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Multiomic characterization of RNA microenvironments by oligonucleotide-mediated proximity-interactome mapping

Nature Methods volume 21, pages 2058–2071 (2024)Cite this article

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Abstract

RNA molecules form complex networks of molecular interactions that are central to their function and to cellular architecture. But these interaction networks are difficult to probe in situ. Here, we introduce Oligonucleotide-mediated proximity-interactome MAPping (O-MAP), a method for elucidating the biomolecules near an RNA of interest, within its native context. O-MAP uses RNA-fluorescence in situ hybridization-like oligonucleotide probes to deliver proximity-biotinylating enzymes to a target RNA in situ, enabling nearby molecules to be enriched by streptavidin pulldown. This induces exceptionally precise biotinylation that can be easily optimized and ported to new targets or sample types. Using the noncoding RNAs 47S, 7SK and Xist as models, we develop O-MAP workflows for discovering RNA-proximal proteins, transcripts and genomic loci, yielding a multiomic characterization of these RNAs’ subcellular compartments and new regulatory interactions. O-MAP requires no genetic manipulation, uses exclusively off-the-shelf parts and requires orders of magnitude fewer cells than established methods, making it accessible to most laboratories.

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Fig. 1: O-MAP design and implementation.
Fig. 2: O-MAP is portable across specimen types and applicable to diverse RNA targets.
Fig. 3: O-MAP-MS for probing RNA-proximal proteomes.
Fig. 4: Proteomics of the Xi by Xist O-MAP-MS.
Fig. 5: O-MAP-seq for probing RNA-proximal transcripts.
Fig. 6: O-MAP-ChIP for probing RNA-proximal genomic loci.

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Data availability

MS data are available on MASSIVE under accession number MSV000094186. RNA-seq and DNA-seq data are available on the Gene Expression Omnibus under SuperSeries accession number GSE217566. Raw imaging files used in this work are available upon request. HyPro-seq data were downloaded from the European Nucleotide Archive (ENA) under accession number PRJEB44394; NAD Fractionation DNA-seq data were downloaded from the ENA under accession number PRJEB2031. Genome assemblies hg38 (human) and mm10 (mouse) were used for alignment. Gene sets used in ROC analysis were assembled from data provided by the HPA (for 47S O-MAP)91 and the Jax Mouse Genome Informatics database (for Xist O-MAP)92. Source data are provided with this paper.

Code availability

Settings used for routine computational analyses (for example, high-throughput sequencing alignment and quantification) are described in detail in the Methods. Code used for k-medoid clustering (Fig. 3i and Supplementary Fig. 10), Xi allelic proportion analysis (Fig. 6c) and statistical testing of HeLa NADs (Fig. 6f) is available on the GitHub repository of the laboratory of D.M.S. via https://github.com/SheqLab/Tsue_Kania_et_al_2024_OMAP_paper/.

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Acknowledgements

We thank J. Gianopulos, C. Hsu, C. Anderson, H. Gizinski, C. Lin and J. Cabarrus for general technical assistance; Y. Sancak, A. Jain, M. Das, R. Akilesh, P. Valdmanis, J. Scott and S. Smukowski for the kind donation of materials; T. Moss for rRNA computational tools; N. Peters, K. Collins, M. Soruco, E. Hacisüleyman and A. Fenix for imaging assistance; A. Bertero, S. Attar and E. Nichols for help with cell-based models; H. -T. Lau and M. G. Golkowski for MS assistance; G. Yardimci, G. Bonara, A. Mishra, R.D. Hawkins and W. Noble for sequencing assistance; and members of the laboratories of D.M.S., Y. Sancak and R. Gardner for thoughtful discussions and critiques. This work was supported by National Institutes of Health (NIH) grants 1R01GM138799-01 and 1R01HL160825-01 (to D.M.S., A.F.T. and E.E.K.), 1R35GM137916 (to B.J.B. and E.H.), 1R35GM150919-01 (to. D.K.S. and R.F.), R01GM129090 (to S.-E.O. and D.M.M.), UM1HG011586 and GM131745 (to X.D. and C.M.D.), R37CA241472 (to S.K.), T32GM007750 (to A.F.T. and E.E.K.) and T32HG000035 (to C.D.M.), a Safeway Albertsons Early Career Award in Cancer Research (to D.M.S.), the UW Royalty Research Fund (to D.M.S.), a Brotman Baty Institute Catalytic Collaborations Award (to D.M.S. and S.K.) and the UW Student Technology Fund. E.E.K. was supported by the NSF DEB2016186 and the AHA 902616. Imaging at the UW Keck Center was supported by NIH S10 OD016240 and the UW Student Technology Fee. This work used an EASY-nLC 1200 UHPLC and Thermo Scientific Orbitrap Fusion Lumos Tribrid mass spectrometer purchased with funding from NIH grant S10OD021502 (to S.-E.O. and D.M.M.). Next-generation sequencing data analysis was facilitated through the use of advanced computational, storage and networking infrastructure provided by the Hyak supercomputer system and funded by the STF at the UW.

Author information

Author notes

  1. Evan E. Kania

    Present address: Shape Therapeutics, Seattle, WA, USA

  2. Diana Q. Lei

    Present address: Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada

  3. Rose Fields

    Present address: Fred Hutchinson Cancer Center, Seattle, WA, USA

  4. Elliot A. Hershberg

    Present address: Department of Genetics, Stanford University, Stanford, CA, USA

  5. These authors contributed equally: Ashley F. Tsue, Evan E. Kania.

Authors and Affiliations

  1. Department of Pharmacology, University of Washington, Seattle, WA, USA

    Ashley F. Tsue, Evan E. Kania, Diana Q. Lei, Daphnée M. Marciniak, Maryanne Kihiu, Shao-En Ong & David M. Shechner

  2. Institute of Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA

    Ashley F. Tsue, Evan E. Kania, Diana Q. Lei & David M. Shechner

  3. Brotman Baty Institute for Precision Medicine, Seattle, WA, USA

    Ashley F. Tsue, Evan E. Kania, Sita Kugel, Brian J. Beliveau, Devin K. Schweppe & David M. Shechner

  4. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    Rose Fields, Christopher D. McGann, Elliot A. Hershberg, Brian J. Beliveau & Devin K. Schweppe

  5. Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA

    Xinxian Deng & Christine M. Disteche

  6. Department of Medicine, University of Washington, Seattle, WA, USA

    Christine M. Disteche

  7. Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Sita Kugel

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Contributions

A.F.T., E.E.K. and D.M.S. conceived of, designed and analyzed all experiments, which were principally performed by A.F.T and E.E.K., and assisted by D.Q.L. and M.K. MS analysis was performed by R.F., C.D.M., D.M.M., S.-E.O. and D.K.S. S.K. assisted with PDA and organoid experiments. X.D. and C.M.D. assisted with all Xist- and Firre-related experiments. B.J.B. and E.A.H. assisted with oligonucleotide probe design. D.M.S. directed research. All authors contributed to writing.

Corresponding author

Correspondence to David M. Shechner.

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Competing interests

A.F.T., E.E.K., B.J.B. and D.M.S. have filed for a patent concerning the use of oligonucleotide-directed proximity labeling to elucidate and visualize subcellular interactions in situ. D.Q.L., R.F., C.D.M., D.M.M., E.H., X.D., M.K., S.-E.O., C.M.D. and S.K. declare no competing interests.

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Nature Methods thanks Gene Yeo, Hsueh-Ping Chu, Piero Carninci and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Rita Strack, in collaboration with the Nature Methods team.

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Extended data

Extended Data Fig. 1 Alternative O-MAP designs.

a, summary of RNA-targeted HRP-recruitment strategies tested. Design 1 uses biotinylated primary probes to recruit a streptavidin-HRP conjugate. Designs 2 and 3 use Digoxigenin (DIG)-labeled primary or secondary probes to recruit an HRP–conjugated anti-DIG antibody. Our final O-MAP design, which uses HRP-conjugated oligo probes, is shown for comparison (see also, Fig. 1a). The same anti-DIG antibody is used in designs 2 and 3; the same “universal landing pad” sequences are used in designs 3 and 4. b–e, limitations of Designs 1–3. In all cases biotin was visualized by staining with a fluorescent neutravidin conjugate. b, Design 1 was disfavored because in situ biotinylation cannot be distinguished from biotinylated probes. HeLa cells over-expressing NPM1~eGFP were probed using the same 47S-targeting probes as in (Supplementary Fig. 1), bearing 3´–terminal biotin. c, Design 2—analogous to HyPro—in HeLa cells. This sometimes produced well-resolved nucleolar-targeted biotinylation, but was disfavored due to antibody irreproducibility (see below). The high cost of DIG-labeled oligos would also limit its use with low-abundance transcripts, which can require dozens-to-hundreds of probes. d, Design 3 overcomes the oligo cost issue but still suffers from antibody background binding and irreproducibility. HeLa cells were probed with the same 47S-targeting primary probe set and DIG-labeled secondary oligo, using four different lots of commercial HRP-conjugated antibodies. In some cases, we observed well-resolved RNA-targeted biotinylation (panel i), though other lots from the same vendor exhibited significant off-target labeling (panel ii, arrows). Regents from other vendors exhibited varying degrees of blurring (panel iii), or excessive off-target biotinylation (panel iv). e, Design 3 is particularly problematic with lower-abundance RNA targets. Patski cells were probed with the same Xist-targeting, landing-pad-extended probes as used in the main text, divided into two sub-pools. Anti-DIG-HRP was from Vendor 1. Note that all conditions—both probe sub-pools and the omit-primary negative control–induced substantial off-target biotinylation. All scale bars, 20 µm.

Source data

Extended Data Fig. 2 More O-MAP controls and imaging analysis.

a, O-MAP and negative control experiments were performed in HeLa cells, as indicated. Biotin was imaged using a fluorescent neutravidin conjugate; NPM1 via immunofluorescence. Note that omitting any component of the O-MAP pipeline ablated biotinylation signal. The 47S-O-MAP, 7SK-O-MAP, omit primary and scrambled primary conditions (left four columns) are the same images presented in (Fig. 1b). Scale bars, 20 µm. b, Representative line traces of each condition. Note the overlap in NPM1 and O-MAP signals in the 47S O-MAP condition; the lack of overlap in 7SK O-MAP, and the nearly complete absence of biotinylation under all other conditions. Scale bars, 10 µm. The 47S O-MAP image are the same data presented in (Fig. 1b). In all “omit-H2O2” conditions (marked *), cells were pre-quenched with sodium azide and ascorbic acid prior to the addition of biotin-phenol and H2O2. Simply removing H2O2 from the O-MAP protocol still resulted in targeted in situ biotinylation (that is nucleolar labeling using 47S probes), presumably due to photoactivation by HRP.

Source data

Extended Data Fig. 3 O-MAP is more spatially precise than genetically encoded proximity-labeling systems.

a, Qualitative assessment of off-target biotinylation by 47S-targeted O-MAP (left) and nucleolar-targeted APEX2 ~ GFP (right). NPM1 was visualized by immunofluorescence; APEX2 ~ GFP by autofluorescence. O-MAP data are the same as presented in (Fig. 1b and Extended Data Fig. 1); APEX2 data are the same as in (Fig. 1c). Note prominent nucleoplasmic (off-target) labeling by APEX2, indicated by arrowheads. Scale bars, 10 µm. b, More head-to-head comparisons between 47S O-MAP and APEX2~NoLS, quantified in line-traces. Green: APEX2 ~ GFP biotinylation; Purple: 47S O-MAP biotinylation. Scale bars: 10 µm. c, Quantifying 47S O-MAP background biotinylation. Left: DAPI signal was used to identify Regions of Interest (ROI; yellow outlines), corresponding to individual nuclei, and the biotinylation intensities for all pixels within each ROI were measured. Right: histogram summarizing the distribution of biotinylation intensities observed within Cell 1. Note the prominent peak near zero, indicating that most pixels contained little or no signal. Inset highlights a minor peak of intense biotinylation (>17,500). d, The same approach was applied to APEX2~NoLS cells. Left: ROIs defined; Right: Histogram of observed biotinylation intensities within cell 1. Note the lower proportion of pixels near zero, fewer pixels at high signal intensities (inset) and the prominent peak between 1500–3600—suggesting significant background labeling. In c, d, a single z-slice is shown; scale bars, 20 µm. e, Cumulative distribution functions of biotinylation intensities for O-MAP and APEX2~NoLS (n = 4). Mean values over all ROIs (dark lines). Lighter bands correspond to the maximum and minimum values observed. Larger plot highlights the distributions at intensities below 10,000; inset shows all data. 59% of pixels in the O-MAP experiment were below 1000; only 14% of APEX2 pixels fell within this range, suggesting that O-MAP’s lower background biotinylation. Furthermore, 57% of APEX2 pixels fell between 1500—3600, while only 8% of O-MAP data fall in this range. All images under direct comparison (for example, all images in a; all in b; and the images in c and d) were collected on the same day, using the same acquisition parameters.

Source data

Extended Data Fig. 4 Probing the nucleolar proteome with 47S O-MAP-MS.

a, Receiving-Operator Characteristic (ROC) analysis of the 47S vs 7SK O-MAP-MS experiment. True Positive and False Positive proteins were defined using lists of exclusively nucleolar and exclusively nucleoplasmic proteins, respectively, as reported by the Human Protein Atlas (HPA). An Area Under the Curve (AUC) of nearly 1.0 suggests strong and highly sensitive selectivity for nucleolar proteins over the nucleoplasmic proteome. b, These data were used to derive an optimal Log2(fold change, 47S/7SK) cutoff value of 0.523, and to define a putative list of 258 O-MAP core nucleolar proteins, as described in (Fig. 3). c–e, Parallel analysis using (47S/Scramble controls), instead of (47S/7SK). c, Volcano plot and histograms of showing the enrichment of HPA-nucleolar, HPA-Nucleoplasmic, and HPA-bilocalized proteins, using the same protein marker reference lists as in (a, b), and (Fig. 3c, d). Benjamini Hochberg corrected p-values (FDR). d, ROC analysis of the (47S/Scramble) data demonstrates slightly lower sensitivity than that of the (47S/7SK) analysis, though still exceptionally. e, As in (b), these data were used to determine an optimal Log2(fold change, 47S/Scramble) cutoff value of 2.201, defining a putative cohort of 286 O-MAP core nucleolar proteins. f, The putative nucleolar proteomes derived from the (47S/7SK) and (47S/Scramble) ROC analyses show considerable overlap (66–73%). Outliers were used for Gene Ontology (GO)-term analysis. Factors uniquely captured by the (47S/7SK) analysis were highly enriched for ribosome biogenesis factors, while those unique to the (47S/Scramble) analysis were enriched for nucleoplasmic functions. This suggests that the (47S/7SK) comparison more precisely captures the nucleolar proteome. Hypergeometric test with Benjamini-Hochberg correction.

Source data

Extended Data Fig. 5 Coverage of the nucleolar proteome during the 47S O-MAP labeling time course.

a, Volcano plots for each 47S O-MAP labeling point, calculated relative to the 7SK/10-min label condition. Enrichment of nucleolar proteins derived from our k-medoid analysis (Fig. 3h–j; Supplementary Fig. 10) are highlighted in pink. Significance cutoffs were assigned at padj ≤ 0.05 and Fold Change (47S/7SK) ≥ 2.0. (dotted lines). Benjamini Hochberg corrected p-values (FDR) b, Table summarizing the recovery of the nucleolar proteome at each labeling time point. Note that coverage appears to plateau at 10 min.

Source data

Extended Data Fig. 6 Comparison of 47S-targeted O-MAP-MS and HyPro-MS.

Top: Venn diagram of enriched proteins from 47S-targeted HyPro-MS (gray) and the k-medoid-derived high-confidence O-MAP nucleolar proteome (Fig. 3j; Supplementary Table 3). Note that O-MAP enriches more than 50% of the HyPro nucleolar proteome. Bottom: Gene Ontology analysis of the outlier proteins observed by HyPro-MS (101 proteins; left) and by O-MAP-MS (194 proteins, right). The top 10 most highly enriched terms are shown. Note that the O-MAP-MS outliers are predominantly nucleolar proteins and ribosome biogenesis factors, suggesting that HyPro has undersampled the nucleolar proteome. In contrast, HyPro outliers exhibit little association with the nucleolus or its central functions, suggesting that they may be noise. Hypergeometric test with Benjamini-Hochberg correction.

Source data

Extended Data Fig. 7 Probing the Xi compartment with O-MAP-MS.

a, Receiving-Operator Characteristic (ROC) analysis of the Xist O-MAP-MS experiment. True Positives (183 proteins; Supplementary Table 2) were defined as Xist-interacting proteins observed previously via antisense oligonucleotide purification methods. False Positives (104 proteins; Supplementary Table 2) were defined using the Jackson Laboratory Mouse Genome Informatics portal (JAX MGI; https://www.informatics.jax.org/vocab/gene_ontology) list of tRNA modifying factors, manually curated to remove duplicate entries and factors that lack nuclear localization. An Area Under the Curve (AUC) of 0.92 suggests strong and highly sensitive selectivity for known Xist interactors relative to the broader proteome. b, These data were used to derive an optimal Log2(fold change, Xist/mock) cutoff value of 0.916, and to define a putative list of 621 O-MAP Xi-proximal proteins, as described in (Fig. 4). c, Behavior of other prominent gene sets in the Xist O-MAP experiment, based on their subnuclear localization (top) or molecular function (bottom). All sets were defined using JAX MGI, manually curated to remove duplicate entries and factors in our Xist True Positive list. None of these gene sets is more highly enriched than known Xist interactors en toto (Fig. 4c). However, each set contains a subset of factors that are enriched by Xist O-MAP-MS, suggesting a functional role for these factors within the Xi compartment. Student’s two-tailed T-Test, with a permutation-based FDR adjustment.

Source data

Extended Data Fig. 8 47S O-MAP-Seq enriches known and novel nucleolar transcripts.

a, the 47S pre-rRNA. Note prominent enrichment for the 5´–ETS, IT1, and ITS2 “transcribed spacer” domains. Sequences corresponding to the mature 18S, 5.8S, and 28S rRNAs are removed during sequencing library preparation. Reads are aligned to a custom genome assembly containing a single copy of the rDNA consensus sequence (courtesy of T. Moss, U. Laval) annotated as a unique chromosome. b, the U3 noncoding RNA, which directs key cleavage events during ribosomal biogenesis, c, d, exemplar Box C/D (c) and H/ACA (d) small nucleolar RNAs (snoRNAs). SnoRNAs are often expressed within the introns of protein-coding genes (gray). e, RNase MRP (enriched) upstream of the CCDC107 gene (not enriched). f, lncRNA SLERT, which is processed from the two H/ACA snoRNAs embedded in the TBRG4 gene. g, Example of a novel nucleolar transcript—a processed pseudogene—discovered by O-MAP-Seq.

Extended Data Fig. 9 Comparison of 47S O-MAP-Seq and HyPro-Seq.

a, Heatmap of Pearson correlations, comparing three biological replicates of 47S O-MAP-Seq and two biological replicates (four technical replicates each) of the analogous HyPro-Seq experiment. Comparisons were made between log2(fold change, enriched/input) for each replicate. Note the high inter-replicate reproducibility of O-MAP (r = 0.87–0.93), and that its correlation to HyPro-Seq Replicate A (r = 0.53–0.58) is comparable to the correlation between HyPro-Seq biological replicates (r = 0.55–0.56). b, Example scatter plots of the data used in (a), highlighting O-MAP-Seq’s superior reproducibility. c, O-MAP-Seq and HyPro-Seq enrich many of the same transcripts. Volcano plot of 47S O-MAP-Seq data, equivalent to (Fig. 5b), with coding and noncoding isoforms merged. 47S O-MAP-Seq prominently enriches many of the transcripts that were enriched by 47S HyPro-Seq (red), in addition to a smaller set of unique RNAs (blue). Note that relatively few of the transcripts that were uniquely enriched by HyPro-Seq (dark gray) were de-enriched by O-MAP; the majority of these RNAs fell below the significance cutoff of detection, presumably due to the O-MAP-Seq dataset’s lower sequencing depth. Significance testing: Wald’s test. d, Breakdown of 47S-proximal RNAs by transcript class, for those enriched by O-MAP-Seq (blue), HyPro-Seq (gray), and both methods (red). Although the overall composition is similar, HyPro-Seq enriches more protein coding genes and mitochondrial tRNAs, which are not thought to be nucleolar, and fewer lncRNAs and pseudogenic transcripts. e, Isoform-level analysis of enriched transcripts arising from protein-coding genes, analogous to (Fig. 5d). Note that HyPro-Seq enriches a higher proportion of protein-coding isoforms, and lower proportion of retained intron-containing transcripts. Collectively, these data suggest that O-MAP-Seq largely recapitulates the data obtained by analogous methods, but with lower noise and higher reproducibility.

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Extended Data Fig. 10 Xist O-MAP-Seq enriches nascent transcripts of XCI-escape genes.

a, Enrichment of the Xist gene itself. Note different scales for input RNA and O-MAP-Seq tracks. The lack of intronic reads suggests that O-MAP-Seq has predominantly targeted and captured the mature Xist transcript. The absence of reads mapping to the antisense noncoding RNA TsiX, a lncRNA that is monoallelically expressed from the Xa (gray), confirms that O-MAP-Seq is precisely labeling the Xi. b, Enriched XCI-escape genes appear to be nascent transcripts. In all cases, read densities for both Input RNA (gray) and O-MAP-Seq (red) are shown, using matched scales. Note prominent intronic read density for all XCI-escape genes (Shroom4, Pbdc1, Magee1, Mid1, Erdr1). Transcript structures denoting the most prominent isoforms are displayed; other nearby genes not known to escape XCI are denoted in gray. c, Xist O-MAP-Seq does not appear to preferentially capture nascent transcripts of non-XCI-escape genes. Two examples (Tspan6 and Rps4x) are shown, neither of which was enriched by Xist O-MAP-Seq. Note the absence of prominent intronic read density, suggesting that the intronic signatures observed in (b) are not general artifacts of the O-MAP-Seq pipeline.

Supplementary information

Supplementary Information

Supplementary Discussion, References and Figs. 1–13; Source Data for Supplementary Fig. 4.

Reporting Summary

Supplementary Data 1

Source microscopy images for Supplementary Fig. 1.

Supplementary Data 2

Source microscopy images for Supplementary Fig. 2.

Supplementary Data 3

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Supplementary Data 4

Source microscopy images for Supplementary Fig. 4.

Supplementary Data 5

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Supplementary Data 6

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Supplementary Tables

Supplementary Tables 1–5.

Source data

Source Data Fig. 1

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Source microscopy images and statistical source data for Fig. 3. Imaging archive contains eight imaging files. Source data archive contains five statistical source data files, for 3b,c,e,f.

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Source microscopy images and statistical source data for Fig. 3. Imaging archive contains eight imaging files. Source data archive contains five statistical source data files, for 3b,c,e,f.

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Source statistical data for Fig. 4b,d,e.

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Source microscopy images and statistical source data for Fig. 5. Imaging archive contains nine images. Source data archive contains seven statistical source data files, for 5b,c,f,h.

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Source microscopy images and statistical source data for Fig. 5. Imaging archive contains nine images. Source data archive contains seven statistical source data files, for 5b,c,f,h.

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Source Data Extended Data Fig. 3

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Tsue, A.F., Kania, E.E., Lei, D.Q. et al. Multiomic characterization of RNA microenvironments by oligonucleotide-mediated proximity-interactome mapping. Nat Methods 21, 2058–2071 (2024). https://doi.org/10.1038/s41592-024-02457-6

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