value = 3.6e-3**, P-value = 1.0e-4***; P-value = 0.32 not significant, P-value = 0.02*, P-value = 2.1e-3, P-value = 0.95 not significant – one-way ANOVA with two-sided Dunnett’s multiple comparison test against not co-bound regions (grey dots) (n = number of 3’UTR regions specified in the plot). (k) Scatter plots indicate the Z score values estimated against control siRNA in HNRNPC and UPF1 irCLIPv2 knockdown data of the 30 EGF-responsive 3’UTRs of RI transcripts co-BR by HNRNPC and UPF1 at 0 and 60 min EGF stimulations. Blue lines and grey bands: fitted linear regression with standard error. (l) De-novo motifs discovered in EGF-responsive co-bound and co-regulated (co-BR) 3’UTRs of RI transcripts. (m) Volcano plot indicates the -log10(FDR) and max(log2FC) values as determined by DESeq262 analysis of DE genes in bulk RNAseq from EGF-treated (0, 15, 30, and 60 min) A431 cells. Orange and blue dots: up and downregulated genes. (n) Heatmap indicates the Z score of normalized counts of 10 co-BR RI genes by HNRNPC and UPF1 showing time-dependent-response to EGF stimulation. Orange and blue boxes: up and downregulated genes (o) IGV tracks indicate normalized coverage of HNRNPC and UPF1 irCLIPv2 timecourse/knockdown and HNRNPC-UPF1 Re-CLIP samples at 3’UTR of DDX3X. Tracks show the signal sum of two replicates. Shades of blue boxes: EGF stimulation time points. (p) De novo structure prediction using AlphaFold 3 and manual refinement provides a three-dimensional model of the protein-RNA multimer complex comprising HNRNPC, UPF1, and 3’ UTR mRNA of DDX3X. (q) Representative immunoblot image of n = 3 independent experiments indicates HNRNPC and UPF1 protein signal after native RNA pull-down of biotinylated RND3 and DDX3X 3’UTR binding regions in 1 h EGF-stimulated A431 cells. (r) Bars indicate fold change against scramble control of HNRNPC and UPF1 protein signal after native RNA pull-down in 1 h EGF-stimulated A431 cells. Data are mean ± SD; UPF1-DDX3X P-value = 0.015*, UPF1-RND3 P-value = 7.6e-3**, HNRNPC-DDX3X P-value = 0.014*, HNRNPC-RND3 P-value = 0.016* – unpaired two-tailed Student’s t-test against scramble control 1 and 2 (n = 3 biologically independent experiments).Source DataSupplementary informationSupplementary InformationSupplementary Discussion, descriptions for Supplementary Tables 1–12 and Supplementary References.Reporting SummarySupplementary Fig. 1Raw immunoblot images.Supplementary Tables 1–12Supplementary Tables 1–12.Source dataSource Data Fig. 1Source Data Fig. 2Source Data Fig. 3Source Data Fig. 4Source Data Extended Data Fig. 1Source Data Extended Data Fig. 2Source Data Extended Data Fig. 3Source Data Extended Data Fig. 4Source Data Extended Data Fig. 5Source Data Extended Data Fig. 6Source Data Extended Data Fig. 7Source Data Extended Data Fig. 8Source Data Extended Data Fig. 9Source Data Extended Data Fig. 10Rights and permissionsReprints and permissionsAbout this articleCite this articleDucoli, L., Zarnegar, B.J., Porter, D.F. et al. irCLIP-RNP and Re-CLIP reveal patterns of dynamic protein assemblies on RNA.
Nature (2025). https://doi.org/10.1038/s41586-025-08787-5Download citationReceived: 21 July 2023Accepted: 13 February 2025Published: 26 March 2025DOI: https://doi.org/10.1038/s41586-025-08787-5Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard
Provided by the Springer Nature SharedIt content-sharing initiative