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Decoding post-transcriptional regulatory networks by RNA-linked CRISPR screening in human cells

Nature Methods volume 22, pages 1237–1246 (2025)Cite this article

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Abstract

RNAs undergo a complex choreography of metabolic processes that are regulated by thousands of RNA-associated proteins. Here we introduce ReLiC, a scalable and high-throughput RNA-linked CRISPR approach to measure the responses of diverse RNA metabolic processes to knockout of 2,092 human genes encoding all known RNA-associated proteins. ReLiC relies on an iterative strategy to integrate genes encoding Cas9, single-guide RNAs (sgRNAs) and barcoded reporter libraries into a defined genomic locus. Combining ReLiC with polysome fractionation reveals key regulators of ribosome occupancy, uncovering links between translation and proteostasis. Isoform-specific ReLiC captures differential regulation of intron retention and exon skipping by SF3B complex subunits. Chemogenomic ReLiC screens decipher translational regulators upstream of messenger RNA (mRNA) decay and identify a role for the ribosome collision sensor GCN1 during treatment with the anti-leukemic drug homoharringtonine. Our work demonstrates ReLiC as a powerful framework for discovering and dissecting post-transcriptional regulatory networks in human cells.

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Fig. 1: Development of ReLiC screening.
Fig. 2: Polysome ReLiC identifies regulators of mRNA translation.
Fig. 3: Isoform-specific splicing screens using ReLiC.
Fig. 4: Dissecting cotranslational quality control using chemogenomic ReLiC screening.
Fig. 5: GCN1 regulates cellular responses to an anti-leukemic drug.

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Data availability

All high-throughput sequencing data are publicly available in the NCBI SRA database under BioProject PRJNA1059490. SRA accession numbers with sample annotations are provided in Supplementary Table 5. All other data are publicly available at https://github.com/rasilab/nugent_2024. Source data are provided with this paper.

Code availability

All software used in this study is publicly available as Docker images at https://github.com/orgs/rasilab/packages. Data analysis and visualization code are publicly available at https://github.com/rasilab/nugent_2024. Information not included in the study can be publicly requested at https://github.com/rasilab/nugent_2024/issues.

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Acknowledgements

We thank members of the Subramaniam laboratory, the Basic Sciences Division and the Computational Biology Program at Fred Hutch for assistance with the project and discussions. We thank A. Geballe, C. Lapointe, A. Rajan and B. Zid for feedback on the paper. This research was funded by NIH R35 GM119835 (A.R.S.), NSF MCB 1846521 (A.R.S.), NIH T32 GM008268 (P.J.N.), NIH R37 CA230617 (A.C.H.), NIH R01 CA276308 (A.C.H.) and NIH GM135362 (A.C.H.). This research was supported by the Genomics and Flow Cytometry Shared Resources of the Fred Hutch/University of Washington Cancer Consortium (P30 CA015704) and Fred Hutch Scientific Computing (NIH grants S10-OD-020069 and S10-OD-028685). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Basic Sciences Division and Computational Biology Section of the Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA

    Patrick J. Nugent, Heungwon Park, James N. Yelland, Katharine Y. Chen, Christine Bynum, Grace Quarterman & Arvind Rasi Subramaniam

  2. Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, WA, USA

    Patrick J. Nugent & Katharine Y. Chen

  3. Human Biology Division, Fred Hutchinson Cancer Center, Seattle, WA, USA

    Cynthia L. Wladyka & Andrew C. Hsieh

  4. Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA

    Sayantani Sinha & Stanley C. Lee

  5. Department of Biology, Spelman College, Atlanta, GA, USA

    Christine Bynum & Grace Quarterman

  6. Department of Medicine and Department of Genome Sciences, University of Washington, Seattle, WA, USA

    Andrew C. Hsieh

  7. Department of Biochemistry and Department of Genome Sciences, University of Washington, Seattle, WA, USA

    Arvind Rasi Subramaniam

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  1. Patrick J. Nugent
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Contributions

P.J.N. designed research, performed experiments, analyzed data and wrote the paper. H.P. performed experiments. C.L.W., J.N.Y. and A.C.H. assisted with polysome fractionation experiments. S.S. and S.C.L. performed experiments on the U937 cell line. C.B., G.Q. and K.Y.C. performed gene ontology analyses. A.R.S. conceived the project, designed research, analyzed data, wrote the paper, supervised the project and acquired funding.

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Correspondence to Arvind Rasi Subramaniam.

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Nature Methods thanks Junyue Cao and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Lei Tang, in collaboration with the Nature Methods team.

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Extended data

Extended Data Fig. 1 ReLiC library design and validation.

a. Validation of Cas9 activity in U2OS. sgEYFP and sgCTRL are single guide RNAs targeting EYFP or a non-targeting control, respectively. Each histogram represents fluorescence of 10,000 cells as measured by flow cytometry. ‘Days post Cas9’ refers to days after addition of doxycycline to induce Cas9 expression. b.Comparison of integration into 293T and U2OS landing pads. BFP and mCherry fluorescence were measured for 10,000 cells, depicted as individual points. Proportion of cells that are mCherry+ and BFP- (orange points) is indicated. No cells in either parental control are mCherry+ and BFP-. c. Depiction of cloning scheme for ReLiC library and reporters. d. Distribution of sgRNA-linked barcode counts in mRNA and genomic DNA. e. Number of unique barcodes linked to each sgRNA in ReLiC library. f. Correlation between distinct barcode sets in ReLiC fitness screens. Each point represents a unique sgRNA pair from the ReLiC RBP library. For each sgRNA pair, individual linked barcodes were randomly partitioned into two sets of equal size (or to within a barcode for odd number of detected barcodes). r refers to Pearson correlation coefficient between the barcode sets.

Extended Data Fig. 2 Polysome ReLiC screen for regulators of mRNA translation.

a. Correlation between replicates. Points represent individual sgRNAs in the ReLiC library. Polysome to monosome ratios are median-centered across sgRNAs in the library. r refers to Pearson correlation coefficient. b. Gene ontology analysis of perturbations that decrease heavy polysome to monosome ratio. Gene ontology analysis performed using GOrilla [@Eden2009] and a subset of enriched terms representative of specific gene classes are shown. c. Change in polysome to supernatant ratio for ribosomal protein and ribosome biogenesis genes. Closed circles correspond to gene hits (FDR < 0.05 with 3 or more concordant sgRNAs). d. Comparison of heavy polysome to monosome and heavy polysome to supernatant ratios for selected translation-related factors. e. Polysome profiles of cell lines depleted of screen hits Profiles are normalized by 80S peak height. P/M indicates ratio of area under the curve for polysome fractions to monosome fractions. f. Comparison of heavy polysome to monosome ratio with growth fitness measured by mRNA and genomic DNA barcode seqencing. g. Comparison of heavy polysome to monosome ratio with growth fitness measured by genomic DNA barcode sequencing for gene knockouts in specific groups. Points correspond to genes targeted in the ReLiC-RBP library. Shaded areas correspond to 95% confidence intervals for a linear fit of polysome to monosome ratio to growth fitness within each gene group.

Extended Data Fig. 3 Isoform-specific splicing screen using ReLiC.

a. Number of gene hits that increase the level of the indicated reporter isoform on various days after Cas9 induction. b. Correlation between barcode sets. For each sgRNA, individual linked barcodes were randomly partitioned into two sets, as in Extended Data Fig. 1f. Each point represents a unique gene that was classified as a hit either with barcode Set A or barcode set B. r refers to Pearson correlation coefficient between barcode sets. c. Correlation between relative levels of different mRNA isoforms. Values represent Pearson correlation coefficients for pairwise comparison between the two barcode sets in b. d. Depletion of genomic DNA barcodes corresponding to SF3b complex subunits after Cas9 induction.

Extended Data Fig. 4 Dissecting mRNA quality control using ReLiC.

a. Validation ofβ-globin NMD reporters. Vertical axis represents − ΔΔCt value of indicated reporter mRNA relative to mCherry-Puro control mRNA. Error bars denote standard error of qPCR across 3 technical replicates. b. Gene ontology analysis of perturbations that increase PTC reporter mRNA levels. c. Volcano plot of reporter mRNA levels with dual barcode screen. Each point corresponds to a gene targeted by the ReLiC library. Marker shape and color denotes one of highlighted gene groups. Genes with FDR < 0.05 and belonging to one of the highlighted groups are listed in the legend. Vertical axis indicates P-values from a permutation test as calculated by MAGeCK. d. PTC reporter mRNA levels for individual translation initiation complex subunits. Error bars denote standard deviation across all detected sgRNAs for that gene. Vertical axis indicates P-values from a permutation test as calculated by MAGeCK; ***: P < 0.001, **: 0.001 < P < 0.01, *: 0.01 < P < 0.05; all other genes have P > 0.05. e. Growth fitness after depletion of translation initiation complex subunits.

Extended Data Fig. 5 GCN1 regulates cellular responses to an anti-leukemic drug.

a. Regulation of EYFP reporter levels by GCN2 after HHT treatment. Cell lines were treated with 1 μM GCN2i for 30m prior to 6h of 1 μM HHT treatment. Vertical axis represents the ratio of EYFP reporter barcode counts during indicated treatment compared to the DMSO-treated control in cells expressing indicated sgRNA. b. ZAK-dependent phosphorylation of p38 in HEK293T cells +/- GCN1 treated with HHT. Cells were treated with nilotinib (1 μM) or DMSO for 30 minutes prior to addition of homoharringtonine (1 μM) treatment or DMSO for 1 hour. c. GCN1-dependent changes to endogenous mRNA expression after HHT treatment in U937 cells +/- GCN1. U937 cell lines were treated with indicated HHT concentrations or DMSO as a vehicle control for 6h. Vertical axis represents − ΔΔCt value of either EGR1 or JUN mRNA relative to GAPDH mRNA as measured by RT-qPCR. Error bars denote standard error of qPCR across 3 technical replicates. d. Regulation of endogenous mRNA expression by GCN2 and ZAK after HHT treatment. Cell lines were treated with 1 μM GCN2i or 1 μM of the ZAK inhibitors nilotinib and vemurafenib for 30m prior to 6h of 1 μM HHT treatment. Vertical axis represents − ΔΔCt value of either EGR1 or JUN mRNA relative to GAPDH mRNA as measured by RT-qPCR. Error bars denote standard error of qPCR across 3 technical replicates. e. Polysome profiles of GCN1-depleted and control cell lines after HHT treatment. Cells were treated with 1 μM HHT or DMSO for 1 hour prior to lysis. Polysome lysates were digested with 1 U micrococcal nuclease / μg of RNA prior to sucrose gradient sedimentation. f. Ribosome P-site density on JUN and MYC mRNAs from previous ribosome profiling studies using harringtonine or lactimidomycin to arrest initiating ribosomes.

Source data

Supplementary information

Supplementary Information

Supplementary Methods.

Reporting Summary

Supplementary Table 1

sgRNA pairs and genes targeted in the ReLiC-RBP library.

Supplementary Table 2

Plasmids used for this study.

Supplementary Table 3

Oligonucleotides used for this study.

Supplementary Table 4

Cell lines used for this study.

Supplementary Table 5

SRA accession numbers.

Supplementary Table 6

Read counts for sgRNAs.

Supplementary Table 7

MAGeCK output for sgRNA comparisons.

Supplementary Table 8

MAGeCK output for gene comparisons.

Source data

Source Data Fig. 5

Unprocessed western blot image for Fig. 5d.

Source Data Extended Data Fig. 5

Unprocessed western blot image for Extended Data Fig. 5b.

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Nugent, P.J., Park, H., Wladyka, C.L. et al. Decoding post-transcriptional regulatory networks by RNA-linked CRISPR screening in human cells. Nat Methods 22, 1237–1246 (2025). https://doi.org/10.1038/s41592-025-02702-6

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