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Genome-wide mapping of RNA-protein associations through sequencing

Nature Biotechnology (2025)Cite this article

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Abstract

RNA-protein interactions critically regulate gene expression and cellular processes, yet their comprehensive mapping remains challenging due to their structural diversity. We introduce PRIM-seq (protein-RNA interaction mapping by sequencing), a method for concurrent de novo identification of RNA-binding proteins and their associated RNAs. PRIM-seq generates unique chimeric DNA sequences by proximity ligation of RNAs with protein-linked DNA barcodes, which are subsequently decoded through sequencing. We apply PRIM-seq to two human cell lines and construct a human RNA-protein association network (HuRPA), encompassing >350,000 associations involving ~7,000 RNAs and ~11,000 proteins, including 2,610 proteins that each interact with at least 10 distinct RNAs. We experimentally validate the tumorigenesis-associated lincRNA LINC00339, the RNA with the highest number of protein associations in HuRPA, as a protein-associated RNA. We further validate the RNA-associating abilities of chromatin-conformation regulators SMC1A, SMC3 and RAD21, as well as the metabolic enzyme PHGDH. PRIM-seq enables systematic discovery and prioritization of RNA-binding proteins and their targets without gene- or protein-specific reagents.

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Fig. 1: PRIM-seq experimental pipeline.
Fig. 2: The REILIS procedure.
Fig. 3: HuRPA network.
Fig. 4: RBDs and RNA-binding motifs.
Fig. 5: PHGDH as an RNA-associating protein.

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Data availability

All PRIM-seq sequencing data has been deposited in GEO (GSE270010)93. All RIP-seq sequencing data have been deposited in GEO (GSE270009)94. Source data are provided with this paper.

Code availability

PRIMseqTools and its source code and complete documentation are available at GitHub95. A web interface for downloading, searching and visualizing the HuRPA network is available at https://sysbiocomp.ucsd.edu/prim/.

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Acknowledgements

We thank UCSD IGM Genomics Center for support with sequencing. This work is supported by National Institutes of Health grants R01GM138852 (S.Z.), DP1DK126138 (S.Z.), UH3CA256960 (S.Z.) and R01HD107206 (S.Z.).

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Author notes

  1. These authors contributed equally: Zhijie Qi, Shuanghong Xue.

Authors and Affiliations

  1. Institute of Engineering in Medicine, University of California, San Diego, La Jolla, CA, USA

    Zhijie Qi, Shuanghong Xue & Sheng Zhong

  2. Shu Chien-Gene Lay Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA

    Junchen Chen, Wenxin Zhao, Kara Johnson, John Lalith Charles Richard, Pei Lin & Sheng Zhong

  3. Program in Bioinformatics and Systems Biology, University of California, San Diego, La Jolla, CA, USA

    Xingzhao Wen & Sheng Zhong

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Contributions

Z.Q., S.X., K.J. and S.Z. designed the PRIM-seq technology. S.X. and K.J. generated the PRIM-seq libraries. Z.Q., X.W. and P.L. carried out the data analysis. S.X. carried out the RNA-PLA experiments. J.C. and W.Z. carried out RIP-seq and PHGDH perturbation experiments. Z.Q., S.X. and S.Z. took the lead in writing the manuscript. Z.Q., S.X., J.C., W.Z., P.L., J.L.C.R. and S.Z. contributed to the interpretation of the results, provided critical feedback and helped to shape the research, analysis and manuscript. J.C, W.Z. and K.J. contributed equally to this paper.

Corresponding author

Correspondence to Sheng Zhong.

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Competing interests

S.Z. is a founder and shareholder of Genemo and Neurospan. The other authors declare no competing interests.

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Nature Biotechnology thanks David Tollervey and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 PRIM-seq’s experimental modules.

(a) SMART-display. (b-i) REILIS (Reverse transcription, Incubation, Ligation, and Sequencing).

Extended Data Fig. 2 PRIM-seq data processing.

(a) A cartoon showing the decoding of the protein-end and the RNA-end reads. As the sequencing reads from both ends are always from 5′ to 3′, the protein-end reads are always antisense sequences and the RNA-end reads are alway sense strand sequences. (b) The contingency table for testing the independence of a RNA (RNA A) and a protein (Protein B) from PRIM-seq data. Xij are the read counts. A Chi-square test is constructed from this contingency table for each pair of RNA and protein. (c) Flowchart of PRIMseqTools for processing PRIM-seq data. Adaptor sequences were trimmed (Adaptor trimming) and low-quality reads were removed (Quality filtering). The resulting read pairs were mapped to Refseq genes (Mapping). The read pairs with the two ends mapped to two different genes are retained (Identification of chimeric read pairs) and deduplicated. Non-duplicated chimeric read pairs with one end mapped to the sense strand of a gene and the other end mapped to the antisense strand of a protein-coding gene (RNA/protein end assignment) were used as the input for the Chi-square test (Statistical test).

Extended Data Fig. 3 Comparison of PRIM-seq and eCLIP data.

(a) Binding profiles of RPS3, FUS, SRSF1, and TAF15 on the TARDBP RNA comparing PRIM-seq (red tracks), eCLIP (green tracks), and eCLIP peaks called with Size‐matched Input (SMInput) controls (green bars beneath eCLIP tracks). Five binding sites shared by all four RBPs are consistently detected in both methods. Arrows mark notable site-specific differences: (1) a site bound by RPS3, FUS, and SRSF1 (but not TAF15); (2) a site bound by RPS3, FUS, and TAF15 (but not SRSF1); and (3) a site bound exclusively by FUS. Conversely, eCLIP identified a binding site for RPS3 not detected by PRIM-seq (Arrow 4). (b) Similar analysis on SNRPD3 RNA, where both PRIM-seq (red) and eCLIP (green) consistently identify multiple shared binding sites.

Extended Data Fig. 4 Gene Ontology analysis.

(a, d, f) The number of genes (x axis) in each GO term (dot) is plotted against the significance level (y axis) of this term in the HuRPA proteins. To avoid very general GO terms, we restricted our analysis to Biological Process (BP) terms with no more than 1,000 genes per term (a), and Cellular Component (CC) and Molecular Function (MF) terms with no more than 100 genes per term (d, f). Additionally, we annotated the most notable BP, CC, and MF terms with no more than 1,100, 110, and 110 genes, respectively (a, d, f). (b, e, g) The numbers of RNA-protein associations (RPAs), RNAs, and proteins in the HuRPA subnetworks associated with the most notable GO terms: ‘RNA processing’ (b), ‘cytoplasmic stress granule’ (e), and ‘translation factor activity, RNA binding’ (g). (c) Distribution of ‘RNA processing’ associated HuRPA proteins across different protein classes.

Extended Data Fig. 5 PTBP1-Associated RNAs.

(a) Distribution of PTBP1 target RNAs identified by PRIM-seq, highlighting the subset that overlaps with entries in the RNAInter database (pink) versus novel targets (green). (b) Motif frequency (y-axis) and statistical enrichment (x-axis, –log[p-value]) for each of the two de novo motifs uncovered by PRIM-seq. (c, d) An example depicting the ‘CUCUCUCUGG’ motif (red block) within a PRIM-seq peak (pink track). (e) Alignment of the top de novo motif (top) with the known PTBP1 motif (bottom), underscoring their strong similarity.

Extended Data Fig. 6 RNA-PLA analysis of RNA-protein pairs involving LINC00339 noncoding RNA.

(a–c) Representative images of the tests (protein name + LINC00039) (a), antibody-only controls (protein name + none) (b), other negative controls including no-probe-no-antibody control (none + none), RNA probe-only control (none + LINC00339), and four RNA-protein pairs not included in HuRPA (GFP + LINC0039, CD40 + LINC0039, CD32 + LINC0039, LTBR + LINC0039 (c). Quantification is provided in Fig. 3k. Blue: DAPI staining. Red: RNA-PLA signal. Scale bar = 20 μm. (d) ROC curve plotting the true positive rate (TPR, y-axis) against the false positive rate (FPR, x-axis), using HuRPA as the reference dataset and RNA-PLA as the test dataset, while varying the threshold applied to RNA-PLA signals.

Extended Data Fig. 7 Comparison between RNA Immunoprecipitation Sequencing (RIP-seq) and PRIM-seq data.

(a) Confusion matrix of all human RNA genes categorized according to their identification as SMC1A targets by PRIM-seq (rows) and RIP-seq (columns). (b) Chi-square test results derived from the confusion matrix in (a). (c–f) ROC curves evaluating PRIM-seq data against RIP-seq results for SMC1A, SMC3, RAD21, and HDAC2. RIP-seq-identified targets serve as the reference sets, and varying thresholds on PRIM-seq read counts are applied to define PRIM-seq-identified targets. (g–j) Expanded views of the ROC curves in (c–f), highlighting lower values on the x-axis. (k) Confusion matrix based solely on PRIM-seq reads within the HuRPA network, categorizing reads according to whether the protein-end maps to SMC1A (rows) and whether the RNA-end corresponds to SMC1A RIP-seq–identified target RNAs (columns). (l) Chi-square test results from the confusion matrix shown in (k). (m) Chi-square test results from a restricted version of the confusion matrix in (k), using only PRIM-seq reads involving SMC1A, SMC3, RAD21, and HDAC2 and their respective RIP-seq–identified target RNAs.

Extended Data Fig. 8 RIP-seq and RNA-PLA analysis of PHGDH.

(a) Categorization of PHGDH-associated RNAs in HuRPA by RNA classes. (b) The average RPM (reads per million, y axis) of each RNA gene (dot) in PHGDH RIP-seq vs. the enrichment level (-log10(adjusted p-value), x axis) of PHGDH as compared to IgG. Purple and red dots: RIP-seq identified PHGDH-associated RNAs. Red dots: PHGDH-associated RNAs identified by both RIP-seq and HuRPA. (c) Comparison of PHGDH-associated RNAs in HuRPA and detected by RIP-seq. Odds ratio (y axis) is greater than 1, indicating a strong overlap. Error bar: 95% confidence interval. As the threshold for calling PHGDH-associated RNAs from RIP-seq (x axis) increases, the odds ratio also increases, indicating a stronger overlap. (d) Among the PHGDH-associated RNAs in HuRPA, the RNA-end reads (y axis) of the RNAs detected (blue) by PHGDH RIP-seq are more than the RNAs not detected by PHGDH RIP-seq (orange). (e–h) Representative RNA-PLA images of the PHGDH protein and ATF4 mRNA (e), RNA-probe-only control (f), antibody-only control (g), and no-antibody-no-RNA-probe control (h). Scale bar = 20 μm.

Extended Data Fig. 9 Cellular responses to PHGDH knockdown in HEK293T cells.

Immunofluorescence staining and quantification of autophagosome (a, b), BrdU (c, d), and activated Caspase-3 (aCaspase3) (e,f) in scramble siRNA (Control) and PHGDH-targeting siRNAs (si-1, si-2) treated HEK293T cells. P-values are derived from two-sided t-tests. Error bar: SEM. n: number of replicates. Scale bars = 100 μm.

Extended Data Fig. 10 Cellular responses to PHGDH knockdown in mouse neural stem cells (mNSCs).

P-values are derived from two-sided t-tests. Error bar: SEM. n: number of replicates. Scale bars = 20 μm. (a) PHGDH RNA levels in scramble siRNA (Control) and PHGDH-targeting siRNAs (si-1, si-2) treated mNSCs. (b–g) Immunofluorescence staining and quantification of autophagosome (b, c), BrdU (d, e), and activated Caspase-3 (aCaspase3) (f, g) in scramble siRNA (Control) and PHGDH-targeting siRNAs (si-1, si-2) treated mNSCs. (h–j) Cell morphology analysis. Immunofluorescence staining of Nestin (h), normalized average dendrite length (i), and the number of dendrite intersections (y axis) as a function of distance from the soma (x axis) (j) in scramble siRNA (Control) and PHGDH-targeting siRNAs (si-1, si-2) treated mNSCs. Scale bars = 100 μm.

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The Supplementary Information file contains Supplementary Figs. 1–9 and Tables 1–7.

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Source Data Fig. 5

The Source Data file contains all the uncropped blots for the main Fig. 5f,j,l,n,r,t.

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Qi, Z., Xue, S., Chen, J. et al. Genome-wide mapping of RNA-protein associations through sequencing. Nat Biotechnol (2025). https://doi.org/10.1038/s41587-025-02780-z

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