Skip to main content

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

Robust single-cell discovery of RNA targets of RNA-binding proteins and ribosomes

Abstract

RNA-binding proteins (RBPs) are critical regulators of gene expression and RNA processing that are required for gene function. Yet the dynamics of RBP regulation in single cells is unknown. To address this gap in understanding, we developed STAMP (Surveying Targets by APOBEC-Mediated Profiling), which efficiently detects RBP–RNA interactions. STAMP does not rely on ultraviolet cross-linking or immunoprecipitation and, when coupled with single-cell capture, can identify RBP-specific and cell-type–specific RNA–protein interactions for multiple RBPs and cell types in single, pooled experiments. Pairing STAMP with long-read sequencing yields RBP target sites in an isoform-specific manner. Finally, Ribo-STAMP leverages small ribosomal subunits to measure transcriptome-wide ribosome association in single cells. STAMP enables the study of RBP–RNA interactomes and translational landscapes with unprecedented cellular resolution.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: RBP–STAMP edits mark specific RBP binding sites.
Fig. 2: Ribo–STAMP edits mark highly translated coding sequences.
Fig. 3: Long-read STAMP reveals isoform-specific binding profiles.
Fig. 4: STAMP allows RBP binding-site detection at single-cell resolution.
Fig. 5: Deconvolution of multiple RBPs and cell type–specific targets.
Fig. 6: Ribo–STAMP reveals ribosome occupancy from individual cells.

Data availability

Raw and assembled sequencing data from this study have been deposited in NCBI’s Gene Expression Omnibus (GEO) under accession code GSE155729. Processed edit coordinates are available in Supplementary Tables 1, 2 and 4. Differential edit and gene expression data are available in Supplementary Tables 3 and 59. Published ribosome profiling data used in this study are deposited in the GEO under accession code GSE94460 and polysome sequencing data are deposited in the GEO under accession code GSE109423.

Code availability

Source code and analysis scripts for edit quantification are available as Supplementary Software. Updated versions can be found at https://github.com/YeoLab/sailor/ and https://github.com/YeoLab/Yeo_STAMP_Nature_Methods/.

References

  1. 1.

    Singh, G. et al. The clothes make the mRNA: past and present trends in mRNP fashion. Annu. Rev. Biochem. 84, 325–354 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

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

    CAS  PubMed  Google Scholar 

  3. 3.

    Van Nostrand, E. L. et al. Principles of RNA processing from analysis of enhanced CLIP maps for 150 RNA-binding proteins. Genome Biol. 21, 90 (2020).

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    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 

  5. 5.

    Wheeler, E. C., E. L. Van Nostrand, E. L. & Yeo, G. W. Advances and challenges in the detection of transcriptome-wide protein–RNA interactions. Wiley Interdiscip. Rev. RNA 9, e1436 (2018).

  6. 6.

    Van Nostrand, E. L. et al. Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP). Nat. Methods 13, 508–514 (2016).

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Perez-Perri, J. I. et al. Discovery of RNA-binding proteins and characterization of their dynamic responses by enhanced RNA interactome capture. Nat. Commun. 9, 4408 (2018).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Calviello, L. & Ohler, U. Beyond read-counts: Ribo-seq data analysis to understand the functions of the transcriptome. Trends Genet. 33, 728–744 (2017).

    CAS  PubMed  Google Scholar 

  9. 9.

    Ingolia, N. T. et al. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218–223 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

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

    CAS  PubMed  Google Scholar 

  11. 11.

    Clamer, M. et al. Active ribosome profiling with ribolace. Cell Rep. 25, 1097–1108 (2018).

    CAS  PubMed  Google Scholar 

  12. 12.

    Buenrostro, J. D. et al. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Hwang, B., Lee, J. H. & Bang, D. Single-cell RNA-sequencing technologies and bioinformatics pipelines. Exp. Mol. Med. 50, 1–14 (2018).

    PubMed  Google Scholar 

  14. 14.

    Tang, F. et al. mRNA-seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    CAS  PubMed  Google Scholar 

  15. 15.

    Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Shahi, P. et al. Abseq: ultrahigh-throughput single-cell protein profiling with droplet microfluidic barcoding. Sci. Rep. 7, 44447 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Nguyen, D. T. T. et al. HyperTRIBE uncovers increased MUSASHI-2 RNA binding activity and differential regulation in leukemic stem cells. Nat. Commun. 11, 2026 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Medina-Munoz, H. C. et al. Records of RNA locations in living yeast revealed through covalent marks. Proc. Natl Acad. Sci. USA 117, 23539–23547 (2020).

  19. 19.

    Jin, H. et al. TRIBE editing reveals specific mRNA targets of eIF4E-BP in Drosophila and in mammals. Sci. Adv. 6, eabb8771 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    McMahon, A. C. et al. TRIBE: hijacking an RNA-editing enzyme to identify cell-specific targets of RNA-binding proteins. Cell 165, 742–753 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Lapointe, C. P. et al. Protein–RNA networks revealed through covalent RNA marks. Nat. Methods 12, 1163–1170 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Xu, W., Rahman, R. & Rosbash, M. Mechanistic implications of enhanced editing by a HyperTRIBE RNA-binding protein. RNA 24, 173–182 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Rahman, R. et al. Identification of RNA-binding protein targets with HyperTRIBE. Nat. Protoc. 13, 1829–1849 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Matthews, M. M. et al. Structures of human ADAR2 bound to dsRNA reveal base-flipping mechanism and basis for site selectivity. Nat. Struct. Mol. Biol. 23, 426–433 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Navaratnam, N. et al. The p27 catalytic subunit of the apolipoprotein B mRNA editing enzyme is a cytidine deaminase. J. Biol. Chem. 268, 20709–20712 (1993).

    CAS  PubMed  Google Scholar 

  26. 26.

    Meyer, K. D. DART-seq: an antibody-free method for global m6A detection. Nat. Methods 16, 1275–1280 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Deffit, S. N. et al. The C. elegans neural editome reveals an ADAR target mRNA required for proper chemotaxis. Elife 6, e28625 (2017).

  28. 28.

    Washburn, M. C. et al. The dsRBP and inactive editor ADR-1 utilizes dsRNA binding to regulate A-to-I RNA editing across the C. elegans transcriptome. Cell Rep. 6, 599–607 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Lovci, M. T. et al. Rbfox proteins regulate alternative mRNA splicing through evolutionarily conserved RNA bridges. Nat. Struct. Mol. Biol. 20, 1434–1442 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Yeo, G. W. et al. An RNA code for the FOX2 splicing regulator revealed by mapping RNA–protein interactions in stem cells. Nat. Struct. Mol. Biol. 16, 130–137 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Ponthier, J. L. et al. Fox-2 splicing factor binds to a conserved intron motif to promote inclusion of protein 4.1R alternative exon 16. J. Biol. Chem. 281, 12468–12474 (2006).

    CAS  PubMed  Google Scholar 

  32. 32.

    Van Nostrand, E. L. et al. CRISPR–Cas9-mediated integration enables TAG-eCLIP of endogenously tagged RNA-binding proteins. Methods 118–119, 50–59 (2017).

    PubMed  Google Scholar 

  33. 33.

    Li, Q. H. et al. Measuring reproducibility of high-throughput experiments. Ann. Appl. Stat. 5, 1752–1779 (2011).

    Google Scholar 

  34. 34.

    Marzluff, W. F., Wagner, E. J. & Duronio, R. J. Metabolism and regulation of canonical histone mRNAs: life without a poly(A) tail. Nat. Rev. Genet. 9, 843–854 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Gilks, N. et al. Stress granule assembly is mediated by prion-like aggregation of TIA-1. Mol. Biol. Cell 15, 5383–5398 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Li, B. B. et al. Targeted profiling of RNA translation reveals mTOR-4EBP1/2-independent translation regulation of mRNAs encoding ribosomal proteins. Proc. Natl Acad. Sci. USA 115, E9325–E9332 (2018).

    CAS  PubMed  Google Scholar 

  37. 37.

    Yang, F. et al. MALAT-1 interacts with hnRNP C in cell cycle regulation. FEBS Lett. 587, 3175–3181 (2013).

    CAS  PubMed  Google Scholar 

  38. 38.

    Zhang, P. et al. Genome-wide identification and differential analysis of translational initiation. Nat. Commun. 8, 1749 (2017).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Tan, F. E. et al. A transcriptome-wide translational program defined by LIN28B expression level. Mol. Cell 73, 304–313 (2019).

    CAS  PubMed  Google Scholar 

  40. 40.

    Wagner, S. et al. Selective translation complex profiling reveals staged initiation and co-translational assembly of initiation factor complexes. Mol. Cell 79, 546–560 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Archer, S. K. et al. Dynamics of ribosome scanning and recycling revealed by translation complex profiling. Nature 535, 570–574 (2016).

    CAS  PubMed  Google Scholar 

  42. 42.

    Miettinen, T. P. & Bjorklund, M. Modified ribosome profiling reveals high abundance of ribosome protected mRNA fragments derived from 3′ untranslated regions. Nucleic Acids Res. 43, 1019–1034 (2015).

    CAS  PubMed  Google Scholar 

  43. 43.

    Thoreen, C. C. et al. An ATP-competitive mammalian target of rapamycin inhibitor reveals rapamycin-resistant functions of mTORC1. J. Biol. Chem. 284, 8023–8032 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Thoreen, C. C. et al. A unifying model for mTORC1-mediated regulation of mRNA translation. Nature 485, 109–113 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Jain, M. et al. The Oxford Nanopore MinION: delivery of nanopore sequencing to the genomics community. Genome Biol. 17, 239 (2016).

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Ardui, S. et al. Single molecule real-time (SMRT) sequencing comes of age: applications and utilities for medical diagnostics. Nucleic Acids Res. 46, 2159–2168 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Rhoads, A. & Au, K. F. PacBio sequencing and its applications. Genomics Proteomics Bioinformatics 13, 278–289 (2015).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Fu, S., Wang, A. & Au, K. F. A comparative evaluation of hybrid error correction methods for error-prone long reads. Genome Biol. 20, 26 (2019).

    PubMed  PubMed Central  Google Scholar 

  49. 49.

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

    CAS  PubMed  Google Scholar 

  50. 50.

    Beaudoin, J. D. et al. Analyses of mRNA structure dynamics identify embryonic gene regulatory programs. Nat. Struct. Mol. Biol. 25, 677–686 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Lorenz, D. A. et al. Direct RNA sequencing enables m6A detection in endogenous transcript isoforms at base-specific resolution. RNA 26, 19–28 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Li, Y. et al. A comprehensive library of familial human amyotrophic lateral sclerosis induced pluripotent stem cells. PLoS ONE 10, e0118266 (2015).

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    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  Google Scholar 

  54. 54.

    Li, H., Ruan, J. & Durbin, R. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res. 18, 1851–1858 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Gordon, S. P. et al. Widespread polycistronic transcripts in fungi revealed by single-molecule mRNA sequencing. PLoS ONE 10, e0132628 (2015).

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank G. Viero at the Institute of Biophysics, CNR Unit at Trento for helpful advice concerning analysis of Ribo-seq data. We thank T. Michael at the J. Craig Venter Institute for use of the Oxford Nanopore PromethION system. We are grateful to K. D. Meyer for the gift of the YTH-APOBEC1 construct. We thank J. Rothstein at Johns Hopkins University School of Medicine for the gift of the NPC cell line. We are grateful to the La Jolla Institute’s Immunology Sequencing Core and the IGM Genomics Center, University of California San Diego, for use of the 10x Chromium and Illumina sequencing platforms. This work was partially supported by the National Institutes of Health (NIH; HG004659 and HG009889 to G.W.Y.). I.A.C. is a San Diego IRACDA Fellow supported by an NIH/NIGMS award (K12GM068524). R.J.M. was supported in part by an institutional award to the UCSD Genetics Training Program from the National Institute for General Medical Sciences, (T32GM008666) and a Ruth L. Kirschstein National Research Service award (F31NS111859). K.W.B is a University of California President’s Postdoctoral Fellow supported by an NIH/NINDS Career Transition Award (K22NS112678).

Author information

Affiliations

Authors

Contributions

Conceptualization: K.W.B. and G.W.Y.; methodology: K.W.B. and G.W.Y.; investigation: K.W.B., I.A.C., K.D.D., A.A.M., D.A.L. and R.J.M.; formal analysis: E.K., P.J., K.W.B., B.A.Y., I.A.C., D.A.L. and R.J.M.; writing of original draft: K.W.B. and G.W.Y.; writing of review and editing: G.W.Y., K.W.B., I.A.C., B.A.Y., D.A.L. and R.J.M.; funding acquisition: G.W.Y.; supervision: G.W.Y.

Corresponding author

Correspondence to Gene W. Yeo.

Ethics declarations

Competing interests

G.W.Y. is a cofounder, a member of the Board of Directors, on the Scientific Advisory Board, an equity holder and a paid consultant for Locanabio and Eclipse BioInnovations. G.W.Y. is a visiting professor at the National University of Singapore. G.W.Y.’s interests have been reviewed and approved by the University of California San Diego, in accordance with its conflict-of-interest policies. The authors declare no other competing interests.

Additional information

Peer review information Nature Methods thanks Alfredo Castello and the other, anonymous, reviewers for their contribution to the peer review of this work. Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 RBP-STAMP reproducibility and concordance with eCLIP, related to Figure 1.

a, Irreproducible Discovery Rate (IDR) analysis comparing ≥ 0.5 confidence edit windows for increasing levels of RBFOX2-STAMP at 24, 48 and 72 hours. b, Differential expression (DEseq2) analysis of RBFOX2-STAMP for increasing levels of RBFOX2-STAMP at 72 hours. c, Fraction of RBFOX2-APOBEC1 eCLIP peaks overlapping low and high induction RBFOX2-STAMP edit sites at increasing expression (TPM) thresholds. d, STAMP edit-site filtering and cluster-calling workflow. e, Number of control- and RBFOX2-STAMP edit sites and clusters retained after each filtering step in D. f, Cumulative distance measurement from RBFOX2-STAMP distal edit-clusters to eCLIP peaks on targets genes. g, Pie chart showing the proportion of N-terminally fused RBFOX2-APOBEC1 STAMP edit-clusters overlapping with either 1) RBFOX2-APOBEC1 N-terminal fusion high-confidence eCLIP peaks (l2fc>2 and l10p>3 over input) containing the conserved RBFOX2 binding motif (GCAUG), 2) equally stringent eCLIP peaks not containing the conserved motif, 3) the conserved motif falling outside of eCLIP peaks, or 4) neither eCLIP peaks nor conserved motifs. h, Quantification of expression from no dox (0ng/ml) low (50ng/ml) or high (1µg/ml) doxycycline induction of SLBP-APOBEC1 and TIA1-APOBEC1 fusions compared to endogenous expression. i, Irreproducible Discovery Rate (IDR) analysis comparing 0.5 ≥ confidence level edit windows for increasing levels of TIA1-STAMP at 72 hours. j, Fraction of SLBP eCLIP peaks (log2fc>2 and -log10p>3 over size-matched input, reproducible by IDR) with SLBP-STAMP edit-clusters, compared to size-matched shuffled regions, calculated at different edit site confidence levels before and after site filtering (see Materials and Methods for filtering procedure). Numbers atop bars are Z-scores computed comparing observed with the distribution from random shuffles. *** denotes statistical significance at p = 0, one-sided exact permutation test. k, Fraction of TIA1-APOBEC1 eCLIP peaks (log2fc>2 and -log10p>3 over size-matched input) with TIA1-STAMP edit-clusters, compared to size-matched shuffled regions, calculated at different edit site confidence levels before and after site filtering (see Materials and Methods for filtering procedure). Numbers atop bars are Z-scores computed comparing observed with the distribution from random shuffles. *** denotes statistical significance at p = 0, one-sided exact permutation test. l, Motif enrichment using HOMER and shuffled background on TIA1-STAMP edit-clusters.

Extended Data Fig. 2 Ribo-STAMP reproducibility and response to mTOR pathway perturbations, related to Figure 2.

a, Quantification of expression from no dox (0ng/ml) low (50ng/ml) or high (1µg/ml) doxycycline induction of RPS2-APOBEC1 fusion compared to endogenous expression. b–d, Scatterplot comparisons of CDS+3′UTR EPKM values from RPS2-STAMP replicate experiments showing high, dose-dependent correlation at 24 (B), 48 (C) and 72 hours (D). e, Scatterplot comparison of CDS EPKM values with CDS+3′UTR EPKM values for RPS2-STAMP. f, Pearson R2 values for low and high induction control- or RPS2-STAMP EPKM compared to poly-ribosome-enriched polysome-seq RPKM. g, Comparison of EPKM from vehicle treated 72-hour high-induction control-STAMP compared to Torin-1 treated 72-hour high-induction control-STAMP showing no significant signal reduction for top ribosome occupied quartile genes containing Torin-1 sensitive TOP genes as detected by ribo-seq (Q1 p = 1.0, n = 3589 genes, Wilcoxon rank-sum one-sided) and polysome profiling (Q1 p = 1.0, n = 3589 genes, Wilcoxon rank-sum one-sided). h, Scatterplot comparison of CDS+3′UTR EPKM values on ribo-seq top quartile genes (n = 3589) for Torin-1 treated and vehicle treated RPS2-STAMP 72-hour high (1µg/ml) doxycycline inductions as in Figure 2H. i, Scatterplot comparison of CDS+3′UTR RPKM values on ribo-seq quartile-1 genes (n = 3589) for Torin-1 treated and vehicle treated RPS2-STAMP 72-hour high (1µg/ml) doxycycline inductions.

Extended Data Fig. 3 Long-read STAMP reveals isoform specific binding profiles, related to Figure 3.

a, Heatmap of control- and RBFOX2-STAMP edit fractions calculated from the final exon of all detected primary and secondary alternative polyadenylation (APA) isoforms meeting coverage criteria (see materials and methods). b, IGV tracks showing RBFOX2-APOBEC1 eCLIP peaks, control- and RBFOX2-STAMP short-read edit clusters, compared to control- and RBFOX2-STAMP long-read (PB) alignments on long, middle and short APA isoforms of the target gene PIGN, with green colored C-to-U conversions on different isoforms.

Extended Data Fig. 4 Comparison of bulk STAMP to single-cell STAMP, related to Figure 4.

a, Overlap between single-cell and bulk RBFOX2-STAMP target genes containing edit-clusters. b, Fraction of RBFOX2-APOBEC1 eCLIP peaks overlapping low and high induction single-cell RBFOX2-STAMP edit-clusters at increasing expression (TPM) thresholds.

Extended Data Fig. 5 Single-cell RBP-RNA interaction detection by STAMP for multiple RBPs and in multiple cell types, related to figure 5.

a, UMAP plot using ε score from RBFOX2-STAMP and TIA1-STAMP mixture with capture sequence RBFOX2-STAMP (blue, n = 844) and TIA1-STAMP cells (red, n = 527) highlighted. b, UMAP plot as in A color-coded by Louvain clustering into RBFOX2-cluster (blue), and TIA1-cluster (red), or background-cluster (gray) populations. c, UMAP plot of gene expression for ε score Louvain clusters defined in B. d, Motif enrichment using HOMER from ≥ 0.99 confidence edits from combined RBFOX2-cluster and control-STAMP cells. e, UMAP plot showing expression of neural precursor cell markers NES, PAX6, SOX2 and DCX. f, Motif enrichment using HOMER from ≥ 0.99 confidence edits from combined control- and RBFOX2-STAMP HEK293T and NPC cells.

Extended Data Fig. 6 Single Ribo-STAMP detects ribosome occupancy from individual cells, related to Figure 6.

a, Genome-wide comparison of CDS+3′UTR EPKM values for bulk and single-cell EPKM-derived RPS2-population. b, Comparison of EPKM-derived RPS2-population CDS and CDS+3′UTR EPKM values. c, Comparison of EPKM-derived RPS2-population total mRNA RPKM values with total mRNA RPKM values from polysome-seq input. d, Comparison of EPKM-derived RPS2-population CDS+3′UTR EPKM values with total mRNA RPKM values from polysome-seq input. e, UMAP analysis of ε score from merged 72-hour high-induction RPS2-STAMP (green), control-STAMP (orange) and mixed-cell RBFOX2:TIA1-STAMP (purple) single-cell experiments. f, UMAP plot as in E with only capture sequence RBFOX2-STAMP (blue, n = 844) and TIA1-STAMP cells (red, n = 527) highlighted. d, Individual cell barcode overlap for EPKM-derived and ε score-derived RPS2-populations.

Supplementary information

Reporting Summary

Supplementary Table 1

All control–STAMP edit site coordinates and levels; related to Figs. 1 and 2 and Extended Data Figs. 1 and 2.

Supplementary Table 2

All RBP–STAMP (RBFOX2, SLBP and TIA1) edit site coordinates and levels; related to Fig. 1 and Extended Data Fig. 1.

Supplementary Table 3

Differential gene expression from RBFOX2–STAMP as evaluated by DEseq2; related to Extended Data Fig. 1.

Supplementary Table 4

All Ribo-STAMP (RPS2 and RPS3) edit site coordinates and levels; related to Fig. 2 and Extended Data Fig. 2.

Supplementary Table 5

All control–STAMP and RBFOX2–STAMP edit percentages on final exons of primary and secondary APA isoforms from PB long-read sequencing; related to Fig. 3 and Extended Data Fig. 3.

Supplementary Table 6

Differential gene editing between background population and RBFOX2 and TIA1 populations; related to Fig. 5.

Supplementary Table 7

Differential gene expression between HEK293T cells and NPCs; related to Fig. 5 and Extended Data Fig. 5.

Supplementary Table 8

Differential gene editing between HEK293T cells and NPCs for RBFOX2–STAMP; related to Fig. 5.

Supplementary Table 9

Differential gene editing between background population and RPS2, RBFOX2 and TIA1 populations; related to Fig. 6.

Supplementary Table 10

Oligonucleotides used in this study.

Supplementary Software

Source code and analysis scripts for edit quantification.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Brannan, K.W., Chaim, I.A., Marina, R.J. et al. Robust single-cell discovery of RNA targets of RNA-binding proteins and ribosomes. Nat Methods 18, 507–519 (2021). https://doi.org/10.1038/s41592-021-01128-0

Download citation

Further reading

Search

Quick links

Nature Briefing

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

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