SPIDR enables multiplexed mapping of RNA-protein interactions and uncovers a mechanism for selective translational suppression upon cell stress

Erica Wolin; Jimmy K Guo; Mario R Blanco; Isabel N Goronzy; Darvesh Gorhe; Wenzhao Dong; Andrew A Perez; Abdurrahman Keskin; Elizabeth Valenzuela; Ahmed A Abdou; Carl R Urbinati; Ross Kaufhold; H Tomas Rube; Jailson Brito Querido; Mitchell Guttman; Marko Jovanovic

doi:10.1016/j.cell.2025.06.042

. Author manuscript; available in PMC: 2025 Aug 30.

Published in final edited form as: Cell. 2025 Jul 22;188(19):5384–5402.e25. doi: 10.1016/j.cell.2025.06.042

SPIDR enables multiplexed mapping of RNA-protein interactions and uncovers a mechanism for selective translational suppression upon cell stress

Erica Wolin ^1,¹⁰, Jimmy K Guo ^2,^3,¹⁰, Mario R Blanco ^2,¹⁰, Isabel N Goronzy ^2,¹¹, Darvesh Gorhe ^1,¹¹, Wenzhao Dong ^4,^5,^6,¹¹, Andrew A Perez ², Abdurrahman Keskin ¹, Elizabeth Valenzuela ¹, Ahmed A Abdou ¹, Carl R Urbinati ⁷, Ross Kaufhold ^4,^5,⁶, H Tomas Rube ⁸, Jailson Brito Querido ^4,^5,^6,^*, Mitchell Guttman ^2,^9,^*, Marko Jovanovic ^1,^9,^12,^*

PMCID: PMC12396178 NIHMSID: NIHMS2099475 PMID: 40701149

SUMMARY

RNA-binding proteins (RBPs) regulate all stages of the mRNA life cycle, yet current methods generally map RNA targets of RBPs one protein at a time. To overcome this limitation, we developed SPIDR (split-and-pool identification of RBP targets), a highly multiplexed split-pool method that profiles the binding sites of dozens of RBPs simultaneously. SPIDR identifies precise, single-nucleotide binding sites for diverse classes of RBPs. Using SPIDR, we uncovered an interaction between LARP1 and the 18S rRNA and resolved this interaction to the mRNA entry channel of the 40S ribosome using cryoelectron microscopy (cryo-EM), providing a potential mechanistic explanation for LARP1’s role in translational suppression. We explored changes in RBP binding upon mTOR inhibition and identified that 4EBP1 preferentially associates with translationally repressed mRNAs upon mTOR inhibition. SPIDR has the potential to significantly advance our understanding of RNA biology by enabling rapid, de novo discovery of RNA-protein interactions at an unprecedented scale.

Graphical Abstract

graphic file with name nihms-2099475-f0001.jpg

In brief

SPIDR, a massively multiplexed method that simultaneously maps dozens of RNA-binding proteins to their RNA targets at single-nucleotide resolution, uncovers new RNA-protein interactions and provides comprehensive insights into RNA regulation throughout the mRNA life cycle.

INTRODUCTION

RNA-binding proteins (RBPs) control all stages of the mRNA life cycle, including transcription, processing, export, translation, and degradation.^1-5 Up to 30% of all human proteins have been proposed to bind RNA^6-10 and mutations in RBPs have been causally linked to various human diseases,^2-4,11 indicative of their importance in cell biology. Yet, we still do not know what specific roles most of these RBPs play because the RNAs they bind remain mostly unknown.

Additionally, there are many thousands of non-coding RNAs (ncRNAs) whose functional roles remain largely unknown^12,13; understanding how they work requires defining the proteins they bind.^13-15 For example, uncovering the mechanism by which the Xist long non-coding RNA (lncRNA) silences transcription on the inactive X chromosome required identifying its Binding to SPEN/SHARP^16-20 – a process that took >25 years after the initial discovery of Xist.¹⁴ Given the large numbers of ncRNAs and putative RBPs identified, and the limited number of these RNA-protein interactions that have been characterized, there is an urgent need to increase the scale at which high-resolution RNA-protein-binding maps can be generated.¹⁴

Currently, the most widely used method to characterize RBP-RNA interactions is crosslinking and immunoprecipitation followed by sequencing (CLIP).^21-26 Briefly, CLIP works by utilizing UV light to covalently crosslink RNA and directly interacting proteins, followed by cell lysis, immunoprecipitation under stringent conditions (e.g., 1M salt washes) to purify a protein of interest, gel electrophoresis, transfer to a nitrocellulose membrane, and excision of the protein-RNA complex prior to sequencing and identification of the bound RNAs. CLIP and its related variants have greatly expanded our knowledge of RNA-RBP interactions and our understanding of gene expression from mRNA splicing to microRNA (miRNA) targeting.^21-26

Yet, CLIP and its variants, with one recent exception,²⁷ are limited to mapping a single RBP at a time. As such, efforts to generate reference maps for hundreds of RBPs in even a limited number of cell types have required major financial investment and the work of large teams working in international consortia (e.g., ENCODE).^23,28,29 Despite these herculean efforts and the important insights they have uncovered, there are critical limitations: (1) only a small fraction of the predicted RBPs have been mapped using genome-wide methods; (2) of these, most have been mapped in only a small number of cell lines (mainly K562 and HepG2). Because RNA-protein interactions are highly cell-type specific, the maps generated within specific cell lines are not directly useful for studying these RBPs within other cell types or model systems (e.g., patient samples, animal models, or perturbations). (3) Because each protein map is generated from an individual experiment, a large number of cells is required to map each individual protein, making scaling to dozens or hundreds of RBPs particularly challenging for studying primary cells, disease models, or other populations of rare cells. Thus, it is critically important to enable the generation of comprehensive RBP binding for any cell type of interest in a manner that is widely accessible.

To overcome these challenges, we developed split-and-pool identification of RBP targets (SPIDR), a massively multiplexed method to simultaneously profile the global RNA-binding sites of dozens to hundreds of RBPs in a single experiment. SPIDR is based on our split-and-pool barcoding strategy that maps multiway nucleic acid interactions using high-throughput sequencing^30-32; the simplified version of split-and-pool barcoding we present here, when combined with antibody-bead barcoding, increases the throughput of current CLIP methods by two orders of magnitude. We used SPIDR to identify the precise, single-nucleotide RNA-binding sites of dozens of RBPs simultaneously and detect changes in RBP binding upon perturbation. We identified several novel protein-binding sites on ribosomal RNAs (rRNAs), including an interaction between LARP1 and 18S rRNA located within the mRNA channel entry site on the 40S small ribosomal subunit, and resolved this structure at 2.8 Å using single-particle cryoelectron microscopy (cryo-EM). This structure provides a potential mechanistic explanation for the role of LARP1 in translational suppression. Finally, we show that 4EBP1 preferentially associates with LARP1-bound mRNAs in an mTOR-dependent manner, which may explain the selective translational repression of specific mRNAs by mTOR signaling.

RESULTS

SPIDR: A highly multiplexed method for mapping RBP-RNA interactions

We developed SPIDR to enable highly multiplexed mapping of RBPs to individual RNAs transcriptome-wide. Briefly, SPIDR involves (1) generating highly diverse antibody-bead pools by coupling individual bead-oligonucleotide conjugates with specific antibodies, (2) performing RBP purification using these antibody-bead pools in UV-crosslinked cell lysates, and (3) linking individual antibodies to their associated RNAs using split-and-pool barcoding (Figures 1A and S1). We recently developed a similar approach for highly multiplexed mapping of DNA-binding proteins.^33,34

Figure 1. — (A) Schematic overview of the SPIDR method. The bead pool is incubated with UV-crosslinked lysate in a single tube. After IP, each bead is uniquely labeled by split-and-pool barcoding. Oligos and RNA molecules and their linked barcodes are sequenced and RNAs are matched to proteins based on their shared barcodes.

(B) RBPs mapped by SPIDR in K562 and/or HEK293T cells. For details, refer to STAR Methods and Table S1.

(C) Example of raw alignment data for the pool (top, all reads) and for individual RBPs (below, reads assigned to specific antibody beads) across the *XIST* RNA. Blocks represent exons, lines introns, and thick blocks are the annotated *XIST* repeat regions (A–E).

(D) Raw alignment data for SLBP, other RBPs and negative control IgG across the *H3C2* histone mRNA. For (C and D), negative control tracks are scaled to the minimum of the positive control tracks.

See also Figures S1 and S2.

SPIDR works as follows:

We generate antibody-bead-oligonucleotide conjugates using a highly modular scheme where each bead is labeled with a specific oligonucleotide tag and a single antibody. Sets of different antibody-bead-oligonucleotide conjugates are combined to generate an antibody-bead pool (Figures 1A and S1). Because this approach does not require direct chemical modification of the antibody, we can utilize any antibody (in any storage buffer) and associate it with a defined sequence by coupling it on a bead-oligonucleotide conjugate using the same efficient coupling procedure utilized in CLIP-based approaches (see STAR Methods and Perez et al.³³).
Using this antibody-bead pool, we perform on-bead immunopurification (IP) of RBPs in UV-crosslinked lysates and associate RNAs and bead-bound oligonucleotides using split-and-pool barcoding, where the same barcode strings are added to both the oligonucleotide tag and immunopurified RNA on the same bead (Figure 1A). The split-and-pool barcoding protocol can be performed without the need for specialized equipment in ~1 h (see STAR Methods).
After library preparation, we sequence barcoded DNA molecules (antibody oligonucleotides and the converted cDNA of bound RNAs) and match antibody oligonucleotides and RNA reads by their shared barcodes. We refer to all reads that share the same barcode as a “cluster” (Figure 1A). We combine all RNA reads from clusters with the same protein identity (specified by the antibody-oligonucleotide) to generate a high-resolution binding map for each protein. The resulting datasets are analogous to those generated by traditional individual CLIP approaches.

To ensure that IPs using a pool of antibodies generate comparable yield and specificity as individual IPs, we purified 10 RBPs in a pool (as done in SPIDR) and separately purified 5 of these RBPs individually (as done in CLIP) and measured the purified proteins by liquid chromatography tandem mass spectrometry (LC-MS/MS). For all RBPs, we observed comparable protein yields between the pooled and individual IPs, but higher specificity of each target protein (i.e., fewer non-targeted proteins) in the pooled IP-MS experiment (Figure S2; Datasets S1 and S2; see STAR Methods, Note 1). We observed similar enrichment within pools containing larger numbers of antibodies. For example, when we performed an IP-MS experiment using a pool of antibodies against 39 RBPs, we observed clear enrichment for 35 of the 39 targeted RBPs (>2-fold enrichment, Figure S1; Dataset S3). The few exceptions were RBPs that were not detected, likely reflecting either a poor antibody or lack of expression in this cell line.

SPIDR accurately maps dozens of RBPs within a single experiment

To evaluate its accuracy, we performed SPIDR in two widely studied human cell lines: K562 and HEK293T cells. We mapped >50 RBPs in each cell line corresponding to a total of 62 unique RBPs involved in key processes across the RNA life cycle, including splicing, processing, and translation (Figure 1B; Table S1, STAR Methods). As negative controls, we included antibodies against epitopes not present in endogenous human cells (GFP and V5), antibodies that lack affinity to any epitope (mouse immunoglobulin G [IgG]), and control beads with no antibody (“empty/sponge”; see STAR Methods, Note 1).

Focusing on the data generated in K562 cells (which were sequenced at greater depth), we obtained a median of 4 oligonucleotide tags per SPIDR cluster with the majority (>80%) of clusters containing tags representing a single antibody type (Figure S1), indicating that there is minimal “crosstalk” between beads in a SPIDR experiment. This specificity enables us to uniquely assign RNA molecules to their corresponding RBPs.

After removing PCR duplicates, we assigned each sequenced RNA read to its associated RBP and identified high confidence binding sites for each RBP by comparing read coverage across an RNA to the coverage for all proteins in the pooled IP (Figure S1; Tables S1 and S2; see STAR Methods for details). Using this approach, we detected the precise binding sites for SAF-A (also called HNRNPU), PTBP1, SPEN (also called SHARP), and HNRNPK on the XIST RNA^17,20,23 (Figure 1C), as well as for stem loop binding protein (SLBP) specifically at the 3′ ends of histone mRNAs²⁹ (Figure 1D).

To explore the reproducibility of these data, we performed two independent replicates of the SPIDR experiment within K562 cells (total of 59 bead populations, including 55 protein targets and 4 [negative] controls, Table S1). We observed high reproducibility between the replicate datasets. Specifically, for the same RBPs in the two replicates we observed the following:

High correlations across high-coverage regions (Pearson R = 0.91 [0.21], median [interquartile range, IQR]; Figure S3A; Table S3), with low correlations observed among different RBPs between replicates (Pearson R = −0.005 [0.17], median [IQR]). The few exceptions were RBPs that are known to interact (e.g., DROSHA and DGCR8).
High correlations across individual RNA features transcriptome-wide (for example, all introns: R = 0.92 [0.08], all exons: R = 0.84 [0.15]; median [IQR], Figures S3B and S3C; Table S3). In both replicates, the targets with lower correlation scores (<0.7, 12/59 for high-coverage regions, 2/59 for all introns, 8/59 for all exons) were negative controls (e.g., IgG, GFP, and V5) or targets with low sequencing coverage (Figures S3B and S3C).
High reproducibility for detected protein-binding locations—such as exons, 5′ UTRs, 3′ UTRs, etc.—within an RNA (Figure S3D) with cosine angle between the 10-component feature binding vectors (e.g., 3′ UTR, CDS, 5′ UTR, etc.) of replicates of 0.995 (0.03) (median [IQR]) for targets (Table S4).

These results demonstrate that SPIDR replicates are highly comparable; for this reason, we merged replicate datasets to increase coverage for subsequent analyses.

To systematically assess the quality, accuracy, and resolution of our SPIDR binding maps and the scope of the SPIDR method, we explored several key features.

Accurate mapping of classical RNPs

We mapped RBPs of diverse function (e.g., those which bind preferentially to RNAs coding for proteins and/or lncRNAs, to introns, exons, miRNAs, etc.) as well as classical ribonuclear protein (RNP) complexes (e.g., the ribosome and spliceosome; Figure 2A). We observed precise binding to the expected RNAs and binding sites. For example, we observed binding of the following:

Figure 2. — (A) RNA-binding patterns of selected RBPs (rows) relative to 100-nt windows across each classical non-coding RNA (columns). Each bin is colored based on the enrichment of read coverage per RBP relative to background.

(B–E) Sequence read coverage for (B) LSM11 binding to U7 snRNA, (C) WDR43, and (D) LIN28B over the 5′ ETS region of 45S RNA, LIN28B binding to let-7 miRNAs, and (E) DROSHA/DGCR8, UPF1, SPEN (SHARP), and TARDBP to their respective mRNAs. For all tracks, “pool” refers to all reads (gray), and individual tracks (teal) reflect reads after assignment to specific antibody beads.

(F) Sequence reads coverage for two distinct antibodies to HNRNPL in a single SPIDR experiment.

Read coverage assigned to the IgG antibody beads (negative control), and ENCODE-generated eCLIP data (bright green) are shown where available.

See also Figures S3.

LSM11 to the U7 small nuclear RNA (snRNA)³⁵ and the telomerase RNA component (TERC)³⁶ (Figures 2A and 2B).
WDR43, a protein that is involved in rRNA processing, to the 45S pre-rRNA and the U3 small nucleolar RNA (snoRNA), which is involved in rRNA modification³⁷ (Figures 2A and 2C).
LIN28B to a distinct region of the 45S pre-rRNA, consistent with recent reports of its role in rRNA biogenesis in the nucleolus³⁸ (Figures 2A and 2C).
NOLC1 (also known as NOPP140), a protein that localizes within the nucleolus and Cajal bodies,^39,40 to both the 45S pre-rRNA (enriched within the nucleolus) and various small Cajal-body-associated RNAs (scaRNAs) (Figure 2A).
DDX52, a DEAD-box protein predicted to be involved in the maturation of the small ribosomal subunit^41,42 and RPS3, a structural protein contained within the small rRNA subunit, to distinct sites on the 18S rRNA (Figure 2A).
FUS and TAF15 to distinct locations on the U1 snRNA^43,44 (Figure 2A).
SMNDC1 to the U2 snRNA⁴⁵ (Figure 2A).
Single-strand break (SSB, also known as La) binding to tRNA precursors consistent with its known role in the biogenesis of RNA polymerase III transcripts^46,47 (Figure 2A).
LIN28B to the let-7 miRNA^48-52 (Figure 2D).
LARP7 binding to 7SK⁵³ (Figure 2A).

Many RBPs bind their own mRNAs to autoregulate expression levels

Many RBPs have been reported to bind their own mRNAs to control their overall protein levels through post-transcriptional regulatory feedback.^54-59 For example, SPEN/SHARP protein binds its own mRNA to suppress its transcription,⁶⁰ UPF1 binds its mRNA to target it for nonsense-mediated decay,⁶¹ TARDBP binds its 3′ UTR to trigger an alternative splicing event that results in degradation of its own mRNA,^62,63 and DGCR8 (together with DROSHA as the microprocessor complex) binds a hairpin structure in DGCR8 mRNA to induce cleavage and destabilization of the mRNA⁶⁴ (Figure 2E). In addition to these cases, we observed autoregulatory binding of proteins to their own mRNAs for over 40% (23) of our targeted RBPs (Figure S4A).

Different antibodies that capture the same protein or multiple proteins within the same complex show similar binding

We considered the possibility that including antibodies against multiple proteins contained within the same complex, or that otherwise bind to the same RNA, within the same pooled sample could compete against each other and therefore limit the utility of large-scale multiplexing. However, we did not observe this to be the case; in fact, antibodies against different proteins known to occupy the same complex displayed highly comparable binding sites on the same RNAs. For example, microprocessor complex proteins DROSHA and DGCR8 showed highly consistent binding patterns across known miRNA precursors with significant overlap in their binding sites (865-fold enrichment relative to the expected overlap, hypergeometric p value < 10⁻⁴⁰, Figure S4B). Similarly, when we included two distinct antibodies targeting HNRNPL, we observed highly comparable binding profiles for both (38-fold enrichment relative to the expected overlap, hypergeometric p value < 10⁻⁴⁰; 90% peak overlap, binding preference cosine angle 0.975, Figures 2F and S4B). Taken together, our results indicate that SPIDR can be used to map different RBPs that bind to the same RNA targets and can successfully map multiple antibodies targeting the same protein. As such, SPIDR may be a particularly useful tool for directly screening multiple antibodies targeting the same protein to evaluate utility for use in CLIP-like studies.

SPIDR generates maps highly comparable with CLIP maps

We benchmarked our SPIDR results directly to those generated using eCLIP by comparing the profiles for each of the RBPs that are present in both SPIDR and ENCODE eCLIP datasets^23,28,29 (48 proteins, see Table S2 and STAR Methods). On visual inspection, we observed similar binding patterns between the SPIDR and ENCODE datasets for multiple proteins. For example: HNRNPK binding to POLR2A (Figure 3A), PTBP1 binding to AGO1 (Figure 3B), RBFOX2 to NDEL1 (Figure 3C), and the binding of several known nuclear RBPs to XIST (Figure 3D). To globally quantify this, we computed the transcriptome-wide correlations across individual RNA features (e.g., introns, exons, CDS, 5′ and 3′ UTRs) and ncRNAs (see STAR Methods). We found that SPIDR and ENCODE datasets were highly correlated across all RNA features (introns: R = 0.849 [0.05]; exons: R = 0.761 [0.05]; ncRNAs: R = 0.831 [0.06]; median [IQR]; Figure 3E; Table S3). In addition, we found comparable protein-binding preferences—such as exons, 5′ UTRs, 3′ UTRs, etc.—within an RNA (cosine angle = 0.843 [0.45], median [IQR] for SPIDR versus ENCODE between the 10-component feature binding vectors; 18/48 proteins with cosine angle > 0.95; Figures 3F and S4C; Table S4).

Figure 3. — (A–C) Sequence reads coverage for individual proteins measured by ENCODE (green) and SPIDR (teal) along with a negative control (IgG) for HNRNPK, PTBP1, and RBFOX2.

(D) Comparison of ENCODE and SPIDR data for PTBP1, HNRNPU (SAF-A), and HNRNPK bound to the *XIST* lncRNA.

(E) Violin plots of transcriptome-wide correlations between ENCODE and SPIDR data across individual RNA features and non-coding RNAs (e.g., 18S rRNA, and U1 snRNA). Top: schematic of RNA transcript pre- and post-processing with individual features labeled. Center: individual points within violin plots represent the correlation coefficients for each of the 48 proteins, which were measured in both ENCODE eCLIP and SPIDR experiments (Pearson R correlation coefficients of log₁₀ (read coverage + 1), Table S3). Lines within violins represent 1^st, 2^nd, and 3^rd quartiles, respectively. Bottom: representative scatterplots of log₁₀ (read coverage + 1) in SPIDR versus ENCODE over selected RNA features for individual proteins.

(F) Stacked bar plots showing the percentage of peaks detected in the SPIDR (S) or ENCODE (E) datasets to indicate binding site preference to RNA features for selected proteins. The cosine angle between the annotation vectors for SPIDR versus ENCODE is shown on top.

(G) *De novo* RNA-binding motifs identified within SPIDR versus eCLIP datasets for selected proteins. The pair of motifs with the highest correlation similarity match score (right column) and the relative rank of the motif (adjacent to motif) is displayed for each.

See also Figures S4.

Finally, we generated de novo motifs from regions that were consistently bound by each RBP in the SPIDR or ENCODE datasets (using an irreproducible discovery rate⁶⁵ between replicate experiments for each dataset to determine the input regions, see STAR Methods). Focusing on the proteins that generated any significant motifs in both the SPIDR and ENCODE datasets (20 proteins), we observed comparable motifs for nearly all proteins (overall comparison score = 1 [0.16], median [IQR]; 18/20 with a comparison score > 0.6; see STAR Methods), with the identical motifs detected for the majority (11/20) (see STAR Methods and Figures 3G and S4D and Table S5). Taken together, SPIDR data show strong agreement with eCLIP data across all metrics investigated.

In summary, our data demonstrate that SPIDR generates reproducible datasets, comparable to eCLIP, and can simultaneously map numerous RBPs representing diverse functions and binding modalities, including RBPs that bind within thousands of RNAs (e.g., CPSF6), only a few very specific RNAs (e.g., SLBP), primarily within intronic regions (e.g., PTBP1), or primarily to exonic regions (e.g., UPF1).

SPIDR maps the structural organization of protein-RNA interactions at single-nucleotide resolution

Mapping the high-resolution, single-nucleotide binding sites of RBPs on RNA can provide important functional mechanistic insights. For example, in previous work, we mapped the SARS-CoV-2 NSP1 protein to a precise location on the 18S rRNA located within the mRNA entry channel, which provided key insights into the mechanism by which the virus disrupts host mRNA translation.⁶⁶ Because SPIDR utilizes UV-crosslinking, which creates a covalent adduct at the site of RBP-RNA crosslinking, reverse transcriptase should preferentially terminate at these sites. Accordingly, we explored whether we could use this information to map RBP-RNA interactions at single-nucleotide resolution and potentially reveal novel mechanistic insights.

We first computed the number of reads that end at each position of an RNA (truncations) and compared these counts with the truncation counts in all other targets in the pooled IP. We focused on 3 RBPs that bind to distinct but well-defined sequence motifs within different locations of mRNAs: HNRNPC, a splicing regulator that binds within specific intronic sequences of mRNAs through recognition and binding to RNA containing the HUUUUUK sequence motif; PTBP1, a splicing regulator that binds within introns containing the HYUUUYU sequence motif; and LARP1, a translation factor that binds to the 5′ UTR of mRNAs containing a terminal oligopyrimidine (TOP) motif (YYYYY sequence motif). For each of these proteins, we observed specific enrichment within individual mRNAs with reads preferentially terminating immediately proximal to their well-known motif sequences²⁹ (Figure 4).

Figure 4. — (A) Schematic showing how reverse transcription pause sites can be used to map RBP-RNA interactions at single-nucleotide resolution.

(B) HNRNPC-binding sites for *STRN3* (left) and *MRPL52* (right). Both raw read alignments (“Reads,” top) and 3′ -end truncations of the cDNA (“Truncations,” bottom) are shown. The lower two panels are zoomed-in on the indicated region. The known binding motifs for HNRNPC are depicted in magenta.

(C) Truncation frequency (3′ ends of the mapped cDNA reads) fold change over all significantly enriched HNRNPC peaks is shown centered on the motif position. The region of the steep frequency fold change rise of truncations is shown by the pink line and corresponds to the sequence shown in pink.

(D) Examples of PTBP1-binding sites on *PTBP1* (left) and *XIST* (right).

(E) Truncation frequency fold change over all significantly enriched PTBP1 peaks relative to the motif position within each peak.

(F) Examples of LARP1 binding for mRNAs containing TOP motifs in their 5′ UTRs – *TPT1* (left) and *RPS8* (right). For *RPS8*, we show the transcription start site (TSS) that is most prevalent in our RNA-seq data (input sample from CLAP data, see STAR Methods), rather than the annotated RefSeq TSS. The TSS shown corresponds to the following *RPS8* RNA variant – Ensembl Transcript ID: ENST00000372209.3.

(G) Truncation frequency fold change over canonical TOP-motif-containing mRNAs (as previously published⁶⁷) is shown relative to the 5′ end of the RNA.

Having determined that SPIDR accurately detects known RBP-RNA interactions with high precision on a global scale, we next explored its ability to detect novel interactions between RNA and specific RBPs. Because of the importance of protein-RNA interactions in ribosome structure and function, we explored the RBPs in our panel that bind to rRNA. We confirmed known RBP-rRNA interaction sites, identified novel binding sites for known ribosomal binding RBPs, and identified novel ribosomal binding proteins. Specifically, we observed the following:

(1) Binding of RPS2 and RPS6 (distinct structural components of the small rRNA subunit) at the precise locations where they are known to contact the 18S rRNA in the resolved ribosome structure⁶⁸ (Figure 5A), indicating that SPIDR accurately detects these known binding interactions with high accuracy and precision.

(2) The known binding sites of eIF3B (a key translational initiation protein) on the 18S rRNA corresponding to the solvent-exposed side of the mRNA entry site of the 40S ribosomal subunit⁶⁸ (Figure 5B). Interestingly, we also observed binding of eIF3B not just to this well-established binding site⁶⁸ but also to a distinct site corresponding to the intersubunit interface on rRNA helix 44, (Figure 5B). A similar relocation of eIF3B on the ribosome was previously described in Saccharomyces cerevisiae,^69,70 where it was proposed to play a crucial role in ensuring the fidelity of start codon selection. The observation of this relocation in mammals suggests that this role for eIF3B in ensuring the fidelity of start codon selection is a conserved role across eukaryotes.

(3) Two binding sites for eIF4A on the 18S rRNA of the 40S small ribosomal subunit (Figure 5B). The first binding site is located near the mRNA channel entry site, while the second is positioned on the opposite side, close to the mRNA channel exit site. These findings align with a recent structural study of the human translation initiation complex, which showed that, in addition to the eIF4A molecule associated with the eIF4F complex near the mRNA exit channel,⁶⁸ a second eIF4A is present at the mRNA entry channel where it may act to unwind mRNA secondary structures to facilitate ribosome movement along the 5′ UTR of mRNA.⁷¹

In addition to detecting the interactions of well-established ribosomal proteins, we identified novel ribosomal binding proteins and their interactions at specific sites on rRNAs (Figures 5 and S5A). For example, we observed binding of SLBP (a protein that binds specifically to the 3′ end of non-polyadenylated histone mRNAs) to the 18S rRNA at the helix 16 region that is near the mRNA entry channel (Figure 5B). Interestingly, a previous study has shown the importance of helix 16 for translation of the histone H4 mRNA.⁷² Our data suggest that SLBP might be required for this increased translational efficiency of histone mRNAs by positioning the start codon of the histone mRNA in the ribosome.

LARP1 binds in the mRNA entry channel of the 40S ribosome

We also observed an interaction between LARP1 and 18S rRNA at nucleotides 1,698–1,702, which corresponds to a position within the 48S structure that is directly adjacent to the mRNA entry channel (Figures 5B and 5C). We explored this novel interaction of LARP1 in more detail because its role in translation has been debated—it has been reported to both promote and repress translation of TOP-containing mRNAs.^67,73-78 We performed IP-MS on LARP1 and observed significant enrichment of nearly all 40S ribosomal proteins but not 60S-specific ribosomal proteins (Figure S5B), consistent with previous reports that LARP1 is found in the same polysome fractions as 40S ribosomal subunits.^78,79 These data suggest that the LARP1-18S interaction occurs within the 40S complex.

To investigate the detailed structural interaction between LARP1 and the 40S ribosome, we purified recombinant human LARP1 and used it to assemble the human 40S-LARP1 complex. We then used single-particle cryo-EM to determine the structure of the human 40S-LARP1 complex to an overall resolution of 2.7 Å (Figures 6A-6I and S6A-S6C; see STAR Methods). Notably, the cryo-EM reconstruction reveals an additional density within the mRNA channel of the 40S that has not been observed in any known structure of human ribosomal complexes. A three-dimensional (3D) focus classification targeting the head of the 40S followed by an additional focus classification on the additional density within the mRNA channel yields a cryo-EM map with an overall resolution of 2.8 Å, resolving this density to ~2.2 Å (Figures 6D-6H). This improved local resolution enabled us to assign it to LARP1 and build the atomic model de novo.

Figure 6. — (A–C) Overall view of the 40S-LARP1 structure in three orientations. LARP1 (dark blue) fitted into the cryo-EM map (light blue) to highlight its position within the mRNA channel. Although the full-length protein was used for *in vitro* reconstitution, the cryo-EM structure revealed only the core region that directly interacts with the mRNA channel in the 40S ribosomal subunit; remaining domains of LARP1 remain unresolved due to their high flexibility.

(D and E) Cryo-EM map and atomic model to highlight the interaction between LARP1 and ribosomal proteins uS3 and eS30 located at the mRNA entry site.

(F and G) LARP1 interacts with the ribosomal protein uS5 within the mRNA channel.

(H and I) Close-up to highlight the interaction between LARP1 and the universal conserved decoding bases in the P site and the A site, as identified by SPIDR.

(J) Sucrose gradient fractionation followed by western blotting of WT LARP1 and a version with the ribosome-binding region (RBR) mutated.

See also Figures S6.

The structure indicates that LARP1 binds to the mRNA channel in the 40S, where it interacts with ribosomal proteins uS3, uS5, and eS30 (Figures 6E-6I). Notably, the binding of the ribosome-binding region (RBR) of LARP1 (amino acids 652–697) within the mRNA channel extends from the rRNA helix 16 (h16) at the mRNA entry site to the decoding center of the ribosome, where it interacts with the universal conserved decoding bases C1698 and C1701 in the A- and P-sites, respectively. This corresponds precisely to the interaction sites mapped by SPIDR (Figures 5B and 5C).

To determine the role of this interaction in stabilizing LARP1 binding to the 40S ribosomal subunit, we generated a mutant of LARP1 lacking part of the RBR (amino acids 660–697) and measured its ability to form a complex with 40S. Consistent with the structure, we find that this mutation abolishes the ability for LARP1 to co-migrate with 40S in a sucrose density gradient (Figure 6J). A complementary study recently resolved the structure of LARP1 and 40S and observed the same binding domain of LARP1 and same locations on the 18S rRNA.⁸⁰ Superimposing our cryo-EM structure with structures of a translating ribosomal complex reveals a steric clash with both the A- and P-site tRNAs. Moreover, the position of LARP1 in our structure is not compatible with the accommodation of mRNA into the channel, which strongly indicates that LARP1 binds to a 40S subunit when the mRNA channel is empty (Figures S6D and S6E). Consistent with this, we observe a large head swivel movement of the 40S when bound to LARP1 (Figure S6F) that is identical to that seen in the structure of 40S bound to the human tumor suppressor protein PDCD4 (Figures S6G and S6H), which also binds to the mRNA channel to suppress translational initiation.⁸¹

Because LARP1 binding to 40S appears to be incompatible with active translation, yet LARP1 is bound to 5′-TOP mRNAs that are highly translated in normal physiological contexts, the TOP-motif might act to evict LARP1 from the entry channel to enable selective translation of these mRNAs. Consistent with this, the LARP1 domain that binds to 40S (RBR) is distinct from the domain previously shown to be essential for binding to TOP-containing mRNAs (DM15).⁸² This eviction mechanism would be analogous to that observed for eviction of SARS-CoV-2 encoded protein NSP1 from the mRNA entry channel when bound at viral mRNAs containing a viral SL1 stem loop.⁶⁶

4EBP1 preferentially associates with LARP1-bound mRNAs upon mTOR inhibition

Translation of TOP motif-containing mRNAs is selectively repressed upon inhibition of the mTOR kinase, which occurs in conditions of physiological stress.^83-86 Recent studies have shown that under these conditions, LARP1 binds the 5′ UTR of TOP-containing mRNAs, and it has been postulated that this binding activity is responsible for the specific translational repression of these mRNAs.^67,87 Yet, LARP1 binding alone does not appear to suppress translation because these same mRNAs are bound by LARP1 even in the presence of mTOR. Accordingly, the mechanism by which LARP1 binding might repress translation remains unknown.

The canonical model of translational suppression by mTOR inhibition involves selective phosphorylation of 4EBP1^84,86: phosphorylated 4EBP1 (i.e., in the presence of mTOR) cannot bind to eIF4E, the critical initiation factor that binds to the 5′ cap of mRNA and recruits the remaining initiation factors through direct binding with eIF4G.^88,89 Unphosphorylated 4EBP1 (i.e., in the absence of mTOR) binds to eIF4E and prevents it from binding to eIF4G and initiating translation. While this differential binding of 4EBP1 to eIF4E upon mTOR modulation is well established, precisely how it leads to selective modulation of TOP mRNA translation has remained unclear. Specifically, direct competition between 4EBP1 and eIF4G for binding to eIF4E should impact translation of all eIF4E-dependent mRNAs, yet the observed translational downregulation preferentially impacts TOP-containing mRNAs,^84,86,90 and this specificity is dependent on LARP1 binding.⁶⁷

To explore this, we treated HEK293T cells with Torin1, a drug that inhibits mTOR kinase.⁹¹ To ensure that Torin1 treatment leads to robust inhibition of mTOR in these cells, we quantified protein phosphorylation and protein levels in Torin1-treated and untreated cells using quantitative MS. Consistent with successful inhibition of mTOR, we observed reduction in phosphorylation of known mTOR-dependent protein targets, including 4EBP1 and eIF4G1 (see STAR Methods). More generally, upon Torin1 treatment, we observed a striking reduction in the level of proteins encoded from the strongest TOP-motif-containing mRNAs, even though the level of most proteins did not change (Figure 7B; Table S6; Dataset S4).

We adapted SPIDR to map multiple independent samples within a single split-and-pool barcoding experiment (Figure 7; Table S1, see STAR Methods) and used this approach to perform SPIDR on >50 distinct RBPs, including LARP1, numerous translational initiation factors, and 4 negative controls in both Torin1-treated and untreated conditions.

To explore changes in RBP binding upon mTOR inhibition, we measured the number of RNA reads observed for each protein upon Torin1 treatment relative to control. While the majority of proteins showed no change in the number of RNA reads, the sole exception was 4EBP1, which showed a dramatic increase (~20-fold) in the overall number of RNA reads produced upon mTOR inhibition (Figure 7C). Although the number of 4EBP1 reads increased for all RNAs, the largest increase was observed at mRNAs containing a TOP-motif (p value < 1 × 10⁻⁵, Mann-Whitney, Figures 7D and 7E). Interestingly, this preferential association of 4EBP1 corresponds to the 5′end of mRNAs (Figure S7A). We confirmed that this increased RNA coverage cannot be explained by differential 4EBP1 protein capture in these two conditions (Figure S7B; Dataset S5).

Notably, this increase did not simply reflect an increase in observed contacts of 4EBP1 with mRNA but instead corresponded to a nearly complete absence of RNA association in the presence of mTOR activity (control samples) and strong RNA association only upon mTOR inhibition. We confirmed this Torin1-dependent increase in 4EBP1 association in an independent SPIDR experiment (Table S1; Figure S7C) and by purification in denaturing conditions of an expressed Halo-tagged 4EBP1 (covalent linkage and affinity purification [CLAP]^20,66,92) (Figures S7D and S7E). Consistent with these observations, a previous study observed that upon mTOR inhibition, 4EBP1 localizes in proximity to translationally suppressed mRNAs.⁹³ It remains possible that the increased association of 4EBP1 with mRNA upon mTOR inhibition measured with SPIDR is the result of close proximity of 4EBP1 binding to eIF4E on the mRNA and not due to interactions with specific mRNA sequences.

In contrast to 4EBP1, we did not observe a global change in the number of RNA reads purified by LARP1 upon mTOR inhibition (Figure 7C). Indeed, in both Torin1-treated and untreated samples we observed strong binding of LARP1 to TOP-motif mRNAs and to the 18S rRNA, suggesting that this interaction with the 40S ribosome and TOP mRNAs occurs independently of mTOR activity. However, we did observe an ~2-fold increase in LARP1 binding to 18S rRNA (Figure S7F) and TOP mRNAs (p value < 1 × 10⁻⁵, Mann-Whitney, Figures 7D and 7F) upon mTOR inhibition. This increased enrichment may reflect the fact that the LARP1 complex is more stably associated with the 40S and each TOP-containing mRNA due to translational repression. Consistent with the global decrease in translation observed upon mTOR inhibition, we observed that the two translation initiation factors we measured—eIF4A and eIF3B—showed reduced binding to TOP-containing mRNAs upon Torin1 treatment (Figures S7G and S7H).

Taken together, our results suggest a model by which LARP1 achieves selective mTOR-dependent translational repression (Figure 7G). Specifically, LARP1 binds to the 40S ribosome and the 5′ UTR of mRNAs containing a TOP motif regardless of mTOR activity. In the presence of mTOR (Figure 7G, right side), this dual binding may act to evict LARP1 from the 40S and enable translation of these mRNAs. In the absence of mTOR (Figure 7G, left side), 4EBP1 associates with TOP-containing mRNAs, potentially via the LARP1 protein already bound to these mRNAs and/or directly via its known eIF4E interaction. Indeed, many of the mRNAs associated with 4EBP1 are also bound by LARP1 under Torin1 treatment (enrichment ratio of 15.8-fold, hypergeometric p value < 10⁻²⁰). By preferentially associating with TOP-containing mRNAs, 4EBP1 can bind to eIF4E and prevent formation of the full eIF4F complex on the mRNA, a necessary requirement for initiation of translation. In this way, LARP1/4EBP1 binding to specific mRNAs would enable sequence-specific repression of mRNA translation. While it remains possible based on our data that 4EBP1 and LARP1 bind to TOP-containing mRNAs in a mutually exclusive manner, this alternative model is inconsistent with previous demonstrations that loss of either 4EBP1 or LARP1 leads to translational de-repression of TOP mRNAs upon mTOR inhibition.^77,94 Our data, in conjunction with previous functional studies, suggest a model where binding of 4EBP1 and LARP1 together to certain mRNAs confers selective translational inhibition (Figure 7G).

DISCUSSION

Here, we present SPIDR, a massively multiplexed method to generate high-quality, high-resolution, transcriptome-wide maps of RBP-RNA interactions. SPIDR can map RBPs with a wide-range of RNA-binding characteristics and functions and will enable the study of diverse RNA processes within a single experiment and at an unprecedented scale.

We show that SPIDR can accurately map dozens of RBPs within a single experiment, but we expect that this approach can readily be applied to pool sizes of hundreds or thousands of proteins. For example, in our recent ChIP-DIP (chromatin immunoprecipitation done in parallel) paper, which describes a related approach for mapping protein-DNA interactions, we map > 225 proteins in a single experiment.³³ Because of this, we expect SPIDR will be a critical technology for exploring the many thousands of putative RBPs^6-10 and for assessing the putative functions of the > 20,000 ncRNAs that remain largely uncharacterized.

Because the number of cells required to perform SPIDR is comparable to that of a traditional CLIP experiment, yet a single SPIDR experiment reports on the binding behavior of dozens (and likely hundreds) of RBPs, this approach dramatically reduces the number of cells required to map an individual RBP. Accordingly, we anticipate that SPIDR will be a valuable tool for studying RBP-RNA interactions in many different contexts, including within rare cell types and patient samples where large numbers of cells may be difficult to obtain.

We showed that SPIDR generates single-nucleotide contact maps that accurately recapitulate contacts observed within structural models. Accordingly, SPIDR is well suited to add high-resolution binding information for entire RNP complexes as it allows for simultaneous mapping of all proteins within a complex. Because SPIDR can detect changes in binding of RBPs within RNP complexes—such as increased LARP1 binding to the 18S rRNA upon mTOR inhibition, we envision that it will help elucidate the dynamics of various RNP complexes, including for mapping proteins that are not currently resolved within these structures or structural conformations that might be difficult to capture by traditional structural methods.

Beyond measuring multiple proteins, because of the nature of the split-and-pool barcoding strategy used, SPIDR also allows for mapping multiple samples within a single experiment. The ability to simultaneously map multiple proteins across multiple samples will enable exploration of RBP-binding patterns and their changes across diverse biological processes and disease states. Until now, systematic comparative studies of RBP-RNA interaction changes at scale have been challenging, even for large consortia (e.g., ENCODE). Our 4EBP1 results highlight the critical value of SPIDR for enabling exploration of RBP dynamics across samples. Specifically, 4EBP1 was not commonly thought to directly bind to mRNA, yet including 4EBP1 within our larger pool of target proteins allowed us to uncover changes across two different experimental conditions that may explain how specificity of mTOR-mediated translational suppression is achieved.

Although we focused here on the differential RNA-binding properties of 4EBP1 and LARP1, we expect that many additional insights into RBP biology can be uncovered from further exploration of this dataset. For example, we identified several additional proteins that bind to the ribosome that may provide new mechanistic insights into specialized translation. Beyond translation, our data may provide key insights into other mechanisms. For example, we observed that TARDBP (TDP43) shows strong binding to U6 snRNA and to multiple scaRNAs, a class of ncRNAs that play critical roles in spliceosome-associated snRNA biogenesis.⁹⁵ TDP43 is an RBP of great interest because of its well-known genetic link to various neurodegenerative disorders, such as amyotrophic lateral sclerosis (ALS).^96-99 These observations could provide new mechanistic insights into how disruption of this RBP impacts splicing changes and pathogenesis in neurodegeneration.

Thus, we expect that SPIDR will enable a fundamental shift for studying mechanisms of transcriptional and post-transcriptional regulation. Rather than depending on large consortium efforts to generate reference maps within selected cell types, SPIDR enables any standard molecular biology lab to rapidly generate a comprehensive and high-resolution genome-wide map within any cell-type or experimental system of interest without the need for specialized training or equipment.

Limitations of the study

Because SPIDR is dependent on antibodies, the protein yield (i.e., amount of protein obtained from cells) and target specificity (i.e., amount of protein of interest versus non-target proteins) depend on the quality of the antibody used. While this issue affects all antibody-based methods (not just SPIDR), in this context it could lead to reads associated with a protein that are due to capture of other non-target proteins. Although SPIDR does not attempt to address this antibody issue, we note that careful validation of antibodies is important to interpreting the results of this assay or any antibody-based method.

Because all antibodies are pooled together in SPIDR, the same IP conditions are applied to all antibodies, and the IP cannot be optimized for every single antibody, as it could potentially be if antibodies were profiled individually via CLIP experiments. Accordingly, antibodies requiring highly specialized IP conditions are not well suited for SPIDR.

Because there are multiple protein, RNA, and antibody-specific factors (e.g., protein level, UV-crosslinking efficiency, and antibody quality) that influence the detected interaction patterns of different proteins, direct comparisons between proteins profiled in a single SPIDR experiment are challenging. In contrast, interactions of the same protein and RNA in different samples (e.g., perturbation versus control) can be compared because the only difference is experimental condition.

Finally, one key difference relative to CLIP is that SPIDR does not include SDS-PAGE separation and purification of the RNA-protein complex, which is often included to improve specificity. While we have not found this to be an issue in our studies, we note that SPIDR can be directly adapted to include SDS-PAGE separation and purification by users in specific contexts where this is considered essential.

We discuss these issues in more detail in STAR Methods, Note 1.

STAR★METHODS

EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

Cell culture

K562 cells (ATCC, CCL-243) and HEK293T cells (ATCC, CRL-3216) were purchased from ATCC and cultured under standard conditions. K562 cells were cultured in K562 media consisting of 1X DMEM (Gibco), 1 mM Sodium Pyruvate (Gibco), 2 mM L-Glutamine (Gibco), 1X FBS (Seradigm), 100 U/mL Penicillin-Streptomycin (Life Technologies). HEK293T cells were cultured in HEK293T media consisting of 1X DMEM media (Gibco), 1 mM MEM non-essential amino acids (Gibco), 1 mM Sodium Pyruvate (Gibco), 2 mM L-Glutamine (Gibco), 1X FBS (Seradigm).

UV-crosslinking

Crosslinking was performed as previously described.²³ Briefly, K562 cells were washed once with 1X PBS and diluted to a density of ~10 million cells/mL in 1X PBS for plating onto culture dishes. HEK293T cells were washed once with 1X PBS and crosslinked directly on culture dishes. RNA-protein interactions were crosslinked on ice using 0.25 J cm⁻² (UV 2.5k) of UV at 254 nm in a Spectrolinker UV Crosslinker. Cells were then scraped from culture dishes, washed once with 1X PBS, pelleted by centrifugation at 330 x g for 3 minutes, and flash-frozen in liquid nitrogen for storage at −80°C.

Torin1 treatment

HEK293T cells were treated at a final concentration of 250 nM Torin1 (Cell Signaling Technology, #14379) in standard HEK293T media for 18 hours prior to UV-crosslinking and harvesting. We verified that Torin1 treatment successfully inhibited mTOR by performing global proteomic measurements after drug treatment (Figure 7B; Table S6) and exploring the phosphorylation changes of well-characterized mTOR affected phosphorylation sites in 4EBP1⁸⁹ and eIF4G1.^100,101

Protein	Phosphosite	Intensity Signal Control Repl. 01	Intensity Signal Control Repl. 02	Intensity Signal Control Repl. 03	Intensity Signal Torinl Repl. 01	Intensity Signal Torinl Repl. 02	Intensity Signal Torinl Repl. 03
EIF4EBP1	Serine 65	14412.1	10033.3	9810.3	Not Detected	Not Detected	Not Detected
EIF4G1	Serine 1231	60894.8	35172.7	45025.5	Not Detected	Not Detected	Not Detected

Open in a new tab

Overview of all performed SPIDR experiments

Experiment	Cell type	Conditions	# of targets (negative controls)	Supplemental Table
K562	K562	1	59 (4)	1, “K562” tab
“CTL” versus “Torin1”	HEK293T	2 (“CTL”, “Torin1”)	58 (4)	1, “Large HEK” tab
“CTL” versus “Torin1” Small Scale	HEK293T	2 (“CTL”, “Torin1”)	13 (3)	1, “Small HEK” tab

Open in a new tab

METHOD DETAILS

Bead biotinylation

1 mL of Protein G Dynabeads (Invitrogen, #10003D) were washed once with 1X PBST (1X PBS + 0.1% Tween-20) and resuspended in 1mL PBST. Beads were then incubated with 20 μL of 5 mM EZ-Link Sulfo-NHS-Biotin (ThermoFisher, #21217) on a HulaMixer for 30 minutes at room temperature. Following NHS reaction, beads were placed on a magnet and 500 μL of buffer was removed and replaced with 500 μL of 1M Tris pH 7.4 to quench the reaction for an additional 30 minutes at room temperature. Beads were then washed twice with 1 mL PBST and resuspended in their original storage buffer until use.

Labeling biotinylated beads with oligonucleotide tags

Unique biotinylated oligonucleotides (Table S1) were first coupled to streptavidin (BioLegend, #280302) in a 96-well PCR plate. In each well, 20 μL of 10 μM oligo was added to 75 μL 1X PBS and 5 μL 1 mg/mL streptavidin. The 96-well plate was then incubated with shaking at 1600 rpm on a ThermoMixer for 30 minutes at room temperature. Each well was then diluted 1:4 in 1X PBS for a final concentration of 227 nM.

For each experiment, the appropriate amount of biotinylated Protein G beads (10 μL beads per capture antibody) was washed once in 1X PBST. Beads were then resuspended in oligo binding buffer (0.5X PBST, 5 mM Tris pH 8.0, 0.5 mM EDTA, 1M NaCl). 200 μL of the bead suspension was aliquoted into individual wells of a 96-well plate, followed by addition of 4 μL of 227 nM streptavidin-coupled oligo to each well. The 96-well plate was then incubated with shaking at 1200 rpm on a ThermoMixer for 30 minutes at room temperature. Beads were then washed twice with M2 buffer (20 mM Tris 7.5, 50 mM NaCl, 0.2% Triton X-100, 0.2% Na-Deoxycholate, 0.2% NP-40), twice with 1X PBST, and resuspended in 200 μL of 1X PBST.

Binding antibody to labeled Protein G beads

2.5 μg of each capture antibody was added to each well of the 96-well plate containing labeled beads in 1X PBST. The plate was incubated with shaking at 1200 rpm on a ThermoMixer for 30 minutes at room temperature. After incubation, beads were washed twice with 1X PBST + 2 mM biotin (Sigma, #B4639-5G), resuspended in 200 μL of 1x PBST + 2 mM biotin, and left shaking at 1200 rpm for 10 minutes at room temperature. All wells containing beads were then pooled together and washed twice with 1 mL 1X PBST + 2 mM biotin. At this stage, each bead in the bead pool contains a single type of capture antibody with a corresponding unique oligonucleotide tag.

Pooled immunopurification

For each experiment, 10 million cells were lysed in 1 mL RIPA buffer (50 mM HEPES pH 7.4, 100 mM NaCl, 1% NP-40, 0.5% Na-Deoxycholate, 0.1% SDS) supplemented with 20 μL Protease Inhibitor Cocktail (Sigma, #P8340-5mL), 10 μL of Turbo DNase (Invitrogen, #AM2238), 1X Manganese/Calcium mix (2.5 mM MnCl₂, 0.5 mM CaCl₂), and 5 μL of RiboLock RNase Inhibitor (Thermo Fisher, #EO0382)). Samples were incubated on ice for 10 minutes to allow lysis to proceed. After lysis, cells were sonicated at 3-4 W of power for 3 minutes (pulses 0.7 s on, 3.3 s off) using the Branson sonicator and then incubated at 37°C for 10 minutes to allow for DNase digestion. DNase reaction was quenched with addition of 0.25 M EDTA/EGTA mix for a final concentration of 10 mM EDTA/EGTA. RNase If (NEB, #M0243L) was then added at a 1:500 dilution and samples were incubated at 37°C for 10 minutes to allow partial fragmentation of RNA to obtain RNAs of approximately ~300-400 bp in length. RNase reaction was quenched with addition of 500 μL ice cold RIPA buffer supplemented with 20 μL Protease Inhibitor Cocktail and 5 μL of RiboLock RNase Inhibitor, followed by incubation on ice for 3 minutes. Lysates were then cleared by centrifugation at 15000 x g at 4°C for 2 minutes. The supernatant was transferred to new tubes and diluted in additional RIPA buffer such that the final volume corresponded to 1 mL lysate for every 100 μL of Protein G beads used. Lysate was then combined with the labeled antibody-bead pool and 1 M biotin was added to a final concentration of 10 mM as to quench any disassociated streptavidin-coupled oligos. Beads were left rotating overnight at 4°C on a HulaMixer. Following immunoprecipitation, beads were washed twice with RIPA buffer, twice with high salt wash buffer (50 mM HEPES pH 7.4, 1 M NaCl, 1% NP-40, 0.5% Na-Deoxycholate, 0.1% SDS), and twice with Tween buffer (50 mM HEPES pH 7.4, 0.1% Tween-20).

We note that several factors will impact the exact protein abundance required to successfully map its interactions, including expression level, how effectively the protein is purified, how many RNAs it binds, the crosslinking efficiency of the protein and target, and the percentage of the protein that binds to RNA. Users can increase the amount of starting material used in a SPIDR experiment (similar to CLIP) if protein levels are low and one can scale the amount of antibody used for specific proteins, which should improve capture of specific proteins.

Ligation of the RNA Phosphate Modified (“RPM”) tag

After immunoprecipitation, 3’ ends of RNA were modified to have 3’ OH groups compatible for ligation using T4 Polynucleotide Kinase (NEB, #M0201L). Beads were incubated at 37°C for 10 minutes with shaking at 1200 rpm on a ThermoMixer. Following end repair, beads were buffer exchanged by washing twice with high salt wash buffer and twice with Tween buffer. RNA is subsequently ligated with an “RNA Phosphate Modified” (RPM) adaptor (Quinodoz et al.³¹) using High Concentration T4 RNA Ligase I (NEB, M0437M). Beads were incubated at 24°C for 1 hour 15 minutes with shaking at 1400 rpm, followed by three washes in Tween buffer. After RPM ligation, RNA was converted to cDNA using SuperScript III (Invitrogen, #18080093) at 42°C for 20 minutes using the “RPM Bottom” RT primer to facilitate on-bead library construction and a 5’ sticky end to ligate tags during split-and-pool barcoding. Excess primer is digested with Exonuclease I (NEB, #M0293L) at 37°C for 15 minutes.

Split-and-pool barcoding to identify RNA-protein interactions

The complexity of the split-and-pool barcode generated depends on the number of individual tags used in each split-and-pool round and the number of split-and-pool rounds. For example, after 8 rounds of split-and-pool barcoding, using 12 barcodes in each round, the likelihood that two beads will end up with same barcode is ~ 1 in 430 million (1/12⁸).

Split-and-pool barcoding was performed as previously described³¹ with minor modifications. Specifically, beads were split-and-pool ligated over ≥ 6 rounds with a set of “Odd,” “Even,” and “Terminal” tags. The number of barcoding rounds performed for each SPIDR experiment was determined based on the complexity of the given bead pool. All split-and-pool ligation steps were performed for 5 minutes at room temperature and supplemented with 2 mM biotin and 1:40 RiboLock RNase Inhibitor to prevent RNA degradation. We ensured that virtually all barcode clusters (>95%) represented molecules belonging to unique, individual beads. See Table S7 for a template to calculate the number of barcoding rounds required to resolve a given number of starting beads.

Compared to previously published approaches, we reduced the number of barcodes per round, but increased the rounds of split-and-pool barcoding as we optimized the ligation step. Therefore, the barcoding procedure was significantly simplified in contrast to previous versions. For example, for the K562 cells pooled experiment, 6 rounds of 24 barcodes were used for combinatorial barcoding (with a scheme of Odd, Even, Odd, Even, Odd, Terminal tag).³⁰ For the HEK293T cells mTOR inhibition experiment, 6 rounds of 36 barcodes were used for combinatorial barcoding to achieve sufficient barcode complexity. Of the 36 barcodes used in round one of the ligations, 18 were used to label the control condition and the remaining 18 were used to label the Torin1 treated condition. The samples were then pooled together for the remaining 5 rounds of ligation.

Library preparation

After split-and-pool barcoding, beads were aliquoted into 5% aliquots for library preparation and sequencing. RNA in each aliquot was degraded by incubating with RNase H (NEB, #M0297L) and RNase cocktail (Invitrogen, #AM2286) at 37°C for 20 minutes. 3’ ends of the resulting cDNA were ligated to attach dsDNA oligos containing library amplification sequences using a “splint” ligation as previously described (Quinodoz et al.³¹). The “splint” ligation reaction was performed with 1X Instant Sticky End Master Mix (NEB #M0370) at 24°C for 1 hour with shaking at 1400 rpm on a ThermoMixer. Barcoded cDNA and biotinylated oligo tags were then eluted from beads by boiling in NLS elution buffer (20 mM Tris-HCl pH 7.5, 10 mM EDTA, 2% N-lauroylsarcosine, 2.5 mM TCEP) for 6 minutes at 91°C, with shaking at 1350 rpm.

Biotinylated oligo tags were first captured by diluting the eluant in 1X oligo binding buffer (0.5X PBST, 5 mM Tris pH 8.0, 0.5 mM EDTA, 1 M NaCl) and subsequently binding to MyOne Streptavidin C1 Dynabeads (Invitrogen, #65001) at room temperature for 30 minutes. Beads were placed on a magnet and the supernatant, containing cDNA, was moved to a separate tube. Biotinylated oligo tags were amplified on-bead using 2X Q5 Hot-Start Mastermix (NEB #M0494) with primers that add the indexed full Illumina adaptor sequences.

To isolate barcoded cDNA, the supernatant was first incubated with a biotinylated antisense ssDNA (“anti-RPM”) probe that hybridizes to the junction between the reverse transcription primer and splint sequences to reduce empty insertion products. This mixture was then bound to MyOne Streptavidin C1 Dynabeads at room temperature for 30 minutes. Beads were placed on a magnet and the supernatant, containing the remaining cDNA products, was cleaned up on Silane beads (Invitrogen, #37002D) as previously described.¹⁰² Finally, cDNA was amplified using 2X Q5 Hot-Start Mastermix (NEB #M0494) with primers that add the indexed full Illumina adaptor sequences.

After amplification, libraries were cleaned up using 1X SPRI (AMPure XP), size-selected on a 2% agarose gel, and cut at either ~300 nt (barcoded oligo tag) or between 300-1000 nt (barcoded cDNA). Libraries were subsequently purified with Zymoclean Gel DNA Recovery Kit (Zymo Research, #4007).

Sequencing

Paired-end sequencing was performed on either an Illumina NovaSeq 6000 (S4 flowcell), NextSeq 550, or NextSeq 2000 with read lengths ≥ 100 x 200 nucleotides. For the K562 data, 37 SPIDR aliquots were generated and sequenced from two technical replicate experiments. The two experiments were generated using the same batch of UV-crosslinked lysate processed on the same day. For the HEK293T data, 9 SPIDR aliquots were generated from a single technical replicate. Each SPIDR library corresponds to a distinct aliquot that was separately amplified with different indexed primers, providing an additional round of barcoding as previously described.³¹ Minimum required sequencing depth for each experiment was determined by the estimated number of beads and unique molecules in each aliquot. For oligo tag libraries, each library was sequenced to a depth of observing ~4-5 unique oligo tags per bead on average. For cDNA libraries, each library was sequenced with at least 2X coverage of the total estimated library complexity.

For the “smaller-scale” SPIDR experiment in HEK293T cells treated with Torin1 or Control (see Table S1), sequencing was performed on the Element Biosciences AVITI system. Briefly, the SPIDR libraries were converted to from their standard Illumina based flow cell binding sequences to be compatible with the AVITI system. This was accomplished by utilizing the Element Biosciences Adept Rapid PCR-Plusw workflow. Briefly, libraries constructed with standard Illumina sequences were were pooled to a final concentration of 2.5 nM in 20 μL. They were subsequently amplified using PCR for five cycles with Adept Rapid PCR Plus primers, cleaned using 1X Ampure beads and re-quantified prior to proceeding with standard dilute and denature procedure for AVITI systems.

Analysis and processing pipeline

Read processing and alignment

Paired-end RNA sequencing reads were trimmed to remove adaptor sequences using Trim Galore! v0.6.2 and assessed with FastQC v0.12.1. Subsequently, the RPM (ATCAGCACTTA) sequence was trimmed using Cutadapt v3.4 from both 5’ and 3’ read ends. The barcodes of trimmed reads were identified with Barcode ID v1.2.0 (https://github.com/mjlab-Columbia/spidr-paper-pipeline) and the ligation efficiency was assessed. Reads with or without an RPM sequence were split into two separate files to process RNA and oligo tag reads individually downstream, respectively.

RNA read pairs were then aligned to a combined genome reference containing the sequences of repetitive and structural RNAs (ribosomal RNAs, snRNAs, snoRNAs, 45S pre-rRNAs, tRNAs) using Bowtie2. The remaining reads were then aligned to the human (hg38) genome using STAR aligner. Only reads that mapped uniquely to the genome were kept for further analysis.

Barcode matching and filtering

Mapped RNA and oligo tag reads were merged, and a cluster file was generated for all downstream analysis, as previously described. MultiQC v1.28 was used to aggregate all reports. To unambiguously exclude ligation events that could not have occurred sequentially, we utilized unique sets of barcodes for each round of split-and-pool. All clusters containing barcode strings that were out-of-order or contained identical repeats of barcodes were filtered from the merged cluster file. To determine the amount of unique oligo tags present in each cluster, sequences sharing the same Unique Molecular Identifier (UMI) were removed and the remaining occurrences were counted. To remove PCR duplication events within the RNA library, sequences sharing identical start and stop genomic positions were removed.

Splitting alignment files by protein identity

Barcode strings from filtered cluster files were then used to assign protein identities to the alignment file containing all mapped RNA reads. Because each cluster represents an individual bead, the frequency of oligo tags (each representing unique protein type) was used to determine protein assignments. For each cluster we required ≥1 observed oligo tags and that the most common protein type represented ≥80% of all observed tags. We observed that read assignment was highly consistent regardless of threshold for number of required oligos (e.g., at least 1 oligo versus a more stringent threshold of at least 3 oligos). RNA reads were then split into separate alignment files by barcode strings corresponding to protein type. Moreover, we applied an additional filter to allow per cluster a maximum of 100 mapped RNA reads. Clusters with more than 100 mapped RNA reads were filtered out.

Background correction and peak calling

To determine what portion of the observed signal is specific to a particular capture antibody, rather than common pileups regardless of the protein captured, we normalized coverage for each protein relative to the coverage detected for all other proteins within the same capture pool. Specifically, for a protein of interest, we computed the number of reads that were mapped to that protein relative to the number of total reads assigned to all other proteins (p). We then computed the observed number of reads within each window across the transcriptome (either 10 nts, 100 nts or 1000 nts) for the protein of interest and the number of total reads across all remaining proteins. We computed a normalized enrichment as the number of observed reads within the window (observed, n) divided by the expected number of read counts if we sampled p reads from the total for the region. This expected read count is distributed as a Poisson where $I = n \times p$ . To determine the expected number, we conservatively estimated the count that exceeded 95% of all values sampled from this distribution (p-value < 0.05). We report enrichment values as the observed counts per region over the 95^th percentile of the Poisson. We also computed a p-value for each region and protein by assigning the percentile of the observed count within the Poisson distribution. For continuous enrichment values throughout the paper, all windows with a p-value less than 0.05 (95th percentile) were considered significantly enriched for our analysis (Figure 2A). For comparison of antibody or protein pairs (e.g. Bethyl vs CST antibodies), we calculated the significance of overlap of enriched 100 nts windows with at least 10 reads, 2-fold enrichment and p-value < 0.05 using a hypergeometric distribution. The advantage of comparing enriched windows is that different proteins either have full or no overlap for each window. For discrete peak analysis, we generated a candidate set of peak regions by merging windows that had a minimum of 10 reads and a p-value less than 0.001 (99.9th percentile) and filtering for regions with an average 2-fold enrichment over the expected.

Irreproducible Discovery Ratio (IDR)

The candidate set of peak regions was then further refined to determine concordant binding regions using irreproducible discovery ratio (IDR). IDR is an approach to measure the reproducibility of findings identified from replicate experiments (see the next two paragraphs for more detail). Specifically, we used the software package idr (https://github.com/nboley/idr). For input, we used the read count per 100 bp window for each replicate and the candidate set of peak regions determined from the merged replicates. We then filtered for an idr threshold of 0.05 to generate a set of concordant binding regions (Table S2). This analysis was performed for each protein the SPIDR K562 and ENCODE eCLIP experiments. In addition, this analysis was performed after down-sampling the SPIDR or eCLIP datasets to equalize read coverage per protein. To check the consistency of concordant regions between SPIDR and eCLIP, we treated SPIDR and eCLIP as pseudo-replicates and performed this IDR analysis using the read count per 100 bp window from SPIDR or eCLIP and the set of candidate binding peaks from the SPIDR experiment as input. Numbers for candidate peak sets and IDR determined concordant binding regions are provided in Table S2.

Analytical considerations for determining concordant binding sites from SPIDR data

As described above, to identify concordant binding sites for each protein, we used the irreproducible discovery rate (IDR) framework. Briefly, the IDR method is designed to measure the reproducibility of finding a binding site in replicate experiments and provides thresholds based on reproducibility of detection, avoiding the need to select an arbitrary threshold. The idea behind IDR is that if two replicates indeed measure the same underlying biology, we would expect that the most significant peaks (true binding events) have high consistency, while less significant peaks (noise) have low consistency. We can find a transition point in consistency, providing the threshold between signal and noise. To do so, the IDR method compares a ranked list of candidate peaks sites between replicates and fits a bivariate rank distribution to separate true binding events from noise. This approach is extensively used by ENCODE (including in the processing of eCLIP data).²⁹ Using this IDR approach, we find that SPIDR and ENCODE perform similarly, with ~10-20% of candidate peaks passing the 0.05 IDR threshold in both cases (SPIDR: median = 0.14, IQR = 0.15; ENCODE: median = 0.20, IQR = 0.15) (Table S2).

To explore the consistency of SPIDR-identified binding sites with eCLIP results, we performed an IDR-like analysis between SPIDR and ENCODE eCLIP data by treating these two datasets as “replicates” of each other to score the reproducibility of SPIDR derived candidate peaks. Like the replicate IDR analysis, we find that ~15% of candidate peaks are identified as consistent (median = 0.16, IQR = 0.36, Table S2). Furthermore, the median peak overlap between SPIDR-ENCODE “replicates” and SPIDR replicate derived peaks is 89% (IQR = 16%). This demonstrates that SPIDR and ENCODE are consistent within the set of peak regions identified solely using SPIDR and supports the notion that these peak regions are true concordant binding events.

RNA feature binding profiles

Sets of candidate peak regions (minimum 10 reads and p-value < 0.001 per 100 bp window, average 2-fold enrichment over the region) were used to compute RNA feature binding profiles for each RBP. These peak regions were then annotated based on overlap with GENCODE v42 transcripts or alignment to the custom structural and repetitive RNA genome. In the case of overlapping annotations, the final assigned annotation was chosen based on the following priority list: miRNA, CDS, 5’UTR, 3’UTR, proximal intron (within 500 nts of the splice site region), distal intron (further than 500 nts of the splice site region), non-coding exon, and finally non-coding intron (both proximal and distal). This annotation was performed using the software annotator (https://github.com/byee4/annotator). Peaks aligned to miRNA and peaks aligned to the custom genome were both considered to be the category non-coding RNA. To compare the RNA feature binding profiles between pairs of samples (e.g., replicates), the cosine angle between the 10-compenent feature vectors (3’UTR, 5’UTR, CDS, noncoding exon, proximal intron, proximal non-coding intron, distal intron, distal-noncoding intron, stop codon, noncoding RNA) of the respective samples was calculated. For replicate comparisons, summary statistics for cosine angle were calculated over all RBP targets, excluding negative controls.

Single-nucleotide resolution analysis

We computed the frequency of reads ending at the 3’ end of the cDNA. We computed enrichment for each of these counts by randomly downsampling all reads not assigned to the specific protein and computing the same 3’ end coverage. Enrichments and p-values were computed as described above and as previously reported in Banerjee et al.⁶⁶

Truncation plots for PTBP1 and HNRNPC were generated using custom scripts that analyzed read truncations centered at their respective motifs.²⁹ The analysis window encompassed 100 nucleotides upstream and downstream of the TSS. Truncation enrichment was calculated by dividing the number of truncations at each position by the expected truncation frequency, assuming an even distribution across the 200-nucleotide window.

Truncation plots for LARP1 were generated using custom scripts that analyzed read truncations centered at the transcription start site (TSS) of canonical TOP mRNAs, as defined by Philippe et al.⁶⁷ The analysis window encompassed 100 nucleotides upstream and downstream of the TSS. Truncation enrichment was calculated by dividing the number of truncations at each position by the expected truncation frequency, assuming an even distribution across the 200-nucleotide window.

Processing of eCLIP datasets

43 of the proteins (2 proteins were targeted twice with different antibodies: PUM1 and HNRNPL) included in our SPIDR experiments were also profiled by ENCODE using eCLIP in K562 cells. To compare the corresponding SPIDR and ENCODE data, we downloaded the raw FASTQ files for these datasets from the ENCODE website (https://www.encodeproject.org/, Table S2) and aligned them to the genome using the same parameters as used for the SPIDR dataset. For 3 additional proteins (FUBP3, SRSF9, and PCBP2) that were only profiled by ENCODE in HEPG2 cells, we compared these data to the corresponding SPIDR data performed in K562. Sets of candidate peak regions were calculated for eCLIP using a similar background correction strategy as above with minor modifications. Specifically, the background was defined as IgG input control from ENCODE instead of the other proteins within the pool (since there is no pool in the case of individual eCLIP experiments).

Transcriptome-wide correlations

Transcriptome-wide correlations of read coverage between SPIDR and eCLIP datasets or between SPIDR replicates (Figure 3E and Figures S3B and S3C, respectively, and Table S3) were calculated as follows: 1) The top 10,000 expressed genes were determined using K562 RNAseq from ENCODE, 2) The annotation features associated with each of these genes (5’UTR, 3’UTR, CDS, proximal intron, distal intron and exon) were collected. Multiple annotation features of a single type (e.g., multiple exons) for the same gene were merged and considered together as one region, 3) Read coverage across all feature regions of a single type were calculated, 4) Feature regions with low coverage in the SPIDR experiment (<10 reads total) were removed, and 5) The Pearson correlation of log₁₀(read coverage + 1) between samples was calculated for each feature type. In addition, correlations were similarly calculated across the set of non-coding RNAs from the custom structural and repetitive RNA genome. Specifically, expressed ncRNAs were first determined by coverage in K562 RNAseq, then read coverage for each ncRNA was calculated, ncRNAs with low coverage (< 10 reads total) in the SPIDR experiment were removed and the Pearson correlation of log₁₀(read coverage +1) was calculated across remaining ncRNAs.

To compute transcriptome-wide correlations of enrichment scores between SPIDR replicates (Figure S3A; Table S3), we defined high coverage regions as 1000 bp windows with nonzero coverage and calculated the Pearson correlation of enrichment (calculated as described above) between pairs of targets accross high coverage regions.

De novo motif discovery

De novo motif discovery was performed for SPIDR and eCLIP datsets using the software HOMER and the command line: “findMotifsGenome.pl targetPeaks.bed hg38_ncRNA.fasta output/target -norevopp -rna -len 5,6,7 -bg top10000_ncRNA.bed -chopify -size given” where targetPeaks.bed are the set of concordant binding regions determined through IDR, hg38_ncRNA.fasta is the merged genome containing both hg38 and the custom structural and repetitive RNA genome used for read alignment and top10000_ncRNA. bed is the set of top 10,000 genes and expressed ncRNAs from the custom genome identified for correlation analysis. Motifs with a p-value of < 1 x 10⁻¹⁵ were considered significant. To determine the similarity of SPIDR and eCLIP motifs, a comparison score was calculated between all pairs of significant motifs using the HOMER function “compareMotifs.pl”. This function uses Pearson correlation coefficient to compare motifs matrices; a score of 0.6 or higher is considered a similar motif. The pair of motifs with the greatest similarity score is displayed in the figures; all significant motifs for RBPs with significant motifs found in SPIDR and eCLIP datasets are listed in Table S5.

mTOR-related analysis

TOP motif binding analysis for the “CTL versus Torin1” SPIDR experiment

To determine if the change in mRNA binding for certain RBPs, such as LARP1 and 4EBP1, upon mTOR inhibition by Torin1 is dependent on the TOP motif being presented in the mRNA target, we looked if RBP binding changes were different for distinct categories of mRNAs. To do this, we generated background corrected bedgraphs from control and Torin1-treated conditions for each RBP. These bedgraph values were then mapped on to Refseq genes using the bedtools map command (arguments: -c 4 -o absmax). Where multiple isoforms were present for the same gene, the isoform with the highest map count was used. To normalize for possible detection bias due to fewer antibody beads in one condition versus the other we adjusted the map value by the ratio of antibody beads as determined by number of bead clusters corresponding to each antibody in each respective condition. Number of antibody (bead clusters) were defined and calculated using the same values used to generate the split bam files for each protein (options: minimum number of oligos=1,fraction unique=0.8, max number of RNAs in clusters=100). The ratio of cluster-corrected values for each gene across the two conditions was then compared per gene and separated based on TOP score. Published TOP scores⁶⁷ were used to generate categories for violin plots.

Normalizing Control/Torin Ratios by PTBP1 for the “smaller scale CTL versus Torin1” SPIDR experiment

For the “smaller scale” SPIDR experiment, we normalized Control/Torin1 ratios to make sure that different read depth for TOP motif containing mRNAs, which are in generally higher expressed, did not skew our conclusions. We used the PTBP1 Control/Torin1 ratios for that as we had good read coverage for PTBP1 in this experiment and PTBP1 is generally not linked to the mTOR pathway. Each BAM file within the smaller scale SPIDR experiment was compared with known TOP-motif scores (citation) at the gene level and grouped into 4 TOP-motif score intervals. The TOP-motif score intervals are [0, 1), [1, 2), [2, 3), [3, inf). For each gene and TOP-motif category, the ratio of the pseudo-count of the control condition and the pseudo-count of the Torin1 condition was calculated. Pseudo-counts were used to avoid divisions by 0 in for cases with 0 reads. Each of these pseudo-count ratios was then normalized by the median value for the corresponding TOP-motif score category in PTBP1 across all genes. These adjusted ratios were used for the violin plots in Figure S7.

Determining if protein changes upon mTOR inhibition are TOP score-dependent

For the protein changes CDF plots, we first selected for the 2000 highest expressed genes based on previous RNA-seq data.⁶⁶ Input TPM values for HEK293 cells were taken from input CLAP (sub_input.merged.bam) data from HEK293T cell in Banerjee et al.⁶⁶ The input samples were downsampled to 20 million reads prior to TPM calculation. Featurecounts was used to calculate read overlaps with hg38 protein coding refseq genes and further converted to TPM values. The top 2000 expressed genes (based on HEK293 input TPM) were used to plot the average protein log₂ fold changes (Torin1 versus control) vs TOP score. Published TOP scores⁶⁷ were used to plot CDF values.

Mass spectrometry

Pooled immunopurification (IP) of 39 RBPs for mass spectrometry

10 million K562 cells were lysed in 1 mL of RIPA on ice for 10 minutes. The lysate was clarified by centrifugation at 15000 g for 2 minutes, and then split in half for either the pooled IP with 39 antibodies or the negative control IP with an anti-V5 antibody. Each half of the lysate was combined with 10 μg total antibody (0.25 μg per each antibody for the pooled IP) and 100 μL of Protein G beads and left rotating at 4°C overnight. The beads were then washed twice with RIPA, twice with High Salt Wash Buffer, twice with Clap-Tween, and finally three times with Mass Spec IP Wash Buffer (150 mM NaCl, 50 mM Tris-HCl pH 7.5, 5% Glycerol). Each sample was then reduced, alkylated, Trypsin digested, and desalted as described in Parnas et al.¹⁰³ Peptides were reconstituted in 12 μL 3% acetonitrile/0.1% formic acid. The corresponding protein quantification data is available in Dataset S3.

Pooled IP of 10 RBPs and corresponding single IPs for mass spectrometry

For each IP 10 million HEK293T cells were lysed in 0.5 mL of lysis buffer (150 mM NaCl, 50 mM Tris pH 7.5, 1% NP-40, 5% Glycerol, Protease (2 μg/ml Apoptin; 10 μg/ml Leupeptin; 1 mM PMSF)) on ice for 20 minutes. The lysate was clarified by centrifugation at 15000 g for 2 minutes. For the pooled IP 2.5 μg of 10 antibodies against 10 RBPs (FASTKD2, EWSR1, NOLC1, HNRNPK, PTBP1, RPS2, LARP1, SLBP, 4EBP1, LARP7) was combined lysate and 100 μL of Protein G beads and left rotating at 4°C overnight. For the 5 single IPs (EWSR1, HNRNPK, PTBP1, RPS2, LARP1) 2.5 μg of antibody against was combined lysate and 100 μL of Protein G beads and left rotating at 4°C overnight. For the negative control IP 2.5 μg of total antibodies (an equal mix of antibodies against V5, GFP and IgG) was combined lysate and 100 μL of Protein G beads and left rotating at 4°C overnight. Each of the above-mentioned IPs was done in duplicate. The beads were then washed once with Lysis buffer, twice with High Salt Wash Buffer (1 M NaCl, 50 mM Tris pH 7.5, 1% NP-40, 5% Glycerol), twice with Mass Spec IP Wash Buffer (150 mM NaCl, 50 mM Tris-HCl pH 7.5, 5% Glycerol) plus 0.05% NP-40, and finally two times with Mass Spec IP Wash Buffer (150 mM NaCl, 50 mM Tris-HCl pH 7.5, 5% Glycerol). Each sample was then reduced, alkylated, Trypsin digested, and desalted as described in Parnas et al.¹⁰³ Peptides were reconstituted in 12 μL 3% acetonitrile/0.1% formic acid. The corresponding protein quantification data is available in Dataset S1.

Sample preparation for global proteomics

40 μg of protein of two input lysates and the supernatant after the above-mentioned pooled IPs and single IPs (duplicate each) were processed for further LC-MS/MS measurements by single-pot, solid-phase-enhanced sample-preparation (SP3) method as described in Hughes et al.¹⁰⁴

The corresponding protein quantification data is available in Dataset S2.

Proteomic quantification of Torin1-treated samples

5 million cells each of control and 250 nM Torin1 treated HEK cells were lysed in 250 μL Mass Spec Lysis Buffer (8 M urea, 75 mM NaCL, 50 mM Tris pH 8.0, 1 mM EDTA) for 30 min at room temperature. Samples were then clarified by centrifugation at 23000 g for 5 minutes, and the protein content in the supernatant was measured by BCA assay (ThermoFisher, #PI23227). 40 μg of protein for each sample was reduced with 5 mM final dithriothreitol (DTT) for 45 minutes at room temperature and subsequently alkylated with 10 mM final iodoacetamide (IAA) for 45 minutes in the dark at room temperature. 50 mM Tris (pH 8.0) was then added to each sample such that the final concentration of urea was less than 2 M. Samples were digested overnight with 0.4 μg Trypsin (Promega, #V5113) for a 1:100 enzyme to protein ratio. Peptides were desalted on C18 StageTips according to Rappsilber et al.¹⁰⁵

The corresponding protein quantification data is available in Dataset S4.

4EBP1 IP upon Torin1 and Control treatment

Antibodies against 4EBP1 or targeting a protein that is not present (V5) were used to perform IP from HEK293T lysates treated either with Torin1 or solvent only (control). Each IP and condition were performed in triplicate. The IPs were performed as described in Parnas et al.¹⁰³ Briefly, 10 μg of antibody (4EBP1 or V5) were added to 1 mg of protein lysate (volume brought up to 400 μL with lysis buffer), and 100 μL of Protein G Dynabeads beads were added per IP. IPs were rotated overnight at 4°C. Immunopurified material was separated by magnet, washed 2X in IP wash buffer + 0.05% NP-40 Igepal (150 mM NaCl, 50 mM Tris pH 7.5, 5% glycerol) and 2X in IP wash buffer without NP-40. Peptides were eluted from beads by adding 80 μL of 2 M Urea, 50 mM Tris pH 7.5, 1 mM DTT, 5 μg/mL Trypsin prepared in HPLC water, and shaking for 1 hour at RT 1,000 rpm. Eluate was collected, and beads were washed twice with 2 M Urea, 50 mM Tris pH 7.5. Washes and eluate were combined. Proteins were reduced by adding 4 mM DTT and incubating 45 minutes at RT, shaking 1,000 rpm. Proteins were alkylated by adding 10 mM iodoacetamide, protected from light and incubated 45 minutes RT, shaking 1,000 rpm. Peptides were digested by adding 0.5 μg Trypsin and incubating at RT overnight, shaking 700 rpm. The following day, 1% formic acid was added to acidify the peptide solution. Peptides were desalted on C18 StageTips according to Rappsilber et al.¹⁰⁵

The corresponding protein quantification data is available in Dataset S5.

V5 versus IgG IP

The same experimental procedure as for the “4EBP1 IP upon Torin1 and Control treatment” described above, but that the IP was performed from untreated HEK293T lysates. The IPs were performed either with a mouse IgG antibody or a V5 antibody. All IPs were done in triplicates. The corresponding protein quantification data is available in Dataset S6.

LC-MS/MS

LC-MS/MS analysis was performed on a Q-Exactive HF. 5 μL of total peptides were analyzed on a Waters M-Class UPLC using either a C18 25 cm Thermo EASY-Spray column (2 μm, 75 μm x 25 cm) or IonOpticks Aurora ultimate column (1.7 μm, 75 μm x 25 cm (or 15 cm)) coupled to a benchtop ThermoFisher Scientific Orbitrap Q Exactive HF mass spectrometer. Peptides were separated at a flow rate of 400 nL/min with either a linear 95 min gradient from 2% to 22% solvent B (100% acetonitrile, 0.1% formic acid); followed by a linear 20 min gradient from 22 to 30% solvent B; followed by a linear 9 min gradient from 30 to 60% solvent B (Datasets S2 and S4) or a linear 30 min gradient from 5% to 10% solvent B (100% acetonitrile, 0.1% formic acid); followed by a linear 27 min gradient from 10 to 22% solvent B; followed by a linear 5 min gradient from 22 to 30% solvent B, followed by a linear 4 min gradient from 30 to 60% solvent B (Datasets S1, S3, S5, and S6). Each sample was run for either 160 min (Datasets S2 and S4) or 95 min (Datasets S1, S3, S5, and S6) total, including sample loading, column washing and equilibration times. Data was acquired using Xcalibur 4.1 software.

The IP samples were measured in a Data Dependent Acquisition (DDA) mode. MS1 Spectra were measured with a resolution of 120000, an AGC target of 3e6 and a mass range from 300 to 1800 m/z. Up to 12 MS2 spectra per duty cycle were triggered at a resolution of 15000, an AGC target of 1e5, an isolation window of 1.6 m/z and a normalized collision energy of 28.

The Torin1 treated and control total lysate samples (Dataset S4) as well as the IP supernatant and IP input samples (Dataset S3) were measured in a Data Independent Acquisition (DIA) mode. MS1 Spectra were measured with a resolution of 120000, an AGC target of 5e6 and a mass range from 350 to 1650 m/z. 47 isolation windows of 28 m/z were measured at a resolution of 30000, an AGC target of 3e6, normalized collision energies of 22.5, 25, 27.5, and a fixed first mass of 200 m/z.

Database searching of the proteomics raw files of the global proteomics samples

Proteomics raw files were analyzed using the directDIA method on SpectroNaut v16.0 for DIA runs (Biognosys) using a human UniProt database (Homo sapiens, UP000005640), under BSG factory settings, with automatic cross-run median normalization and imputation. Protein group data were exported for subsequent analysis.

Database searching of the proteomics raw files of the IP samples

The raw data were analyzed with MaxQuant software¹⁰⁶ version 2.0.3.0 using a human UniProt database (Homo sapiens, UP000005640), and MS/MS searches were performed with the following parameters: label free MS1 quantification, oxidation of methionine and protein N-terminal acetylation as variable modifications; carbamidomethylation as fixed modification; Trypsin/P as the digestion enzyme; precursor ion mass tolerances of 20 parts per million (p.p.m.) for the first search (used for nonlinear mass re-calibration) and 4.5 p.p.m. for the main search, and a fragment ion mass tolerance of 20 p.p.m. For identification, we applied a maximum FDR of 1% separately on protein and peptide level. We required 1 or more unique/razor peptides for protein identification. Label free quantification (LFQ) was enabled.

Finally, only protein groups that had at least 5 MS2 counts on average for one of the triplicate IP measurements were not filtered out. To each protein group and IP sample a random pseudocount value between 250000 to 500000 was added to the LFQ value in order to account for the noise level and make our fold change calls more robust for small intensity values. Afterwards we normalized these LFQ intensities such that at each condition these intensity values added up to exactly 1000000, therefore each protein group value can be regarded as a normalized microshare.

4EBP1 and PTBP1 Torin and DMSO CLAP

Expression, UV-crosslinking, and lysis of HEK293T cells were performed as previously described.^66,92 After 18 hours of expression, HEK293T cells were then treated with 250 nM Torin1 as described above. After 24 hours of Torin1 treatment, cells were washed once with PBS and then crosslinked on ice using 0.25 J cm-2 (UV 2.5k) of UV at 254 nm in a Spectrolinker UV Crosslinker. Cells were then scraped from culture dishes, washed once with PBS, pelleted by centrifugation at 1500 g for 4 min, and flash-frozen in liquid nitrogen for storage at −80°C.

CLAP was performed on HEK293T cells as previously described.^66,92 Briefly, post-crosslinking, cells were resuspended in 1 mL of cold lysis buffer (50 mM HEPES pH 7.4, 100 mM NaCl, 1% NP-40, 0.1% SDS, 0.5% sodium deoxycholate) supplemented with 1X Protease Inhibitor Cocktail (Promega), 200 U of RiboLock, 20 U of TURBO DNase (Ambion), and 1X manganese/calcium mix (0.5 mM CaCl2, 2.5 mM MnCl2). Samples were incubated on ice for 10 minutes and then at 37°C for 10 minutes at 1150 rpm shaking on a ThermoMixer (Eppendorf). Lysates were cleared by centrifugation at 15000 g for 2 minutes and the supernatant was collected for capture to HaloLink Resin (Promega). 50 μL of lysate was taken prior to immunoprecipitation for input processing. For each CLAP capture, 200 μL of 25% HaloLink Resin (50 μL of HaloLink Resin total) was used per 10 million cells. Resin was washed three times with 2 mL of 1X TBS (50 mM Tris pH 7.5, 150 mM NaCl) and incubated in 1X Blocking Buffer (50 mM HEPES, pH 7.5, 10 μg/mL Random 9-mer, 100 μg/mL BSA) for 20 minutes at room temperature with continuous rotation. After the incubation, resin was washed three times with 1X TBS. Cleared lysate was mixed with 50 μL of HaloLink Resin and incubated at 4°C overnight with continuous rotation. The captured protein bound to resin was washed three times with lysis buffer at room temperature and then three times at 90°C for 3 minutes while shaking at 1200 rpm with each of the following buffers: 1X ProK/NLS buffer (50 mM HEPES, pH 7.5, 2% NLS, 10 mM EDTA, 0.1% NP-40, 10 mM DTT), high salt buffer (50 mM HEPES, pH 7.5, 10 mM EDTA, 0.1% NP-40, 1M NaCl), 8 M urea buffer (50 mM HEPES, pH 7.5, 10 mM EDTA, 0.1% NP-40, 8 M Urea), and Tween buffer (50 mM HEPES, pH 7.5, 0.1% Tween 20, 10 mM EDTA). After the last wash, samples were centrifuged at 7500g for 30 seconds and supernatant was discarded. For elution, HaloLink Resin was resuspended in 100 μL of ProK/NLS buffer + 10 μL of Proteinase K (NEB) and incubated at 50°C for 20 minutes while shaking at 1200 rpm. Elutions were then transferred to microspin cups (Pierce, Thermo Fisher), centrifuged at 2000 g for 30 seconds, and purified with RNA Clean and Concentrate-5 (Zymo, >17 nucleotides protocol).

After purification, RNA was dephosphorylated (FastAP), cyclic phosphates were removed (T4 PNK), and RNA was ligated to an RNA adapter containing a RT primer binding site. RNA was then reverse transcribed into single stranded cDNA and subsequently degraded with NaOH. Following RT, a second adapter was ligated to the single stranded DNA. PCR amplification was achieved using primers that targeted the 3′ and 5′ ligated adapters.

Structural resolution of LARP1-40S using cryo-EM

Purification of LARP1 and LARP1 mutant proteins

The wild-type LARP1 protein (UniProt ID Q6PKG0) and a mutant variant lacking amino acids 660-697 were cloned into the pET28C vector. This resulted in constructs expressing the protein with an N-terminal 6xHis-MBP tag, followed by a tobacco etch virus (TEV) protease cleavage site. The fusion proteins were expressed in Escherichia coli (BL21) using autoinduction at 37°C for 3 hours, followed by incubation at 18°C for 20 hours.After harvesting, the cell pellet was resuspended in IMAC lysis buffer and sonicated. The lysate was clarified by centrifugation, and the supernatant was loaded onto a Hitrap FF column. The target protein was eluted using an imidazole gradient. The N-terminal tag was cleaved by TEV protease in dialysis buffer (25 mM Tris, 200 mM NaCl, 20 mM Imidazole, 10% glycerol, 5 mM 2-Mecaptoethanol, pH 7.5) at 4°C overnight. The protein was further purified using NiNTA and HiTrap SP columns. Final polishing was performed using Superdex 200 columns. The final sample was concentrated to a concentration of 20 μM (2.5 mg/ml) using Amicon filters with a molecular weight cutoff of 30 kDa.

LARP1 used for the cryo-EM analysis was cloned into a pcDNA3.1 vector and expressed in Expi293 cells (ThermoFisher) following standard protocols. Cells were lysed using 3 cycles of freeze-thaw.

Assembly and purification of 40S-LARP1 complex

We used recombinant LARP1 to assemble and purify the 40S-LARP1 complex using HeLa cell lysate (Ipracell). Initially, the cell lysate was incubated with 1 μM LARP1 at room temperature. To enrich the reaction mixture with the 40S-LARP1 complex, we supplemented the lysate with 0.3 μM of purified 40S ribosomal subunits and allowed the reaction to proceed for an additional 30 minutes at 30°C. The reaction mixture was then loaded onto 10-30% sucrose density gradients prepared in a buffer containing 20 mM HEPES (pH 7.5), 1 mM DTT, 100 mM KCl, and 3 mM MgCl2, followed by centrifugation for 5 hours at 40,000 rpm using the SW41 Ti rotor. Fractions containing the 40S complex were collected and subjected to buffer exchange into a solution containing 20 mM HEPES-KOH (pH 7.5), 100 mM potassium acetate, 4 mM magnesium acetate, 2% glycerol, 0.1 mM spermidine, and 1 mM DTT to remove sucrose.

The purified complex was analyzed by western blot using antibodies against ribosomal protein uS17 (AB157101; Abcam) and LARP1 (PA5117002; Invitrogen).

Cryo-EM grid preparation, data acquisition and processing

The LARP1-40S complex was assembled using recombinant LARP1 expressed in Expi293 cells (ThermoFisher). The 40S small ribosomal subunit was also purified from the same cell line. To prevent the complex from dissociating during grid preparation, the sample was crosslinked with 1.5 mM BS3 on ice for 45 minutes. 3 μl of 180 nM 40S-LARP1 complex were applied onto glow-discharged UltraAUFoil Gold R1.2/1.3 grids (Quantifoil) pre-coated with a thin layer of graphene oxide (Sigma-Aldrich) made in-house. Cryo-EM grids were prepared using a Vitrobot (ThermoScientific) at 4°C and 100% humidity and then plunged into liquid ethane at approximately 93 K.

The dataset was collected on a Titan Krios G4i (ThermoFisher) equipped with a Gatan K3 direct detector and a BioQuantum imaging filter at a magnification of 105,000x, resulting in a pixel size of 0.832 Å/pixel. For each exposure, 50 frames were collected with a dose of 1 e/Å² per frame. The target defocus range was set between −1.2 μm and −3.0 μm.

Image processing

Micrographs were processed using Relion-5.0.¹⁰⁷ Motion correction was carried out using Relion’s built-in implementation. Contrast Transfer Function (CTF) estimation was performed using CTFFIND4.1.¹⁰⁸ The selected 2D classes were utilized for reference-based 3D classification. A cryo-EM map of a human 40S ribosome was used as a reference after low-pass filtering to 60 A. Classes of particles exhibiting LARP1 density were selected for Bayesian polishing in RELION to correct beam-induced motion. Following to polishing and CTF refinement, mask classification was conducted focusing on the mRNA channel and the 40S head.

Model building and refinement

We initiated model building and refinement by rigid-body fitting our prior structure of the human ribosome⁶⁸ and an AlphaFold prediction¹⁰⁹ of the LARP1 structures into the cryo-EM map using Coot.¹¹⁰ Subsequently, we refined the model using Phenix for real-space refinement,¹¹¹ followed by manual fitting to address any outliers.

Cryo-EM data collection, refinement and validation statistics

The following table presents essential statistics for Cryo-EM data collection, including defocus range, dose, and magnification. It also includes validation metrics for model quality, such as the Ramachandran plot and MolProbity score.

Data collection and processing
	#40S-LARP1 (EMDB- 47929) (PDB 9ED0)
Magnification	105,000
Voltage (kV)	300
Electron exposure (e–/Å²)	1.0461
Defocus range (μm)	−1.2 to −3.0
Pixel size (Å)	0.832
Symmetry imposed	C1
Initial particle images (no.)	465,812
Final particle images (no.)	124,913
Map resolution (Å) FSC threshold	2.8 0.143
Map resolution range (Å)	2.4 to 5.2
Refinement
Initial model used (PDB code)	6ZMW
Model resolution (Å) FSC threshold	2.8 0.5
Model resolution range (Å)	2.4 to 2.8
Map sharpening B factor (Å²)	−50
Model composition Non-hydrogen atoms Protein residues Ligands	73,475 4,839 166
B factors (Å²) Protein Ligands	72.28 53.30
R.m.s. deviations Bond lengths (Å) Bond angles (°)	0.007 0.920
Validation MolProbity score Clashscore Poor rotamers (%)	1.62 2.85 3.22
Ramachandran plot Favored (%) Allowed (%) Disallowed (%)	97.10 2.88 0.02

Open in a new tab

Cryo-EM figure generation

Figures were made using ChimeraX.¹¹²

NOTE 1: Method considerations for performing a SPIDR experiment

Antibody specificity

Because SPIDR is dependent on antibodies, the protein yield (i.e., amount of protein obtained from cells) and target specificity (i.e., amount of protein of interest vs. non-target proteins) depend on the quality of the antibody used. While this issue affects all antibody-based methods (not just SPIDR), in this context it could lead to reads associated with a protein that are due to capture of other non-target proteins. Although SPIDR does not attempt to address this antibody issue, we note that careful validation of antibodies is important to interpreting the results of this assay or any antibody-based method. For example, antibodies should be validated using standard approaches/recommendations such as those outlined in Uhlen et al.¹¹³ One approach to explore this is using a pooled IP-MS strategy which would detect potential antibody specificity issues. This approach would allow users to identify potential problematic antibodies and/or non-target RBPs that might be purified (see Figures S1B and S2). In addition, our data indicates that the pooled IP strategy itself improves the specificity of RBP capture. Although we see a number of other non-target proteins that are enriched, this is not a pooled IP-specific issue, because we also see enrichment of non-target proteins when performing individual IPs of these same proteins (Figure S2). While SPIDR does not address this general IP issue, we note that it does improve the signal-to-noise ratio relative to methods that map proteins individually, because we found that non-target protein background is higher in single IPs than pooled IPs. Specifically, we observed comparable enrichment levels of all target RBPs, but a significant reduction (>2-fold) in enrichment of non-target proteins in the pooled IP-MS compared to the individual IP-MS experiments (Figure S2). This highlights a key advantage of SPIDR: that it allows comparison of signal enrichment relative to the other RBPs, potentially correcting for shared background signal and improving the signal-to-noise relative to individual approaches (see ‘Analytical considerations’ below for more detail). We also note that SPIDR may offer the opportunity to directly assess the specificity question because its multiplexing capability allows for the inclusion of several distinct antibodies, including those that may not have been previously validated, against the same RBP without increasing the experimental burden.

IP conditions

The SPIDR protocol requires that each experiment is performed under the same IP conditions for all RBPs. Efficient IP of target proteins and associated RNA fragments will depend on many distinct factors including the expression level of the target protein, its crosslinking efficiency to RNA, the antibody specificity, and elution conditions. As such, the multiplexing strategy of SPIDR is not optimized for each target RBP. However, this is not SPIDR-specific, but true for many CLIP protocols where the IP conditions are not optimized for each protein. For example, the commonly used eCLIP method (used by the ENCODE project), uses a single condition for each protein measured, including a single UV-crosslinking condition, RNase condition, and antibody amount for all proteins studied within a cell type. This feature is essential for being able to scale the approach and for comparing results across conditions. Although we show that standard conditions work for many diverse proteins, they may not be suitable for all RBPs. One possible solution is to match antibodies (and target RBPs) by similar IP conditions, which can be explored by using a pooled IP-MS experiment (Figures S1B and S2) as a fast way to predetermine if the experimental conditions provide strong enough enrichment for specific proteins of interest. However, it should be noted that for distinct antibodies that need very specific IP conditions which are incompatible with other antibodies, SPIDR is not the method of choice and a single antibody CLIP-seq approach is best suited for such an antibody.

Direct binding versus protein-mediated complex binding

RBPs often do not work in isolation but as components of larger protein complexes. As such, one potential concern could be that SPIDR does not necessarily identify binding sites that are directly bound by the targeted protein, but also binding sites that are bound by other proteins contained within the associated protein complex. As SPIDR utilizes the same high stringency washes (e.g. 1 M salt) as those used by CLIP, many protein-protein interactions should be disrupted. However, there are some protein complexes that remain stably associated in these conditions. This is true for all IP-methods, including CLIP methods (see comment on SDS-PAGE purification below). We note that discovery of any novel RBPs, especially those lacking canonical RNA binding domains, requires detailed follow-up experiments.

SDS-PAGE separation and purification

Many CLIP approaches utilize isolation of crosslinked RNA-protein complexes following separation by SDS-PAGE. This was introduced to decrease the chances of identifying indirect binding sites. However, this only adds a layer of selectivity to separate RBPs with a significant size difference; it does not address the issue of potential non-target protein capture because SDS-PAGE purification does not lend selectivity to the IP specificity itself. Accordingly, an increasingly large number of CLIP methods and studies are forgoing the SDS-PAGE step (e.g., seCLIP,¹¹⁴ abcCLIP,²⁷ TLC-Clip^115,116, SPYCLIP,^115,116 chimeric eCLIP,¹¹⁷ CLAP⁶⁶). Therefore, while we have not found the identification of indirect binding sites to be an issue (e.g., we observe strong agreement between ENCODE and our datasets), we note that SDS-PAGE purification could be readily incorporated into the SPIDR procedure. For example, after the IP and split-and-pool, the samples can be eluted from beads and run on an SDS-PAGE gel as is normally done for CLIP. However, the beads themselves and the oligos associated with them should be saved for PCR amplification and sequencing. The gel can be transferred to nitrocellulose and the membrane can be cut into defined slices based on the protein sizes (defined by the ladder). The RNA/cDNA can be extracted from each slice as done in standard protocols and PCR amplified with an indexed primer to demarcate which slice it came from. After that step, all the amplified cDNA can be pooled, sequenced, and then matched to their corresponding RBP (split-and-pool barcode) and size fraction (slice barcode).

Antibody-to-target concentrations

In our experiments, we did not aim to achieve saturation of target protein capture because this would require excess antibody to protein target and might lead to increased non-specific binding of antibodies to non-target proteins. Using the antibody concentrations in our experiments, we found that no protein was completely depleted (saturated) from the supernatant in our IP-MS experiments (Figure S2). Nonetheless, we recovered enough RNA to robustly characterize each protein.

Number of sequencing reads

Another important experimental consideration is how to allocate sequencing reads between RNA and bead ID oligos. Because our focus in developing SPIDR was to increase the throughput of mapping multiple proteins, we were less concerned about the additional sequencing reads needed and we aimed to sequence more bead ID oligos than RNA to maximize the assignment of each RNA fragment. Specifically, the minimum required sequencing depth for each experiment was determined by the estimated number of beads and unique molecules in each aliquot. For cDNA libraries, each library was sequenced with at least 2X coverage of the total estimated library complexity. For oligo tag libraries, each library was sequenced to a depth of observing ~4 unique oligo tags per bead on average. In practice, we have found that assignment based on a single oligo ID per cluster works as well as conservative assignment requiring multiple oligo ID tags per cluster. Accordingly, the number of sequencing reads allocated to oligos could be considerably reduced, thereby reducing the associated sequencing cost.

Analytical considerations

Although different proteins have different numbers of detected peaks, this does not indicate that some experiments failed simply because they have fewer identified peaks. For example, LSM11 only generated 7 significant peaks but this includes U11 and U7 snRNAs, which are the known binding targets of LSM11. As illustrated by this example, the low numbers of peaks are not necessarily due to low read coverage or lack of binding, but, in many cases, represent the specificity of protein binding. In future experiments, we envision that if more reads should be allocated to certain RBPs that have more binding sites, the ratio of the distinct bead populations targeting these RBPs can be adjusted to allocate a higher number of beads these RBPs.

One big challenge in CLIP experiments is to differentiate background signal from true signal and different CLIP protocols apply different strategies to distinguish these two signals from each other. We believe that SPIDR has a distinct advantage relative to other CLIP approaches: because SPIDR experiments are done in a pool, a protein that binds poorly to RNA (because it does not bind RNA, the antibody is not efficient, or because it is lowly expressed) will have few RNA reads associated with this protein compared to other proteins. Nonetheless, there is often a baseline background when pulling down any protein and we have observed that there is often a common background that reflects RNA abundance and crosslinking of the RNAs. For this reason, we define “background” based on all of the samples mapped. A protein that does not bind to specific RNAs would be expected to roughly approximate this background. Indeed, we use this precise background to i) define significant binding sites by calculating enrichment relative to background and ii) exclude sites that may have high absolute counts which simply reflect this background. For example, the raw read coverage for LARP1 over 18S ribosomal RNA predominantly reflects sites that are shared with all other proteins (which likely reflect RNA modification sites on 18S). However, when we normalize for these expected patterns and calculate relative enrichment, we clearly see strong enrichment at specific sites that we were able to confirm using Cryo-EM (see Figures 5C and 6).

There are other sources of non-specific background, including specific non-target capture by individual antibodies. For example, we found that of the three “negative” controls that we included, only beads containing non-specific “IgG” antibodies showed nearly no specific signal enrichment. Beads containing antibodies against “V5” and “GFP”, respectively, appear to consistently purify a number of other specific non-target proteins (see Figure S2I; Dataset S6). Accordingly, it may be useful to estimate this expected non-target background in defining binding sites in future analytical frameworks.

Our “empty/sponge” control corresponds to protein G beads that contain a unique oligo but no antibody (empty beads). The purpose of these empty beads is to act as a “sponge” for antibodies that dissociate during the IP incubation step and prevent their “reassociation” with protein G present on other beads which would lead to misassignments. As such, the “empty” beads are not expected to be devoid of RNA, but instead roughly reflect the RNA content of the pooled experiment. However, they may not be identical to the pool because different antibodies could have preferential dissociation kinetics based on their binding affinity to protein G. These differences are likely what this metric is capturing.

Finally, we want to emphasize that direct quantitative comparisons between proteins in the same SPIDR experiment are difficult because of differences in UV-crosslinking efficiency in a protein- and RNA-specific manner, as well as differences in antibody efficiency and specificity. In contrast, quantitative comparisons of RNA-protein interactions between experimental samples using the same antibodies may can be compared if the only distinction is the experimental condition (i.e., our Torin1 and control treatment experiment). In addition, even in such a scenario, we have found that a pooled IP-MS experiment can be highly informative to ensure that protein enrichments are comparable for different conditions or to estimate specificity and yield of specific antibodies. Because IP-MS experiments are quantitative, comparing the enrichment levels directly between conditions enables normalization of potential differences in protein enrichment between conditions. As an example, we compared IP efficiency (by IP-MS) for 4EBP1 in control and Torin1 treatment conditions and observed that 4EBP1 is very efficiently IP’d under both conditions (Figure S7E), with no significant difference in IP efficiency between conditions. This implies that the strong increase in RNA binding of 4EBP1 upon Torin1 treatment is solely due to the effects of drug treatment and not IP differences.

QUANTIFICATION AND STATISTICAL ANALYSIS

Statistical analysis for each experiment can be found in the figures and figure legends. Details of statistical analysis and software used are described within the relevant sections in STAR Methods and key resources table.

KEY RESOURCES TABLE

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
See Table S1		N/A
Chemicals, peptides, and recombinant proteins
Torin1	Cell Signaling Technology	Cat# 14379
EZ-Link Sulfo-NHS-Biotin	Thermofisher	Cat# 21217
Streptavidin	BioLegend	Cat# 280302
Biotin	Sigma-Aldrich	Cat# B4639-5G
Protease Inhibitor Cocktail	Sigma-Aldrich	Cat# P8340
Turbo DNase	Invitrogen	Cat# AM2238
RiboLock RNase Inhibitor	Thermofisher	Cat# EO0382
RNase If	NEB	Cat# M0243L
T4 Polynucleotide Kinase	NEB	Cat# M0201L
High Concentration T4 RNA Ligase I	NEB	Cat# M0437M
Superscript III	Invitrogen	Cat# 18080093
Exonuclease I	NEB	Cat# M0293L
RNase H	NEB	Cat# M0297L
RNase Cocktail	Invitrogen	Cat# AM2286
N-Lauroylsarcosine sodium salt solution (NLS)	Sigma-Aldrich	Cat# L7414
Instant Sticky-End Ligase Master Mix	NEB	Cat#M0370L
Q5 Hot Start High-Fidelity 2X Master Mix	NEB	Cat# M0494
NEBNext Quick Ligation Reaction Buffer	NEB	Cat# B6058S
Critical commercial assays
Zymoclean Gel DNA Recovery Kit	Zymo Research	Cat# 4007
RNA Clean & Concentrator-5	Zymo Research	Cat# R1015
Deposited data
Mass Spectrometry	This paper	MassIVE: MSV000096455
Next generation sequencing	This paper	GEO: GSE299553
Coordinates of human LARP1-40S complex	This paper	PDB:9ED0
Cryo-EM map of human LARP1-40S complex	This paper	EMDB: EMD-47929
Datasets S1 to S6	This paper	Mendeley Data: https://doi.org/10.17632/2f8czsk233.2
Experimental models: Cell lines
Human: K562 Cell line	ATCC	Cat# CCL-243
Human: HEK293T Cell Line	ATCC	Cat# CRL-3216
Oligonucleotides
See Table S1		N/A
Software and algorithms
Python		Version 3.6
Pigz		Version 2.8
seqkit		Version 2.10.0
Bedtools		Version 2.30.0
trim-galore		Version 0.6.2
Cutadapt		Version 3.4
bowtie2		Version 2.3.5
Star		Version 2.5.3
Samtools		Version 1.9
Pysam		Version 0.16.0.1
Numpy		Version 1.17.2
Pandas		Version 1.1.3
Multiqc		Version 1.28
Fastqc		Version 0.12.1
subr		Version 4.38.0
Subread		Version 2.0.1
umi_tools		Version 1.1.6
Deeptools		Version 3.1.3
MaxQuant		Version 2.0.3.0
SpectroNaut	Biognosys	Version 16.0
annotator		https://github.com/byee4/annotator
idr		https://github.com/nboley/idr
SPIDR pipeline		https://github.com/mjlab-Columbia/spidr-paper-pipeline
Other
Protein G Dynabeads	Invitrogen	Cat# 10003D
MyOne Streptavidin C1 Dynabeads	Invitrogen	Cat# 65001
Silane beads	Invitrogen	Cat# 37002D
CleanNGS DNA & RNA Clean-Up Magnetic Beads	Bulldog Bio	Cat# CNGS500

Open in a new tab

Supplementary Material

MMC1

NIHMS2099475-supplement-MMC1.xlsx^{(21.6KB, xlsx)}

MMC3

NIHMS2099475-supplement-MMC3.xlsx^{(48.1KB, xlsx)}

MMC6

NIHMS2099475-supplement-MMC6.xlsx^{(67.6KB, xlsx)}

MMC2

NIHMS2099475-supplement-MMC2.xlsx^{(28.2KB, xlsx)}

MMC7

NIHMS2099475-supplement-MMC7.xlsx^{(15.6KB, xlsx)}

MMC5

NIHMS2099475-supplement-MMC5.xlsx^{(255.1KB, xlsx)}

MMC4

NIHMS2099475-supplement-MMC4.xlsx^{(38.8KB, xlsx)}

NIHMS2099475-supplement-8.pdf^{(5.5MB, pdf)}

Supplemental information can be found online at https://doi.org/10.1016/j.cell.2025.06.042.

Highlights.

SPIDR maps dozens of RBPs at single-nucleotide resolution in a single experiment
High-resolution binding maps of RBPs involved in all stages of mRNA life cycle
SPIDR identifies a precise interaction between LARP1 and entry channel of ribosome
4EBP1 binds 5′ UTRs of translationally repressed mRNAs upon mTOR inhibition

ACKNOWLEDGMENTS

We thank all members of the Jovanovic and Guttman labs for technical help and helpful feedback, Shawna Hiley for editing, and Inna-Marie Strazhnik for illustrations. This work was funded by grants from NIH (R35GM128802 [to M.J.], R01AG071869 [to M.J.], R01HG012216 [to M.J./M.G.], U01DK127420 [to M.G.], R01DA053178 [to M.G.], and R01NS140149 [to J.B.Q.]), NCI F30CA278005 (to J.K.G.), the University of Southern California MD/PhD program (to J.K.G.), NSF (award 2224211 [to M.J.] and award 2243705 [to M.G.]), CZI Ben Barres Early Career Acceleration Award (to M.G.), funds from Columbia University (to M.J.), Rogel Cancer Center (NIH Award Number P30 CA046592) (to J.B.Q.), LSI computer cluster (S10OD030275) (to J.B.Q.), and GTP (T32GM149391) (to R.K.). U-M cryo-EM Facility is supported by the U-M Life Sciences Institute, U-M Biosciences Initiative, and Beckman Foundation.

Footnotes

DECLARATION OF INTERESTS

M.G., M.J., E.W., J.K.G., M.R.B., A.A.P., and I.N.G. are inventors of submitted patents covering the SPIDR method.

RESOURCE AVAILABILITY

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Marko Jovanovic (mj2794@columbia.edu).

Materials availability

This study did not generate new, unique reagents.

Data and code availability

The sequencing data, mass spectrometry raw files, cryo-EM model, and codes are all available under links provided in the key resources table in the STAR Methods section.

REFERENCES

1.Sonenberg N, and Hinnebusch AG (2009). Regulation of Translation Initiation in Eukaryotes: Mechanisms and Biological Targets. Cell 136, 731–745. 10.1016/j.cell.2009.01.042. [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Castello A, Fischer B, Hentze MW, and Preiss T (2013). RNA-binding proteins in Mendelian disease. Trends Genet. 29, 318–327. 10.1016/j.tig.2013.01.004. [DOI] [PubMed] [Google Scholar]
3.Gao F-B, and Taylor JP (2012). RNA-binding proteins in neurological disease. Brain Res. 1462, 1–2. 10.1016/j.brainres.2012.05.038. [DOI] [PubMed] [Google Scholar]
4.Gebauer F, Schwarzl T, Valcárcel J, and Hentze MW (2021). RNA-binding proteins in human genetic disease. Nat. Rev. Genet 22, 185–198. 10.1038/s41576-020-00302-y. [DOI] [PubMed] [Google Scholar]
5.Gerstberger S, Hafner M, and Tuschl T (2014). A census of human RNA-binding proteins. Nat. Rev. Genet 15, 829–845. 10.1038/nrg3813. [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Caudron-Herger M, Jansen RE, Wassmer E, and Diederichs S (2021). RBP2GO: a comprehensive pan-species database on RNA-binding proteins, their interactions and functions. Nucleic Acids Res. 49, D425–D436. 10.1093/nar/gkaa1040. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Trendel J, Schwarzl T, Horos R, Prakash A, Bateman A, Hentze MW, and Krijgsveld J (2019). The Human RNA-Binding Proteome and Its Dynamics during Translational Arrest. Cell 176, 391–403. 10.1016/J.CELL.2018.11.004. [DOI] [PubMed] [Google Scholar]
8.Queiroz RML, Smith T, Villanueva E, Marti-Solano M, Monti M, Pizzinga M, Mirea DM, Ramakrishna M, Harvey RF, Dezi V, et al. (2019). Comprehensive identification of RNA–protein interactions in any organism using orthogonal organic phase separation (OOPS). Nat. Biotechnol 37, 169–178. 10.1038/s41587-018-0001-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Urdaneta EC, Vieira-Vieira CH, Hick T, Wessels HH, Figini D, Moschall R, Medenbach J, Ohler U, Granneman S, Selbach M, et al. (2019). Purification of cross-linked RNA-protein complexes by phenoltoluol extraction. Nat. Commun 10, 990. 10.1038/S41467-019-08942-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Perez-Perri JI, Rogell B, Schwarzl T, Stein F, Zhou Y, Rettel M, Brosig A, and Hentze MW (2018). Discovery of RNA-binding proteins and characterization of their dynamic responses by enhanced RNA interactome capture. Nat. Commun 9, 4408. 10.1038/S41467-018-06557-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.King OD, Gitler AD, and Shorter J (2012). The tip of the iceberg: RNA-binding proteins with prion-like domains in neurodegenerative disease. Brain Res. 1462, 61–80. 10.1016/j.brainres.2012.01.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Guttman M, Amit I, Garber M, French C, Lin MF, Feldser D, Huarte M, Zuk O, Carey BW, Cassady JP, et al. (2009). Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals. Nature 458, 223–227. 10.1038/nature07672. [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Statello L, Guo CJ, Chen LL, and Huarte M (2021). Gene regulation by long non-coding RNAs and its biological functions. Nat. Rev. Mol. Cell Biol 22, 96–118. 10.1038/s41580-020-00315-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Guo JK, and Guttman M (2022). Regulatory non-coding RNAs: everything is possible, but what is important? Nat. Methods 19, 1156–1159. 10.1038/s41592-022-01629-6. [DOI] [PubMed] [Google Scholar]
15.Guttman M, and Rinn JL (2012). Modular regulatory principles of large non-coding RNAs. Nature 482, 339–346. 10.1038/nature10887. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Chu C, Zhang QC, Da Rocha ST, Flynn RA, Bharadwaj M, Calabrese JM, Magnuson T, Heard E, and Chang HY (2015). Systematic Discovery of Xist RNA Binding Proteins. Cell 161, 404–416. 10.1016/J.CELL.2015.03.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.McHugh CA, Chen CK, Chow A, Surka CF, Tran C, McDonel P, Pandya-Jones A, Blanco M, Burghard C, Moradian A, et al. (2015). The Xist lncRNA interacts directly with SHARP to silence transcription through HDAC3. Nature 521, 232–236. 10.1038/nature14443. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Robert-Finestra T, Tan BF, Mira-Bontenbal H, Timmers E, Gontan C, Merzouk S, Giaimo BD, Dossin F, van IJcken WFJ, Martens JWM, et al. (2021). SPEN is required for Xist upregulation during initiation of X chromosome inactivation. Nat. Commun 12, 7000. 10.1038/s41467-021-27294-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Engreitz JM, Pandya-Jones A, McDonel P, Shishkin A, Sirokman K, Surka C, Kadri S, Xing J, Goren A, Lander ES, et al. (2013). The Xist lncRNA Exploits Three-Dimensional Genome Architecture to Spread Across the X Chromosome. Science 341, 1237973. 10.1126/science.1237973. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Jachowicz JW, Strehle M, Banerjee AK, Blanco MR, Thai J, and Guttman M (2022). Xist spatially amplifies SHARP/SPEN recruitment to balance chromosome-wide silencing and specificity to the X chromosome. Nat. Struct. Mol. Biol 29, 239–249. 10.1038/S41594-022-00739-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Hafner M, Landthaler M, Burger L, Khorshid M, Hausser J, Berninger P, Rothballer A, Ascano M Jr., Jungkamp A-C, Munschauer M, et al. (2010). Transcriptome-wide Identification of RNA-Binding Protein and MicroRNA Target Sites by PAR-CLIP. Cell 141, 129–141. 10.1016/j.cell.2010.03.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Chi SW, Zang JB, Mele A, and Darnell RB (2009). Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature 460, 479–486. 10.1038/nature08170. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Van Nostrand EL, Pratt GA, Shishkin AA, Gelboin-Burkhart C, Fang MY, Sundararaman B, Blue SM, Nguyen TB, Surka C, Elkins K, et al. (2016). Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP). Nat. Methods 13, 508–514. 10.1038/nmeth.3810. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Zhang C, and Darnell RB (2011). Mapping in vivo protein-RNA interactions at single-nucleotide resolution from HITS-CLIP data. Nat. Biotechnol 29, 607–614. 10.1038/nbt.1873. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Ule J, Jensen KB, Ruggiu M, Mele A, Ule A, and Darnell RB (2003). CLIP identifies Nova-regulated RNA networks in the brain. Science 302, 1212–1215. 10.1126/science.1090095. [DOI] [PubMed] [Google Scholar]
26.Ramanathan M, Porter DF, and Khavari PA (2019). Methods to study RNA–protein interactions. Nat. Methods 16, 225–234. 10.1038/s41592-019-0330-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Lorenz DA, Her H-L, Shen KA, Rothamel K, Hutt KR, Nojadera AC, Bruns SC, Manakov SA, Yee BA, Chapman KB, et al. (2023). Multiplexed transcriptome discovery of RNA-binding protein binding sites by antibody-barcode eCLIP. Nat. Methods 20, 65–69. 10.1038/s41592-022-01708-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Van Nostrand EL, Pratt GA, Yee BA, Wheeler EC, Blue SM, Mueller J, Park SS, Garcia KE, Gelboin-Burkhart C, Nguyen TB, et al. (2020). Principles of RNA processing from analysis of enhanced CLIP maps for 150 RNA binding proteins. Genome Biol. 21, 90. 10.1186/s13059-020-01982-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Van Nostrand EL, Freese P, Pratt GA, Wang X, Wei X, Xiao R, Blue SM, Chen J-Y, Cody NAL, Dominguez D, et al. (2020). A large-scale binding and functional map of human RNA-binding proteins. Nature 583, 711–719. 10.1038/s41586-020-2077-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Quinodoz SA, Bhat P, Chovanec P, Jachowicz JW, Ollikainen N, Detmar E, Soehalim E, and Guttman M (2022). SPRITE: a genome-wide method for mapping higher-order 3D interactions in the nucleus using combinatorial split-and-pool barcoding. Nat. Protoc 17, 36–75. 10.1038/s41596-021-00633-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Quinodoz SA, Jachowicz JW, Bhat P, Ollikainen N, Banerjee AK, Goronzy IN, Blanco MR, Chovanec P, Chow A, Markaki Y, et al. (2021). RNA promotes the formation of spatial compartments in the nucleus. Cell 184, 5775–5790. 10.1016/J.CELL.2021.10.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Quinodoz SA, Ollikainen N, Tabak B, Palla A, Schmidt JM, Detmar E, Lai MM, Shishkin AA, Bhat P, Takei Y, et al. (2018). Higher-Order Inter-chromosomal Hubs Shape 3D Genome Organization in the Nucleus. Cell 174, 744–757. 10.1016/j.cell.2018.05.024. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Perez AA, Goronzy IN, Blanco MR, Yeh BT, Guo JK, Lopes CS, Ettlin O, Burr A, and Guttman M (2024). ChIP-DIP maps binding of hundreds of proteins to DNA simultaneously and identifies diverse gene regulatory elements. Nat. Genet 56, 2827–2841. 10.1038/s41588-024-02000-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Goronzy IN (2025). Unravelling the complexity of gene regulation through multiplexed protein mapping. Nat. Rev. Mol. Cell Biol 26, 251. 10.1038/s41580-025-00830-7. [DOI] [PubMed] [Google Scholar]
35.Pillai RS, Grimmler M, Meister G, Will CL, Lührmann R, Fischer U, and Schümperli D (2003). Unique Sm core structure of U7 snRNPs: assembly by a specialized SMN complex and the role of a new component, Lsm11, in histone RNA processing. Genes Dev. 17, 2321–2333. 10.1101/GAD.274403. [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Singh RN, Howell MD, Ottesen EW, and Singh NN (2017). Diverse role of survival motor neuron protein. Biochim. Biophys. Acta Gene Regul. Mech 1860, 299–315. 10.1016/J.BBAGRM.2016.12.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Bi X, Xu Y, Li T, Li X, Li W, Shao W, Wang K, Zhan G, Wu Z, Liu W, et al. (2019). RNA Targets Ribogenesis Factor WDR43 to Chromatin for Transcription and Pluripotency Control. Mol. Cell 75, 102–116. 10.1016/J.MOLCEL.2019.05.007. [DOI] [PubMed] [Google Scholar]
38.Sun Z, Yu H, Zhao J, Tan T, Pan H, Zhu Y, Chen L, Zhang C, Zhang L, Lei A, et al. (2022). LIN28 coordinately promotes nucleolar/ribosomal functions and represses the 2C-like transcriptional program in pluripotent stem cells. Protein Cell 13, 490–512. 10.1007/s13238-021-00864-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Chen HK, Pai CY, Huang JY, and Yeh NH (1999). Human Nopp140, which interacts with RNA polymerase I: implications for rRNA gene transcription and nucleolar structural organization. Mol. Cell. Biol 19, 8536–8546. 10.1128/MCB.19.12.8536. [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Bizarro J, Bhardwaj A, Smith S, and Meier UT (2019). Nopp140-mediated concentration of telomerase in Cajal bodies regulates telomere length. Mol. Biol. Cell 30, 3136–3150. 10.1091/mbc.E19-08-0429. [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Tseng TL, Wang YT, Tsao CY, Ke YT, Lee YC, Hsu HJ, Poss KD, and Chen CH (2021). The RNA helicase Ddx52 functions as a growth switch in juvenile zebrafish. Development 148, dev199578. 10.1242/DEV.199578. [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Tafforeau L, Zorbas C, Langhendries JL, Mullineux ST, Stamatopoulou V, Mullier R, Wacheul L, and Lafontaine DLJ (2013). The Complexity of Human Ribosome Biogenesis Revealed by Systematic Nucleolar Screening of Pre-rRNA Processing Factors. Mol. Cell 51, 539–551. 10.1016/J.MOLCEL.2013.08.011. [DOI] [PubMed] [Google Scholar]
43.Jutzi D, Campagne S, Schmidt R, Reber S, Mechtersheimer J, Gypas F, Schweingruber C, Colombo M, von Schroetter C, Loughlin FE, et al. (2020). Aberrant interaction of FUS with the U1 snRNA provides a molecular mechanism of FUS induced amyotrophic lateral sclerosis. Nat. Commun 11, 6341. 10.1038/s41467-020-20191-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Jobert L, Pinzón N, Van Herreweghe E, Jády BE, Guialis A, Kiss T, and Tora L (2009). Human U1 snRNA forms a new chromatin-associated snRNP with TAF15. EMBO Rep. 10, 494–500. 10.1038/EMBOR.2009.24. [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Rappsilber J, Ajuh P, Lamond AI, and Mann M (2001). SPF30 Is an Essential Human Splicing Factor Required for Assembly of the U4/U5/U6 Tri-small Nuclear Ribonucleoprotein into the Spliceosome. J. Biol. Chem 276, 31142–31150. 10.1074/jbc.M103620200. [DOI] [PubMed] [Google Scholar]
46.Bayfield MA, and Maraia RJ (2009). Precursor-product discrimination by La protein during tRNA metabolism. Nat. Struct. Mol. Biol 16, 430–437. 10.1038/nsmb.1573. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Blewett NH, and Maraia RJ (2018). La involvement in tRNA and other RNA processing events including differences among yeast and other eukaryotes. Biochim. Biophys. Acta Gene Regul. Mech 1861, 361–372. 10.1016/j.bbagrm.2018.01.013. [DOI] [PubMed] [Google Scholar]
48.Piskounova E, Polytarchou C, Thornton JE, LaPierre RJ, Pothoulakis C, Hagan JP, Iliopoulos D, and Gregory RI (2011). Lin28A and Lin28B Inhibit let-7 MicroRNA Biogenesis by Distinct Mechanisms. Cell 147, 1066–1079. 10.1016/j.cell.2011.10.039. [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Nam Y, Chen C, Gregory RI, Chou JJ, and Sliz P (2011). Molecular basis for interaction of let-7 MicroRNAs with Lin28. Cell 147, 1080–1091. 10.1016/j.cell.2011.10.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Mayr F, Schütz A, Döge N, and Heinemann U (2012). The Lin28 coldshock domain remodels pre-let-7 microRNA. Nucleic Acids Res. 40, 7492–7506. 10.1093/NAR/GKS355. [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Heo I, Joo C, Kim YK, Ha M, Yoon MJ, Cho J, Yeom KH, Han J, and Kim VN (2009). TUT4 in Concert with Lin28 Suppresses MicroRNA Biogenesis through Pre-MicroRNA Uridylation. Cell 138, 696–708. 10.1016/J.CELL.2009.08.002. [DOI] [PubMed] [Google Scholar]
52.Piskounova E, Viswanathan SR, Janas M, LaPierre RJ, Daley GQ, Sliz P, and Gregory RI (2008). Determinants of MicroRNA Processing Inhibition by the Developmentally Regulated RNA-binding Protein Lin28. J. Biol. Chem 283, 21310–21314. 10.1074/jbc.C800108200. [DOI] [PubMed] [Google Scholar]
53.Markert A, Grimm M, Martinez J, Wiesner J, Meyerhans A, Meyuhas O, Sickmann A, and Fischer U (2008). The La-related protein LARP7 is a component of the 7SK ribonucleoprotein and affects transcription of cellular and viral polymerase II genes. EMBO Rep. 9, 569–575. 10.1038/EMBOR.2008.72. [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Müller-Mcnicoll M, Rossbach O, Hui J, and Medenbach J (2019). Auto-regulatory feedback by RNA-binding proteins. J. Mol. Cell Biol 11, 930–939. 10.1093/JMCB/MJZ043. [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Hogan DJ, Riordan DP, Gerber AP, Herschlag D, and Brown PO (2008). Diverse RNA-Binding Proteins Interact with Functionally Related Sets of RNAs, Suggesting an Extensive Regulatory System. PLoS Biol. 6, e255. 10.1371/journal.pbio.0060255. [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Mittal N, Scherrer T, Gerber AP, and Janga SC (2011). Interplay between Posttranscriptional and Posttranslational Interactions of RNA-Binding Proteins. J. Mol. Biol 409, 466–479. 10.1016/J.JMB.2011.03.064. [DOI] [PubMed] [Google Scholar]
57.Hlevnjak M, Polyansky AA, and Zagrovic B (2012). Sequence signatures of direct complementarity between mRNAs and cognate proteins on multiple levels. Nucleic Acids Res. 40, 8874–8882. 10.1093/NAR/GKS679. [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Zagrovic B, Adlhart M, and Kapral TH (2023). Coding From Binding? Molecular Interactions at the Heart of Translation. Annu. Rev. Biophys 52, 69–89. 10.1146/ANNUREV-BIOPHYS-090622-102329. [DOI] [PubMed] [Google Scholar]
59.Kapral TH, Farnhammer F, Zhao W, Lu ZJ, and Zagrovic B (2022). Widespread autogenous mRNA–protein interactions detected by CLIP-seq. Nucleic Acids Res. 50, 9984–9999. 10.1093/NAR/GKAC756. [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Carter AC, Xu J, Nakamoto MY, Wei Y, Zarnegar BJ, Shi Q, Broughton JP, Ransom RC, Salhotra A, Nagaraja SD, et al. (2020). Spen links rna-mediated endogenous retrovirus silencing and x chromosome inactivation. eLife 9, e54508. 10.7554/eLife.54508. [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Yepiskoposyan H, Aeschimann F, Nilsson D, Okoniewski M, and Mühlemann O (2011). Autoregulation of the nonsense-mediated mRNA decay pathway in human cells. RNA 17, 2108–2118. 10.1261/RNA.030247.111. [DOI] [PMC free article] [PubMed] [Google Scholar]
62.White MA, Kim E, Duffy A, Adalbert R, Phillips BU, Peters OM, Stephenson J, Yang S, Massenzio F, Lin Z, et al. (2018). TDP-43 gains function due to perturbed autoregulation in a Tardbp knock-in mouse model of ALS-FTD. Nat. Neurosci 21, 552–563. 10.1038/s41593-018-0113-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Ayala YM, De Conti L, Avendaño-Vázquez SE, Dhir A, Romano M, D’Ambrogio A, Tollervey J, Ule J, Baralle M, Buratti E, et al. (2011). TDP-43 regulates its mRNA levels through a negative feedback loop. EMBO J. 30, 277–288. 10.1038/emboj.2010.310. [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Han J, Pedersen JS, Kwon SC, Belair CD, Kim Y-K, Yeom K-H, Yang W-Y, Haussler D, Blelloch R, and Kim VN (2009). Post-transcriptional Crossregulation between Drosha and DGCR8. Cell 136, 75–84. 10.1016/j.cell.2008.10.053. [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Li Q, Brown JB, Huang H, and Bickel PJ (2011). Measuring reproducibility of high-throughput experiments. Ann. Appl. Stat 5, 1752–1779. 10.1214/11-AOAS466. [DOI] [Google Scholar]
66.Banerjee AK, Blanco MR, Bruce EA, Honson DD, Chen LM, Chow A, Bhat P, Ollikainen N, Quinodoz SA, Loney C, et al. (2020). SARS-CoV-2 Disrupts Splicing, Translation, and Protein Trafficking to Suppress Host Defenses. Cell 183, 1325–1339. 10.1016/J.CELL.2020.10.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
67.Philippe L, van den Elzen AMG, Watson MJ, and Thoreen CC (2020). Global analysis of LARP1 translation targets reveals tunable and dynamic features of 5′ TOP motifs. Proc. Natl. Acad. Sci. USA 117, 5319–5328. 10.1073/pnas.1912864117. [DOI] [PMC free article] [PubMed] [Google Scholar]
68.Brito Querido JB, Sokabe M, Kraatz S, Gordiyenko Y, Skehel JM, Fraser CS, and Ramakrishnan V (2020). Structure of a human 48S translational initiation complex. Science 369, 1220–1227. 10.1126/SCIENCE.ABA4904. [DOI] [PMC free article] [PubMed] [Google Scholar]
69.Llácer JL, Hussain T, Dong J, Villamayor L, Gordiyenko Y, and Hinnebusch AG (2021). Large-scale movement of eIF3 domains during translation initiation modulate start codon selection. Nucleic Acids Res. 49, 11491–11511. 10.1093/NAR/GKAB908. [DOI] [PMC free article] [PubMed] [Google Scholar]
70.Llácer JL, Hussain T, Marler L, Aitken CE, Thakur A, Lorsch JR, Hinnebusch AG, and Ramakrishnan V (2015). Conformational Differences between Open and Closed States of the Eukaryotic Translation Initiation Complex. Mol. Cell 59, 399–412. 10.1016/J.MOLCEL.2015.06.033. [DOI] [PMC free article] [PubMed] [Google Scholar]
71.Brito Querido J, Sokabe M, Díaz-López I, Gordiyenko Y, Fraser CS, and Ramakrishnan V (2024). The structure of a human translation initiation complex reveals two independent roles for the helicase eIF4A. Nat. Struct. Mol. Biol 31, 455–464. 10.1038/S41594-023-01196-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
72.Martin F, Ménétret JF, Simonetti A, Myasnikov AG, Vicens Q, Prongidi-Fix L, Natchiar SK, Klaholz BP, and Eriani G (2016). Ribosomal 18S rRNA base pairs with mRNA during eukaryotic translation initiation. Nat. Commun 7, 12622. 10.1038/NCOMMS12622. [DOI] [PMC free article] [PubMed] [Google Scholar]
73.Ogami K, Oishi Y, Sakamoto K, Okumura M, Yamagishi R, Inoue T, Hibino M, Nogimori T, Yamaguchi N, Furutachi K, et al. (2022). mTOR- and LARP1-dependent regulation of TOP mRNA poly(A) tail and ribosome loading. Cell Rep. 41, 111548. 10.1016/j.celrep.2022.111548. [DOI] [PubMed] [Google Scholar]
74.Berman AJ, Thoreen CC, Dedeic Z, Chettle J, Roux PP, and Blagden SP (2021). Controversies around the function of LARP1. RNA Biol. 18, 207–217. 10.1080/15476286.2020.1733787. [DOI] [PMC free article] [PubMed] [Google Scholar]
75.Tcherkezian J, Cargnello M, Romeo Y, Huttlin EL, Lavoie G, Gygi SP, and Roux PP (2014). Proteomic analysis of cap-dependent translation identifies LARP1 as a key regulator of 5′ TOP mRNA translation. Genes Dev. 28, 357–371. 10.1101/GAD.231407.113. [DOI] [PMC free article] [PubMed] [Google Scholar]
76.Lahr RM, Fonseca BD, Ciotti GE, Al-Ashtal HA, Jia JJ, Niklaus MR, Blagden SP, Alain T, and Berman AJ (2017). La-related protein 1 (LARP1) binds the mRNA cap, blocking eIF4F assembly on TOP mRNAs. eLife 6, e24146. 10.7554/eLife.24146. [DOI] [PMC free article] [PubMed] [Google Scholar]
77.Fonseca BD, Zakaria C, Jia JJ, Graber TE, Svitkin Y, Tahmasebi S, Healy D, Hoang HD, Jensen JM, Diao IT, et al. (2015). La-related protein 1 (LARP1) represses terminal oligopyrimidine (TOP) mRNA translation downstream of mTOR complex 1 (mTORC1). J. Biol. Chem 290, 15996–16020. 10.1074/jbc.M114.621730. [DOI] [PMC free article] [PubMed] [Google Scholar]
78.Fuentes P, Pelletier J, Martinez-Herráez C, Diez-Obrero V, Iannizzotto F, Rubio T, Garcia-Cajide M, Menoyo S, Moreno V, Salazar R, et al. (2021). The 40S-LARP1 complex reprograms the cellular translatome upon mTOR inhibition to preserve the protein synthetic capacity. Sci. Adv 7, eabg9275. 10.1126/SCIADV.ABG9275. [DOI] [PMC free article] [PubMed] [Google Scholar]
79.Gentilella A, Morón-Duran FD, Fuentes P, Rocha GZ, Riaño-Canalias F, Pelletier J, Ruiz M, Turón G, Castaño J, Tauler A, et al. (2017). Autogenous Control of 5′ TOP mRNA Stability by 40S Ribosomes. Mol. Cell 67, 55–70. 10.1016/J.MOLCEL.2017.06.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
80.Saba JA, Huang Z, Schole KL, Ye X, Bhatt SD, Li Y, Timp W, Cheng J, and Green R (2024). LARP1 binds ribosomes and TOP mRNAs in repressed complexes. EMBO J. 43, 6555–6572. 10.1038/s44318-024-00294-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
81.Brito Querido J, Sokabe M, Díaz-López I, Gordiyenko Y, Zuber P, Du Y, Albacete-Albacete L, Ramakrishnan V, and Fraser CS (2024). Human tumor suppressor protein Pdcd4 binds at the mRNA entry channel in the 40S small ribosomal subunit. Nat. Commun 15, 6633. 10.1038/S41467-024-50672-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
82.Lahr RM, Mack SM, Héroux A, Blagden SP, Bousquet-Antonelli C, Deragon JM, and Berman AJ (2015). The La-related protein 1-specific domain repurposes HEAT-like repeats to directly bind a 5′ TOP sequence. Nucleic Acids Res. 43, 8077–8088. 10.1093/NAR/GKV748. [DOI] [PMC free article] [PubMed] [Google Scholar]
83.Ma XM, and Blenis J (2009). Molecular mechanisms of mTOR-mediated translational control. Nat. Rev. Mol. Cell Biol 10, 307–318. 10.1038/nrm2672. [DOI] [PubMed] [Google Scholar]
84.Thoreen CC, Chantranupong L, Keys HR, Wang T, Gray NS, and Sabatini DM (2012). A unifying model for mTORC1-mediated regulation of mRNA translation. Nature 485, 109–113. 10.1038/nature11083. [DOI] [PMC free article] [PubMed] [Google Scholar]
85.Yang M, Lu Y, Piao W, and Jin H (2022). The Translational Regulation in mTOR Pathway. Biomolecules 12, 802. 10.3390/biom12060802. [DOI] [PMC free article] [PubMed] [Google Scholar]
86.Liu GY, and Sabatini DM (2020). mTOR at the nexus of nutrition, growth, ageing and disease. Nat. Rev. Mol. Cell Biol 21, 183–203. 10.1038/s41580-019-0199-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
87.Hong S, Freeberg MA, Han T, Kamath A, Yao Y, Fukuda T, Suzuki T, Kim JK, and Inoki K (2017). LARP1 functions as a molecular switch for mTORC1-mediated translation of an essential class of mRNAs. eLife 6, e25237. 10.7554/eLife.25237. [DOI] [PMC free article] [PubMed] [Google Scholar]
88.Qin X, Jiang B, and Zhang Y (2016). 4E-BP1, a multifactor regulated multifunctional protein. Cell Cycle 15, 781–786. 10.1080/15384101.2016.1151581. [DOI] [PMC free article] [PubMed] [Google Scholar]
89.Gingras AC, Gygi SP, Raught B, Polakiewicz RD, Abraham RT, Hoekstra MF, Aebersold R, and Sonenberg N (1999). Regulation of 4E-BP1 phosphorylation: a novel two-step mechanism. Genes Dev. 13, 1422–1437. 10.1101/gad.13.11.1422. [DOI] [PMC free article] [PubMed] [Google Scholar]
90.Hsieh AC, Liu Y, Edlind MP, Ingolia NT, Janes MR, Sher A, Shi EY, Stumpf CR, Christensen C, Bonham MJ, et al. (2012). The translational landscape of mTOR signalling steers cancer initiation and metastasis. Nature 485, 55–61. 10.1038/nature10912. [DOI] [PMC free article] [PubMed] [Google Scholar]
91.Thoreen CC, Kang SA, Chang JW, Liu Q, Zhang J, Gao Y, Reichling LJ, Sim T, Sabatini DM, and Gray NS (2009). An ATP-competitive mammalian target of rapamycin inhibitor reveals rapamycin-resistant functions of mTORC1. J. Biol. Chem 284, 8023–8032. 10.1074/jbc.M900301200. [DOI] [PMC free article] [PubMed] [Google Scholar]
92.Guo JK, Blanco MR, Walkup WG, Bonesteele G, Urbinati CR, Banerjee AK, Chow A, Ettlin O, Strehle M, Peyda P, et al. (2024). Denaturing purifications demonstrate that PRC2 and other widely reported chromatin proteins do not appear to bind directly to RNA in vivo. Mol. Cell 84, 1271–1289. 10.1016/J.MOLCEL.2024.01.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
93.Jin H, Xu W, Rahman R, Na D, Fieldsend A, Song W, Liu S, Li C, and Rosbash M (2020). TRIBE editing reveals specific mRNA targets of eIF4E-BP in Drosophila and in mammals. Sci. Adv 6, eabb8771. 10.1126/sciadv.abb8771. [DOI] [PMC free article] [PubMed] [Google Scholar]
94.Hochstoeger T, Papasaikas P, Piskadlo E, and Chao JA (2024). Distinct roles of LARP1 and 4EBP1/2 in regulating translation and stability of 5′ TOP mRNAs. Sci. Adv 10, eadi7830. 10.1126/SCIADV.ADI7830. [DOI] [PMC free article] [PubMed] [Google Scholar]
95.Darzacq X, Jády BE, Verheggen C, Kiss AM, Bertrand E, and Kiss T (2002). Cajal body-specific small nuclear RNAs: a novel class of 2′-O-methylation and pseudouridylation guide RNAs. EMBO J. 21, 2746–2756. 10.1093/EMBOJ/21.11.2746. [DOI] [PMC free article] [PubMed] [Google Scholar]
96.De Boer EMJ, Orie VK, Williams T, Baker MR, De Oliveira HM, Polvikoski T, Silsby M, Menon P, Van Den Bos M, Halliday GM, et al. (2020). TDP-43 proteinopathies: a new wave of neurodegenerative diseases. J. Neurol. Neurosurg. Psychiatry 92, 86–95. 10.1136/JNNP-2020-322983. [DOI] [PMC free article] [PubMed] [Google Scholar]
97.Yahara M, Kitamura A, and Kinjo M (2017). U6 snRNA expression prevents toxicity in TDP-43-knockdown cells. PLoS One 12, e0187813. 10.1371/JOURNAL.PONE.0187813. [DOI] [PMC free article] [PubMed] [Google Scholar]
98.Izumikawa K, Nobe Y, Ishikawa H, Yamauchi Y, Taoka M, Sato K, Nakayama H, Simpson RJ, Isobe T, and Takahashi N (2019). TDP-43 regulates site-specific 2′-O-methylation of U1 and U2 snRNAs via controlling the Cajal body localization of a subset of C/D scaRNAs. Nucleic Acids Res. 47, 2487–2505. 10.1093/NAR/GKZ086. [DOI] [PMC free article] [PubMed] [Google Scholar]
99.Asakawa K, Handa H, and Kawakami K (2021). Multi-phaseted problems of TDP-43 in selective neuronal vulnerability in ALS. Cell. Mol. Life Sci 78, 4453–4465. 10.1007/S00018-021-03792-Z. [DOI] [PMC free article] [PubMed] [Google Scholar]
100.Raught B, Gingras AC, Gygi SP, Imataka H, Morino S, Gradi A, Aebersold R, and Sonenberg N (2000). Serum-stimulated, rapamycin-sensitive phosphorylation sites in the eukaryotic translation initiation factor 4GI. EMBO J. 19, 434–444. 10.1093/EMBOJ/19.3.434. [DOI] [PMC free article] [PubMed] [Google Scholar]
101.Martínez-Alonso E, Guerra-Pérez N, Escobar-Peso A, Peracho L, Vera-Lechuga R, Cruz-Culebras A, Masjuan J, and Alcázar A (2022). Phosphorylation of Eukaryotic Initiation Factor 4G1 (eIF4G1) at Ser1147 Is Specific for eIF4G1 Bound to eIF4E in Delayed Neuronal Death after Ischemia. Int. J. Mol. Sci 23, 1830. 10.3390/IJMS23031830. [DOI] [PMC free article] [PubMed] [Google Scholar]
102.Shishkin AA, Giannoukos G, Kucukural A, Ciulla D, Busby M, Surka C, Chen J, Bhattacharyya RP, Rudy RF, Patel MM, et al. (2015). Simultaneous generation of many RNA-seq libraries in a single reaction. Nat. Methods 12, 323–325. 10.1038/nmeth.3313. [DOI] [PMC free article] [PubMed] [Google Scholar]
103.Parnas O, Jovanovic M, Eisenhaure TM, Herbst RH, Dixit A, Ye CJ, Przybylski D, Platt RJ, Tirosh I, Sanjana NE, et al. (2015). A Genome-wide CRISPR Screen in Primary Immune Cells to Dissect Regulatory Networks. Cell 162, 675–686. 10.1016/j.cell.2015.06.059. [DOI] [PMC free article] [PubMed] [Google Scholar]
104.Hughes CS, Moggridge S, Müller T, Sorensen PH, Morin GB, and Krijgsveld J (2019). Single-pot, solid-phase-enhanced sample preparation for proteomics experiments. Nat. Protoc 14, 68–85. 10.1038/s41596-018-0082-x. [DOI] [PubMed] [Google Scholar]
105.Rappsilber J, Mann M, and Ishihama Y (2007). Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat. Protoc 2, 1896–1906. 10.1038/nprot.2007.261. [DOI] [PubMed] [Google Scholar]
106.Cox J, and Mann M (2008). MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol 26, 1367–1372. 10.1038/nbt.1511. [DOI] [PubMed] [Google Scholar]
107.Kimanius D, Jamali K, Wilkinson ME, Lövestam S, Velazhahan V, Nakane T, and Scheres SHW (2024). Data-driven regularization lowers the size barrier of cryo-EM structure determination. Nat. Methods 21, 1216–1221. 10.1038/S41592-024-02304-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
108.Rohou A, and Grigorieff N (2015). CTFFIND4: Fast and accurate defocus estimation from electron micrographs. J. Struct. Biol 192, 216–221. 10.1016/J.JSB.2015.08.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
109.Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589. 10.1038/S41586-021-03819-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
110.Casañal A, Lohkamp B, and Emsley P (2020). Current developments in Coot for macromolecular model building of Electron Cryo-microscopy and Crystallographic Data. Protein Sci. 29, 1069–1078. 10.1002/PRO.3791. [DOI] [PMC free article] [PubMed] [Google Scholar]
111.Afonine PV, Poon BK, Read RJ, Sobolev OV, Terwilliger TC, Urzhumtsev A, and Adams PD (2018). Real-space refinement in PHENIX for cryo-EM and crystallography. Acta Crystallogr. D Struct. Biol 74, 531–544. 10.1107/S2059798318006551. [DOI] [PMC free article] [PubMed] [Google Scholar]
112.Goddard TD, Huang CC, Meng EC, Pettersen EF, Couch GS, Morris JH, and Ferrin TE (2018). UCSF ChimeraX: Meeting modern challenges in visualization and analysis. Protein Sci. 27, 14–25. 10.1002/PRO.3235. [DOI] [PMC free article] [PubMed] [Google Scholar]
113.Uhlen M, Bandrowski A, Carr S, Edwards A, Ellenberg J, Lundberg E, Rimm DL, Rodriguez H, Hiltke T, Snyder M, and Yamamoto T (2016). A proposal for validation of antibodies. Nat. Methods 13, 823–827. 10.1038/nmeth.3995. [DOI] [PMC free article] [PubMed] [Google Scholar]
114.Blue SM, Yee BA, Pratt GA, Mueller JR, Park SS, Shishkin AA, Starner AC, Van Nostrand EL, and Yeo GW (2022). Transcriptome-wide identification of RNA-binding protein binding sites using seCLIPseq. Nat. Protoc 17, 1223–1265. 10.1038/S41596-022-00680-Z. [DOI] [PMC free article] [PubMed] [Google Scholar]
115.Ernst C, Duc J, and Trono D (2023). Efficient and sensitive profiling of RNA-protein interactions using TLC-CLIP. Nucleic Acids Res. 51, e70. 10.1093/NAR/GKAD466. [DOI] [PMC free article] [PubMed] [Google Scholar]
116.Zhao Y, Zhang Y, Teng Y, Liu K, Liu Y, Li W, and Wu L (2019). SpyCLIP: an easy-to-use and high-throughput compatible CLIP platform for the characterization of protein-RNA interactions with high accuracy. Nucleic Acids Res. 47, e33. 10.1093/NAR/GKZ049. [DOI] [PMC free article] [PubMed] [Google Scholar]
117.Manakov SA, Shishkin AA, Yee BA, Shen KA, Cox DC, Park SS, Foster HM, Chapman KB, Yeo GW, and Van Nostrand EL (2022). Scalable and deep profiling of mRNA targets for individual micro-RNAs with chimeric eCLIP. Preprint at bioRxiv, 2022.02.13.480296. 10.1101/2022.02.13.480296. [DOI] [Google Scholar]
118.Schwanhäusser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J, Chen W, and Selbach M (2011). Global quantification of mammalian gene expression control. Nature 473, 337–342. 10.1038/nature10098. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

MMC1

NIHMS2099475-supplement-MMC1.xlsx^{(21.6KB, xlsx)}

MMC3

NIHMS2099475-supplement-MMC3.xlsx^{(48.1KB, xlsx)}

MMC6

NIHMS2099475-supplement-MMC6.xlsx^{(67.6KB, xlsx)}

MMC2

NIHMS2099475-supplement-MMC2.xlsx^{(28.2KB, xlsx)}

MMC7

NIHMS2099475-supplement-MMC7.xlsx^{(15.6KB, xlsx)}

MMC5

NIHMS2099475-supplement-MMC5.xlsx^{(255.1KB, xlsx)}

MMC4

NIHMS2099475-supplement-MMC4.xlsx^{(38.8KB, xlsx)}

NIHMS2099475-supplement-8.pdf^{(5.5MB, pdf)}

[R1] 1.Sonenberg N, and Hinnebusch AG (2009). Regulation of Translation Initiation in Eukaryotes: Mechanisms and Biological Targets. Cell 136, 731–745. 10.1016/j.cell.2009.01.042. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R2] 2.Castello A, Fischer B, Hentze MW, and Preiss T (2013). RNA-binding proteins in Mendelian disease. Trends Genet. 29, 318–327. 10.1016/j.tig.2013.01.004. [DOI] [PubMed] [Google Scholar]

[R3] 3.Gao F-B, and Taylor JP (2012). RNA-binding proteins in neurological disease. Brain Res. 1462, 1–2. 10.1016/j.brainres.2012.05.038. [DOI] [PubMed] [Google Scholar]

[R4] 4.Gebauer F, Schwarzl T, Valcárcel J, and Hentze MW (2021). RNA-binding proteins in human genetic disease. Nat. Rev. Genet 22, 185–198. 10.1038/s41576-020-00302-y. [DOI] [PubMed] [Google Scholar]

[R5] 5.Gerstberger S, Hafner M, and Tuschl T (2014). A census of human RNA-binding proteins. Nat. Rev. Genet 15, 829–845. 10.1038/nrg3813. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R6] 6.Caudron-Herger M, Jansen RE, Wassmer E, and Diederichs S (2021). RBP2GO: a comprehensive pan-species database on RNA-binding proteins, their interactions and functions. Nucleic Acids Res. 49, D425–D436. 10.1093/nar/gkaa1040. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R7] 7.Trendel J, Schwarzl T, Horos R, Prakash A, Bateman A, Hentze MW, and Krijgsveld J (2019). The Human RNA-Binding Proteome and Its Dynamics during Translational Arrest. Cell 176, 391–403. 10.1016/J.CELL.2018.11.004. [DOI] [PubMed] [Google Scholar]

[R8] 8.Queiroz RML, Smith T, Villanueva E, Marti-Solano M, Monti M, Pizzinga M, Mirea DM, Ramakrishna M, Harvey RF, Dezi V, et al. (2019). Comprehensive identification of RNA–protein interactions in any organism using orthogonal organic phase separation (OOPS). Nat. Biotechnol 37, 169–178. 10.1038/s41587-018-0001-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R9] 9.Urdaneta EC, Vieira-Vieira CH, Hick T, Wessels HH, Figini D, Moschall R, Medenbach J, Ohler U, Granneman S, Selbach M, et al. (2019). Purification of cross-linked RNA-protein complexes by phenoltoluol extraction. Nat. Commun 10, 990. 10.1038/S41467-019-08942-3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R10] 10.Perez-Perri JI, Rogell B, Schwarzl T, Stein F, Zhou Y, Rettel M, Brosig A, and Hentze MW (2018). Discovery of RNA-binding proteins and characterization of their dynamic responses by enhanced RNA interactome capture. Nat. Commun 9, 4408. 10.1038/S41467-018-06557-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R11] 11.King OD, Gitler AD, and Shorter J (2012). The tip of the iceberg: RNA-binding proteins with prion-like domains in neurodegenerative disease. Brain Res. 1462, 61–80. 10.1016/j.brainres.2012.01.016. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R12] 12.Guttman M, Amit I, Garber M, French C, Lin MF, Feldser D, Huarte M, Zuk O, Carey BW, Cassady JP, et al. (2009). Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals. Nature 458, 223–227. 10.1038/nature07672. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R13] 13.Statello L, Guo CJ, Chen LL, and Huarte M (2021). Gene regulation by long non-coding RNAs and its biological functions. Nat. Rev. Mol. Cell Biol 22, 96–118. 10.1038/s41580-020-00315-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R14] 14.Guo JK, and Guttman M (2022). Regulatory non-coding RNAs: everything is possible, but what is important? Nat. Methods 19, 1156–1159. 10.1038/s41592-022-01629-6. [DOI] [PubMed] [Google Scholar]

[R15] 15.Guttman M, and Rinn JL (2012). Modular regulatory principles of large non-coding RNAs. Nature 482, 339–346. 10.1038/nature10887. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R16] 16.Chu C, Zhang QC, Da Rocha ST, Flynn RA, Bharadwaj M, Calabrese JM, Magnuson T, Heard E, and Chang HY (2015). Systematic Discovery of Xist RNA Binding Proteins. Cell 161, 404–416. 10.1016/J.CELL.2015.03.025. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R17] 17.McHugh CA, Chen CK, Chow A, Surka CF, Tran C, McDonel P, Pandya-Jones A, Blanco M, Burghard C, Moradian A, et al. (2015). The Xist lncRNA interacts directly with SHARP to silence transcription through HDAC3. Nature 521, 232–236. 10.1038/nature14443. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R18] 18.Robert-Finestra T, Tan BF, Mira-Bontenbal H, Timmers E, Gontan C, Merzouk S, Giaimo BD, Dossin F, van IJcken WFJ, Martens JWM, et al. (2021). SPEN is required for Xist upregulation during initiation of X chromosome inactivation. Nat. Commun 12, 7000. 10.1038/s41467-021-27294-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R19] 19.Engreitz JM, Pandya-Jones A, McDonel P, Shishkin A, Sirokman K, Surka C, Kadri S, Xing J, Goren A, Lander ES, et al. (2013). The Xist lncRNA Exploits Three-Dimensional Genome Architecture to Spread Across the X Chromosome. Science 341, 1237973. 10.1126/science.1237973. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R20] 20.Jachowicz JW, Strehle M, Banerjee AK, Blanco MR, Thai J, and Guttman M (2022). Xist spatially amplifies SHARP/SPEN recruitment to balance chromosome-wide silencing and specificity to the X chromosome. Nat. Struct. Mol. Biol 29, 239–249. 10.1038/S41594-022-00739-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R21] 21.Hafner M, Landthaler M, Burger L, Khorshid M, Hausser J, Berninger P, Rothballer A, Ascano M Jr., Jungkamp A-C, Munschauer M, et al. (2010). Transcriptome-wide Identification of RNA-Binding Protein and MicroRNA Target Sites by PAR-CLIP. Cell 141, 129–141. 10.1016/j.cell.2010.03.009. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R22] 22.Chi SW, Zang JB, Mele A, and Darnell RB (2009). Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature 460, 479–486. 10.1038/nature08170. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R23] 23.Van Nostrand EL, Pratt GA, Shishkin AA, Gelboin-Burkhart C, Fang MY, Sundararaman B, Blue SM, Nguyen TB, Surka C, Elkins K, et al. (2016). Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP). Nat. Methods 13, 508–514. 10.1038/nmeth.3810. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R24] 24.Zhang C, and Darnell RB (2011). Mapping in vivo protein-RNA interactions at single-nucleotide resolution from HITS-CLIP data. Nat. Biotechnol 29, 607–614. 10.1038/nbt.1873. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R25] 25.Ule J, Jensen KB, Ruggiu M, Mele A, Ule A, and Darnell RB (2003). CLIP identifies Nova-regulated RNA networks in the brain. Science 302, 1212–1215. 10.1126/science.1090095. [DOI] [PubMed] [Google Scholar]

[R26] 26.Ramanathan M, Porter DF, and Khavari PA (2019). Methods to study RNA–protein interactions. Nat. Methods 16, 225–234. 10.1038/s41592-019-0330-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R27] 27.Lorenz DA, Her H-L, Shen KA, Rothamel K, Hutt KR, Nojadera AC, Bruns SC, Manakov SA, Yee BA, Chapman KB, et al. (2023). Multiplexed transcriptome discovery of RNA-binding protein binding sites by antibody-barcode eCLIP. Nat. Methods 20, 65–69. 10.1038/s41592-022-01708-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R28] 28.Van Nostrand EL, Pratt GA, Yee BA, Wheeler EC, Blue SM, Mueller J, Park SS, Garcia KE, Gelboin-Burkhart C, Nguyen TB, et al. (2020). Principles of RNA processing from analysis of enhanced CLIP maps for 150 RNA binding proteins. Genome Biol. 21, 90. 10.1186/s13059-020-01982-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R29] 29.Van Nostrand EL, Freese P, Pratt GA, Wang X, Wei X, Xiao R, Blue SM, Chen J-Y, Cody NAL, Dominguez D, et al. (2020). A large-scale binding and functional map of human RNA-binding proteins. Nature 583, 711–719. 10.1038/s41586-020-2077-3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R30] 30.Quinodoz SA, Bhat P, Chovanec P, Jachowicz JW, Ollikainen N, Detmar E, Soehalim E, and Guttman M (2022). SPRITE: a genome-wide method for mapping higher-order 3D interactions in the nucleus using combinatorial split-and-pool barcoding. Nat. Protoc 17, 36–75. 10.1038/s41596-021-00633-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R31] 31.Quinodoz SA, Jachowicz JW, Bhat P, Ollikainen N, Banerjee AK, Goronzy IN, Blanco MR, Chovanec P, Chow A, Markaki Y, et al. (2021). RNA promotes the formation of spatial compartments in the nucleus. Cell 184, 5775–5790. 10.1016/J.CELL.2021.10.014. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R32] 32.Quinodoz SA, Ollikainen N, Tabak B, Palla A, Schmidt JM, Detmar E, Lai MM, Shishkin AA, Bhat P, Takei Y, et al. (2018). Higher-Order Inter-chromosomal Hubs Shape 3D Genome Organization in the Nucleus. Cell 174, 744–757. 10.1016/j.cell.2018.05.024. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R33] 33.Perez AA, Goronzy IN, Blanco MR, Yeh BT, Guo JK, Lopes CS, Ettlin O, Burr A, and Guttman M (2024). ChIP-DIP maps binding of hundreds of proteins to DNA simultaneously and identifies diverse gene regulatory elements. Nat. Genet 56, 2827–2841. 10.1038/s41588-024-02000-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R34] 34.Goronzy IN (2025). Unravelling the complexity of gene regulation through multiplexed protein mapping. Nat. Rev. Mol. Cell Biol 26, 251. 10.1038/s41580-025-00830-7. [DOI] [PubMed] [Google Scholar]

[R35] 35.Pillai RS, Grimmler M, Meister G, Will CL, Lührmann R, Fischer U, and Schümperli D (2003). Unique Sm core structure of U7 snRNPs: assembly by a specialized SMN complex and the role of a new component, Lsm11, in histone RNA processing. Genes Dev. 17, 2321–2333. 10.1101/GAD.274403. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R36] 36.Singh RN, Howell MD, Ottesen EW, and Singh NN (2017). Diverse role of survival motor neuron protein. Biochim. Biophys. Acta Gene Regul. Mech 1860, 299–315. 10.1016/J.BBAGRM.2016.12.008. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R37] 37.Bi X, Xu Y, Li T, Li X, Li W, Shao W, Wang K, Zhan G, Wu Z, Liu W, et al. (2019). RNA Targets Ribogenesis Factor WDR43 to Chromatin for Transcription and Pluripotency Control. Mol. Cell 75, 102–116. 10.1016/J.MOLCEL.2019.05.007. [DOI] [PubMed] [Google Scholar]

[R38] 38.Sun Z, Yu H, Zhao J, Tan T, Pan H, Zhu Y, Chen L, Zhang C, Zhang L, Lei A, et al. (2022). LIN28 coordinately promotes nucleolar/ribosomal functions and represses the 2C-like transcriptional program in pluripotent stem cells. Protein Cell 13, 490–512. 10.1007/s13238-021-00864-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R39] 39.Chen HK, Pai CY, Huang JY, and Yeh NH (1999). Human Nopp140, which interacts with RNA polymerase I: implications for rRNA gene transcription and nucleolar structural organization. Mol. Cell. Biol 19, 8536–8546. 10.1128/MCB.19.12.8536. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R40] 40.Bizarro J, Bhardwaj A, Smith S, and Meier UT (2019). Nopp140-mediated concentration of telomerase in Cajal bodies regulates telomere length. Mol. Biol. Cell 30, 3136–3150. 10.1091/mbc.E19-08-0429. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R41] 41.Tseng TL, Wang YT, Tsao CY, Ke YT, Lee YC, Hsu HJ, Poss KD, and Chen CH (2021). The RNA helicase Ddx52 functions as a growth switch in juvenile zebrafish. Development 148, dev199578. 10.1242/DEV.199578. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R42] 42.Tafforeau L, Zorbas C, Langhendries JL, Mullineux ST, Stamatopoulou V, Mullier R, Wacheul L, and Lafontaine DLJ (2013). The Complexity of Human Ribosome Biogenesis Revealed by Systematic Nucleolar Screening of Pre-rRNA Processing Factors. Mol. Cell 51, 539–551. 10.1016/J.MOLCEL.2013.08.011. [DOI] [PubMed] [Google Scholar]

[R43] 43.Jutzi D, Campagne S, Schmidt R, Reber S, Mechtersheimer J, Gypas F, Schweingruber C, Colombo M, von Schroetter C, Loughlin FE, et al. (2020). Aberrant interaction of FUS with the U1 snRNA provides a molecular mechanism of FUS induced amyotrophic lateral sclerosis. Nat. Commun 11, 6341. 10.1038/s41467-020-20191-3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R44] 44.Jobert L, Pinzón N, Van Herreweghe E, Jády BE, Guialis A, Kiss T, and Tora L (2009). Human U1 snRNA forms a new chromatin-associated snRNP with TAF15. EMBO Rep. 10, 494–500. 10.1038/EMBOR.2009.24. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R45] 45.Rappsilber J, Ajuh P, Lamond AI, and Mann M (2001). SPF30 Is an Essential Human Splicing Factor Required for Assembly of the U4/U5/U6 Tri-small Nuclear Ribonucleoprotein into the Spliceosome. J. Biol. Chem 276, 31142–31150. 10.1074/jbc.M103620200. [DOI] [PubMed] [Google Scholar]

[R46] 46.Bayfield MA, and Maraia RJ (2009). Precursor-product discrimination by La protein during tRNA metabolism. Nat. Struct. Mol. Biol 16, 430–437. 10.1038/nsmb.1573. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R47] 47.Blewett NH, and Maraia RJ (2018). La involvement in tRNA and other RNA processing events including differences among yeast and other eukaryotes. Biochim. Biophys. Acta Gene Regul. Mech 1861, 361–372. 10.1016/j.bbagrm.2018.01.013. [DOI] [PubMed] [Google Scholar]

[R48] 48.Piskounova E, Polytarchou C, Thornton JE, LaPierre RJ, Pothoulakis C, Hagan JP, Iliopoulos D, and Gregory RI (2011). Lin28A and Lin28B Inhibit let-7 MicroRNA Biogenesis by Distinct Mechanisms. Cell 147, 1066–1079. 10.1016/j.cell.2011.10.039. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R49] 49.Nam Y, Chen C, Gregory RI, Chou JJ, and Sliz P (2011). Molecular basis for interaction of let-7 MicroRNAs with Lin28. Cell 147, 1080–1091. 10.1016/j.cell.2011.10.020. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R50] 50.Mayr F, Schütz A, Döge N, and Heinemann U (2012). The Lin28 coldshock domain remodels pre-let-7 microRNA. Nucleic Acids Res. 40, 7492–7506. 10.1093/NAR/GKS355. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R51] 51.Heo I, Joo C, Kim YK, Ha M, Yoon MJ, Cho J, Yeom KH, Han J, and Kim VN (2009). TUT4 in Concert with Lin28 Suppresses MicroRNA Biogenesis through Pre-MicroRNA Uridylation. Cell 138, 696–708. 10.1016/J.CELL.2009.08.002. [DOI] [PubMed] [Google Scholar]

[R52] 52.Piskounova E, Viswanathan SR, Janas M, LaPierre RJ, Daley GQ, Sliz P, and Gregory RI (2008). Determinants of MicroRNA Processing Inhibition by the Developmentally Regulated RNA-binding Protein Lin28. J. Biol. Chem 283, 21310–21314. 10.1074/jbc.C800108200. [DOI] [PubMed] [Google Scholar]

[R53] 53.Markert A, Grimm M, Martinez J, Wiesner J, Meyerhans A, Meyuhas O, Sickmann A, and Fischer U (2008). The La-related protein LARP7 is a component of the 7SK ribonucleoprotein and affects transcription of cellular and viral polymerase II genes. EMBO Rep. 9, 569–575. 10.1038/EMBOR.2008.72. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R54] 54.Müller-Mcnicoll M, Rossbach O, Hui J, and Medenbach J (2019). Auto-regulatory feedback by RNA-binding proteins. J. Mol. Cell Biol 11, 930–939. 10.1093/JMCB/MJZ043. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R55] 55.Hogan DJ, Riordan DP, Gerber AP, Herschlag D, and Brown PO (2008). Diverse RNA-Binding Proteins Interact with Functionally Related Sets of RNAs, Suggesting an Extensive Regulatory System. PLoS Biol. 6, e255. 10.1371/journal.pbio.0060255. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R56] 56.Mittal N, Scherrer T, Gerber AP, and Janga SC (2011). Interplay between Posttranscriptional and Posttranslational Interactions of RNA-Binding Proteins. J. Mol. Biol 409, 466–479. 10.1016/J.JMB.2011.03.064. [DOI] [PubMed] [Google Scholar]

[R57] 57.Hlevnjak M, Polyansky AA, and Zagrovic B (2012). Sequence signatures of direct complementarity between mRNAs and cognate proteins on multiple levels. Nucleic Acids Res. 40, 8874–8882. 10.1093/NAR/GKS679. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R58] 58.Zagrovic B, Adlhart M, and Kapral TH (2023). Coding From Binding? Molecular Interactions at the Heart of Translation. Annu. Rev. Biophys 52, 69–89. 10.1146/ANNUREV-BIOPHYS-090622-102329. [DOI] [PubMed] [Google Scholar]

[R59] 59.Kapral TH, Farnhammer F, Zhao W, Lu ZJ, and Zagrovic B (2022). Widespread autogenous mRNA–protein interactions detected by CLIP-seq. Nucleic Acids Res. 50, 9984–9999. 10.1093/NAR/GKAC756. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R60] 60.Carter AC, Xu J, Nakamoto MY, Wei Y, Zarnegar BJ, Shi Q, Broughton JP, Ransom RC, Salhotra A, Nagaraja SD, et al. (2020). Spen links rna-mediated endogenous retrovirus silencing and x chromosome inactivation. eLife 9, e54508. 10.7554/eLife.54508. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R61] 61.Yepiskoposyan H, Aeschimann F, Nilsson D, Okoniewski M, and Mühlemann O (2011). Autoregulation of the nonsense-mediated mRNA decay pathway in human cells. RNA 17, 2108–2118. 10.1261/RNA.030247.111. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R62] 62.White MA, Kim E, Duffy A, Adalbert R, Phillips BU, Peters OM, Stephenson J, Yang S, Massenzio F, Lin Z, et al. (2018). TDP-43 gains function due to perturbed autoregulation in a Tardbp knock-in mouse model of ALS-FTD. Nat. Neurosci 21, 552–563. 10.1038/s41593-018-0113-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R63] 63.Ayala YM, De Conti L, Avendaño-Vázquez SE, Dhir A, Romano M, D’Ambrogio A, Tollervey J, Ule J, Baralle M, Buratti E, et al. (2011). TDP-43 regulates its mRNA levels through a negative feedback loop. EMBO J. 30, 277–288. 10.1038/emboj.2010.310. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R64] 64.Han J, Pedersen JS, Kwon SC, Belair CD, Kim Y-K, Yeom K-H, Yang W-Y, Haussler D, Blelloch R, and Kim VN (2009). Post-transcriptional Crossregulation between Drosha and DGCR8. Cell 136, 75–84. 10.1016/j.cell.2008.10.053. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R65] 65.Li Q, Brown JB, Huang H, and Bickel PJ (2011). Measuring reproducibility of high-throughput experiments. Ann. Appl. Stat 5, 1752–1779. 10.1214/11-AOAS466. [DOI] [Google Scholar]

[R66] 66.Banerjee AK, Blanco MR, Bruce EA, Honson DD, Chen LM, Chow A, Bhat P, Ollikainen N, Quinodoz SA, Loney C, et al. (2020). SARS-CoV-2 Disrupts Splicing, Translation, and Protein Trafficking to Suppress Host Defenses. Cell 183, 1325–1339. 10.1016/J.CELL.2020.10.004. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R67] 67.Philippe L, van den Elzen AMG, Watson MJ, and Thoreen CC (2020). Global analysis of LARP1 translation targets reveals tunable and dynamic features of 5′ TOP motifs. Proc. Natl. Acad. Sci. USA 117, 5319–5328. 10.1073/pnas.1912864117. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R68] 68.Brito Querido JB, Sokabe M, Kraatz S, Gordiyenko Y, Skehel JM, Fraser CS, and Ramakrishnan V (2020). Structure of a human 48S translational initiation complex. Science 369, 1220–1227. 10.1126/SCIENCE.ABA4904. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R69] 69.Llácer JL, Hussain T, Dong J, Villamayor L, Gordiyenko Y, and Hinnebusch AG (2021). Large-scale movement of eIF3 domains during translation initiation modulate start codon selection. Nucleic Acids Res. 49, 11491–11511. 10.1093/NAR/GKAB908. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R70] 70.Llácer JL, Hussain T, Marler L, Aitken CE, Thakur A, Lorsch JR, Hinnebusch AG, and Ramakrishnan V (2015). Conformational Differences between Open and Closed States of the Eukaryotic Translation Initiation Complex. Mol. Cell 59, 399–412. 10.1016/J.MOLCEL.2015.06.033. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R71] 71.Brito Querido J, Sokabe M, Díaz-López I, Gordiyenko Y, Fraser CS, and Ramakrishnan V (2024). The structure of a human translation initiation complex reveals two independent roles for the helicase eIF4A. Nat. Struct. Mol. Biol 31, 455–464. 10.1038/S41594-023-01196-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R72] 72.Martin F, Ménétret JF, Simonetti A, Myasnikov AG, Vicens Q, Prongidi-Fix L, Natchiar SK, Klaholz BP, and Eriani G (2016). Ribosomal 18S rRNA base pairs with mRNA during eukaryotic translation initiation. Nat. Commun 7, 12622. 10.1038/NCOMMS12622. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R73] 73.Ogami K, Oishi Y, Sakamoto K, Okumura M, Yamagishi R, Inoue T, Hibino M, Nogimori T, Yamaguchi N, Furutachi K, et al. (2022). mTOR- and LARP1-dependent regulation of TOP mRNA poly(A) tail and ribosome loading. Cell Rep. 41, 111548. 10.1016/j.celrep.2022.111548. [DOI] [PubMed] [Google Scholar]

[R74] 74.Berman AJ, Thoreen CC, Dedeic Z, Chettle J, Roux PP, and Blagden SP (2021). Controversies around the function of LARP1. RNA Biol. 18, 207–217. 10.1080/15476286.2020.1733787. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R75] 75.Tcherkezian J, Cargnello M, Romeo Y, Huttlin EL, Lavoie G, Gygi SP, and Roux PP (2014). Proteomic analysis of cap-dependent translation identifies LARP1 as a key regulator of 5′ TOP mRNA translation. Genes Dev. 28, 357–371. 10.1101/GAD.231407.113. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R76] 76.Lahr RM, Fonseca BD, Ciotti GE, Al-Ashtal HA, Jia JJ, Niklaus MR, Blagden SP, Alain T, and Berman AJ (2017). La-related protein 1 (LARP1) binds the mRNA cap, blocking eIF4F assembly on TOP mRNAs. eLife 6, e24146. 10.7554/eLife.24146. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R77] 77.Fonseca BD, Zakaria C, Jia JJ, Graber TE, Svitkin Y, Tahmasebi S, Healy D, Hoang HD, Jensen JM, Diao IT, et al. (2015). La-related protein 1 (LARP1) represses terminal oligopyrimidine (TOP) mRNA translation downstream of mTOR complex 1 (mTORC1). J. Biol. Chem 290, 15996–16020. 10.1074/jbc.M114.621730. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R78] 78.Fuentes P, Pelletier J, Martinez-Herráez C, Diez-Obrero V, Iannizzotto F, Rubio T, Garcia-Cajide M, Menoyo S, Moreno V, Salazar R, et al. (2021). The 40S-LARP1 complex reprograms the cellular translatome upon mTOR inhibition to preserve the protein synthetic capacity. Sci. Adv 7, eabg9275. 10.1126/SCIADV.ABG9275. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R79] 79.Gentilella A, Morón-Duran FD, Fuentes P, Rocha GZ, Riaño-Canalias F, Pelletier J, Ruiz M, Turón G, Castaño J, Tauler A, et al. (2017). Autogenous Control of 5′ TOP mRNA Stability by 40S Ribosomes. Mol. Cell 67, 55–70. 10.1016/J.MOLCEL.2017.06.005. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R80] 80.Saba JA, Huang Z, Schole KL, Ye X, Bhatt SD, Li Y, Timp W, Cheng J, and Green R (2024). LARP1 binds ribosomes and TOP mRNAs in repressed complexes. EMBO J. 43, 6555–6572. 10.1038/s44318-024-00294-z. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R81] 81.Brito Querido J, Sokabe M, Díaz-López I, Gordiyenko Y, Zuber P, Du Y, Albacete-Albacete L, Ramakrishnan V, and Fraser CS (2024). Human tumor suppressor protein Pdcd4 binds at the mRNA entry channel in the 40S small ribosomal subunit. Nat. Commun 15, 6633. 10.1038/S41467-024-50672-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R82] 82.Lahr RM, Mack SM, Héroux A, Blagden SP, Bousquet-Antonelli C, Deragon JM, and Berman AJ (2015). The La-related protein 1-specific domain repurposes HEAT-like repeats to directly bind a 5′ TOP sequence. Nucleic Acids Res. 43, 8077–8088. 10.1093/NAR/GKV748. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R83] 83.Ma XM, and Blenis J (2009). Molecular mechanisms of mTOR-mediated translational control. Nat. Rev. Mol. Cell Biol 10, 307–318. 10.1038/nrm2672. [DOI] [PubMed] [Google Scholar]

[R84] 84.Thoreen CC, Chantranupong L, Keys HR, Wang T, Gray NS, and Sabatini DM (2012). A unifying model for mTORC1-mediated regulation of mRNA translation. Nature 485, 109–113. 10.1038/nature11083. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R85] 85.Yang M, Lu Y, Piao W, and Jin H (2022). The Translational Regulation in mTOR Pathway. Biomolecules 12, 802. 10.3390/biom12060802. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R86] 86.Liu GY, and Sabatini DM (2020). mTOR at the nexus of nutrition, growth, ageing and disease. Nat. Rev. Mol. Cell Biol 21, 183–203. 10.1038/s41580-019-0199-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R87] 87.Hong S, Freeberg MA, Han T, Kamath A, Yao Y, Fukuda T, Suzuki T, Kim JK, and Inoki K (2017). LARP1 functions as a molecular switch for mTORC1-mediated translation of an essential class of mRNAs. eLife 6, e25237. 10.7554/eLife.25237. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R88] 88.Qin X, Jiang B, and Zhang Y (2016). 4E-BP1, a multifactor regulated multifunctional protein. Cell Cycle 15, 781–786. 10.1080/15384101.2016.1151581. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R89] 89.Gingras AC, Gygi SP, Raught B, Polakiewicz RD, Abraham RT, Hoekstra MF, Aebersold R, and Sonenberg N (1999). Regulation of 4E-BP1 phosphorylation: a novel two-step mechanism. Genes Dev. 13, 1422–1437. 10.1101/gad.13.11.1422. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R90] 90.Hsieh AC, Liu Y, Edlind MP, Ingolia NT, Janes MR, Sher A, Shi EY, Stumpf CR, Christensen C, Bonham MJ, et al. (2012). The translational landscape of mTOR signalling steers cancer initiation and metastasis. Nature 485, 55–61. 10.1038/nature10912. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R91] 91.Thoreen CC, Kang SA, Chang JW, Liu Q, Zhang J, Gao Y, Reichling LJ, Sim T, Sabatini DM, and Gray NS (2009). An ATP-competitive mammalian target of rapamycin inhibitor reveals rapamycin-resistant functions of mTORC1. J. Biol. Chem 284, 8023–8032. 10.1074/jbc.M900301200. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R92] 92.Guo JK, Blanco MR, Walkup WG, Bonesteele G, Urbinati CR, Banerjee AK, Chow A, Ettlin O, Strehle M, Peyda P, et al. (2024). Denaturing purifications demonstrate that PRC2 and other widely reported chromatin proteins do not appear to bind directly to RNA in vivo. Mol. Cell 84, 1271–1289. 10.1016/J.MOLCEL.2024.01.026. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R93] 93.Jin H, Xu W, Rahman R, Na D, Fieldsend A, Song W, Liu S, Li C, and Rosbash M (2020). TRIBE editing reveals specific mRNA targets of eIF4E-BP in Drosophila and in mammals. Sci. Adv 6, eabb8771. 10.1126/sciadv.abb8771. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R94] 94.Hochstoeger T, Papasaikas P, Piskadlo E, and Chao JA (2024). Distinct roles of LARP1 and 4EBP1/2 in regulating translation and stability of 5′ TOP mRNAs. Sci. Adv 10, eadi7830. 10.1126/SCIADV.ADI7830. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R95] 95.Darzacq X, Jády BE, Verheggen C, Kiss AM, Bertrand E, and Kiss T (2002). Cajal body-specific small nuclear RNAs: a novel class of 2′-O-methylation and pseudouridylation guide RNAs. EMBO J. 21, 2746–2756. 10.1093/EMBOJ/21.11.2746. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R96] 96.De Boer EMJ, Orie VK, Williams T, Baker MR, De Oliveira HM, Polvikoski T, Silsby M, Menon P, Van Den Bos M, Halliday GM, et al. (2020). TDP-43 proteinopathies: a new wave of neurodegenerative diseases. J. Neurol. Neurosurg. Psychiatry 92, 86–95. 10.1136/JNNP-2020-322983. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R97] 97.Yahara M, Kitamura A, and Kinjo M (2017). U6 snRNA expression prevents toxicity in TDP-43-knockdown cells. PLoS One 12, e0187813. 10.1371/JOURNAL.PONE.0187813. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R98] 98.Izumikawa K, Nobe Y, Ishikawa H, Yamauchi Y, Taoka M, Sato K, Nakayama H, Simpson RJ, Isobe T, and Takahashi N (2019). TDP-43 regulates site-specific 2′-O-methylation of U1 and U2 snRNAs via controlling the Cajal body localization of a subset of C/D scaRNAs. Nucleic Acids Res. 47, 2487–2505. 10.1093/NAR/GKZ086. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R99] 99.Asakawa K, Handa H, and Kawakami K (2021). Multi-phaseted problems of TDP-43 in selective neuronal vulnerability in ALS. Cell. Mol. Life Sci 78, 4453–4465. 10.1007/S00018-021-03792-Z. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R100] 100.Raught B, Gingras AC, Gygi SP, Imataka H, Morino S, Gradi A, Aebersold R, and Sonenberg N (2000). Serum-stimulated, rapamycin-sensitive phosphorylation sites in the eukaryotic translation initiation factor 4GI. EMBO J. 19, 434–444. 10.1093/EMBOJ/19.3.434. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R101] 101.Martínez-Alonso E, Guerra-Pérez N, Escobar-Peso A, Peracho L, Vera-Lechuga R, Cruz-Culebras A, Masjuan J, and Alcázar A (2022). Phosphorylation of Eukaryotic Initiation Factor 4G1 (eIF4G1) at Ser1147 Is Specific for eIF4G1 Bound to eIF4E in Delayed Neuronal Death after Ischemia. Int. J. Mol. Sci 23, 1830. 10.3390/IJMS23031830. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R102] 102.Shishkin AA, Giannoukos G, Kucukural A, Ciulla D, Busby M, Surka C, Chen J, Bhattacharyya RP, Rudy RF, Patel MM, et al. (2015). Simultaneous generation of many RNA-seq libraries in a single reaction. Nat. Methods 12, 323–325. 10.1038/nmeth.3313. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R103] 103.Parnas O, Jovanovic M, Eisenhaure TM, Herbst RH, Dixit A, Ye CJ, Przybylski D, Platt RJ, Tirosh I, Sanjana NE, et al. (2015). A Genome-wide CRISPR Screen in Primary Immune Cells to Dissect Regulatory Networks. Cell 162, 675–686. 10.1016/j.cell.2015.06.059. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R104] 104.Hughes CS, Moggridge S, Müller T, Sorensen PH, Morin GB, and Krijgsveld J (2019). Single-pot, solid-phase-enhanced sample preparation for proteomics experiments. Nat. Protoc 14, 68–85. 10.1038/s41596-018-0082-x. [DOI] [PubMed] [Google Scholar]

[R105] 105.Rappsilber J, Mann M, and Ishihama Y (2007). Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat. Protoc 2, 1896–1906. 10.1038/nprot.2007.261. [DOI] [PubMed] [Google Scholar]

[R106] 106.Cox J, and Mann M (2008). MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol 26, 1367–1372. 10.1038/nbt.1511. [DOI] [PubMed] [Google Scholar]

[R107] 107.Kimanius D, Jamali K, Wilkinson ME, Lövestam S, Velazhahan V, Nakane T, and Scheres SHW (2024). Data-driven regularization lowers the size barrier of cryo-EM structure determination. Nat. Methods 21, 1216–1221. 10.1038/S41592-024-02304-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R108] 108.Rohou A, and Grigorieff N (2015). CTFFIND4: Fast and accurate defocus estimation from electron micrographs. J. Struct. Biol 192, 216–221. 10.1016/J.JSB.2015.08.008. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R109] 109.Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589. 10.1038/S41586-021-03819-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R110] 110.Casañal A, Lohkamp B, and Emsley P (2020). Current developments in Coot for macromolecular model building of Electron Cryo-microscopy and Crystallographic Data. Protein Sci. 29, 1069–1078. 10.1002/PRO.3791. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R111] 111.Afonine PV, Poon BK, Read RJ, Sobolev OV, Terwilliger TC, Urzhumtsev A, and Adams PD (2018). Real-space refinement in PHENIX for cryo-EM and crystallography. Acta Crystallogr. D Struct. Biol 74, 531–544. 10.1107/S2059798318006551. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R112] 112.Goddard TD, Huang CC, Meng EC, Pettersen EF, Couch GS, Morris JH, and Ferrin TE (2018). UCSF ChimeraX: Meeting modern challenges in visualization and analysis. Protein Sci. 27, 14–25. 10.1002/PRO.3235. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R113] 113.Uhlen M, Bandrowski A, Carr S, Edwards A, Ellenberg J, Lundberg E, Rimm DL, Rodriguez H, Hiltke T, Snyder M, and Yamamoto T (2016). A proposal for validation of antibodies. Nat. Methods 13, 823–827. 10.1038/nmeth.3995. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R114] 114.Blue SM, Yee BA, Pratt GA, Mueller JR, Park SS, Shishkin AA, Starner AC, Van Nostrand EL, and Yeo GW (2022). Transcriptome-wide identification of RNA-binding protein binding sites using seCLIPseq. Nat. Protoc 17, 1223–1265. 10.1038/S41596-022-00680-Z. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R115] 115.Ernst C, Duc J, and Trono D (2023). Efficient and sensitive profiling of RNA-protein interactions using TLC-CLIP. Nucleic Acids Res. 51, e70. 10.1093/NAR/GKAD466. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R116] 116.Zhao Y, Zhang Y, Teng Y, Liu K, Liu Y, Li W, and Wu L (2019). SpyCLIP: an easy-to-use and high-throughput compatible CLIP platform for the characterization of protein-RNA interactions with high accuracy. Nucleic Acids Res. 47, e33. 10.1093/NAR/GKZ049. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R117] 117.Manakov SA, Shishkin AA, Yee BA, Shen KA, Cox DC, Park SS, Foster HM, Chapman KB, Yeo GW, and Van Nostrand EL (2022). Scalable and deep profiling of mRNA targets for individual micro-RNAs with chimeric eCLIP. Preprint at bioRxiv, 2022.02.13.480296. 10.1101/2022.02.13.480296. [DOI] [Google Scholar]

[R118] 118.Schwanhäusser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J, Chen W, and Selbach M (2011). Global quantification of mammalian gene expression control. Nature 473, 337–342. 10.1038/nature10098. [DOI] [PubMed] [Google Scholar]

PERMALINK

SPIDR enables multiplexed mapping of RNA-protein interactions and uncovers a mechanism for selective translational suppression upon cell stress

Erica Wolin

Jimmy K Guo

Mario R Blanco

Isabel N Goronzy

Darvesh Gorhe

Wenzhao Dong

Andrew A Perez

Abdurrahman Keskin

Elizabeth Valenzuela

Ahmed A Abdou

Carl R Urbinati

Ross Kaufhold

H Tomas Rube

Jailson Brito Querido

Mitchell Guttman

Marko Jovanovic

SUMMARY

Graphical Abstract

In brief

INTRODUCTION

RESULTS

SPIDR: A highly multiplexed method for mapping RBP-RNA interactions

Figure 1. SPIDR—A highly multiplexed method to map protein-RNA interactions.

SPIDR accurately maps dozens of RBPs within a single experiment

Accurate mapping of classical RNPs

Figure 2. SPIDR accurately maps binding of a diverse set of RBPs.

Many RBPs bind their own mRNAs to autoregulate expression levels

Different antibodies that capture the same protein or multiple proteins within the same complex show similar binding

SPIDR generates maps highly comparable with CLIP maps

Figure 3. SPIDR data are comparable to previous eCLIP datasets.

SPIDR maps the structural organization of protein-RNA interactions at single-nucleotide resolution

Figure 4. SPIDR enables high-resolution RBP mapping at single-nucleotide resolution.

Figure 5. LARP1 binds to 18S rRNA near the mRNA entry channel and at TOP motifs contained within the 5′ UTRs of mRNAs.

LARP1 binds in the mRNA entry channel of the 40S ribosome

Figure 6. Cryo-EM structure of human LARP1 bound to the 40S subunit.

4EBP1 preferentially associates with LARP1-bound mRNAs upon mTOR inhibition

Figure 7. 4EBP1 binds specifically to LARP1-bound mRNAs upon mTOR inhibition.

DISCUSSION

Limitations of the study

STAR★METHODS

EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

Cell culture

UV-crosslinking

Torin1 treatment

Overview of all performed SPIDR experiments

METHOD DETAILS

Bead biotinylation

Labeling biotinylated beads with oligonucleotide tags

Binding antibody to labeled Protein G beads

Pooled immunopurification

Ligation of the RNA Phosphate Modified (“RPM”) tag

Split-and-pool barcoding to identify RNA-protein interactions

Library preparation

Sequencing

Analysis and processing pipeline

Read processing and alignment

Barcode matching and filtering

Splitting alignment files by protein identity

Background correction and peak calling

Irreproducible Discovery Ratio (IDR)

Analytical considerations for determining concordant binding sites from SPIDR data

RNA feature binding profiles

Single-nucleotide resolution analysis

Processing of eCLIP datasets

Transcriptome-wide correlations

De novo motif discovery

mTOR-related analysis

TOP motif binding analysis for the “CTL versus Torin1” SPIDR experiment

Normalizing Control/Torin Ratios by PTBP1 for the “smaller scale CTL versus Torin1” SPIDR experiment

Determining if protein changes upon mTOR inhibition are TOP score-dependent

Mass spectrometry

Pooled immunopurification (IP) of 39 RBPs for mass spectrometry

Pooled IP of 10 RBPs and corresponding single IPs for mass spectrometry

Sample preparation for global proteomics

Proteomic quantification of Torin1-treated samples

4EBP1 IP upon Torin1 and Control treatment

V5 versus IgG IP

LC-MS/MS