Mapping human pre-rRNA processing and modification at single nucleotide resolution using long read nanopore sequencing

Domain: biology
Published: 2026-03-31
Editorial outcome: minor_revision

Abstract
Ribosome biogenesis requires the synthesis and sequential processing of precursor rRNAs (pre-rRNAs) into mature rRNAs. Traditional methods such as northern blotting and metabolic labeling provide limited resolution. Here, we present NanoRibolyzer, a nanopore-based long-read sequencing approach that enables ab initio identification and quantification of rRNA precursors while simultaneously mapping RNA modifications. Using supervised and unsupervised mapping, we detect both known and previously uncharacterized pre-rRNAs and delineate cleavage events at single-nucleotide resolution. A simple cell-fractionation protocol further separates nuclear and cytoplasmic pre-rRNAs, allowing spatial deconvolution of processing pathways. By projecting each sequenced molecule in a two-dimensional space using its starting and ending coordinates, we generate an intuitive representation in which the activity of the 5′→3′ and 3′→5′ exoRNases can be tracked as they mature pre-rRNAs one nucleotide at a time. Targeted knockdowns of ribosome-assembly factors quantify accumulation of intermediates and reveal condition-specific processing “fingerprints” with biomarker potential. High-resolution re-analysis of known factors uncovers unexpected functions. Additionally, pseudouridine mapping shows that the primary 47S transcript is extensively modified, whereas aberrant intermediates (34S and 36S-C) are hypomodified. With its high resolution and unique discovery mode, NanoRibolyzer provides new insights into rRNA processing and modification, greatly advancing our understanding of ribosome biogenesis.

Introduction
Ribosomes are ribonucleoprotein nanomachines responsible for protein synthesis in all living cells1,2.
Ribosome biogenesis is a complex process involving the synthesis, processing, and modification of precursor ribosomal RNAs (pre-rRNAs), as well as RNA folding and packaging into functional ribosomal subunits. In eukaryotes, this pathway is initiated in the nucleolus, where a large ribosomal RNA precursor (pre-rRNA), the 47S, is synthesized by RNA polymerase I (Pol I)3,4. The 47S contains sequences for three out of four rRNAs (the 18S, 5.8S, and 28S) interspersed with non-coding spacers (Fig 1A and S1). The fourth rRNA, 5S, is produced independently by Pol III, in the nucleoplasm. Following transcription, pre-rRNAs undergo a series of maturation steps, including processing (cleavage), modification, and packaging with ribosomal proteins, to release the mature rRNAs and produce the ribosomal subunits, which are ultimately exported to the cytoplasm where they engage in translation5. Throughout this multistep process, the nascent transcripts undergo extensive processing by endonucleases performing precise cleavages within the external and internal transcribed spacers (ETS and ITS, respectively) often followed by exonucleases that progressively trim pre-rRNAs, ultimately releasing the mature rRNAs (Fig 1A and S1).

The progressive trimming of pre-rRNAs by exonucleases results in the production of transient, metastable species that likely remain largely uncharacterized. Additionally, these processes contribute to the generation of poorly defined RNA ends, further highlighting the complexity and incomplete understanding of pre-rRNA processing. Disruptions occurring at any stage of the pathway may activate regulatory cascades, including surveillance leading to the accumulation of distinctive intermediates, which can significantly impact ribosome function, cellular protein synthesis, and overall cellular homeostasis4,6,7.

More than two decades of research have led to major advances in identifying discrete processing sites and pre-rRNA intermediates, which now serve as critical markers for assessing ribosome biogenesis efficiency (Fig 1A and S1). Knowledge of these intermediates has been particularly valuable for investigating aberrant precursor production that arises during processing perturbations2. Conventional approaches for analyzing rRNA processing intermediates, such as northern blotting, metabolic labelling, or primer extension, allow for the identification of these accumulated precursors and cleavage sites, respectively6,8,9,10. However, these assays require important input material (often µg range), have limited resolution and throughput. Additionally, studying pre-rRNA processing by sequencing has remained challenging due to the highly repetitive nature of rDNA arrays and poor genome annotations making it difficult to accurately map reads and distinguish between individual rDNA copies, especially with short-read sequencing technologies11,12,13,14.

Nanopore sequencing (nanopore-seq) has emerged as a promising technology to investigate ribosome biogenesis15,16,17. The key advantage of nanopore-seq lies in its ability to sequence long reads, such as cDNAs, as well as native RNA molecules via direct RNA sequencing (DRS), allowing for the investigation of entire transcripts including their modifications 18,19. These capabilities are particularly valuable for studying rRNA intermediates, which can vary significantly in length and abundance, and possibly modification levels. To this date, there are no tools that exploit long-read sequencing for the analysis of human ribosomal RNA precursors.

Here, we present NanoRibolyzer, a method that integrates state-of-the-art long-read nanopore sequencing with advanced bioinformatics to achieve spatially resolved, single-nucleotide analysis of pre-rRNA intermediates. Using a streamlined nuclei isolation protocol, we systematically profile precursor and mature rRNA species in both nuclear and cytoplasmic compartments. We further introduce precursor-specific modification analysis, uncovering the spatio-temporal dynamics of rRNA modifications. Overall, NanoRibolyzer enables comprehensive detection and quantification of both known and novel processing intermediates, including cleavage events generated by endo- and exoribonucleolytic activity, as well as pseudouridine modifications.

Results
Nuclear isolation enabling high-resolution mapping of pre-rRNA precursors
Ribosome biogenesis begins in the nucleolus, a multiphase condensate within the nucleus, continues in the nucleoplasm, and is finalized in the cytoplasm, where mature rRNAs accumulate. In order to develop a nanopore-sequencing approach to study pre-rRNA processing, we adapted a straightforward isolation protocol involving density gradient separation using a simple benchtop centrifuge (Fig 1B and S2). This provided spatial resolution of processing events and access to purified nuclear RNA enriched in low-abundance, short-lived pre-rRNAs that are otherwise masked in total RNA by the highly abundant mature cytoplasmic rRNAs. We applied the protocol to HEK293 cells to produce highly purified nuclear and cytoplasmic RNA fractions, and as a control, whole cell total RNA (‘whole cell’) (Fig 1B and S2A).

After separation of the fractions, a quality assessment of isolated nuclei was conducted using DNA staining with DAPI, revealing debris-free and round intact nuclei20 (Fig SS2C). RNA was extracted and electropherograms produced using a TapeStation. As expected, the whole cell and cytoplasmic fractions displayed the abundant mature 18S and 28S rRNAs (all analysis performed in triplicate throughout this work, R1-R3) (Fig S2D). In contrast, the nuclear fractions exhibited higher molecular weight species, at the expected size for pre-rRNAs (Fig S2D). Northern blot analyses performed throughout the study consistently verified that precursor rRNA is predominantly retained within nuclear fractions, in contrast to cytoplasmic and whole-cell fractions (Fig S3, Source Data 1).

Since Nanopore-based RNA library preparation strategies typically capture poly(A)+ RNA21, we applied an in-vitro polyadenylation strategy followed by long-read cDNA sequencing (Fig 1B). To assess enrichment of transcripts in the nuclear fractions, we quantified two nuclear long non-coding RNAs (lncRNAs): XIST and MALAT1 (Fig 1B, Supplementary Table S1). As expected, comparative analysis of the abundance of these transcripts across different cellular compartments revealed several fold-change enrichments in the nuclear fraction relative to the cytoplasm (up to 165-fold for XIST).

Compared with whole-genome alignments, which are challenging to interpret for rRNAs due to presence of hundreds of rDNA gene copies, NanoRibolyzer aligns long rRNA reads to a single 47S reference template (45SN1; GeneID: 106631777). This approach provides a clear and unambiguous view of reads corresponding to pre-rRNAs and mature rRNAs. We generated an average of ~3 million reads per sample with an average ~75% alignment rate to the 47S template. Our bioinformatic pipeline is depicted in Fig S4A. For reference and to initiate precursor quantification analysis, we retrieved the positions of known processing sites and major precursors from literature8,9,10 (Fig 1A and S1).

We applied NanoRibolyzer to unperturbed HEK293 cells and characterized reads mapping to mature rRNA sequences and the non-coding spacers (5’ ETS, ITS1, ITS2 and 3’ ETS) for the nuclear, cytoplasmic, and whole cell fractions (Fig 1C-D). As expected, the coverage profiles revealed that the nuclear fraction had higher coverage across the spacers (ETSs and ITSs) compared to the cytoplasmic ones, with the whole cell fraction displaying an intermediate coverage level (Fig 1C-D). The start and ends of these reads corresponded to known processing sites, e.g. sites 01,1, 2, 4, etc. (see Refs8,9,10).

These observations agree with the notion that the nuclear fraction predominantly contains ribosomal assembly intermediates, in line with the fact that most steps of ribosome biogenesis occur in the nucleolus. In our initial analyses, we consistently observed that whole-cell fractions are largely redundant with cytoplasmic fractions (see Fig 1D, zoomed IGV). Therefore, throughout the remainder of the manuscript, and unless stated otherwise, we focus on comparisons between nuclear and cytoplasmic fractions.

We also noted that the hyper modified nucleotide 1-methyl-3-α-amino-α-carboxyl-propyl pseudouridine (m1acp3Ψ) at position 1248 on the 18S rRNA was detected in whole cell and cytoplasmic fractions, but strikingly not in nuclear ones (Fig 1C, arrow), consistent with the fact that m1acp3Ψ formation is completed in the cytoplasm22.

Mapping rRNA precursor diversity and processing pathways
Once we confirmed that nuclear sequencing reads extends across spacer regions, we employed two complementary approaches to quantify pre-rRNAs and resolve processing sites: 1) a rather classical supervised or ‘template-based’ strategy, and, 2) an unsupervised or ‘template-free’ method (Fig S4).

The supervised approach detects pre-rRNA intermediates and spacer fragments described in the literature, quantifying their relative abundance by maximizing reciprocal overlap (Fig 2A and S4B). These intermediates and fragments are defined based on published processing sites (Fig 1A and Refs1,2). Quantified data can be visualized either as heatmaps of averaged log10 reads per million (Fig 2B and S5A) or histograms illustrating averaged reads per million for each condition with associated standard deviations and p-values (Fig S5B).

Using the supervised approach, as expected, we found that most pre-rRNA intermediates are substantially more abundant in the nucleus than in the cytoplasm (Fig 2B and Fig S5A-B; statistical analysis in Source Data 1, p-values on histograms). This analysis also highlights that several intermediates historically associated with ribosome biogenesis defects6, such as the 34S, 36S, or 36S-C, are not, or only marginally, produced in unperturbed cells (see, dark shade of blue in the heatmap of Fig 2B and S5A). As we will show later, when mapping rRNA modifications at the level of individual pre-rRNA precursors, these aberrant species are, strikingly, not faithfully modified. The analysis also reveals that precursors associated with early steps of large ribosomal subunit biogenesis (32S) are not detected in the cytoplasm, while precursors associated with late maturation of the small subunit (21S, 21S-C, 18S-E) and the large subunit (12S, 7S) are partially present in the cytoplasm (Fig 2B and Fig S5A-B).

Together, these results demonstrate that NanoRibolyzer provides a quantitative framework to assess the relative abundance of all major pre-rRNA precursors as well as mature rRNAs.

Having assigned long-read sequences to specific pre-rRNA precursors and mature rRNAs, we next sought to pinpoint their predominant start and end positions, thereby defining processing sites. To do so, we computed significant 5’ and 3’ boundaries from the template-based BAM files, using the 45SN1 (hg38) reference sequence. Sites occurring at least two standard deviations above the mean were classified as significant, and results were exported in TSV and BED formats for visualization (Fig S5C). To ensure robustness, we retained only those sites present in at least two of the three biological replicates, enabling us to identify the most reproducible processing sites and quantify their prevalence across the 47S sequence (Source Data 1).

Inspection of the cleavage sites revealed consistent positions within the spacer regions and at the boundaries of the mature rRNAs (Fig S5D; start sites in green, end sites in orange). Cleavages detected in the spacer regions, particularly those proximal to previously reported processing sites, were considered bona fide processing events (Fig S5D-F; see also Fig 1A and Fig S1). In several cases, using the literature information alone was insufficient to recognize with single nucleotide precision the RNA end points. As discussed below, this limitation will be solved by using our complementary unsupervised mapping approach (Fig 2C-D, Fig 3).

As expected, samples from the cytoplasm, where pre-ribosomes are nearly fully matured and where mature ribosomes are engaged in translation or storage, showed cleavage sites mostly restricted to the extremities of mature rRNAs (Fig S5D). In sharp contrast, nuclear samples displayed numerous internal cleavages within the spacer regions, reflecting ongoing pre-rRNA processing (Fig S5E-F).

We also found numerous cleavages within mature rRNA sequences (Fig S5D). These aberrant events, which generate by-products destined for degradation, have been greatly underappreciated using classical approaches such as northern blotting. Their relevance becomes clear below, where we show they can define condition-specific “fingerprints” (e.g. upon URB1 or RPL3 depletion, shown below), with potential biomarker value.

In conclusion, the supervised component of NanoRibolyzer performs very well to quantify the abundance of RNA species previously described in the literature and, with few exceptions, to map their ends with single nucleotide resolution. In essence it performs quite similarly to northern-blotting, except that, it has additionally started to reveal an under-appreciated level of cleavage within mature sequences.

Nonetheless, the supervised approach is not suitable to map the low abundant metastable (transient) species which results from the progressive exoribonucleolytic cleavage, 5’→3’ and 3’→5’, which are known to be of paramount importance in processing, generating thousands of intermediates. To put simply, a huge diversity of species still escape characterization.

To address this limitation, we next implemented an unsupervised approach that provides a completely unbiased mapping strategy, i.e. a method that does not rely on any preconceived knowledge about pre-rRNA processing. We also developed a new display to apprehend processing intuitively.

In this approach, every single individual read sequenced is projected onto a two-dimensional matrix (Fig 2C-D and Fig S4C). The x- and y-axes extend from the transcription start site to the termination site of the primary 47S transcript, respectively. Each rRNA read is positioned on the matrix according to its 5′ and 3′ coordinates, enabling precise, reference-free mapping of transcript boundaries (Fig 2C-D and Fig S4C). This visualization reveals intensity “hubs” corresponding to abundant rRNA species whose ends coincide with established or putative novel processing sites (Fig 2C-D and Fig S4C; see Methods for details).

We applied the unsupervised approach to nuclear and cytoplasmic fractions of unperturbed HEK293 cells to visualize the unbiased aggregation of reads associated with the 47S transcript. In the nuclear fraction, we observed prominent accumulations of reads within the ETS and ITS regions, corresponding to processing sites and appearing as characteristic blue “pyramids” in the matrix (Fig 2D). For example, striking pyramids were detected between site 01 in the 5′ ETS and site 2 in ITS1, defining precursors of 18S rRNA (see below), and between the 5′ end of 5.8S and site 4 in ITS2, corresponding to 3′-extended 5.8S precursors destined for 3′→5′ trimming by the multi-subunit RNA exosome6,23 (zoomed inset in Fig 2D).

Remarkably, this approach also visualizes, the activity of 5’→3’ and 3’→5’ exoRNases as they progressively trim spacer sequences one nucleotide at a time (Fig S6A-B). Low-abundance, metastable intermediates generated during this process, which are difficult to detect with conventional methods, align as continuous signal traces in the matrix: horizontal lines reflecting 5’→3’ exonucleolytic trimming, and vertical lines corresponding to 3’→5’ exonucleolytic activity (Fig S6A-B). Remarkably, the positions where the exoRNases pause can be seen as more intense dots.

In contrast, reads from the cytoplasmic fraction (shown as ‘red pyramids’, in Fig 2D, Fig S6C) mapped almost exclusively to the three mature rRNAs, 18S, 5.8S and 28S (Fig 2D; see inset for 5.8S). The top of each pyramid corresponds to the actual mature RNAs, whereas the interior signal to abundant degradation products. Again, such abundance of mature rRNA fragments was truly unexpected and was not characterized before. Individual matrices for all three biological replicates are shown in Fig S6C, underscoring the robustness of the analysis.

In conclusion, combining supervised and unsupervised mapping approaches not only exploits existing knowledge of abundant, well-characterized pre-rRNA intermediates, but also enables the discovery of previously undetected, low-abundance and metastable processing intermediates as well as degradation by-products. Our analysis reveals the progression of exoRNases on pre-rRNAs, highlighting stalling positions. Finally, we also uncover an exceptionally rich and largely overlooked population of aberrantly cleaved species. As shown below, these aberrant products carry powerful biological information: depletion of factors acting at the same step of ribosome biogenesis yields highly similar patterns of cleavage by-products (Fig 4E), effectively generating condition-specific molecular fingerprints.

Redefining pre-rRNA processing sites to single nucleotide resolution
Next, we followed an agnostic approach to remap the processing sites by analyzing the intensity matrix obtained by unsupervised mapping. To achieve this, we extracted approximate coordinates from detectable intensity “hubs” in the nuclear fraction and annotated the associated 5' and 3' processing sites (Fig 3A). These coordinates should be considered estimates, as minor RNA species often show variability in their end points. We therefore bioinformatically selected the most predominant end points located near putative processing sites, using a threshold of at least ten reads per hub.

Analysis of the intensity matrix revealed obvious cleavages, which could all be mapped to single nucleotide resolution, many were associated with known processing sites 8,9,10,24-32 and there were also new sites. The detailed description of all processing sites mapped, novel and known, is provided in Table 1 and Fig S7. Briefly, we mapped thirteen known processing sites to single-nucleotide resolution, revealing multiple endpoints at two sites (E1 and E2, and 21 and 22). For sites already known in the literature, there was an excellent agreement between sites identified by the supervised and unsupervised approaches (Fig S5D-F).

In summary, we have confirmed all known processing sites and identified new sites to single nucleotide resolution, providing a powerful resource to the community for years to come.

NanoRibolyzer enables quantification of novel pre-rRNAs
The intensity hub mapping strategy (unsupervised approach) led to the identification of novel processing sites, and we next inspected the products resulting from cleavage at these sites (Fig 3B).

We draw several conclusions:
(1) the detection of precursors starting at site 01, in the 5’ ETS, and ending at different positions in ITS1 (including at site 3, E, C, 21 and 22) indicates that cleavage in ITS1 can occur at least to some extent prior to cleavage at site A0 (Fig 3B, Fig S7; Hubs no 1b and 2-4).
(2) we confirmed that the 21S corresponds to two isoforms30: 21S (small) and 21S (large) (Fig 3B, Fig S7; Hubs no 5a-b, and S6B).
(3) we confirmed that maturation of the 3’ end of 5.8S involves progressive exoribonucleolytic trimming of 12S to 7S, and ultimately to 5.8S (Fig 3B, Fig S7; Hubs 6b-7 and S6B), a process known to involves substrate handover between RNA exosome subunits6.

Having identified new cleavage sites and intermediates using the unsupervised model (Fig 3B), we then incorporated this information into the supervised approach to further refine our processing analysis and to formally quantify the newly detected intermediates through reciprocal overlap maximization (see above and Fig S4B).

This analysis revealed that the shorter pre-rRNAs are systematically more abundant than the larger pre-rRNAs from which they derive, as generally expected in a precursor-product relationship. This was true in the pathways of production of 18S, 5.8S, and, also of 28S (Fig 3C, Fig S8A-B).

To further illustrate how NanoRibolyzer can easily be adapted to detect and quantify new pre-rRNA species, we focused on 3’ end maturation of 18S rRNA (Fig S8C). The biogenesis of 18S rRNA 3’ end is particularly interesting because it initiates in the nucleoplasm, it ends in the cytoplasm, and it involves formation of a succession of metastable forms: the 18S-E extended respectively by 10, 24, 36, 40, and 78 nucleotides, which are progressively trimmed to 18S-E and finally 18S (ref.26). Once we incorporated this information in our supervised approach (Fig S8C), it revealed the respective abundance of each intermediate across the nucleus and cytoplasm as the precursors mature across the nuclear pore complexes (Fig S8D). Depletion of ribosomal protein RPS26 (Fig S9A-C), which is known to block the final cytoplasmic steps of 18S rRNA maturation while leaving nuclear processing unaffected33-34 indeed led to accumulation of the 18S-E+24, +36 and +40 in the cytoplasmic fraction but not in the nucleus (Fig S8E-F and Fig S9).

NanoRibolyzer captures pre-rRNA processing perturbations
Having demonstrated the ability of NanoRibolyzer to capture pre-rRNA processing in unperturbed cells, we next aimed to evaluate how efficiently our method can detect processing perturbations. To achieve this, we selected key processing factors involved in the maturation of each spacer sequence and depleted them (Fig 4A and Fig S3).

For the 5’ ETS, we targeted UTP18, a component of the SSU-processome, whose depletion results in accumulation of the aberrant 34S species (Ref.35). For ITS1, we selected DIMT1L and WBSCR22, whose loss results respectively in accumulation of the 21S/21S-C and the 18S-E pre-rRNAs36,37. For ITS2, we selected the endoRNase LAS1L, whose absence causes accumulation of 32S pre-rRNA38. Lastly, for the 3′ ETS, we chose URB1, whose importance for 3’ ETS removal has been demonstrated39. Ribosomal protein RPL3 that binds near the end of 28S rRNA but whose role in 3’ ETS processing had not been established was also tested.

First, we assessed the depletion of each factor using northern blots, confirming the expected precursor accumulation (Fig 4B and Fig S10-15). For NanoRibolyzer analysis, we chose the optimal depletion time point for each factor: 48 h for UTP18, WBSCR22, and LAS1L, and 72 h for DIMT1L, URB1 and RPL3 (Fig 4B).

RNA from nuclear and cytoplasmic fractions was extracted in triplicate and analyzed using cDNA nanopore-seq, as described above. Coverage profiles revealed distinct differences in processing factor knockdowns compared to controls (Fig 4C and S16A-C). The effects observed by northern blotting were all confirmed by nanopore sequencing (Fig 4B-FI and Fig S16).

The depletion of UTP18 led to a substantially increased coverage upstream of site 01, corresponding to the 34S species (Fig 4C, purple rectangle, and S16B). Knocking down DIMT1L or WBSCR22 increased coverage in ITS1 downstream of the 3’ end of 18S, corresponding to 21S/21S-C (in case of DIMT1L, dark brown rectangle) and 18S-E accumulation (WBSCR22, turquoise rectangle), respectively (Fig 4C and S16C). Depletion of LAS1L revealed increased reads across ITS2, in agreement with 32S accumulation (Fig 4C and S16C). Both URB1 and RPL3 depleted samples showed increased coverage across the 3′ ETS (Fig 4C and S16A). For URB1, this confirms the accumulation of 3’ ETS extended species in URB1-deficient cells39. The observation upon RPL3 knockdown reveals it is also indispensable for this maturation step. Each RNA species was quantified and visualized as heatmaps (Fig 4D and S17A, S18B-C) or histograms (Fig S17B-E) confirming the observations on the IGV traces (Source Data 1).

Upon instructing the supervised approach with detailed maturation steps of 18S rRNA 3’ end (see above), it was possible to zoom in further. Notably, it was possible to see that all nuclear precursors of 18S-E pre-rRNA accumulates upon WBSCR22 knockdown (Fig S18D-F), in full agreement with ref.36.

In addition to confirming processing phenotypes previously associated with the selected factors, the nanopore-seq approach revealed several unexpected features. For example, we observed a striking and highly reproducible accumulation of fragments encompassing part of the 5’ ETS starting at site 01 upon URB1 and RPL3 depletion (see green and dark yellow rectangles in Fig 4C and S16B). We also detected a remarkable “processing fingerprint” consisting of multiple consecutive cleavages within the 3’ end regions of the 18S and 28S sequences, which we interpret as increased turnover (Fig 4E and S16F, Source Data 1). This is particularly interesting given that both factors are important for the same maturation step: removal of the 3’ ETS (Fig 4C and S16A). Just as unexpected, we observed that DIMT1L, a small-subunit assembly factor37, also affects 3’ ETS processing and 32S maturation (Fig 4C-D and Fig S16), a phenotype that was not apparent by northern blotting.

Lastly, using the unsupervised approach, we mapped the nuclear reads onto an intensity matrix (Fig 4F and S19A-F). Upon UTP18 depletion, a unique hub corresponding to 34S was detected (purple arrow, Fig 4F and S19A). Upon DIMT1L depletion, hubs associated with 21S/21S-C and 26S were seen (brown arrows), indicating impaired processing between sites C and 2, in combination with defects in the 5′ ETS (Fig 4F and S19B). Additionally, we observed notable accumulation of early precursors such as 47S and 45S, consistent with previous findings37. Depletion of WBSCR22 revealed accumulation of a hub associated with 18S-E (turquoise arrow), with shortened start coordinates, indicating some level of 5'-3' degradation of 18S precursors (Fig 4F and S19C). Knockdown of LAS1L showed reduced aggregated reads at processing site 4 and accumulation at 32S (orange arrow, Fig S19D). URB1 and RPL3 knockdown exhibited accumulation of reads downstream of processing site 02 (3’ end of 28S) corresponding to RNAs that have retained the 3' ETS (green arrows, Fig 4F and S19E-F). Additionally, upon depletion of URB1 and RPL3, we observed an increased accumulation of products upstream of the 01 site, consistent with the coverage profiles (S19E-F), and an increase signal across mature rRNA regions (S19E-F), reflecting by-product buildup and elevated turnover (Fig 4E and S17F).

In summary, NanoRibolyzer captures previously described pre-rRNAs, newly identified pre-rRNA species, and mature rRNAs; it quantifies them, maps processing sites at single-nucleotide resolution, confirms previously assigned functions of processing factors, reveals new involvements at additional steps, and ultimately defines novel unique processing “fingerprints”.

NanoRibolyzer uncovers spatio-temporal dynamics of RNA modification
Ribosomal RNA is abundantly decorated by covalent modifications36,40. To investigate the spatial dynamics of these modifications, we employed direct RNA sequencing (DRS), a technique that sequences native RNA molecules and detects modifications through distinct current signatures that differentiate modified from unmodified nucleotides41.

First, we selected three base modifications known to occur during late 18S biogenesis: N7-methylguanosine m7G, deposited by WBSCR22 at G1639 (Ref.36,37), N6,N6-dimethyladenosine (m62Am62A) deposited by DIMT1L at A1735 and A1736 (Ref.37) and m1acp3Ψ which is finalized by addition of the acp group by TSR3 at U1248 (Ref42) (Fig S20A-C). To obtain ground truth, we generated an in vitro transcribed sample (IVT 18S) to serve as constitutively unmodified negative control. Additionally, we used RNA extracted from cells inactivated for DIMT1L, WBSCR22, or TSR3 to serve as negative controls. We performed signal mapping refinement (conversion of sequence to raw signal) using Remora and analyzed the deviations around each modification site across late precursors (21S, 21S-C, 18S-E) and mature rRNA (18S) in both nuclear and cytoplasmic RNA.

For all three modifications, the signal corresponding to precursors closely resembled that observed in inactive mutants or IVT controls, indicating a lack of modification at these sites (Fig S20D-F). In contrast, cytoplasmic 18S showed a distinct signal shift at these positions, consistent with presence of the modification on mature rRNA. For m7G, the shift between unmodified (IVT, inactive enzyme, and precursors) and mature cytoplasmic RNA was obvious (Fig S20D). For m62Am62A, it was less obvious but visible (Fig S20E). For m1acp3Ψ, the situation was a bit more complex to interpret because this modification is formed in three steps: sugar 2’-O methylation by SNORA13, base methylation by EMG1, and addition of acp by TSR3. In Fig 1C, we discussed that m1acp3Ψ is readily detected by nanopore sequencing in the cytoplasm but not in the nucleus because the addition of the bulky acp moiety is a late event. Here the IVT control lacking all three modifications (yellow trace), and the sample extracted from cells inactivated for TSR3 lacking only the acp group (black trace) are clearly shifted from each other and from the fully modified mature cytoplasmic 18S rRNA (in red). The nuclear precursor traces (21S, 21S-C, 18S-E) superimpose nicely with the sample inactivated for TSR3, confirming earlier prediction that the addition of the acp moiety is a late event.

In conclusion, NanoRibolyzer pipeline can assign nucleotide modification status to particular pre-rRNA precursor based on raw signal analysis.

Next, we aimed to detect the abundant pseudouridine (Ψ), a uridine isomer formed by DKC1 guided by box H/ACA snoRNAs43. For this, we used first our nuclear and cytoplasmic RNA fractions and employed SeqTagger44 to multiplex up to four DRS barcodes per flow cell (two replicates per condition). Following sequencing, pseudouridine levels were quantified using Dorado (v7.2.0) with the “PseU basecaller”. In addition to the Ψ ratio provided by the basecaller, we calculated the U-to-C mismatch ratio, as previous studies have shown that isomerization of U to Ψ may lead to U-to-C mismatches during base calling41,45,46, a feature not integrated in the “PseU basecaller” (see Supplementary Note S1). To ensure selectivity, we filtered out sites with a modification ratio below 10% in each condition, and we used IVT 18S and 28S as negative controls.

We reproducibly detected all known 104 Ψ sites in 18S, 5.8S, and 28S rRNAs, as previously identified by mass spectrometry40 (Fig S20G, Supplementary Data 1). We observed that all positions are not fully modified, confirming the existence of compositionally different ribosomes with potentially specialized functions in cells. We conclude that NanoRibolyzer, coupled with “PseU basecaller”, is a robust tool for detecting ribosomal RNA pseudouridines. Using it in combination with C/U mismatch detection proved useful as it allowed to disregard hits at position not known to be modified, such as in the pre-rRNA spacers (Fig S20G, see e.g. green peaks in the 5’ ETS).

NanoRibolyzer captures cotranscriptional folding of the 5’ external transcribed spacer
Our cDNA sequencing data revealed a consistent ~1,916-nt deletion within the 5’ ETS, a feature that was absent from the corresponding DRS dataset (Fig S21A). When we mapped this deletion onto a secondary-structure model of the 5’ ETS generated with RNAstructure46, we realized that it corresponds to a reverse-transcriptase bypass of a strongly predicted structural element, characterized by very extensive Watson-Crick base-pairing and a notably low ΔG, flanked by cleavage sites A0 and 1 (Fig S21B-C). The extended folding of the 5’ ETS, as noted by pioneers48, brings the distant A0 and 1 sites into close proximity, facilitating coordinated processing (Fig S21C).

Aberrant RNA precursors are hypomodified
Lastly, to gain information about spatio-temporal conversion of U in Ψ, we followed the ‘pre-rRNA sequential extraction’ (PSE) method49, which in our hands led to the isolation of a cytoplasmic (Cp), a nucleoplasmic (Np), and a nucleolar (No) fractions, as confirmed by Western blot analysis with compartment-specific markers (Fig 5A and S22A).

We sequenced cDNA in triplicate and examined coverage and precursor abundance across the subcellular fractions (Fig S22B-D). The nucleolar fraction (No) showed strong enrichment for transcripts spanning the 5’ and 3’ ETS and the ITS1 and ITS2 regions (Fig S22B). In contrast, transcripts in the cytoplasmic fraction (Cp) were dominated by mature rRNAs. Transcripts enriched in the nucleoplasm (Np) displayed intermediate characteristics, having already undergone partial maturation (Fig S22B). Formal quantification revealed that the nucleolar fractions were enriched in the majority of pre-rRNA species, including 30S, 26S, 21S/21S-C, as well as 34S and 36S-C precursors, considered short-lived aberrant species6 (Fig S22C-D, Source Data 1). Progressive processing was reflected by a gradient of precursors abundance, with the highest levels in the nucleolus, followed by the nucleoplasm and then the cytoplasm.

We next performed direct RNA sequencing (DRS), on these fractions to assess the dynamic conversion of U to Ψ from the nucleolus to the cytoplasm. Using the supervised approach revealed highly similar precursor abundance profiles between the cDNA and DRS datasets (Fig 5B, Fig S22E-F). Using the unsupervised approach, we identified in the DRS data, processing hubs corresponding to all key precursors (e.g. 30S, 26S, 21S, etc., see arrowheads in Fig 5C). In addition, we could now also detect in nucleolar fraction the primary transcript, 47S, and its immediate derivative, 45S (Fig 5C and Fig S22G).

Interestingly, this led us to discover novel early intermediates, which we tentatively named 47S-01 (lacking the sequence upstream of the 01 site) and 47S-02 (without the downstream sequence of the 02 site) (Fig 5C and Fig 6). These intermediates were already visible in the processing perturbation intensity matrices, particularly upon DIMT1L depletion (Fig 4F). We now have further proof by direct RNA sequencing that they do exist, as these previously undocumented species presumably escaped detection due to the lower sensitivity and resolution of the methods available at the time.

We conclude that, compared with cDNA sequencing, DRS provides higher resolution, reduced background, and improved capture longer precursors (Fig S22G-H). We attribute these differences to the library-preparation workflow and to the use of a recently released, more processive enzyme (see Supplementary Note S2). Despite these distinctions, precursor abundances measured by cDNA sequencing and by DRS were remarkably similar (Fig 5B, Fig S22E-F).

Lastly, using the PseU basecaller, we reproducibly identified all expected Ψ sites in nucleolar, nucleoplasmic, and cytoplasmic fractions (Fig S23-S24; Supplementary Data 1). Having categorized the reads according to their respective precursor sequences (using the supervised approach), we next performed precursor-specific pseudouridine analysis to investigate the modification dynamics across the different precursors and cell compartments. A key conclusion from the precursor-specific modification mapping is that Ψ sites are already largely present on the primary 47S transcript. Another important observation is that aberrant RNAs, such as the 34S and 36S-C species, are distinctly hypomodified (Fig 5D-E). At this stage, we cannot determine whether these RNAs are hypomodified because they are aberrant, or whether insufficient modification contributes to their aberrant nature.

In conclusion, NanoRibolyzer can map rRNA modification at the individual precursor level.

Methods
Cell lines and culture
HEK293 cells (ATCC CRL-1573) were cultured in DMEM supplemented with 10% FBS, and 1% L-glutamine and maintained in an incubator at 37°C and 5% CO2.
All mutations analyses including tsr3 -/- 42, dimt1l-Y131G55 and wbsr22-D82K55, were introduced in homozygous diploid in HCT116 p53 positive cells.

siRNA inactivation experiments
Cells were revere transfected with silencers (10 nM, except URB1: 15 nM) in a time course (H6, H12, H24, H48, H72) to identify the best condition for Nanoribolyzer analysis6. All DsiRNAs silencers were used at 10 nM final (except for URB1, 15 nM). Silencers against UTP18, WBSCR22, DIMT1L, and LAS1L are Silencer Select (Ambion). Silencers against URB1, RPL3, and RPS26 are DsiRNAs (IDT). The Silencer RNA sequences are shown in Table S2.

Simplified nuclei isolation protocol
Detailed description of the protocol is illustrated in the Supplementary Fig S1. Briefly, samples were trypsined and washed with cold PBS, after which they were resuspended in Nuclei Isolation Buffer (NIB - 10mM Tris-HCl (pH7.4),10mM NaCl, 3mM MgCl2, 0.1% Igepal, 0.1% Tween-20,1% BSA, 0.15mM Spermine, 0.15mM Spermidine, 0.2U/ul RNase inhibitor) and homogenized using a loose pestle with ten strokes. The homogenate was then incubated on ice for 15 minutes and subsequently centrifuged at 100 x g for 5 minutes at 4 °C. The resulting soluble fraction (cytoplasmic) was transferred to a new tube, and Trizol was added to the sample at room temperature (RT) while nuclei isolation continued. The remaining pellet was subjected to two washes with 500 μl of ice-cold NIB buffer and centrifuged at 100 x g for 5 minutes at 4 °C. After the second wash, the supernatant was removed, and the pellet was resuspended with 300 μl of ice-cold NIB buffer. For nuclei isolation an equal volume (300 μl) of 50% Optiprep solution (Stemcell technologies, 07820) was added to the homogenate sample and thoroughly resuspended by pipetting, resulting in a 25% sample/optiprep mix. Then, 600 μl of 40% Optiprep solution, followed by 30% Optiprep solution, were layered in a 2 ml Eppendorf tube. The 25% sample/optiprep mix was layered on top of the 30% solution forming three visible layers. The tube was centrifuged at top speed (20,817 x g) in a bench centrifuge for 20 minutes at 4 °C. After centrifugation, the nuclei (~600 μl) were carefully collected from the 40%-30% phase and transferred into a 1.5 ml Lo-bind tube. 600 μl of Nuclei Wash Buffer (NWB - 10mM Tris-HCl (pH7.4),10mM NaCl, 3mM MgCl2, 0.1% Tween-20,1% BSA, 0.2U/ul RNase inhibitor) was added to the nuclei and thoroughly resuspended by pipetting. The sample was then centrifuged at 150 x g for 10 minutes at 4 °C, and the supernatant was removed. The nuclei were washed again with 500 μl of ice-cold NWB buffer and centrifuged similarly. The supernatant was removed, leaving approximately 20 μl of solution. Quality of the nuclei was assessed using DAPI staining and visualized under the microscope using a UV filter to identify DAPI-positive nuclei. High-quality nuclei were characterized by debris-free, round or oval-shaped DAPI-stained nuclei. Once the quality of isolated nuclei was confirmed, remaining nuclei samples were treated with DNaseI (NEB). The DNase I master mix was supplemented with 0.15 mM spermidine, 0.15 mM spermine, and RNAse inhibitor (U/ul) per sample. The samples were then incubated at 37 °C for 20 minutes. Next, the sample volume was brought to 500 μl using nuclease-free water, and 500 μl of TRIzol reagent (Life Technologies, 15596026) was added to the nuclei samples to proceed with RNA isolation.

Pre-ribosomal sequential extraction method (PSE)
The PSE method was performed as previously described49. Briefly, cells (HEK293) were grown at at ~80% confluency and harvested on ice-cold phosphate-buffered saline. Cell were resupended thoroughly in 0.5 ml of SN1 buffer (20 mM HEPES-NaOH [pH 7.5], 130 mM KCl, 10 mM MgCl2, 0.05% Igepal CA-630), supplemented with Cømplete protease inhibitor cocktail (Roche), followed by centrifugation 1300 × g for 3 min at 4 °C. The supernatant was collected and stored as the SN1 fraction. The pellet was washed with 0.5 ml SN1 buffer, and then resuspended in 0.3 ml of SN2 buffer (10 mM HEPES-NaOH (pH 7.5), 10 mM NaCl, 5 mM MgCl2, 0.1% Igepal CA-630, 0.5 mg/ml heparin, 600 U/ml RNasin (Promega)) supplemented with 100 U RNase-free DNase I (Qiagen), and incubated for 10 min at room temperature with gentle mixing. The lysate was centrifuged 12,300 × g for 10 min at 4 °C and the supernatant collected as the SN2 fraction. The remaining pellet was resuspended in 0.4 ml of SN3 buffer (20 mM HEPES-NaOH (pH 7.5), 200 mM NaCl, 4 mM EDTA, 0.1% Igepal CA-630, 0.04% sodium deoxycholate, 4 mM imidazole, 0.1 mg/ml heparin, 1 mM dithiothreitol (DTT), Cømplete, 600 U/ml RNasin) and incubated for 20 min at room temperature with moderate agitation. The extract was centrifuged 12,300 × g for 10 min in 4 °C and the supernatant was collected as the SN3 fraction. Total RNA was prepared from each fraction using TRIzol reagent (Life Technologies, 15596026) according to the manufacturer’s protocol.

RNA isolation
RNA isolation was performed as previously described59. Nuclear and cytoplasmic (as well as whole cell) fractions (~1 mL) were incubated in RT for at least 5 Min. 200µl Chlorophorm was added to nuclear and cytoplasmic fractions. Samples were Vortexed for 15 sec, incubated in RT fort 3 min and centrifuged for 15 min full speed (FS) at 4 °C. The upper aqueous phase (~550 µl) was transferred into a fresh Eppendorf tube and 500 µl isopropanol was added, thoroughly resuspended and incubated in RT for 15 min. Sample were centrifuge 10 min at full speed at 4 °C. The supernatant was discarded pellet was washed with 75% EtOH in nuclease free water followed by a 5 min centrifugation at 7,500 x g at 4°C. The supernatant was discarded, and pellet was air dried for 5-10 min. RNA was eluted with 30 µl of RNAse free water and mix in Hula mixer for 10 min in RT. RNA concentration was measured with qubit and RNA integrity was assessed via Agilent RNA ScreenTape analysis.

Northern Blotting
Total RNA was extracted using TRIzol reagent, separated on agarose denaturing gel, and analyzed by northern blotting, as previously described6. The depletion optimization was performed on HEK293 cells where each depletion factor displayed the expected phenotype according to the literature. On the basis of this analysis, the following depletion time points were selected for NanoRibolyzer: UTP18 (48h), DIMT1L (72h), WBSCR22 (48h), ALAS1L (48h), and URB1 (72h) (see Fig 3b). The probes used for northing blotting are shown in Table S3.

Western blot controls for SN1, SN2, and SN3 fractionation
Equivalent amounts of SN1, SN2, and SN3 fractions, each extracted from starting material equivalent to 25 µg of total protein (fractionated according to49), along with 25 µg of total protein from whole-cell lysate used as control, were separated on a 10% SDS-PAGE gel. Proteins were transferred to nitrocellulose membranes and probed in TBS containing 0.1% Tween-20 and 3% BSA. Membranes were incubated with either: 1) Anti-rabbit primary antibodies: anti-histone H3 (Proteintech, Ref 17168-1-AP), anti-nucleolin (Proteintech, Ref 10556-1-AP), and anti-α-tubulin (Proteintech, Ref 11224-1-AP), each at 1:5000 for 2 hours, followed by HRP-conjugated anti-rabbit secondary antibody (Cytiva, Ref NA934V) at 1:5000 for 2 hours, or 2) Anti-mouse primary antibodies: anti-fibrillarin (Antibodies-online, Ref ABIN361375) or anti-UBF (Tebu Bio, Ref SC-13125), each at 1:1000 overnight, followed by HRP-conjugated anti-mouse secondary antibody (Jackson ImmunoResearch, Ref 115-035-062) at 1:5000 for 2 hours.

In-vitro poly adenylation using poly(A) tailing of RNA
For in-vitro polyadenylation, “In-vitro poly adenylation using poly(A) tailing of RNA” kit was used (NEB#M0276), according to the manufacturer instructions. Briefly, 1 µg of RNA was taken in 15 µl nuclease free water and supplemented with 2 µl of 10X E. coli Poly(A) Polymerase Reaction Buffer, 2 µl ATP (10mM) and 1 µl E. coli Poly(A) Polymerase (to a total volume 20 µl). Samples were incubated at 37°C for 30 minutes. The polyadenylated RNA samples were purified using RNAClean XP beads (Beckman Coulter, A63987), according to the manufacturer instructions. In the last elution step, the sample was resuspended in 10 µl nuclease-free water and incubated at 37°C for 5 minutes in Hula mixer. The sample was placed on the magnet and once the solution was clear, the elute was transferred into a clean 1.5 ml Eppendorf tube.

In-vitro transcription of 18S and 28S rRNA.
Synthesis of the 18S and 28S rRNA in-vitro transcripts was performed using HiScribe® T7 High Yield RNA Synthesis Kit following manufacturers instruction. Briefly, 2 µg linearized plasmid containing the mature rRNA (GenScript) were used as template material, combined with 10X Reaction Buffer, 10 mM NTP`s and 2U T7 RNA Polymerase Mix. Reactions were incubated for 2h at 37°C and stopped by digestion of the template plasmid through DNase I (ThermoFisher Scientific, EN0525) following manufactures instructions. Purification was performed using Monarch® RNA Cleanup Kit (NEB, T2040) and product quality was assessed using capillary electrophoresis via Agilent RNA ScreenTape Analysis.

Direct cDNA-native barcoding library preparation
Direct cDNA coupled with native barcoding libraries were prepared using Direct cDNA Sequencing Kit (SQK-DCS109), Native Barcoding Expansion 1-24 (EXP-NBD104, EXP-NBD114), following the manufacturer's protocol. Reverse transcription and strand-switching. 1µg of poly(A)-tailed RNA was transferred to a 1.5 ml tube and adjusted to 7.5 µl with nuclease-free water. In a 0.2 ml PCR tube, 7.5 µl of RNA sample were mixed with 2.5 µl of VNP (ONT), 2.5 µl of 10 mM dNTPs (NEB N0447), and the volume was adjusted to 11 µl with nuclease-free water. The samples were incubated for 10 minutes at room temperature and then snap-cooled on a pre-chilled freezer block for 1 minute. Next, a master mix was prepared, containing 4 μl of 5x RT Buffer (ThermoFisher, EP0751), 1 μl RNaseOUT (Life Technologies, 10777019), 1 μl of Nuclease-free water, and 2 μl Strand-Switching Primer (SSP, ONT) per sample, to a total volume of 8 μl. The strand-switching buffer was added to the snap-cooled, annealed mRNA, and the samples were incubated at 42°C for 2 minutes in the thermal cycler. Subsequently, 1 µl of Maxima H Minus Reverse Transcriptase (ThermoFisher, EP0751) was added, and the total volume becomes 20 µl. The samples were incubated following a specific thermal protocol: 42°C for 90 mins, 85°C for 5 mins, and then holding at 4°C. After the reverse transcription, RNA degradation and second strand synthesis were performed. 1 µl of RNase Cocktail Enzyme Mix (ThermoFisher, AM2286) was added to the reverse transcription reaction and incubated for 10 minutes at 37°C. The samples were then subjected to the AMPure XP beads-based (Beckman Coulter A63881) purification method using 0.85x ratio of beads:sample and ultimately cDNA hybrid was eluted in 20 µl of nuclease-free water. Next, the 20 μl of reverse-transcribed samples were prepared with 25 μl of 2x LongAmp Taq Master Mix (NEB, N0447), 2 μl of PR2 Primer (PR2, ONT), and 3 μl of Nuclease-free water, to a total volume of 50 μl. The samples were incubated at specific temperatures in the thermocycler. Afterwards, the sample were then subjected to the AMPure XP beads-based purification method using 0.8x ratio of beads:sample and ultimately cDNA/RNA hybrid was eluted in 21 µl of nuclease-free water. The eluted sample was quantified using a Qubit fluorometer. End-prep. Subsequently, end repair and dA-tailing were performed by mixing 20 µl of cDNA sample with 30 µl Nuclease-free water, 7 µl Ultra II End-prep reaction buffer (NEB, E7546), and 3 µl Ultra II End-prep enzyme (NEB, E7546) mix to a total volume of 60 µl. The samples were incubated at 20°C for 5 minutes and then at 65°C for 5 minutes. Next, the samples were subjected to AMPure XP beads-based purification using 1x ratio of beads:sample, and the cDNA was eluted with 22.5 µl of nuclease-free water. Barcode ligation. Barcode ligation was then performed, where 22.5 µl of End-prepped DNA was mixed with 2.5 µl of Native Barcode and 25 µl of Blunt/TA Ligase Master Mix (NEB, M0367) to a total volume of 50 µl. The reaction was incubated for 15 minutes at room temperature. The samples were then subjected to AMPure XP beads-based purification using 1x ratio of beads:sample, and the cDNA was eluted with 26 µl of nuclease-free water. Lastly, the barcoded samples are pooled to a final volume of 65 μl in a 1.5 ml Eppendorf tube. Adapter ligation. Adapter ligation was performed by adding 65 µl of pooled barcoded sample, 5 µl of Adapter Mix II (AMII, ONT), 20 µl of 5X NEBNext Quick Ligation Reaction Buffer (NEB, B6058), and 10 µl of Quick T4 DNA Ligase (NEB, E6056) to a total volume of 100 µl. The final libraries were incubated for 10 minutes at room temperature and then subjected to AMPure XP beads-based purification, with the cDNA being eluted with 26 µl of nuclease-free water. The sample was loaded and sequenced onto a primed PromethION flow cell as per the manufacturer instruction.

Direct RNA library preparation
Direct RNA libraries were prepared using the SQK-RNA004 kit (ONT) following the manufacturer's protocol. Briefly, 1µg of poly(A)-tailed RNA was adjusted to final volume of 9.5 µl with nuclease-free water. 3 µl of NEBNext Quick Ligation Reaction Buffer (NEB B6058), 1 µl of RT Adapter (RTA) (ONT), and 1.5 µl of T4 DNA Ligase 2M U/ml (NEB M0202), were added to sample resulting in a total volume of 15 µl. The reaction is mixed by pipetting and incubated for 10 minutes at room temperature. Next, the reverse transcription master mix was prepared by mixing 9 µl of Nuclease-free water, 2 µl of 10 mM dNTPs (NEB N0447), 8 µl of 5x first-strand buffer (Thermo Fisher Scientific, 18080044), and 4 µl of 0.1 M DTT, resulting in a total volume of 23 µl. The master mix was added to the RNA sample containing the RT adapter-ligated RNA. 2 µl of SuperScript III reverse transcriptase (Thermo Fisher Scientific, 18080044) were added to the reaction, bringing the final volume to 40 µl. The reaction was incubated at 50°C for 50 minutes, followed by 70°C for 10 minutes, and then brought to 4°C. Agencourt RNAClean XP beads (Beckman Coulter, A63987) were resuspended and 72 µl of the resuspended beads were added to the reaction. The sample was mixed by pipetting and incubated on a Hula mixer for 5 minutes at room temperature. Subsequently, the sample was subjected to two washes with 70% ethanol, and the RNA:DNA hybrids were eluted with 20 µl of nuclease-free water. For the adapter ligation reaction, 8.0 µl of NEBNext Quick Ligation Reaction Buffer, 6.0 µl of RNA Ligation Adapter (RLA), 3.0 µl of Nuclease-free water, and 3.0 µl of T4 DNA Ligase were mixed with 20 µl of the eluted sample to reach a total volume of 40 µl. The reaction was incubated for 10 minutes at room temperature. 30 µl of resuspended RNAClean XP beads were added to the adapter ligation reaction, mixed by pipetting, and incubated on a Hula mixer for 5 minutes at room temperature. The sample was then subjected to two washes with the Wash Buffer (WSB, ONT) using a magnetic rack. Following the washes, the beads were pelleted on the magnet, and the supernatant was pipetted off. The pellet was resuspended in 33 µl of Elution Buffer (EB, ONT) and were incubated at 37°C for 10 minutes in a Hula mixer. Incubation at 37°C allows the release of long fragments from the beads. The eluate was then cleared by pelleting the beads on a magnet, and the eluate was retained and transferred to a clean to 1.5 ml tube. The sample was loaded and sequenced onto primed PromethION flow cell as per the manufacturer instruction.

Implementation of NanoRibolyzer pipeline
NanoRibolyzer was implemented as a Nextflow-based workflow using Docker containers and could be installed as a plugin within Oxford Nanopore Technologies (ONT) Epi2Me platform (https://github.com/stegiopast/wf-nanoribolyzer). Pod5 output files were basecalled using the dorado basecaller (https://github.com/nanoporetech/dorado), trimmed with Porechop (https://github.com/rrwick/Porechop) to remove adapter sequences and aligned to the 45SN1 (equivalent to 47S) (GeneID:106631777; NW_021160023.1:480347-493697) using minimap260 with the map-ont flag. The read IDs of the aligned 45SN1 were used to filter the original pod5 file. The resulting unaligned BAM files were used to collect metadata on reads at a single nucleotide resolution and the rebasecalled reads were realigned to the 45SN1 reference. The final BAM files were processed using both template-based and template-free approaches.

NanoRibolyzer is open-source and has been made freely available to the community through the Epi2ME platform for broader accessibility and use.

Template-based quantification of rRNA precursors
The template-based algorithm associated long-reads with literature-based ribosomal intermediates8,9,10. In this approach, the pairwise minimal reciprocal overlap (MRO) between a query read and all possible intermediates was determined. The MRO was defined by calculating the minimal relative overlap of the query over the intermediate and vice versa. Once the minimal overlap for each query-intermediate pair was established, the pair with the maximal overlap was used to associate the read with the corresponding intermediate (See Fig S3). Read clusters were then stored in a tab-separated values (TSV) table, which included read IDs, absolute and relative read counts, and the start and end sites of all reads associated with each intermediate. Additionally, bed files for each intermediate were generated to facilitate visualization in the Integrative Genomics Viewer61 (IGV). The 45SN1 reference FASTA from the NanoRibolyzer references repository was used for all analyses.

Template-free rRNA precursors
The template-free algorithm was based on the construction of a 2-dimensional (length(45SN1)2) intensity matrix in which reads were embedded using the alignment start and end sites as coordinates. The number of reads sharing start and end site coordinates on the matrix led to the formation of intensity “hubs”. The resulting intensity matrix was stored in CSV format, which included the start site, end site, number of reads, and ID list for each intensity hub. For visualization of the matrices, absolute read counts of intensity hubs were min-max normalized applying an additional contrast enhancement of 2%.

Determination of read cleavage sites
The determination of significant abundant cleavage sites was computed using alignment start and end sites where the absolute abundance of start and end sites was determined along the 45SN1 reference. For each template-based intermediate, the mean abundance and standard deviation (SD) of alignment start and end sites were calculated. Cleavage sites occurring two SDs above the mean in the dataset were considered significant abundant cleavage sites. For overlapping template-based intermediate cleavage site intervals, metrics were determined by calculating the mean of means and the mean of SDs. The output files were stored in TSV and BED file formats, including information about their relative abundance and the cleavage site location. The BED files were visualized using IGV.

DRS Modification analysis
PseudoU detection was performed by using dorado version 7.2.0 with the super accuracy RNA004 model version 5.0.0 using the “pseU” flag. The resulting unaligned bam file was converted into fastq format using samtools62 (https://github.com/samtools/samtools). The fastq file was aligned with minimap260 using the “map-ont” long read alignment flag together with the MD flag to ensure the remainder of the modification information. Subsequently, the aligned bam file was processed using pysam (https://github.com/pysam-developers/pysam). For each possibly modified nucleotide on a read aligned bam files contain a modification probability. A threshold of 95% modification probability was chosen to determine modifications. For the acquisition of the raw signal analysis at specific modification sites, the data was basecalled and aligned as described above. Template-based analysis information was used to determine intermediate-specific datasets. The aligned bam file and the original pod5 together with a 45SN1 reference fasta file were processed using remora (https://github.com/nanoporetech/remora). Remora was used to perform the resquiggleing, which is the process of association of raw signal interval with specific nucleotides on the reference or basecalled sequence. In this case, once the intervals were associated to the reference sequence, the mean of z-normalized raw current signal covering 10 bases up- and downstream from the loci of interest were extracted. For all reads covering the locus of interest the mean of means and semi-standard deviation (deviation below and above mean) of means were determined.

Statistics
All statistical analyses are described in the respective figure legends. Each legend provides detailed information about the statistical metrics (such as the mean and standard deviation), sample sizes, statistical tests used and any relevant adjustments applied.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study presents a novel technical approach for quantification and characterization of pre-rRNA intermediates using long-read nanopore sequencing. The authors provide a comprehensive set of validation data showing that their RNA fractionation, sequencing and analyses tools can detect and quantify all major known pre-rRNA species. In addition, they identify several previously-unknown pre-rRNA processing sites. In the final section of the manuscript, they report the mapping of pseudouridylated sites in different pre-RNA species, and conclude that such sites are modified co-transcriptionally.\n\nGiven the limitations of current methods for analyzing ribosome maturation, the approaches described here might have high potential for broader application and more in-depth characterization of the pathway. However, there are a few points that, in my opinion, need to be addressed.\n\nMy concerns are mostly related to the demonstrations that NanoRibolyzer has sufficient accuracy and sensitivity to quantify various pre-rRNA species. This is essential for detecting altered patterns of ribosome maturation, such as those observed in ribosomopathies.\n\n1. Quantifications of pre-rRNA species and patterns of pre-rRNA contents.\nThe pre-rRNA species most efficiently detected by nanopore cDNA sequencing (Figures 2B and S4B) are 21S, 21S-C, 12S, and 7S. However, these are not the most abundant species in HEK293 cells. Based on published Northern blot data (and also based on the control samples in Figure 4B), the most abundant pre-rRNAs are 45S/47S, 30S, and 32S. NanoRibolyzer detects well the shortest species but has a reduced sensitivity for longer precursors. In my view, this limitation should be explicitly mentioned in the results section, pointing that the method does not accurately reflect the relative abundance of all pre-rRNA species. This limitation does not preclude the use of the method for the purposes shown in the study, but the extended information provided by Northern blots must be acknowledged.\n\nNorthern blots of total RNA (5'-ETS, ITS1 and ITS2 probes) from total cell, nucleus and cytoplasm should be shown for comparison with the results of pre-rRNA contents obtained with NanoRibolyzer.\n\n2. Underestimation of 18S-E contents.\nIn Figures 2B and S4B, nuclear 18S-E contents appear lower than those of 21S and 21S-C; and cytoplasmic 18S-E is barely detectable. However, it is already known that total 18S-E levels are comparable to or even slightly higher than 21S levels (seen in previously published Northern blot analyses of total RNA from HEK293 cells and in nuclear–cytoplasmic pre-rRNA fractionation data from the Gleizes lab). This discrepancy is also apparent in the control samples of Northern blots in Figure 4B. The reduced detection of 18S-E, particularly in the cytoplasmic fraction, is found with the two fractionation methods used in the study. In my opinion, this problem might be due to the progressive 3′-end trimming of 18S-E during nuclear export described by Preti et al. (2013). To address this, a re-analysis of cytoplasmic RNA-seq data that quantifies, as a pool, the 3′-end-shortened forms of 18S-E could improve recovery and more accurately reflect its true abundance. Accurate quantification of this pool is critical for assessing defects in cytoplasmic maturation of the small ribosomal subunit.\nAside from improving its detection in unperturbed cells, this point would be further strengthened by including a knockdown of a protein known to cause cytoplasmic accumulation of 18S-E (for example RPS26) in Figure 4. Demonstrating that NanoRibolyzer reliably detects this phenotype would validate its utility for studying 40S cytoplasmic maturation defects.\n\n3. Monitoring pre-rRNA processing perturbations.\nA complete set of Northern blot analyses, with the different probes for pre-18S and pre-28S precursors, should be provided to demonstrate that all changes detected by NanoRibolyzer are also observed by Northern blot (they might go as supplementary data).\n\n4. Refinement of processing sites and discovery of new pre-rRNA species.\nIt would be very informative to include data on the relative prevalence of each species.\nFor instance, among the 30S variants (30S-E2, 30S-C, 30SS, and 30SL), which are the most abundant?\nA similar analysis could be useful for the 21S variants (21SS and 21SL) in order to be taken as a reference of major and minor precursors in future studies.\nMinor point. The description of the PSE fractionation method used in the final section of the study may not be entirely accurate. Based on the original publication, my understanding is that the SN3 fraction contains preribosomes undergoing early maturation in the nucleolus, the SN2 fraction includes preribosomes at intermediate nucleolar maturation stages, and the SN1 fraction contains the preribosomes in the nucleoplasm and cytoplasm.", "reviewer_2": "In this study, Pastore et al., harness Nanopore sequencing to study rRNA processing and modifications in human cell culture. In principle it is an interesting idea to 1) re-explore rRNA maturation and modifications with this technology and 2) provide a pipeline enabling to study rRNA maturation and modifications. However, the manuscript is rather technical and limited in its biological scope (there is not much new biology discovered). Another serious limitation of this work is that it also lacks conceptual novelty and scientific rigour (among other several novelty claims that are not grounded).\n\nSpecific comments:\n1) Novelty, references to key previous studies and methodological limitations:\nThe authors were doing a poor job of properly acknowledging key previous studies.\nAmong others, studies from the Novoa laboratory, like https://doi.org/10.1038/s41587-021-00915-6 examining pseudouridylation in (r)RNA are ignored.\nStudy from the Ferreira-Cerca and Grohmann groups are also ignored https://doi.org/10.1261/rna.079636.123. This study has previously demonstrated the use of Nanopore sequencing for the de novo determination of rRNA maturation pathways.\nMoreover, this study also provided proof of concept to determine precursor-dependent analysis of base modifications. Two previously published key concepts that are claimed by the authors as novelties in this manuscript.\nThere are also no efforts to critically evaluate the limitations of RiboAnalyzer or how it compares/adds up to what is already existing in the field.\n\n2) rRNA processing site mapping and maturation analysis:\nIt is not clear what is new or not new regarding the rRNA processing sites. Presentation efforts should be done here.\nPreviously known rRNA maturation sites and known positions should be better summarized (e.g previous studies vs this study). Please provide the nucleotide positioning when previously determined in comparison to the current study…\nThe importance of using cellular fractionation to analyse rRNA maturation defects is not clear or justified, especially in the context of analysing ribosome biogenesis factors with known nuclear phenotypes. My impression is that the whole cell analysis does not allow to faithfully capture rRNA maturation in the experimental conditions described (insufficient numbers of reads?). This seems to be expressed indirectly by the authors: “We often observed that whole cell fractions were largely redundant with cytoplasmic fractions”. Meaning: no nuclear precursors are observed when doing total RNA sequencing?! If so, it is a major bottle neck. In this regard the Cyt 18S-E recovery seems to me rather poor/modest (and sometime not used or ignored without justification, see point 3). It would be also crucial to look at conditions where this precursor is accumulating in the cytoplasm to demonstrate that this type of events can be faithfully capture.\nThe author should provide the relevant results and provide clarity on optimal conditions to analyse rRNA maturation (whole cell vs fractionation …). Please provide experimental evidence and comments to better guide the potential users of the method.\n\n3) Precursor-dependent analysis of base modifications:\nThe Lafontaine laboratory has previously reported that the m7G and dimethylations modifications are added in the nucleus in human cells https://doi.org/10.1091/mbc.E15-02-0073. Accordingly, one would expect to detect these base modifications at the level of Nuc 18S-E and particularly at the level of the downstream Cyt 18S-E, which does not seem to be the case. It is also very unfortunate that the Cyt 18S-E precursor, on which the acp3 modification is also expected to be added, is not used/shown in this analysis, please add.\nOverall, the analysis does not provide evidence for the following claim, “In conclusion, NanoRibolyzer pipeline can assign base modification status to particular pre-rRNA precursor based on raw signal analysis.”. It is currently an all or nothing comparison without seeing anything clear on the relevant expected pre-rRNA, for instance nuc/cyt18S-E, vs other pre-rRNA intermediates in comparison to “final modification” state (mature 18S). As such these results are useless.\n\n4) Ribosomal RNA pseudouridine formation is mostly cotranscriptional\nThe conclusion of this analysis is strongly misleading, since the primary 47S rRNA intermediates is NOT a co-transcriptional intermediate. Considering the primary transcript as equal to a co-transcriptional event is an over-simplification and an over-interpretation on the true nature of this pre-rRNA intermediate (where is the evidence of its association with actively transcribing RNA Pol I !!??). At best, the authors can only conclude that most of the pseudouridylation are added rather early.\nSome pseudouridylations seem to have a rather “erratic behavior” between precursors. Some residues appear to have lower “signals” than the upstream precursor (e.g. positions 36, 649, 681, 1244, 16943, 1692), instead of being constant or increasing. Please comment.\nThe density map visualization is also misleading, the darker being the lower modification ratio percentage, the opposite would be more intuitive.\nThere is also no information for Nuc-18S-E?! Please provide.\nA similar depiction related to 28S rRNA pseudouridylation should also be provided somewhere.\nThe relative percentage of Pseudouridylation varies from site to site, and not all are attaining ~100% within the mature rRNA. I believe that there is information related to their true relative abundance. Please comment on those numbers and probable limitations and relevance. I believe a difference in % between mature rRNA and pre-rRNA (like 47S) would probably provide a clearer view on “late” modifications that may increase over time (if any existing). (The true early modifications should remain constant across the precursors).", "reviewer_3": "The 18S, 28S, and 5.8S rRNA species are derived from a common 47S precursor RNA, which is synthesized by RNA polymerase I in the nucleolus. The maturation of these rRNA species from the precursor involves a series of step-wise cleavage and processing events that occur in the nucleolus, nucleoplasm, and cytoplasm. This process generates distinct rRNA intermediates and requires the involvement of numerous trans-acting factors. Although rRNA maturation has been extensively studied over the past 15-20 years, the methodology for defining rRNA intermediates and specific cleavage sites during the maturation process has not seen significant advancements and still primarily relies on Northern blotting and pulse-chase labeling.\n\nHere the authors have developed NanoRibolyzer, an innovative and straightforward rRNA sequencing approach based on long-read nanopore sequencing. They have meticulously benchmarked this method and demonstrated its ability to detect known and unknown pre-rRNA species and pinpoint cleavage sites with single-nucleotide precision. Furthermore, the authors have shown that NanoRibolyzer can detect distinct rRNA modifications as well as alterations in rRNA processing upon depletion of specific processing factors. Overall, this methodology represents a significant advancement for researchers in the field of ribosome biogenesis.\n\nThe data in the manuscript are well-organized and clearly presented, and the NanoRibolyzer technology is described in sufficient detail. My only concern is that due to its very technical focus the data remain at a merely descriptive level. For example, the functional significance of the newly identified cleavage sites and rRNA intermediates is not clear. Regardless of this potential weakness, I believe that the technology described here will have a significant impact on the field."}, "author_response": "Point-by-point responses to the reviewers’ comments:\nReviewer #1 (Remarks to the Author):\nThis study presents a novel technical approach for quantification and characterization of\npre-rRNA intermediates using long-read nanopore sequencing. The authors provide a\ncomprehensive set of validation data showing that their RNA fractionation, sequencing and\nanalyses tools can detect and quantify all major known pre-rRNA species. In addition, they\nidentify several previously-unknown pre-rRNA processing sites. In the final section of the\nmanuscript, they report the mapping of pseudouridylated sites in different pre-RNA species,\nand conclude that such sites are modified co-transcriptionally.\nGiven the limitations of current methods for analyzing ribosome maturation, the\napproaches described here might have high potential for broader application and more in-\ndepth characterization of the pathway. However, there are a few points that, in my opinion,\nneed to be addressed.\nAnswer: We are grateful to Reviewer #1 for their overall extremely positive assessment of\nour work.\nMy concerns are mostly related to the demonstrations that NanoRibolyzer has sufficient\naccuracy and sensitivity to quantify various pre-rRNA species. This is essential for detecting\naltered patterns of ribosome maturation, such as those observed in ribosomopathies.\nAnswer: We thank Reviewer #1 for this comment.\nIn terms of accuracy, our method achieves single-nucleotide resolution, which is the\nhighest possible.\nIn terms of sensitivity, NanoRibolyzer is currently effective with as little as 1 µg of RNA,\nwhich is approximately five times less than what is typically required for Northern blotting.\nIn future work, we aim to further reduce the input requirements. However, we hope Reviewer\n#1 will agree that such further optimization is beyond the scope of the present study.\n1. Quantifications of pre-rRNA species and patterns of pre-rRNA contents.\nThe pre-rRNA species most efficiently detected by nanopore cDNA sequencing (Figures 2B\nand S4B) are 21S, 21S-C, 12S, and 7S. However, these are not the most abundant species\nin HEK293 cells. Based on published Northern blot data (and also based on the control\nsamples in Figure 4B), the most abundant pre-rRNAs are 45S/47S, 30S, and 32S.\nNanoRibolyzer detects well the shortest species but has a reduced sensitivity for longer\nprecursors. In my view, this limitation should be explicitly mentioned in the results section,\npointing that the method does not accurately reflect the relative abundance of all pre-rRNA\nPage 1 of 15\nspecies. This limitation does not preclude the use of the method for the purposes shown in\nthe study, but the extended information provided by Northern blots must be acknowledged.\nAnswer: This is a very good point, thank you. Agree to some extent. A strict comparison is\nchallenging because Northern blotting and nanopore sequencing differ markedly in\nsensitivity. Nonetheless, when comparing the signal captured by NanoRibolyzer (e.g., Fig\n2B) with that obtained by Northern blotting (new Supplementary Data 1, HEK293 lanes), it is\nclear that the species mentioned by the referee—21S/21S-C, 12S (7S is not visible here), and\nalso 18S-E—are at least as abundant, if not more abundant, than 47S/45S, 30S, or 32S. We\ntherefore believe our method largely captures the natural distribution of species\nWe fully agree with Reviewer #1 that NanoRibolyzer, by design, involves selection steps. This\nis true for all sequencing-based approaches. Such selection arises (i) from the cDNA-based\ncloning strategy and (ii) from the physical properties of the nanopores themselves. By\ncontrast, Northern blotting is a “blind” method in which RNAs are directly loaded onto a gel\nwithout selection. In this sense, we agree that Northern blotting will remain a valuable and\nappreciated technique, and we have ourselves used it extensively for two decades.\nWe have added a short paragraph to the Discussion to acknowledge this point. The new\nparagraph reads as follows:\nBy design, NanoRibolyzer, like all sequencing-based approaches, entails inherent selection\nsteps arising from cDNA library preparation and nanopore biophysics; by contrast, Northern\nblotting is a largely “blind” technique in which RNAs are directly resolved without prior\nselection, and therefore remains a valuable and complementary method.\nWe would also like to emphasize that the technology is evolving very rapidly. Over the course\nof this study, improvements in enzymes and the use of DRS steadily enhanced the detection\nof low-abundance species such as 45S/47S (see Fig 5, upper left panel, where 47S and 45S\nare readily detected). Detection became so robust that we were even able to characterize\ntwo previously unknown species—47S-01 and 47S-02 (see amended text for details).\nNorthern blots of total RNA (5'-ETS, ITS1 and ITS2 probes) from total cell, nucleus and\ncytoplasm should be shown for comparison with the results of pre-rRNA contents obtained\nwith NanoRibolyzer.\nAnswer: Done. Related to previous question. See Supplementary Dataset 1. We also\nincluded the 3’ ETS probe.\nPage 2 of 15\n2. Underestimation of 18S-E contents.\nIn Figures 2B and S4B, nuclear 18S-E contents appear lower than those of 21S and 21S-C;\nand cytoplasmic 18S-E is barely detectable. However, it is already known that total 18S-E\nlevels are comparable to or even slightly higher than 21S levels (seen in previously published\nNorthern blot analyses of total RNA from HEK293 cells and in nuclear–cytoplasmic pre-\nrRNA fractionation data from the Gleizes lab). This discrepancy is also apparent in the\ncontrol samples of Northern blots in Figure 4B. The reduced detection of 18S-E, particularly\nin the cytoplasmic fraction, is found with the two fractionation methods used in the study.\nIn my opinion, this problem might be due to the progressive 3′-end trimming of 18S-E during\nnuclear export described by Preti et al. (2013). To address this, a re-analysis of cytoplasmic\nRNA-seq data that quantifies, as a pool, the 3′-end-shortened forms of 18S-E could improve\nrecovery and more accurately reflect its true abundance. Accurate quantification of this\npool is critical for assessing defects in cytoplasmic maturation of the small ribosomal\nsubunit.\nAnswer: Thank you. This is a very interesting point.\nFrom Figs 2B and S4B, it can be seen that the levels of nuclear 21S, 21S-C, and 18S-E are\nquite similar; in particular, 18S-E and 21S-C levels are nearly identical.\nTo further resolve the 3′-end maturation steps of 18S, we instructed our analysis pipeline to\nincorporate sites corresponding to 3′-extended forms of 18S, as described in Preti et al. (see\nnew Fig S7).\nThis re-analysis, suggested by Reviewer #1 (thank you), beautifully resolved 18S-E and its\nmultiple 3′-extended variants as they mature across the nucleus and the cytoplasm.\nAside from improving its detection in unperturbed cells, this point would be further\nstrengthened by including a knockdown of a protein known to cause cytoplasmic\naccumulation of 18S-E (for example RPS26) in Figure 4. Demonstrating that NanoRibolyzer\nreliably detects this phenotype would validate its utility for studying 40S cytoplasmic\nmaturation defects.\nAnswer: We have performed the suggested experiment. RPS26 was depleted, and nuclear\nand cytoplasmic RNA fractions from knockdown and control cells were sequenced (new Fig\nS8). As expected, we observed cytoplasmic accumulation of 3′-extended forms of 18S-E (Fig\nS8F, red signal in the heatmap). We thank the reviewer for this valuable suggestion.\nFurthermore, we applied the same deconvolution analysis to perturbed cells (Fig S17D–F),\nwith particularly striking results upon WBSCR22 depletion.\nPage 3 of 15\n3. Monitoring pre-rRNA processing perturbations.\nA complete set of Northern blot analyses, with the different probes for pre-18S and pre-28S\nprecursors, should be provided to demonstrate that all changes detected by NanoRibolyzer\nare also observed by Northern blot (they might go as supplementary data).\nAnswer: Completely Agree. Thank you. Done. See Supplementary Figures S10-S14.\n4. Refinement of processing sites and discovery of new pre-rRNA species.\nIt would be very informative to include data on the relative prevalence of each species.\nFor instance, among the 30S variants (30S-E2, 30S-C, 30SS, and 30SL), which are the most\nabundant?\nA similar analysis could be useful for the 21S variants (21SS and 21SL) in order to be taken\nas a reference of major and minor precursors in future studies.\nAnswer: Excellent suggestion. Thank you. This is now shown in Fig 17.\n-Minor point. The description of the PSE fractionation method used in the final section of the\nstudy may not be entirely accurate. Based on the original publication, my understanding is\nthat the SN3 fraction contains preribosomes undergoing early maturation in the nucleolus,\nthe SN2 fraction includes preribosomes at intermediate nucleolar maturation stages, and\nthe SN1 fraction contains the preribosomes in the nucleoplasm and cytoplasm.\nAnswer: Agree. But, in our hands, the protocol reproducibly enriched the nucleolus, the\nnucleoplasm, and the cytoplasm, as we thoroughly established with suitable protein marker\ndetection by western blotting. The differences between the excellent original report from the\nteam of Prof. Dosil, which was a great inspiration to us, and our fractionation, may be due to\nslight differences in cell line used, extract preparation (pestle used etc.).\nReviewer #2 (Remarks to the Author):\nIn this study, Pastore et al., harness Nanopore sequencing to study rRNA processing and\nmodifications in human cell culture. In principle it is an interesting idea to 1) re-explore rRNA\nmaturation and modifications with this technology and 2) provide a pipeline enabling to\nstudy rRNA maturation and modifications. However, the manuscript is rather technical and\nlimited in its biological scope (there is not much new biology discovered). Another serious\nlimitation of this work is that it also lacks conceptual novelty and scientific rigour (among\nother several novelty claims that are not grounded).\nPage 4 of 15\nAnswer: We respectfully disagree with Reviewer #2’s assessment, and note that Reviewers\n#1 and #3 do not share this view.\nWe are not entirely clear which aspects of the study are considered to lack scientific rigour.\nWe would welcome more specific guidance from Reviewer #2, as we believe that both the\nexperimental design and the analytical framework are robust, extensively benchmarked,\nand carefully validated throughout the manuscript.\nWe also respectfully disagree with the assertion that the work lacks conceptual novelty.\nNanoRibolyzer introduces a fundamentally new way of interrogating ribosome biogenesis by\nenabling quantitative, single-molecule–level analysis of pre-rRNA processing intermediates\nand RNA modifications within individual precursor species. As highlighted by Reviewers #1\nand #3, the field has relied for several decades on classical biochemical approaches—such\nas Northern blotting and pulse-chase labelling—which, while robust and invaluable, are\nintrinsically low-resolution, require microgram quantities of RNA, and frequently rely on\nradioactive labelling, thereby limiting scalability and clinical applicability.\nFinally, we respectfully disagree with the claim that the biological scope of the study is\nlimited. Using NanoRibolyzer, we uncover multiple layers of new biology, including:\n(i) the assignment of new or refined functions to previously characterised ribosome\nbiogenesis factors (including DIMT1L and URB1);\n(ii) a novel role for the ribosomal protein RPL3 in 3′ ETS processing;\n(iii) the identification of previously undescribed 47S pre-rRNA species (e.g., 47S-01 and 47S-\n02);\n(iv) the confirmation of previously speculated processing sites (such as site 22) and the\ndiscovery of new cleavage sites (including site E2 and others);\n(v) direct evidence that pseudouridylation in human cells occurs very early during ribosome\nbiogenesis, already detectable on unprocessed 47S pre-rRNA;\n(vi) the observation that aberrant 34S and 36S-C RNA species are distinctly hypomodified.\nWhile it remains to be determined whether hypomodification is a cause or a consequence\nof aberrant processing, this finding raises the intriguing possibility that RNA modification is\nactively coupled to RNA surveillance mechanisms.\nTogether, these findings demonstrate that NanoRibolyzer is not merely a technical advance,\nbut a platform that enables biological insights that were previously inaccessible with\nexisting methodologies.\n1) Novelty, references to key previous studies and methodological limitations:\nThe authors were doing a poor job of properly acknowledging key previous studies.\nAmong others, studies from the Novoa laboratory, like https://doi.org/10.1038/s41587-021-\n00915-6 examining pseudouridylation in (r)RNA are ignored.\nPage 5 of 15\nAnswer: Apologies. Due to space limitation, at initial submission we compacted the text,\nand could not cite all references. We take this fully on board. We have added these\nimportant references.\nStudy from the Ferreira-Cerca and Grohmann groups are also ignored\nhttps://doi.org/10.1261/rna.079636.123.This study has previously demonstrated the use of\nNanopore sequencing for the de novo determination of rRNA maturation pathways.\nMoreover, this study also provided proof of concept to determine precursor-dependent\nanalysis of base modifications. Two previously published key concepts that are claimed by\nthe authors as novelties in this manuscript.\nAnswer: All apologies for this oversight. These imporatnt reference were added. Thank you.\nThere are also no efforts to critically evaluate the limitations of RiboAnalyzer or how it\ncompares/adds up to what is already existing in the field.\nAnswer: Thank you. See response above to reviewer #1 and new Discussion.\n2) rRNA processing site mapping and maturation analysis:\nIt is not clear what is new or not new regarding the rRNA processing sites. Presentation\nefforts should be done here. Previously known rRNA maturation sites and known positions\nshould be better summarized (e.g previous studies vs this study). Please provide the\nnucleotide positioning when previously determined in comparison to the current study\nAnswer: We thank Reviewer #2 for their comment. In response, we included Table 1 showing\nthe annotation of pre-rRNA processing sites from three reviews and compared them to our\nreported sites (all in one referenced pre-rRNA template). More detailed information is\npresented in Figure S6. It is important to note that rRNA annotations can vary positionally\ndue to sequence heterogeneity or incomplete rRNA annotation. Therefore, it is crucial to\naccurately document the processing sites with nucleotide resolution using a standard\nreference sequence.\nThe importance of using cellular fractionation to analyse rRNA maturation defects is not\nclear or justified, especially in the context of analysing ribosome biogenesis factors with\nknown nuclear phenotypes. My impression is that the whole cell analysis does not allow to\nfaithfully capture rRNA maturation in the experimental conditions described (insufficient\nnumbers of reads?).\nPage 6 of 15\nAnswer: Indeed, this is correct, and this is because of the large amounts of mature\ncytoplasmic RNAs which are several days stable and preclude detection of short-lived\nnuclear precursors.\nThis seems to be expressed indirectly by the authors: “We often observed that whole cell\nfractions were largely redundant with cytoplasmic fractions”. Meaning: no nuclear\nprecursors are observed when doing total RNA sequencing?! If so, it is a major bottle neck.\nIn this regard the Cyt 18S-E recovery seems to me rather poor/modest (and sometime not\nused or ignored without justification, see point 3). It would be also crucial to look at\nconditions where this precursor is accumulating in the cytoplasm to demonstrate that this\ntype of events can be faithfully captured.\nAnswer: Thank you. Following the suggestion of Reviewer #1, we have quantified\nexonucleolytic products, previously hard to measure. Using this approach, we showed the\ndifference in the 18S-E intermediates with a new perturbed factor RPS26 which showed\naccumulation in cytoplasmic 18S-E variants but not in the nucleus. Similarly, we increased\nthe resolution of the 18S-E intermediates accumulation in WBSCR22 depletion, increasing\nfurther the sensitivity of the tool to quantify potentially metastable pre-rRNA fragments.\nThe author should provide the relevant results and provide clarity on optimal conditions to\nanalyse rRNA maturation (whole cell vs fractionation …). Please provide experimental\nevidence and comments to better guide the potential users of the method.\nAnswer: We have provided a complete set of northern blots for all of the tested condition in\nthis study (with all the relevant probes) in supplementary Data 1 and Figs S8-S14 depletions.\n3) Precursor-dependent analysis of base modifications:\nThe Lafontaine laboratory has previously reported that the m7G and dimethylations\nmodifications are added in the nucleus in human cells https://doi.org/10.1091/mbc.E15-\n02-0073. Accordingly, one would expect to detect these base modifications at the level of\nNuc 18S-E and particularly at the level of the downstream Cyt 18S-E, which does not seem\nto be the case. It is also very unfortunate that the Cyt 18S-E precursor, on which the\nacp3 modification is also expected to be added, is not used/shown in this analysis, please add.\nAnswer: With respect to m¹acp³Ψ, we first demonstrate that position 1248 on the 18S rRNA\nis detected in whole-cell and cytoplasmic fractions but, strikingly, not in nuclear fractions\nPage 7 of 15\n(Fig. 1C, arrow). This observation is fully consistent with the fact that m¹acp³Ψ formation is\ncompleted in the cytoplasm.\nRegarding the findings of Zorbas et al. (2015), the modification at this site was originally\npredicted based on differential primer extension; however, this assay did not include\nnucleo–cytoplasmic fractionation, and therefore could not address the subcellular\nspecificity of modification.\nFinally, after categorizing the exonucleolytic processing products of 18S-E as suggested by\nReviewer #1, we performed precursor-specific raw signal analysis and identified interesting\nalterations among the different precursors (see figures below).\nFor instance, in the case of m7G, the nuclear 18S-E+78 precursor shows a signal similar to\nthe unmodified 18S IVT, suggesting that it is not modified, whereas the other precursors\ndisplay distinct patterns.\nFor m¹acp³Ψ, we again observed clear separation between the different precursors and the\nmature 18S rRNA (relative to the IVT control). We suspect that, because m¹acp³Ψ consists\nof three individual chemical modifications, these intermediate states likely reflect stepwise\nmaturation, illustrated by the difference between the 18S IVT (yellow), the mature\ncytoplasmic 18S (red), and the remaining precursors.\nAs for Nuc-E+9 and Cyt 18S-E+9, we believe that we have reached the limit of our nucleo-\ncytoplasmic purification protocol and we don’t want to over interpret our data. We hope the\nreferee will agree with us, it’s a wise decision.\nPage 8 of 15\nOverall, the analysis does not provide evidence for the following claim, “In conclusion,\nNanoRibolyzer pipeline can assign base modification status to particular pre-rRNA\nprecursor based on raw signal analysis.”. It is currently an all or nothing comparison without\nseeing anything clear on the relevant expected pre-rRNA, for instance nuc/cyt18S-E, vs\nother pre-rRNA intermediates in comparison to “final modification” state (mature 18S). As\nsuch these results are useless.\nAnswer: We are unsure we understand where the reviewer is heading here. We do provide\nindividual species RNA mapping, and this based on raw signal (Fig S19). We do, on the other\nhand, change the sentence to “In conclusion, NanoRibolyzer pipeline can assign\nnucleotide modification status to particular pre-rRNA precursor based on raw signal\nanalysis.” (Page 8, Line 386)\n4) Ribosomal RNA pseudouridine formation is mostly cotranscriptional\nThe conclusion of this analysis is strongly misleading, since the primary 47S rRNA\nintermediates is NOT a co-transcriptional intermediate. Considering the primary transcript\nas equal to a co-transcriptional event is an over-simplification and an over-interpretation on\nthe true nature of this pre-rRNA intermediate (where is the evidence of its association with\nactively transcribing RNA Pol I !!??). At best, the authors can only conclude that most of the\npseudouridylation are added rather early.\nAnswer: Thank you for this important clarification. We agree with the reviewer that equating\nthe primary 47S transcript with a co-transcriptional intermediate was an incorrect use of\nterminology, and we regret this imprecision.\nWe have revised the Results section accordingly, now stating: “A key conclusion from the\nprecursor-specific modification mapping is that Ψ sites are already largely present on the\nprimary 47S transcript” (page 9, line 456).\nWe have also revised the corresponding text in the Discussion to remove any implication of\nco-transcriptional modification, now reading: “…we found that many of the modifications\npresent on mature rRNAs were already detectable on the primary transcript (47S), indicating\nearly modification events” (page 10, line 542).\nThese changes ensure that our conclusions are limited to the timing of modification relative\nto pre-rRNA processing, without implying direct association with actively transcribing RNA\npolymerase I.\nSome pseudouridylations seem to have a rather “erratic behavior” between precursors.\nPage 9 of 15\nSome residues appear to have lower “signals” than the upstream precursor (e.g. positions\n36, 649, 681, 1244, 16943, 1692), instead of being constant or increasing. Please comment.\nAnswer: As noted in Supplementary Note 2, our data reliably identified all previously\nreported Ψ sites (see Taoka et al. (2018)). However, the precise quantification of Ψ levels\nremains challenging, at this stage. Several studies have shown that Ψ residues can induce\ncharacteristic U-to-C mismatches during sequencing (Begik et al., 2021; Tavakoli et al.,\n2023; Makhamreh et al., 2024; Hewel et al., 2024; Schartel et al., 2024), which can influence\nboth detection sensitivity and the accuracy of modification calling. Additionally, recent work\nhas demonstrated that accurate quantification of pseudouridylation by ONT direct-RNA\nsequencing strongly depends on the sequence context around the modification and that\nsignal deviations from Ψ extend beyond the canonical 5-mer window (Makhamreh et al.,\n2024). When combined with our precursor-specific modification analysis, these effects\ncould result in apparent alterations in modification patterns that, at this stage, cannot\nconclusively interpret as technical artefacts or genuine biological differences.\nDespite these limitations, two important observations emerge from our analysis.\n1) First, we were able to annotate all known pseudouridine sites with high confidence,\nshowing that the primary 47S transcript is reproducibly modified at an early stage.\n2) Second, the aberrant 34S and 36S-C intermediates are markedly hypomodified, raising\ninteresting hypothesis for future exploration.\nWe anticipate that ongoing improvements in basecalling models and modification-aware\nalgorithms will progressively overcome current limitations, enabling more accurate and\nreadily accessible datasets for studying RNA modification dynamics.\nThe density map visualization is also misleading, the darker being the lower modification\nratio percentage, the opposite would be more intuitive.\nAnswer: We thank the reviewer for this suggestion. However, we prefer to keep the heat map\nin its current format, as the comment concerns mainly a visual/aesthetic aspect. To\nfacilitate interpretation, we have included a detailed figure legend adjacent to the figure to\nguide the reader. We would also like to emphasize that we have used the same color scheme\nconsistently throughout the manuscript, where: Blue/black represents precursor\nquantification (reads per million, log₁₀), Red/black represents pseudouridine modification\nratios (in %), and Blue/red indicates the comparison between control and knockdown\nconditions (log₂ fold change).\nPage 10 of 15\nThis consistency ensures that readers can easily interpret and compare the data across all\nfigures.\nThere is also no information for Nuc-18S-E?! Please provide.\nAnswer: Please find the requested figure below, which has been included in Fig 5D.\nA similar depiction related to 28S rRNA pseudouridylation should also be provided\nsomewhere.\nAnswer: We thank Reviewer 2 for their suggestion. We have now performed a similar\nanalysis focusing on precursor-specific RNA modifications involved in 28S rRNA biogenesis,\nand we have included in vitro–transcribed 28S RNA as a negative control. Remarkably, we\nPage 11 of 15\nidentified that the aberrant 36S-C species, similar to 34S in 18S biogenesis, was also\nhypomodified. These observations raise interesting question regarding the role of precursor\nmodification and potential surveillance roles\nThe relative percentage of Pseudouridylation varies from site to site, and not all are attaining\n~100% within the mature rRNA. I believe that there is information related to their true relative\nabundance. Please comment on those numbers and probable limitations and relevance.\nAnswer: We have already included in Source Data 3 the detailed modification ratios for each\nindividual site and replicate (demonstrating high reproducibility across replicates) as well\nas the reference modification ratios reported by Taoka et al., 2018 for comparison (see\nexample below).\nRegarding the accurate quantification and their limitations, we would like to highlight three\nmain points:\n1) As stated above and in Supplementary note 2, DRS and the dorado basecaller correctly\nidentified pseudouridine sites, but the quantitative ratios should be interpreted with\ncaution, as nearby modifications and local sequence context may affect base calling.\nWe believe that future improvements in DRS base calling accuracy and stoichiometry\nestimation will improve these issues.\n2) Although Taoka et al., 2018 serves as the “gold standard” for mapping human rRNA\nmodifications, subsequent studies have reported variation in modification ratios across\nspecific sites and between different cell types (Barozzi et al. 2022, Milenkovic et al. 2025).\n3) In fact, Milenkovic et al. 2025 recently showed that ribosomal RNA modification levels\ncan vary across different cell types, indicating that direct comparison of modification\nratios between Taoka et al. (2018; TK6 and HeLa) and our data (HEK293) is not\nunexpected.\nPage 12 of 15\nI believe a difference in % between mature rRNA and pre-rRNA (like 47S) would probably\nprovide a clearer view on “late” modifications that may increase over time (if any existing).\n(The true early modifications should remain constant across the precursors).\nAnswer: We thank the reviewer for their suggestion. As requested, we calculated the\ndifference in modification ratios between each precursor and the mature 18S rRNA (per-\nsite) and the results are shown below. However, we believe that presenting it in detail at this\nstage is not appropriate for the current study. As mentioned above, although we can reliably\nidentify pseudouridine sites, their biological significance remains difficult to interpret and\nlies beyond the scope of this work.\nReviewer #2 (Remarks on code availability):\nI could not access the code on Github.\nPage 13 of 15\nAnswer: We apologize for this. We have created a token that allows the reviewer to access\nthe repository in a read only format. To download the code please follow these instructions\nin the command line.\ngit clone https://github.com/stegiopast/wf-nanoribolyzer.git\nUsername: stegiopast\nPassword:\ngithub_pat_11ANNHK4Q0ACiq2v0l7uiF_C2FEZwDT8b5jSZzIOEw6YSqIHSBfs6puMlH\nX9H0S096QRXFUFD60dHDD5qi\nOnce the repository is downloaded one can download epi2me and place the\nrepository folder into /home/user/epi2me/workflows/foo_folder/\nWhen placed in the right folder the pipeline should work as long as the machine has a\nGPU access and the nvidia-container toolkit, nextflow and docker are installed. These\nare standard installations to set up Epi2Me.\nReviewer #3 (Remarks to the Author):\nThe 18S, 28S, and 5.8S rRNA species are derived from a common 47S precursor RNA, which\nis synthesized by RNA polymerase I in the nucleolus. The maturation of these rRNA species\nfrom the precursor involves a series of step-wise cleavage and processing events that occur\nin the nucleolus, nucleoplasm, and cytoplasm. This process generates distinct rRNA\nintermediates and requires the involvement of numerous trans-acting factors. Although\nrRNA maturation has been extensively studied over the past 15-20 years, the methodology\nfor defining rRNA intermediates and specific cleavage sites during the maturation process\nhas not seen significant advancements and still primarily relies on Northern blotting and\npulse-chase labeling.\nHere the authors have developed NanoRibolyzer, an innovative and straightforward rRNA\nsequencing approach based on long-read nanopore sequencing. They have meticulously\nbenchmarked this method and demonstrated its ability to detect known and unknown pre-\nrRNA species and pinpoint cleavage sites with single-nucleotide precision. Furthermore, the\nauthors have shown that NanoRibolyzer can detect distinct rRNA modifications as well as\nalterations in rRNA processing upon depletion of specific processing factors. Overall, this\nmethodology represents a significant advancement for researchers in the field of ribosome\nbiogenesis.\nAnswer: We are grateful for a positive assessment of our work.\nThe data in the manuscript are well-organized and clearly presented, and the NanoRibolyzer\ntechnology is described in sufficient detail.\nPage 14 of 15\nAnswer: Again, we thank Reviewer #3 for their positive evaluation.\nMy only concern is that due to its very technical focus the data remain at a merely descriptive\nlevel. For example, the functional significance of the newly identified cleavage sites and\nrRNA intermediates is not clear. Regardless of this potential weakness, I believe that the\ntechnology described here will have a significant impact on the field.\nAuthors: Firstly, we truly appreciate Reviewer #3’s positive comments and their recognition\nof the potential impact that NanoRibolyzer can have in the field in years to come.\nWe somehow disagre, since in addition to establishing a compleetly novel pipeline of\nanalysis, we also reveal:\n-new functions for known factors (case of DIMT1L for example, known to be important for\nITS1 processing, for which we show it is also involved in earlier and later maturation steos)\n-new fucntions for ribosomel proteins (3’ ETS processing for RPL3)\n-observation of unique degradation rRNA fingerprints, with biological values since obsreved\nin cells depleted of factors involved in the same maturation steps (URB1 and RPL3)\nAll these elements are novel biology, and not opurely technique.\nReviewer #3 (Remarks on code availability):\nAs far as I see the code was not connected to material that is related to this manuscript.\nAnswer: Similar to reviewer 2, we provide here the token to access the code. To download\nthe code please follow these instructions in the command line.\ngit clone https://github.com/stegiopast/wf-nanoribolyzer.git\nUsername: stegiopast\nPassword:\ngithub_pat_11ANNHK4Q0ACiq2v0l7uiF_C2FEZwDT8b5jSZzIOEw6YSqIHSBfs6puMlH\nX9H0S096QRXFUFD60dHDD5qi\nPage 15 of 15"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have adequately addressed all the concerns raised during the first round of review.\nI recommend the manuscript for publication in Nature Communications.", "reviewer_2": "The manuscript has greatly improved but I will reiterate some of my criticisms to the authors. While the specific \"NanoRibolyzer\" pipeline is somewhat new, the underlying technology of using long-read nanopore sequencing to investigate rRNA modifications and rRNA processing is not entirely novel. Pioneering studies in archaea and other organisms have already demonstrated the utility (and challenges) of long read sequencing for simultaneously identifying the 5' and 3' ends of (r)RNAs and mapping modifications. This study builds upon these established capabilities rather than introducing the concept from scratch. Even though I am convinced that the authors have done a great job developing and benchmarking “Nanoribolyzer” to study human ribosome biogenesis, the manuscript has a certain propensity to over-claim novelty and to not fully acknowledge key previous studies, something I feel unnecessary and that could be avoided without modifying the true impact of this work. Even if not intentional, this reviewer aims to express this concern to the authors.\n\nI also have some additional comments:\n1) Analysis of 18S-E has now improved and since the Gleizes laboratory has nicely demonstrated the addition of non-templated nucleotides during the maturation of 18S-E, the authors should indicate whether these events can be captured by Riboanalyzer? Please provide information in the manuscript.\n2) Intro: I am not sure what is the intention of the authors regarding the following sentence and the associated references, “Nanopore sequencing (nanopore-seq) has emerged as a promising technology to investigate ribosome biogenesis 15,16”.\nUnless I have overseen this, there is nothing related to ribosome biogenesis in Ref. 15-16, so I am not sure why these are used in this context. Or is it supposed to be a deduction from these references? I believe there are pioneering references in the literature demonstrating proof-of-concept that would be a better fit here.\n3) Ref. 57 is about rRNA modification analysis and Ref. 58 is about rRNA processing and stage-dependent rRNA modification analysis, however, in its current form, these two references are only associated with rRNA processing analysed by nanopore sequencing. Please correct.\n4) Last sentence in the discussion: …” with broad applications in basic research and in the study of disease mechanisms, including ribosomopathies and tumorigenesis, as well as in clinical diagnostics.” Considering the cellular fractionation step and the RNA amounts currently required, these aspects particularly limit applications in clinical diagnostics."}, "author_response": "Point-by-point responses to the reviewers’ comments:\nReviewer #1 (Remarks to the Author):\nThe authors have adequately addressed all the concerns raised during the first round\nof review.\nI recommend the manuscript for publication in Nature Communications.(Remarks on\ncode availability)\nAnswer: We sincerely thank Reviewer #1 for their valuable suggestions, which have\nhelped us improve the quality of our work. Thank you!\nReviewer #2 (Remarks to the Author)\nThe manuscript has greatly improved but I will reiterate some of my criticisms to the\nauthors. While the specific \"NanoRibolyzer\" pipeline is somewhat new, the underlying\ntechnology of using long-read nanopore sequencing to investigate rRNA\nmodifications and rRNA processing is not entirely novel. Pioneering studies in\narchaea and other organisms have already demonstrated the utility (and challenges)\nof long read sequencing for simultaneously identifying the 5' and 3' ends of (r)RNAs\nand mapping modifications. This study builds upon these established capabilities\nrather than introducing the concept from scratch. Even though I am convinced that\nthe authors have done a great job developing and benchmarking “Nanoribolyzer” to\nstudy human ribosome biogenesis, the manuscript has a certain propensity to over-\nclaim novelty and to not fully acknowledge key previous studies, something I feel\nunnecessary and that could be avoided without modifying the true impact of this\nwork. Even if not intentional, this reviewer aims to express this concern to the\nauthors.\nAnswer: Thank you. We have already introduced in our manuscript the references to\nwhich Reviewer #2 is alluding. We hope Reviewer #2 understands that this is a\nresearch manuscript, and therefore there is limited space to compare in detail every\nmethod previously developed.\nWe are nevertheless grateful to Reviewer #2, as these comments have prompted us\nto plan a review article in which we will compare the available methods in greater\ndetail.\nI also have some additional comments:\n1) Analysis of 18S-E has now improved and since the Gleizes laboratory has nicely\ndemonstrated the addition of non-templated nucleotides during the maturation of\n18S-E, the authors should indicate whether these events can be captured by\nRiboanalyzer? Please provide information in the manuscript.\nAnswer: We thank Reviewer #2 for their suggestion. While we fully agree that the\naddition of non-templated nucleotides (such as oligo-adenylation/uridylation), is\nimportant, we believe that a detailed analysis of these mechanisms lies beyond the\ncurrent scope of our manuscript. As Gleizes’s lab demonstrated, mechanisms such\nas oligo-adenylation and uridylation, play a double role both in processing as well as\nribosomal RNA surveillance. While our pipeline does capture such events (data shown\nbelow – please keep this unpublished information confidential), we feel that presenting\nsuch results is out of scope of the present manuscript. In fact, we recognize the\nimportance of this topic and are actively working to collect further evidence through\nmore targeted experiments. We look forward to addressing this in a dedicated follow-\nup study.\n2) Intro: I am not sure what is the intention of the authors regarding the following\nsentence and the associated references, “Nanopore sequencing (nanopore-seq) has\nemerged as a promising technology to investigate ribosome biogenesis 15,16”.\nUnless I have overseen this, there is nothing related to ribosome biogenesis in Ref.\n15-16, so I am not sure why these are used in this context. Or is it supposed to be a\ndeduction from these references? I believe there are pioneering references in the\nliterature demonstrating proof-of-concept that would be a better fit here.\nAnswer: We thank the reviewer for this comment. The references included in this\nparagraph (ref 15-18) are focused mostly on introducing Nanopore-seq technology and\nits unique features of long-read sequencing and RNA modification detection as a\ngeneral. Therefore, we included two major reviews that we thought summarize nicely\nthis technology. The following sentences describe why is it a promising technology to\ninvestigate Ribosome biogenesis “The key advantage of nanopore-seq lies in its ability\nto sequence long reads, such as cDNAs, as well as native RNA molecules via direct\nRNA sequencing (DRS), allowing for the investigation of entire transcripts including\ntheir 5’ and 3’ ends and their modifications 17,18”. However, we agree with the\nreviewer that in the case of this sentence, inclusion of a more relevant citation such as\nGrünberger et al. 2021. Therefore, we have included this citation here.\n3) Ref. 57 is about rRNA modification analysis and Ref. 58 is about rRNA processing\nand stage-dependent rRNA modification analysis, however, in its current form, these\ntwo references are only associated with rRNA processing analysed by nanopore\nsequencing. Please correct.\nAnswer: We thank the reviewer for this comment. We have corrected these citations\nand included both citations for the description of rRNA modification analysis.\n4) Last sentence in the discussion: …” with broad applications in basic research and\nin the study of disease mechanisms, including ribosomopathies and tumorigenesis,\nas well as in clinical diagnostics.” Considering the cellular fractionation step and the\nRNA amounts currently required, these aspects particularly limit applications in\nclinical diagnostics.\nAnswer: We thank Reviewer #2 for raising this important point. We respectfully clarify\nthat, although the RNA input required for NanoRibolyzer remains significant, it already\nrepresents more than a tenfold reduction compared with Northern blotting, which\ntypically requires microgram quantities of RNA. This reduction enables analysis from\nhundreds of nanograms of input material, representing a substantial improvement over\nexisting approaches and expanding the range of accessible biological samples.\nWe agree that the current workflow, including the requirement for cellular fractionation\nand relatively high RNA input, may presently limit immediate application in routine\nclinical diagnostics. Nevertheless, NanoRibolyzer offers important practical\nadvantages, including the absence of hazardous radiolabeled reagents and\nsubstantially improved resolution, which make it well suited for future clinical\nimplementation.\nImportantly, we are actively working to further reduce RNA input requirements through\nboth experimental and computational optimization. Ongoing developments aim to\nenable robust analysis from low-input samples and may ultimately reduce or eliminate\nthe need for fractionation."}]

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