An absolute quantification atlas of small non-coding RNAs across diverse mammalian tissues and cell lines

Domain: neuroscience
Published: 2026-02-03
Editorial outcome: minor_revision

Abstract
The low quantitative accuracy of conventional small non-coding RNA sequencing (sncRNA-seq) methods due to extensive ligation bias commonly limits functional investigation of microRNAs (miRNAs) and PIWI-interacting RNAs (piRNAs). Here, we develop 4NBoost, a single-tube sncRNA-seq protocol designed to minimize bias in the estimated absolute quantification of miRNA and piRNA transcripts through the incorporation of quantitative exogenous RNA spike-ins. With 4NBoost, we profile sncRNA expression across 20 murine tissues, 18 macaque tissues, and 24 widely used cell lines, as well as 4 Arabidopsis tissues, to establish a comprehensive quantitative reference atlas. Compared with existing small RNA databases, our data reveal substantial biases in miRNA abundance, strand selection, and tissue-specific expression at both individual and family levels. To further extend its utility, we employ machine learning to model and correct biases in conventional datasets, effectively recovering ground truth transcript abundances. All 4NBoost data and the accompanying bias-correction model are freely available via SmRNA-Quant (http://wulg-lab.sibcb.ac.cn/SmRNAQuant/), a web-based repository for exploring sncRNA expression. Together, the 4NBoost, bias-correction model, and SmRNAQuant provide powerful resources to advance sncRNA research.

Introduction
Small non-coding RNAs (sncRNAs), including microRNAs (miRNAs), small interfering RNAs (siRNAs), and piwi-interacting RNAs (piRNAs), are key regulators in a variety of biological and pathological processes such as organ development, epigenetic modification, and tumorigenesis1–8. Numerous recent studies have greatly expanded our understanding of the diversity, complexity, and function of these sncRNAs9–19. Given their critical regulatory roles, a robust and comprehensive understanding of their biological functions requires accuracy in sncRNA profiling and quantification20. Despite the power and versatility of next-generation sequencing tools for studying sncRNA expression21,22, conventional sncRNA library construction typically relies on T4 RNA ligase for adapter ligation prior to reverse transcription and amplification, which are prone to introducing biases in quantification23–26. These biases primarily arise from the structure-dependent mechanism of T4-mediated ligation of adapters to sncRNAs, causing certain sncRNAs to be over- or under-represented, which can ultimately skew conclusions about their biological functions27–31.

Several methods have been developed to reduce or minimize these biases, including the use of terminal randomized adapter sequences and/or high polyethylene glycol (PEG) concentrations, as reported in methods such as 4N-seq, AQ-seq, IsoSeek, and NEXTflex9,15,29–35. Despite these advances, the large majority of sequencing approaches primarily target miRNAs with a 2′-hydroxyl group (2′-OH) at the 3′ end, which neglects optimization of ligation efficiency for piRNAs and plant miRNAs, which instead harbor 3′ 2′-O-methyl modifications (2′-Ome) that hinder ligation15,36–39. Furthermore, many of these methods do not incorporate unique molecular identifiers (UMIs) to correct for PCR bias by eliminating duplicate sequences. Additionally, few of these sequencing approaches has been adopted to determine estimated absolute quantification of sncRNAs, which is essential for addressing crucial fundamental research questions related to sncRNA stoichiometry. For example, miRNA activity has been shown to depend on a threshold level of expression, with only the most abundant miRNAs capable of effectively mediating target suppression40–44. This indicates that intracellular concentration is tightly coupled to functional activity. However, absolute expression levels have been measured for only a limited subset of miRNAs in specific cell or tissue types43–47. Additionally, understanding the precise miRNA concentrations required for regulatory function can be vital to identifying differences between physiological and pathological processes48,49. Moreover, existing miRNA databases—such as miRbase50, MirGeneDB51, miRmine52, and DIANA-miTED53—as well as tissue atlas of sncRNAs54,55, were constructed using such biased sncRNA sequencing data, and therefore lack the capacity for accurate absolute quantification. Consequently, a comprehensive, bias-minimized, and quantitatively accurate reference for sncRNAs has yet to be established.

To overcome these challenges, in the current study, we developed 4NBoost, a quantitative miRNA and piRNA sequencing technique that minimizes bias in small RNA sequencing through the incorporation of exogenous ratiometric RNA spike-ins. We applied 4NBoost to generate a comprehensive reference atlas of sncRNA expression by profiling 259 samples, including 20 tissues from mice, 18 tissues from crab-eating macaques, 24 commonly used cell lines, and 4 tissues from Arabidopsis thaliana. To the best of our knowledge, this is the most systematic estimated absolute quantification of sncRNAs across mammalian tissues and cell lines. To further expand its utility, we developed an XGBoost-based framework to correct biases in conventional datasets, enabling accurate reconstruction of ground truth transcript abundances. Additionally, we developed a web-based database to facilitate access to 4NBoost data, allowing researchers to retrieve and analyze small RNA expression profiles. Together, the 4NBoost method, correction model, and online resource constitute a robust platform for advancing both fundamental and clinical research in sncRNAs.

Results
Construction of 4NBoost with greatly reduced ligation bias for high accuracy sncRNA quantification
To construct 4NBoost, we modified the 4N-Xu method35,56 by first increasing the size of random nucleotides (NTs) in 5′ adapter from 4 to 7 NTs, while retaining a 3′ adapter of 4 random NTs that ensures a total of 11 random bases to reduce ligation bias and expand the number of unique molecular identifiers (UMIs) essential for removing duplicate reads. Based on AmpUMI model predictions57 and previous studies34, we determined that UMIs with 11 random bases could effectively capture the most abundant molecules with a low probability of UMI collision (1.4%, Supplementary Fig. 1A). To limit the likelihood of generating excessive byproducts associated with long consecutive random sequences, we also introduced 3 fixed nucleotides into the 7 random nucleotides of the 5′ adapter58.

We then designed and synthesized 30 spike-ins, including 7 with 2′-O-methyl modifications at the 3′ end, which enabled the evaluation of the quantitative accuracy of 2′-O-methyl-modified sncRNAs in addition to miRNAs. We prepared two spike-in pools, including an equimolar pool (EM) and a ratiometric pool (RM), to test the accuracy and uniformity of the library (Supplementary Fig. 1B). In the ratiometric pool, the concentrations of 30 spike-ins spanned an approximate 2×105-fold concentration range, with 3 oligos per concentration.

Optimization of reaction conditions showed that, in addition to high PEG8000 concentration32, the coefficient of variation (CV) of equimolar spike-ins gradually decreased with increasing concentration of adapters, suggesting that the higher concentration of adapters could further minimize potential library biases (Supplementary Fig. 1C). However, higher adapter concentrations led to a significant increase in self-ligation byproducts, with up to 90% of raw data comprising such artifactual sequences that had to be discarded (Supplementary Fig. 1D). To balance library uniformity and data quality, we determined that 0.55 µM was the optimal concentration of 3′ and 5′ adapters (Supplementary Fig. 1E). Additionally, we found that Lambda exonuclease was more efficient than RecJ exonuclease in depleting excess 3′ adapters59,60 (Supplementary Fig. 1F, G).

We then evaluated the accuracy of 4NBoost by adding the EM or RM pools to the total cellular RNAs and calculating the relative amounts of spike-ins based on the sequencing results. Compared to TruSeq and AQ-seq, 4NBoost demonstrated the lowest average CV (1.73, 0.56, and 0.51, respectively, for TruSeq, AQ-seq, and 4NBoost), indicating a marked reduction in library ligation biases and resulting in more uniform representation of equimolar spike-ins (Fig. 1A, B and Supplementary Fig. 1H). Correlation analysis between the expected and observed abundance of ratiometric spike-ins yielded Pearson’s coefficients >0.94, indicating that 4NBoost could accurately reflect small RNA expression levels across varying abundances (Fig. 1C). Notably, in 4NBoost libraries, spike-ins with 3′ end methylations showed similar ligation efficiency to non-methylated spike-ins (Supplementary Fig. 1I), in sharp contrast to the TruSeq libraries, where spike-ins with terminal methylation were detected at over ten fold lower efficiency compared to their non-methylated counterparts. This finding was further validated by the 4NBoost library from mouse testis, which displayed a stronger 1U signal for piRNAs compared to the TruSeq library (Supplementary Fig. 1J).

To assess the sensitivity of 4NBoost, we performed sequencing on serial dilutions of input RNA, ranging from microgram (µg) to nanogram (ng) and picogram (pg) amounts. As a benchmark, miRNA expression profiles generated from 1 µg of input RNA were compared with those from lower input amounts (Supplementary Fig. 1K–L). The number of detected miRNA species remained relatively constant down to 10 ng of input RNA. At 1 ng, reproducibility in miRNA detection declined moderately, but Spearman correlation coefficients remained high (>0.7). Below 1 ng, both the number of detected miRNA species and robustness decreased sharply. These results indicate that 4NBoost can faithfully profile sncRNA expression from as little as 1 ng of total RNA without substantial loss of performance.

Finally, we applied 4NBoost to quantify each small RNA by generating standard curves using the ratiometric spike-ins with known absolute molecular numbers. To validate its accuracy, we incorporated four additional external spike-ins and found that 4NBoost could predict the molar quantities of all spike-ins, except the lowest abundance one, with high precision (Pearson’s coefficient=0.93; predicted vs ground truth values; Fig. 1D). Additionally, a comparison between miR-221-3p abundance in ten cell lines predicted by 4NBoost and its copy numbers obtained by RT-qPCR showed a high correlation (Pearson’s coefficient=0.93) between methods (Fig. 1E). Similarly, miR-21-5p, the most abundantly expressed miRNA across most cell lines, showed excellent agreement between RT-qPCR and 4NBoost measurements (Pearson’s coefficient=0.99, p=4.95×10−20; Supplementary Fig. 1M). Collectively, these results further support the high quantitative accuracy of our method. Notably, 4NBoost could accurately quantify sncRNAs with abundances >10 amol per 100 ng total RNAs.

Construction of a quantitative sncRNA expression atlas of mammalian tissues and cell lines
Assembling a set of tissues and cell lines commonly used in miRNA studies (Supplementary Data 1) resulted in a panel of 24 cell lines (n=4), 20 tissues from BALB/C mice (n=4), and 18 tissues from crab-eating monkeys (n=4) (Fig. 2A). Using 4NBoost, we then constructed 244 total libraries, among which 242 passed quality control screening (Fig. 2B). Each library contained over 1 million genome-mapped reads (Supplementary Fig. 2A), which were further annotated into various sncRNA species (Fig. 2B). Among these sncRNAs, miRNA was the most well-represented species in most cell lines and tissues, except in testis samples (Fig. 2C). Correlation analysis of miRNAs between biological replicates showed expression correlations exceeding 0.9 (Supplementary Fig. 2B–D), indicating high reproducibility. The high correlation of the read counts before and after UMI deduplication for the top 100 most abundant miRNAs further supports that 11-nt UMIs are sufficient for accurate deduplication with negligible impact from potential UMI collisions (Supplementary Fig. 2E).

Subsequent validation of quantitative accuracy for sncRNAs in each sample with an additional four spike-ins yielded a correlation coefficient of 0.97 between predicted and ground truth values (Supplementary Fig. 2F, G), thereby confirming the accuracy of 4NBoost. This high accuracy enabled further estimation of the abundance of each miRNA and piRNA in all cell lines and tissues in our panel, which we used to construct a dataset of more reliable expression levels. We also noted that miRNA family quantifications had markedly low CV across biological replicates, further supporting the stability and reliability of quantification by 4NBoost (Supplementary Fig. 2H). Overall, these results indicated that 4NBoost generated robust, quantitative miRNA and piRNA expression datasets across a wide range of tissues and cell lines.

Comparative analysis of miRNA species and expression across cell lines and tissues
We then applied these quantitative sncRNA datasets to analyze the number of miRNA species expressed in each cell line and tissue, mitigating the influence of variations in sequencing depth by randomly selecting 1.5 million mapped molecules from each sample for analysis. In most cell lines, approximately 600 miRNA species were detected (Supplementary Data 2 and Fig. 3A), which was comparable to the 532 average miRNA species reported in the miRmine database52. Notably, U2OS cells had the highest number of miRNAs (715 species), while the fewest were detected in MDA-MB-231 cells (266 species). In tissue samples, typically more than 500 miRNA species were expressed, with the exception of erythrocytes and blood, which had only 328 species (Fig. 3B). Of note, some samples with lower miRNA species numbers, e.g., erythrocytes and blood, also had a small subset of predominant or overrepresented miRNAs, such as miR-451. Intriguingly, we also observed that most samples from monkeys had fewer miRNA species compared to the corresponding tissues from mice, likely due to the less comprehensive miRNA annotation in monkeys. However, detected miRNA numbers may be influenced by factors such as sncRNA composition or incomplete annotation, and should be interpreted with caution.

We then compared miRNA concentrations quantified by 4NBoost (Fig. 3C–E). Similar to the distribution of miRNA species across samples, total miRNA contents were comparable among most cells, with an average of ~2200 amol per 100 ng of total RNAs (Supplementary Data 2 and Fig. 3C), or approximately 15 pg (~0.015% of total RNAs), which was consistent with previous reports45. Across cell lines, SH-SY5Y cells had the highest miRNA expression level, ~4000 amol per 100 ng total RNA. Across tissues, for example, in monkeys, we found that brain samples had the lowest average miRNA levels, while heart and lung samples had the highest. By contrast, in mice, pancreas and testis tissues had the lowest average miRNA levels, whereas lung and skin samples had the highest miRNA contents (Supplementary Data 2 and Fig. 3D). As a result of this variability, Spearman analysis of miRNA concentrations between monkey and mouse tissues showed low correlation (R=0.48; Fig. 3D), potentially reflecting big differences in miRNA expression among species. Additionally, due to the overrepresentation of miR-451 in blood and erythrocyte samples, miRNA quantities were much higher in these samples compared to solid tissues (Fig. 3E).

Notably, although pancreas samples had a higher relative proportion of miRNAs than testis in mice (Fig. 2C), their quantities were not significantly different (Fig. 3D). These findings highlighted the importance of absolute quantification for comparing miRNA expression levels across different tissues or cell lines.

Improved miRNA expression accuracy with 4NBoost
Previous studies have shown that miRNA dosage influences the strength of downstream gene regulation, with highly expressed miRNAs commonly selected as key targets for further investigation48,49. In our study, we focused on these highly expressed miRNAs across various cell lines and tissues, identifying notable discrepancies between 4NBoost datasets and the DIANA-miTED database, which shares the greatest overlap in cell lines with our study, as well as MirGeneDB, a well-established source for miRNA expression data in tissues. Estimated absolute quantification with 4NBoost suggested that the expression levels of numerous miRNA species, including pre-mir-19, pre-mir-29, pre-mir-23a, pre-mir-24, pre-mir-126a, pre-mir-143, pre-mir-26a, and pre-mir-30c were likely underestimated in DIANA-miTED and MirGeneDB, while pre-let-7a, pre-let-7b, pre-let-7c, pre-mir-10a, pre-mir-22, and pre-mir-191 were overestimated in samples from the same tissues or cell lines (Supplementary Data 3).

To test whether these discrepancies arose through differences among library construction methods, we analyzed heterodimer structures for both over- and underestimated miRNAs, as the influence of heterodimer structures formed between RNA and adapters has been widely reported to influence T4 RNA ligase (Rnl) activity, wherein 5′ adapter ligation by Rnl1 favoring RNAs with an unpaired 5′ end29,30,61–63. This analysis of heterodimer structures in over- and underestimated miRNAs with 5′ RNA adapters revealed that over 85% of the overestimated miRNAs had unpaired 5′ ends, while around 70% of underestimated miRNAs had paired 5′ ends (Supplementary Fig. 3A, B), which was consistent with Rnl1 bias for unpaired 5′ ends. By contrast, for 4NBoost data, generated with random adapters, over 90% of these underestimated miRNAs formed secondary structures more favorable for Rnl1 ligation (Supplementary Fig. 3C). These findings suggested that 4NBoost consequently improved the accuracy of miRNA expression quantitation, compared to that in DIANA-miTED or MirGeneDB.

Analysis of miRNA strand preference by 4NBoost
In addition to the above problem of over- or underrepresentation, ligation bias has also been identified as a potential source of misannotation in miRNA strand preference for certain miRNAs, such as pre-mir-423, pre-mir-17, pre-mir-106b, and pre-mir-151a. As bias-reducing methods have been shown to help mitigate this issue29,32, we next examined strand preference in 4NBoost data. This analysis showed that miRNA strand preferences were consistent with previously validated results for pre-mir-32464, pre-mir-42332, pre-mir-22365, and pre-mir-133a66 (Fig. 4A and Supplementary Table 1). Moreover, strand preferences measured by 4NBoost also correlated closely with those obtained by AQ-seq in the HCT116 and HEK293T cell lines (Pearson’s coefficient=0.94 and 0.91, respectively), whereas moderate differences were noted when compared to TruSeq (Pearson’s coefficient=0.79 for the HCT116 cell line and 0.82 for the HEK293T cell line; Fig. 4B). Subsequent application of a strand selection model to all miRNA, as previously described67, and compared with the strand ratio data obtained by 4NBoost and TruSeq showed that 4NBoost data shared a stronger correlation with model predictions (Pearson’s coefficient=0.67) than TruSeq (Pearson’s coefficient=0.59; Fig. 4C), further emphasizing higher accuracy of 4NBoost in strand ratio measurements.

Examination of strand preference for all detected miRNAs across cell lines and tissues in our 4NBoost data (Supplementary data 4) indicated that >50% of miRNAs preferentially expressed the 5p strand, whereas >30% preferentially expressed the 3p strand, regardless of species (Fig. 4D). Additionally, although most miRNAs exhibited consistent strand preferences, a small subset switched the preferred strand depending on the cell type or tissue (Fig. 4A). For example, pre-mir-142a produced markedly more 3p than 5p miRNA in mouse blood; but the 5p and 3p strands are present at similar levels in other tissues. Likewise, pre-mir-324 and pre-mir-339 in monkeys, as well as pre-mir-126a and pre-mir-144 in mice, also showed variable strand preferences.

Notably, 4NBoost identified several miRNAs with opposite strand selection compared to TruSeq or miRBase annotations, including pre-mir-500a, pre-mir-582, pre-mir-671, and pre-mir-362 (Fig. 4E), as well as 15 previously reported miRNAs, such as pre-mir-193a, pre-mir-374a, and pre-mir-45429. Among these, pre-mir-423 and pre-mir-362 were selected, and their strand preference was validated by RT-qPCR. The strand ratios estimated by RT-qPCR were consistent with our dataset but differed from the TruSeq data or miRBase annotations (Fig. 4F, G). Taken together, these results indicated that 4NBoost could provide more reliable and accurate miRNA strand preference data than other current methods.

Re-evaluation of the expression and tissue specificity of miRNA families
miRNA families, which share a common seed sequence and exhibit high sequence similarity, often exert cumulative effects on gene expression68–72. However, the extent to which previous biased sequencing approaches affect the accurate measurement of miRNA family expression has not yet been evaluated. We therefore assessed miRNA family-level expression patterns across various cell lines and tissues for comparison with other current miRNA databases.

We observed that miRNA families such as let-7, miR-10, and miR-21 were broadly expressed across human cell lines in 4NBoost data (Fig. 5A), which agreed well with previous reports1,52,73,74. However, comparison with DIANA-miTED revealed significant discrepancies in certain miRNA family expression patterns compared with 4NBoost data. For example, miR-15, miR-17, miR-19, and miR-29 family expression levels were generally underestimated in DIANA-miTED, while the let-7, miR-10, miR-191, and miR-92 families were overrepresented compared to 4NBoost data (Fig. 5B). Further, even more pronounced discrepancies emerged through comparisons of tissue level data between 4NBoost and MirGeneDB (Supplementary Fig. 4A, B), which indicated that the miR-126, miR-143, miR-19, miR-23, miR-24, miR-26, and miR-29 families were generally underestimated, whereas let-7, miR-1, miR-103, miR-199, miR-10, miR-181, miR-191 and miR-22 families were overrepresented in MirGeneDB (Supplementary Fig. 4C, D). Closer scrutiny of these datasets suggested that these discrepancies likely arose through inaccurate quantification of specific miRNAs, such as miR-19a and miR-19b in the miR-19 family, as well as miR-29a, miR-29b, and miR-29c in the miR-29 family, which led to underestimation of these miRNA families by conventional small RNA sequencing analytical methods (Supplementary Data 3). Consistent with this, previous studies have also shown that miR-29b ranked only 29th in the fixed adapter library derived from DLD-1 cells, whereas it was the most abundant miRNA in the random adapter library29. This discrepancy illustrates how miR-29b might be overlooked in colorectal cancer studies. These cumulative results suggested that 4NBoost data could provide a more accurate reference for miRNA family expression, which is essential for elucidating their biological functions and roles in disease processes.

We then focused on identifying ubiquitously expressed or tissue-specific miRNA families, which revealed that the let-7, miR-10, miR-23, miR-24, miR-26, miR-27, miR-29, and miR-30 families were highly expressed across all solid tissues, and showed remarkably similar expression levels between mice and monkeys (Fig. 5C). These results were in line with the well-documented roles of these miRNA families in various developmental, cellular, and physiological processes essential for most tissues1. In contrast with the above broadly expressed miRNAs, we next searched for tissue-specific miRNA families, which are particularly intriguing due to their potential for specialized functions within specific tissues52,75. To assess tissue specificity, we calculated the tissue specificity index (TSI) for miRNA families using a well-established method76, wherein higher TSI values indicated greater tissue specificity. This analysis uncovered 23 and 30 total tissue-specific miRNA families in 4NBoost data from monkey and mouse tissues, respectively (TSI ≥ 0.85, Table 1). Among these tissue-specific miRNA families, more than 80% were consistent with those identified by MirGeneDB (Fig. 5D and Tables 2 and 3), which may be attributable to the high accuracy of relative expression analysis available through all current small RNA sequencing methods35.

However, we also found some noteworthy differences with 4NBoost results. First, the tissue-specific miRNA families identified using 4NBoost data were largely subsets of those identified using MirGeneDB (12 out of 17). For example, 6 of 11 brain-specific miRNA families identified by MirGeneDB in monkeys were also detected using 4NBoost. Further analysis revealed that the miRNA families missed by 4NBoost were predominantly expressed at low levels (Fig. 5E), and were likely filtered out due to the expression cut-off we applied during the analysis process. Alternatively, 4NBoost analysis identified five tissue-specific miRNA families that were overlooked by MirGeneDB, such as the kidney-specific miR-204 family, heart-specific miR-135 family, and spleen-specific miR-335 family in monkey (Table 2), as well as the ovary-specific miR-135 and miR-202 family in mice (Table 3). The expression levels of these miRNA families may have been underestimated by conventional sequencing data compared to 4NBoost (Fig. 5F), potentially leading to their omission from MirGeneDB. Taken together, the capacity for precise miRNA quantification by 4NBoost facilitates more reliable identification of tissue-specific miRNA families.

Tissue-specific miRNA families are conserved between mice and monkeys
Since tissue-specific miRNAs have been previously shown to exhibit high conservation between mice and humans76,77, we next applied 4NBoost to explore the conservation of tissue-specific miRNA families between mice and monkeys. Our results showed that TSI values were quite similar between species for these miRNA families (Pearson’s coefficient=0.77, Supplementary Fig. 5A), which was consistent with their comparable expression profiles. This analysis also showed a high degree of overlap in the tissue-specific expression of miRNA families between the two species (Supplementary Fig. 5B and Table 1). For example, we identified several well-documented tissue-specific miRNA families, such as the miR-122 family in liver; miR-9, miR-137, and miR-124 families in brain; miR-133 and miR-1 families in muscle and heart; miR-499 family in heart; miR-144 and miR-451 families in blood; miR-205 family in skin; and miR-216 family in the pancreas54,76,78–81. Additionally, we identified some previously unreported tissue-specific miRNA families in mice and monkeys, including the miR-203 family in the skin, miR-459 and miR-217 families in the pancreas, as well as miR-19 and miR-142 families in the blood. These findings suggested that tissue-specific miRNA families are conserved across mouse and monkey tissues.

Re-evaluation of plant sncRNA abundance
Plant miRNAs and most siRNAs are characterized by 3′-terminal 2′-O-methylation, which substantially impedes adapter ligation and introduces biases in library construction using conventional small RNA sequencing methods. To overcome this limitation, we re-evaluated sncRNA abundance in root, stem, leaf, and flower tissues of the model plant Arabidopsis thaliana using 4NBoost. Consistent with the well-established features of plant small RNAs, the 4NBoost profiles were dominated by 21–24 nt sncRNAs, with 21-nt species strongly enriched for 5′ uridine while 24-nt species preferentially carrying adenine (Supplementary Fig. 6A). Compared with the conventional TruSeq method, 4NBoost markedly improved the recovery of 24-nt sncRNAs with a stronger 5′ adenine bias, and significantly enhanced miRNA detection, identifying on average 244 unique miRNAs, nearly 50% more than the 163 miRNAs captured by TruSeq (Supplementary Fig. 6B–D). In addition, TruSeq introduced substantial inaccuracies in miRNA quantification. For instance, miR165a-3p, miR166a-3p, miR168a-3p, miR168a-5p, and miR166e-5p were consistently overestimated, whereas miR161.2, miR172a, miR173-5p, and miR167a-5p were underestimated across all four tissues (Supplementary Fig. 6E). Collectively, these results demonstrate that 4NBoost provides a more accurate and comprehensive representation of plant small RNA abundances, overcoming the limitations of conventional sequencing methods.

Machine learning-based correction of sequencing bias in sncRNA expression profiles
To enhance the utility of existing sncRNA sequencing datasets affected by library preparation bias, we developed a computational framework that corrects biases in conventional expression profiles, generating quantitatively accurate datasets. We analyzed matched samples processed using two protocols: the widely adopted NEBNext small RNA library preparation and our optimized 4NBoost method. Using these paired datasets, we trained XGBoost regression models to learn and correct library-specific artifacts present in conventional sequencing data (Fig. 6A). The correction model exhibited strong performance in reconstructing accurate expression profiles from biased datasets. For NEBNext-prepared libraries, the predicted expression levels showed high concordance with those obtained from 4NBoost, with Pearson correlation coefficients of r=0.87 in the test set and r=0.86 in the validation set (Fig. 6B). Importantly, the model also improved the ranking accuracy of transcript abundances: the correlation with 4NBoost data increased from r=0.51 (uncorrected) to r=0.85 (corrected) in the test set (Fig. 6C). Internal validation further confirmed the robustness of the model, yielding similarly high correlations (Pearson’s r=0.83; Fig. 6D). In summary, this regression-based correction framework enables accurate reinterpretation of existing sncRNA-seq datasets by mitigating protocol-induced biases.

SmRNAQuant: an integrated 4NBoost database
To facilitate access to our 4NBoost data from various cell lines and tissues, we developed SmRNAQuant (Fig. 7), an interactive analytical and visualization platform incorporating Django (v4.2.1), Bootstrap (v3.3.7), jQuery (v3.2.1), Python 3.8, and Echarts. This platform enables detailed quantification of small RNAs across different cell lines and tissues, facilitating deeper insights into expression patterns and biological roles. Users can download the complete dataset or generate customized subsets by selecting specific tissues or cell lines of interest. SmRNAQuant also features a “miRNA view” function, which allows users to query individual miRNAs and visualize their expression profiles through bar charts across various tissues and cell types, providing a comprehensive overview of miRNA expression. Additionally, the platform integrates our regression-based correction algorithm, enabling users to calibrate NEBNext-derived datasets and obtain expression values comparable to those generated by 4NBoost. Collectively, SmRNAQuant improves the accessibility and usability of small RNA sequencing data for a broad range of research applications.

Methods
Cell culture and RNA extraction
All cell lines used in this study were purchased from the American Type Culture Collection (ATCC), National Collection of Authenticated Cell Cultures (NCACC), or Shanghai Chuanqiu Biological Technology Co., Ltd (SCBT) (Supplementary Data 1) and were authenticated by the respective providers. Cell lines were revived and cultured in a 6 cm dish at 37°C with 5% CO2. Once the cells reached 90% confluence, they were collected with 1 mL of TRIzol reagent (Invitrogen). Next, 200 µL of trichloromethane was added and mixed thoroughly. The solution was then centrifuged at 12,000 × g for 15 min at 4°C. The upper aqueous phase (~450 µL) was transferred into a new 1.5 mL EP tube, followed by the addition of 900 µL of ethanol. The solution was mixed by inversion, left at −30°C for 30 min, and then centrifuged again at 12,000 × g for 15 min at 4°C. After removing the supernatant, the pellet was washed twice with 80% ethanol, air-dried for 2–3 min at room temperature, and dissolved in DEPC-treated water. RNA concentration was measured using a Nanodrop spectrophotometer, and RNA quality was assessed using the Agilent Bioanalyzer 2200. Each RNA sample was diluted to a final concentration of 100 ng/µL and stored at −80°C until use.

Tissue collection and RNA extraction
This study was conducted using both male and female mice and Crab-eating monkeys. 4 weeks old male and female BALB/c mice were purchased from Shanghai Lingchang Biotechnology Co., Ltd, and animal protocol was approved by the Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences (SIBCBS2182005007). 4 years old male and female Crab-eating monkey tissues were generously provided by the Jinyi Chen’ lab at Shanghai Institute of Materia Medica, Chinese Academy of Sciences.

Fresh blood samples were collected into EDTA anticoagulant tubes and diluted with PBS at a 1:1 ratio. 3 mL of separation solution (Solarbio) and 3 mL of the diluted blood were added to a 15 mL centrifuge tube and centrifuged at 800 × g for 30 min at room temperature, resulting in distinct layers containing plasma, white blood cells, separation solution, and red blood cells. The red blood cell layer was carefully transferred into a new 1.5 mL EP tube, mixed with 1 mL of Trizol, and stored at −20°C until RNA extraction. The white blood cell layer was transferred into a new 15 mL centrifuge tube, washed with 10 mL of PBS, and centrifuged at 250 × g for 10 min at room temperature. After centrifugation, the supernatant was removed, and the precipitate was resuspended with red blood cell lysis buffer, then centrifuged at 250 × g for another 10 min. The supernatant was discarded, and the precipitate was resuspended with 1 mL Trizol. After thorough mixing, the sample was stored at −20°C until RNA extraction.

Mouse and monkey lung, breast, stomach, colon, rectum, kidney, brain, skin, liver, ovary, testis, fat, pancreas, heart, thymus, muscle, and spleen tissues were collected, cut into small pieces, and submerged in an appropriate amount of RNAlater (Thermo). After being stored overnight at 4°C, the samples were transferred to −20°C. For RNA extraction, 50–100 mg of tissue was ground using a cryogenic grinder or a mortar (see Supplementary Table 2 for the specific grinding method for each tissue), and RNA was purified as described above.

Small RNA spike-in design
We randomly generated 1000 sequences with lengths ranging from 20 to 30 nt and predicted their secondary structures using RNAfold. Sequences were selected based on the following criteria: GC content between 40% and 60%, a predicted secondary structure with a ΔG greater than −3, and no alignment with common model organism genomes. Finally, 23 sequences were chosen as spike-in candidates. To evaluate the ligation efficiency of RNAs with 2′-Ome at the 3′ ends, seven of these candidates were randomly selected. The 2′-OH at their 3′ end was replaced by 2′-Ome, and one of their bases was altered to distinguish them from their 2′-OH counterparts. All spike-ins were synthesized by Suzhou Olipharma Co., Ltd.

Small RNA library preparation
One hundred nanograms of total RNA with 0.05 µL of 20 nM EM or RM spike-in control oligos was ligated to a 0.55 µM 3′ randomized adapter using 25 U/µL T4 RNA ligase 2 truncated KQ (NEB) in 0.83× T4 RNA ligase reaction buffer (NEB), supplemented with 20% PEG 8000 (NEB) at 25°C overnight. The ligated RNA was then annealed with 1.28 µM RTP. To remove excess 3′ adapters, 3.33 U/µL 5′ deadenylase (NEB) and 0.2 U/µL lambda exonuclease (NEB) were added, followed by incubation at 30°C for 1 h and 37°C for 2 h, respectively. Subsequently, the product was ligated to a 0.55 µM 5′ randomized adapter using 0.4 U/µL of T4 RNA ligase 1 (NEB) in 0.74× T4 RNA ligase reaction buffer, supplemented with 0.7 mM ATP and 17.3% PEG 8000 (NEB) at 25°C for 1 h. Reverse transcription was carried out using 5 U/µL of ProtoScript II reverse transcriptase (NEB) in 1× ProtoScript II RT reaction buffer (NEB), containing 0.5 mM dNTPs (Promega) and 10 mM DTT (NEB), at 50°C for 1 h. The resulting cDNA was then amplified using 0.02 U/µL of KOD-Plus-Neo (TOYOBO) in 1× PCR Buffer, along with 0.5 µM of both forward and reverse primers, 1.5 mM MgSO4 (TOYOBO), and 0.2 mM dNTPs (TOYOBO). After PCR amplification, the cDNA was gel-purified on a 6% polyacrylamide gel to remove adapter dimers and sequenced using the Illumina NovaSeq 6000 sequencing system. The sequences of adapters, along with the sequences and specific mixing ratios of EM or RM spike-ins, are provided in Supplementary Table 3. Mouse tissue miRNA libraries (excluding blood, erythrocytes, and leukocytes) were also prepared using the NEBNext® Multiplex Small RNA Library Prep Set for Illumina®, with 100 ng of total RNA as input.

Quantitative real-time PCR
We selected miR-221-3p (whose abundance spanned the entire linear range of our spike-in standard curve) and miR-21-5p (the most abundant in nearly all cell lines) for evaluation of quantification accuracy at different concentrations. The reverse transcriptase reactions consisted of 100 ng of purified total RNA, 50 nM stem-loop RT primer, 1× reverse transcriptase M-MLV buffer (TAKARA), 0.25 mM of each dNTP (Promega), 10 U/µL of reverse transcriptase M-MLV RNase H- (TAKARA), and 1.07 U/µL of RNase inhibitor (Thermo). The total volume of the reactions was 7.5 µl, and they were incubated in a Bio-Rad T100 Thermal Cycler under the following conditions: 30 min at 25°C, 1 h at 42°C, and 15 min at 75°C, before being held at 4°C. RT products were diluted five times with deionized water. Each real-time PCR assay for the microRNA (10 µL volume) contains 2 µL of diluted RT product, 5 µL of 2× TaqPro Universal SYBR qPCR Master Mix (Vazyme), and 1.5 µM of both forward and reverse primers. The reactions were carried out in an Applied Biosystems® QuantStudio™ 6 Flex Real-Time PCR System in 384-well plates, with the following cycling conditions: an initial denaturation at 95°C for 30 s, followed by 40 cycles of denaturation at 95°C for 10 s and annealing/extension at 60°C for 30 s. The sequences of RT and PCR primers are listed in Supplementary Table 4.

To validate the ratio of miRNA-5p and -3p strands, hsa-mir-326 and hsa-mir-423 were selected. Reverse transcription (RT) and dilution steps were performed as described previously. Each real-time PCR reaction was carried out in a 10 µL volume containing 2 µL of diluted RT product, 5 µL of 2× TaqPro HSU+ Probe Master Mix (Vazyme), 1× ROX Reference Dye 2 (Vazyme), 0.1 µM TaqMan probe, and 0.2 µM each of the forward and reverse primers. Amplifications were performed on an Applied Biosystems® QuantStudio™ 6 Flex Real-Time PCR System using 384-well plates under the following cycling conditions: 37°C for 2 min and 95°C for 10 s, followed by 45 cycles of 95°C for 10 s and 60°C for 30 s. The sequences of the RT and PCR primers are provided in Supplementary Table 4.

Analysis of small RNA sequencing
Raw data (read1) were pre-processed using the FASTX Toolkit (v0.0.14). The first 70 bp of each read were retained, and low-quality sequences were filtered out (-q 20, -p 90). After quality filtering, adapter sequences were trimmed, requiring a minimum 10-nucleotide match at the 3′ end. Reads without a detected adapter or shorter than 32 bp were discarded. Only reads with “CGA” at positions 4–6 were retained, corresponding to the 5′ random adapter. UMI extraction was performed with UMI_tools (v0.5.1) using the following parameters: extract --extract-method regex --bc-pattern="^(?P<umi_1>.{3})(?P<cell_1>.{3})(?P<umi_2>.{4})([A,T,C,G]{5,100})(?P<umi_3>.{4})$". Processed reads were aligned to the target genome using Bowtie (v1.2.1.1) with parameters –best -v 0. Duplicate reads were removed with UMI_tools using the following parameters: dedup --edit-distance-threshold 1 --soft-clip-threshold 0 --method adjacency. Unaligned reads were mapped to the spike-in reference sequences using Bowtie with parameters –best -v 1 --norc and de-duplicated in the same manner. The remaining unmapped reads were aligned to the isomiR reference library with the same parameters and de-duplicated. Molecules aligned to the genome were merged with isomiR molecules for downstream analysis. Molecules were annotated based on the following priority: mature miRNA > isomiR > tRNA > rRNA > snoRNA > snRNA > scRNA > scaRNA > YRNA > lncRNA > mRNA > piRNA > novel miRNA. Mature miRNA, isomiR, piRNA, and novel miRNA were annotated based on exact sequence and length matches, while other small RNA categories were aligned to known sequences using Bowtie with parameters -k 100 -v 0 --norc. Reads that aligned to the genome but lacked known annotations were classified as “others”. The expression of molecules in each library was normalized using the standard curve generated from the external spike-ins.

Prediction of novel miRNAs
To identify potential novel miRNAs in our library, we applied miRDeep2 (v0.1.2) to reads that did not align to known annotations. Novel miRNAs predicted by miRDeep2 were further filtered based on the following criteria: (1) no match to any known sequences in the Rfam database; (2) a total of at least 10 reads supporting the miRNA in the library; and (3) the mature miRNA sequence must have a corresponding star sequence. Only miRNAs that satisfied all three conditions were deemed reliable, and their precursor miRNA, mature miRNA, and star sequences were extracted. Additionally, if miRge 3.0 predicted pre-miRNA sequences with expression from both strands, and the 3p or 5p strand had more than 10 reads and was not identified by miRDeep2, those sequences were incorporated into the miRDeep2 prediction results. Together, these sequences constituted the reference set for novel miRNAs.

Expression of miRNA families
To assess the expression patterns of miRNA families, we extracted all miRNA family annotations from the latest version of MirGeneDB (v2.1). For each miRNA precursor, all mature forms have been considered as family members. To reduce potential bias from multiple precursors, duplicated mature miRNAs (e.g., those derived from different precursors within the same family) were counted only once.

Prediction of miRNA arm ratio
We analyzed the miRNA arm ratio as previously described67 with the following model:
ln(5p/3p) = kΔΔG 5p-3p + N 5p-3p (1)
Where k and N represent the constant for the relative thermodynamic stability and the constant corresponding to the 5′ end identity, respectively.

Tissue specificity index
To compute the tissue specificity index, we used the formula described previously76. The same formula was applied to cells to provide a cell specificity index. Specifically, the TSI for a miRNA j is calculated as
TSI j = (Σ (1-x j,i)) / (N-1) (2)
Where N is the total number of tissues measured and x j,i is the expression score of tissue i normalized by the maximal expression of any tissue for miRNA j.

Model development and validation
To enhance model compatibility, we integrated both in-house data generated using NEB kits and an external raw dataset prepared with the same kits. After preprocessing, the datasets were merged. To minimize background noise, miRNAs with expression levels below 5 amol in the 4NBoost data were excluded. The filtered dataset was split into three subsets: 72% for training, 20% for testing, and 8% as an independent validation set not involved in model training. In addition to NEB-biased expression levels as core input features, we extracted several parameters as auxiliary features, including (i) Sequence features: RNA length, nucleotide composition (A/T/C/G ratio), and GC content; (ii) Thermodynamic parameters: minimum free energy, free energy difference between 5′ and 3′ termini, and total duplex free energy; (iii) Terminal motifs: the first 1, 3, 4, and 6 nucleotides at both 5′ and 3′ ends; (iv) miRNA structural features: paired ratio, bulge density, internal loop density, hairpin density, and pairing continuity. Feature correlation analysis showed that NEB-biased expression contributed most significantly to the prediction performance (R=0.614). Terminal sequence features and some thermodynamic parameters (delta_G_sum and MFE) showed moderate correlation (R>0.14), whereas structure-related features contributed minimally (R<0.05). Therefore, the five miRNA-structure-related features were excluded from the final featureset.

Using default hyperparameters, we benchmarked four tree-based algorithms: Random Forest (RF), Gradient Boosting Decision Tree (GBDT), LightGBM, and XGBoost. Comparative performance analysis identified XGBoost as the best-performing framework for our dataset, a robust machine learning method known for its robustness and high predictive accuracy. Model training was conducted on the designated training dataset, with hyperparameter optimization performed using the Hyperopt package in Python.

The final model architecture incorporated the following optimized parameters: a learning rate (eta) of 0.4, num_boost_round of 10, max_depth of 8, min_child_weight of 14, subsample of 0.75, colsample_bynode of 0.5, colsample_bytree of 1.0, and reg_lambda of 4.0. Model performance was evaluated on both a test set and an independent external validation dataset. Two primary evaluation metrics were used: the coefficient of determination (R²) to quantify the variance explained by the model in predicting absolute expression values, and Spearman’s rank correlation coefficient to measure the concordance between predicted and actual expression rankings.

Download of the genome sequence and small RNA annotation
The reference genomes for mouse (mm10), human (hg38), and crab-eating monkey (macFas5) were downloaded from the UCSC Genome Browser (https://hgdownload.soe.ucsc.edu/). Genome annotations for crab-eating monkeys were obtained from UCSC, while annotations for the mouse and human genomes were downloaded from Gencode (https://www.gencodegenes.org/). Small RNA annotations for humans and mice were sourced as follows: miRNA annotations were downloaded from miRBase (release 22, https://mirbase.org/download/) and retained only unique sequences. tRNA annotations were obtained from Ensembl (release 110, https://www.ensembl.org/biomart/martview/) and GtRNAdb (release 21, http://gtrnadb.ucsc.edu/index.html), with duplicate entries removed. rRNA annotations were retrieved from Ensembl and NCBI, and duplicates were similarly removed. piRNA annotations were sourced from the piRBase gold standard set (release v3.0, http://bigdata.ibp.ac.cn/piRBase/). Annotations for snRNA, scRNA, snoRNA, scaRNA, YRNA, mRNA, and lincRNA were downloaded from Ensembl. Small RNA annotations for crab-eating monkeys were sourced as follows: miRNA annotations from miRBase for rhesus macaques, with unique sequences retained. Annotations for rRNA, tRNA, snRNA, and scRNA from UCSC. Additionally, to comprehensively classify small RNAs derived from functional RNAs, we combined the annotated functional RNA sequences from the rhesus macaque genome with corresponding annotations from the crab-eating monkey genome. Annotations for rhesus macaques were retrieved from NCBI.

Web server and database implementation
To create an efficient and engaging web interface for the SmRNAQuant database, we employed the following tools: Django (v4.2.1) for web development, Bootstrap (v3.3.7) for responsive design, jQuery (v3.2.1) for dynamic interactions, Python 3.8 for backend processing, and ECharts for interactive data visualization. Data management and retrieval were conducted using MySQL (v8.0.35).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors claim that they have developed an improved sequencing technology for small noncoding RNA sequencing called qMap-seq. The key improvements involve (1) the 11 random bases over the existing 8 random bases and (2) the inclusion of RNA spike-ins with 2’-O-methyl-modified RNAs. The authors then extensively applied qMap-seq to a panel of 24 cell lines and 20 tissues from mice, and 18 tissues from crab-eating monkeys. With this sequencing data, the authors characterized their findings, developed a machine-learning algorithm for bias-correction, and built a web interface called SmRNAQuant for easy access to their computational results. The sequencing data that the authors provide especially on the crab-eating monkeys is novel and interesting. However, the improvement in sequencing technology is marginal, the innovation of their machine-learning algorithm is unclear, and the physiological significance of their findings in terms of co-sequencing miRNAs and piRNAs, for example, is missing.\nqMap-seq is not the first sequencing method to use random nucleotides and unique molecular identifies. As the authors mention in line 85, the qMap-seq method is a modified version of 4N-Xu method which also uses random nucleotides to reduce ligation bias and is utilized as unique molecular identifiers to correct for PCR biases. Other small RNA sequencing methods also use random nucleotides. The impact of using 11 random bases in qMap-seq over short random bases is only presented theoretically in Figure S1A, but the empirical impact that this decision results in is unclear.\nThe second innovation that the author claims is the use of RNA spike-ins for absolute quantification. In line 58-60, the authors state that “none of these sequencing approaches permit absolution sncRNA quantification”. However, the use of RNA spike-ins for absolute quantification and monitoring ligation bias has also been used in previous methods as well. Other experimental optimizations including the concentration of PEG8000 are also done in previous studies, altogether indicating that the innovations proposed by qMap-seq are marginal.\nLastly the authors’ main comparison is with TruSeq and not the more advanced methods such as 4N-Xu and AQ-Seq that the authors mention in the introduction. To argue for improvement, the authors must compare with more advanced methods other than TruSeq.\nThe fact that it is a single-tube sequencing protocol is interesting. It’d be interesting if the author tested on low RNA input and its robustness in quantification.\nIt is worth mentioning that there is another method called QMAP-Seq that was published in Nature Communications in 2020. Here’s the link to the manuscript for reference: https://www.nature.com/articles/s41467-020-19553-8\nThe comprehensive profiling of multiple cell lines, mouse tissues, and macaque tissues is very impressive. The discovery of miRNA conservation between mouse and macaque tissues is novel but not surprising as it is already known that microRNAs are conserved between mouse and human. Nevertheless, I do expect that this resource will be valuable for the small RNA scientific community.\nThe authors’ comment on 2’-O-methyl-modified sncRNAs was interesting. The authors mention that piRNAs and plant miRNAs harbor 3’ 2’-O-methyl modifications in line 56. Applying their method to plant tissues and germinal cells would help highlight the importance of using 2’-O-methyl-modified RNA spike-ins.\nIn line 24, the authors claim that they revealed substantial biases in miRNA abundance, strand selection and tissue specific expression. However, this bias in TruSeq in particular has been shown previously, especially in AQ-seq which is one of the previous methods that the authors cite. In fact, in Figure 4C, they compare with AQ-seq data and find that the performance of both methods are comparable.\nRegarding their machine learning algorithm, the performance is interesting but the exact details of their approach and how it corrects bias in small RNA sequencing data is unclear. For example, in the result and method section, the authors do not describe how they conducted feature engineering before applying XGBoost.\nIn Figure 2c, the authors show a high abundance of other RNAs in cell lines compared to tissue samples. Also, the amount of rRNA is especially high in cell lines and mouse liver and pancreas tissues. The physiological significance of this is unclear. Could it be that they are artifacts or noise from the sequencing method?\nIn Figure 3a, the authors compare the number of miRNA species across cellines and tissues. However, the number of miRNA species will also depend on sequencing depth and the amount of artifacts and technical noise (e.g., rRNAs) from data generation.\nAs for the web application, it’d be more applicable to the scientific community if it included the sequence alignments and sequence modifications.\nLastly, to claim absolute quantification, the authors must provide experimental confirmation that is not based on sequencing.", "reviewer_2": "This manuscript presents qMap-seq, an optimized protocol designed to minimize bias in small RNA-seq. By incorporating synthetic RNA spike-ins of known quantities, the method enables estimation of the absolute abundance of miRNAs in, e.g. 100 ng of total RNA. Leveraging this approach, the authors generate a comprehensive resource characterizing the miRNA transcriptome across a wide range of cell lines, tissues, and species, offering potentially a more reliable catalog of functional miRNAs. This manuscript also introduces a machine learning-guided model for bias correction, which holds potential for retrospective correction of miRNA-seq datasets. Lastly, the authors provide an interactive platform (SmRNAQuant) to facilitate access to the resource, which will be of value to the community.\nAlthough various methods have been developed to reduce bias in miRNA-seq, few have been applied systematically across diverse biological contexts to generate a unified reference dataset. Furthermore, absolute quantification of miRNAs has not been extensively explored. This paper addresses both limitations. However, I have several points for the authors to address:\nMajor comments:\n1. The authors use 11 random bases for UMIs to collapse PCR duplicates. The collision rate was estimated to be 1.4%, but this may be inaccurate if the authors assumed uniform ligation of all possible 11-nt UMI sequences. Given the known strong ligation bias in miRNA-seq, some UMI sequences are likely overrepresented. I strongly recommend including a scatter plot comparing miRNA read counts before and after UMI deduplication. As PCR is known not to significantly distort the miRNA profile, substantial changes after UMI correction would suggest that 11-nt UMIs may be insufficient for accurate deduplication.\n2. In Fig. 1F, the authors validate qMap-seq using miR-221-3p. In connection with point 1, I suggest including an additional comparison between RT-qPCR and qMap-seq for the most abundant miRNA in average across cell lines. This would help assess whether qMap-seq performs well for highly expressed miRNAs.\n3. qMap-seq still exhibits bias and therefore does not guarantee fully accurate absolute quantification (Fig. 1C). I strongly suggest softening some of the more definitive claims throughout the manuscript. For example, consider modifying the title from “absolute quantification” to “estimated absolute quantification,” and revising line 158 from “true” to “more reliable.”\n4. Most importantly, estimating absolute miRNA quantities is a central contribution of the paper. However, it remains unclear what specific biological insights are uniquely enabled by these absolute estimates, as opposed to relative quantification. For example, do absolute abundance estimates more accurately reflect miRNA activity?\n5. The authors highlight examples in which previous methods or databases misestimate 1) strand bias, 2) family-level expression, or 3) tissue specificity. However, these observations are not experimentally validated, although qMap-seq still has bias. I suggest validating at least one of these examples to substantiate the improved reliability of qMap-seq.\nMinor comments:\n1. Lines 47-50: PCR has been shown not to be a major source of bias in miRNA-seq. Notably, the addition of 50 cycles did not substantially alter miRNA profiles (Hafner et al., RNA, 2011, Jayaprakash et al., Nucleic Acids Research, 2011). Thus, please revise this section or provide a justification for diverging from these findings.\n2. Please acknowledge the prior use of Lambda exonuclease in miRNA library preparation (Hagemann-Jensen et al.,Nat. Biotech., 2018).\n3. Please include strand information (e.g., -5p vs -3p) when referring to mature miRNAs throughout the manuscript unless referring to families or precursor forms (pri- or pre-miRNAs). For example, lines 209-211 currently lack this information.\n4. The machine learning-guided model could potentially be further improved by incorporating information on 5’ and 3’ termini of mapped reads. If the current model aggregates all reads mapped to a given miRNA regardless of their precise ends, isoform-level resolution could be leveraged to refine the model.", "reviewer_3": "In their manuscript \"An absolute quantification atlas of small non-coding RNAs across diverse mammalian tissues and cell lines\" Xiao et al describe their improved method to clone small RNAs with absolute quantification (qMap-seq), and use this to generate an atlas of several dozen datasets of mammalian small RNAs, cell lines and tissues of several species. They also describe a machine learning method to correct small RNA cloning bias.\nThere are many small RNA cloning protocols, and many limitations of the early methods have been recognized and improved. These include use of randomized adaptors on both ends, spike-ins, and UMI to minimize ligation bias and enable absolute quantification of small RNAs. These have all been applied in other studies (eg from Narry Kim lab PMID 30605524 or Phil Zamore lab 30001700), and are implemented in many lab's current workflows. In addition, a new paper from Ildar Gainetdinov lab also includes small RNA cloning absolute quantification (PMID 40436630). A difference with the submitted paper is that they use a series diluted spike-in RNAs with defined concentrations to the total RNA, so they can make a standard curve and calculate the absolute miRNA levels according to the curve, similar to absolute quantitive qPCR for individual genes. This submission is >1 month later than the other recent one, so I would not make a big deal, but only to say the general cloning methods are well-known, but they do a better job for absolute quantification.\nThe authors apply this method to a wide range of mammalian tissues and cell lines, generating a large dataset and providing a public resource. This dataset is useful, but just to note there are also extensive other data exist.\nThis study also developed a machine learning method to help correcting the previous small RNA seq datasets and include in their web database for users to adjust and do absolute quantification of these previous small RNA seq data.\nI think the study is technically very competent. I don't really have substantial feedback that would improve it or make it more confident. It provides some advances but the methodology is not very novel, and the database is extensive but also other small RNA databases exist. I think its an editorial decision whether to publish in Nature Communications. It could also be of interest to a journal with a more focused readership in RNA (such as RNA or NAR), or perhaps genomics (Genome Research or Genome Biology)."}, "author_response": "Dear Referees,\nWe would like to express our sincere gratitude for your valuable feedback and\nencouraging comments on our manuscript titled \"An absolute quantification atlas of\nsmall non-coding RNAs across diverse mammalian tissues and cell lines.\" We\nappreciate the time and effort you have devoted to reviewing our work. We have\ncarefully considered each of suggestions and made substantial revisions accordingly.\nIn particular, we are grateful to Reviewer 1 for drawing our attention to the previously\npublished method QMAP-Seq (Nature Communications, 2020). We recognize that our\nuse of the name “qMap-seq” could lead to confusion in the field, despite the\nfundamental differences between the two approaches. To avoid any nomenclature\nconflict and ensure clarity, we have renamed our method as “4NBoost” in the revised\nmanuscript. Moreover, we rigorously validated the quantification accuracy of 4NBoost\nusing RT-qPCR, and further demonstrated its broader applicability by profiling\nsncRNAs in Arabidopsis thaliana tissues, thereby highlighting its potential as a\nvaluable tool for sncRNA research in plant biology.\nAdditionally, during the revision process, we performed a thorough re-examination\nof the text and a re-analysis of some datasets. This led us to identify and correct a few\nminor errors in the original version, which we have now carefully addressed and\nmarked in blue in the revised manuscript. We would like to emphasize that these\ncorrections do not affect the conclusion of the study.\nFinally, we wish to express our sincere gratitude to all the reviewers for their\nvaluable and constructive feedback. Their guidance has greatly enhanced both the\ntechnical rigor and the scientific significance of our work.\nBelow, we provide our point-by-point responses to the comments.\nReviewer #1 (Remarks to the Author):\nThe authors claim that they have developed an improved sequencing technology for\nsmall noncoding RNA sequencing called qMap-seq. The key improvements involve (1)\nthe 11 random bases over the existing 8 random bases and (2) the inclusion of RNA\nspike-ins with 2’-O-methyl-modified RNAs. The authors then extensively applied\nqMap-seq to a panel of 24 cell lines and 20 tissues from mice, and 18 tissues from\ncrab-eating monkeys. With this sequencing data, the authors characterized their\nfindings, developed a machine-learning algorithm for bias-correction, and built a web\ninterface called SmRNAQuant for easy access to their computational results. The\nsequencing data that the authors provide especially on the crab-eating monkeys is\nnovel and interesting. However, the improvement in sequencing technology is marginal,\nthe innovation of their machine-learning algorithm is unclear, and the physiological\nsignificance of their findings in terms of co-sequencing miRNAs and piRNAs, for\nexample, is missing.\nRESPONSE: We sincerely thank the reviewer for the valuable and insightful comments.\nTo address the concerns, we have conducted additional experiments and performed a\nmore thorough analysis. We have also revised the manuscript accordingly to address\nthe concerns raised.\nqMap-seq is not the first sequencing method to use random nucleotides and unique\nmolecular identifies. As the authors mention in line 85, the qMap-seq method is a\nmodified version of 4N-Xu method which also uses random nucleotides to reduce\nligation bias and is utilized as unique molecular identifiers to correct for PCR biases.\nOther small RNA sequencing methods also use random nucleotides. The impact of\nusing 11 random bases in qMap-seq over short random bases is only presented\ntheoretically in Figure S1A, but the empirical impact that this decision results in is\nunclear.\nRESPONSE: We thank the reviewer’s insightful comments. We address this concern\nalong with our response to next comments, below.\nThe second innovation that the author claims is the use of RNA spike-ins for absolute\nquantification. In line 58-60, the authors state that “none of these sequencing\napproaches permit absolution sncRNA quantification”. However, the use of RNA spike-\nins for absolute quantification and monitoring ligation bias has also been used in\nprevious methods as well. Other experimental optimizations including the\nconcentration of PEG8000 are also done in previous studies, altogether indicating that\nthe innovations proposed by qMap-seq are marginal.\nRESPONSE: We thank the reviewer for the insightful comments.\nWe agree that 4NBoost is not the first study to use random adapters and UMIs.\nIndeed, a series of studies since 2011 have demonstrated that library preparation\nmethods using fixed adapters can introduce severe ligation bias, whereas using\nrandom or pooled adapters can effectively mitigate such bias (Giraldez et al., 2018;\nHafner et al., 2011; Jayaprakash et al., 2011; Kim et al., 2019; Sorefan et al., 2012;\nSun et al., 2011; van Eijndhoven et al., 2023; Xu et al., 2015; Zhuang et al., 2012).\nSimilarly, several studies have employed random sequences as UMIs to remove PCR\nduplicates (Belair et al., 2019; Fu et al., 2018; van Eijndhoven et al., 2023). The use of\nRNA spike-ins for absolute quantification of sncRNAs has also been reported using\nmicroarray (Bissels et al., 2009), RT-qPCR (Brown et al., 2007; Rose et al., 2021) or\nsequencing (Gainetdinov et al., 2018; Gainetdinov et al., 2021). In addition, earlier\nworks have demonstrated that a high concentration of PEG can reduce ligation bias\n(Kim et al., 2019; Song et al., 2014; Zhang et al., 2013). Overall, we acknowledge the\nreviewer’s point that our method is not entirely novel and that several of its individual\ncomponents have been reported to some extent previously. However, the primary\npurpose of our methodological section is not to claim the superiority over existing\napproaches, but rather to demonstrate its accuracy and robustness in achieving\nabsolute quantification of sncRNAs.\nTo this end, we systematically integrated and refined findings from prior studies,\nand optimized key parameters—including the number of random bases in adapters,\nthe required UMI length, PEG concentration, adapter concentration, by-product\nremoval strategy, and the overall workflow. These refinements allowed us to establish\na stable and convenient single-tube protocol that minimizes bias during library\npreparation and sequencing, thereby providing a foundation for absolute quantification\nof sncRNAs. Moreover, unlike most previous studies that used equimolar spike-ins\n(Gainetdinov et al., 2018; Gainetdinov et al., 2021), we employed ratiometric spike-ins\nwith a defined gradient to construct the standard curve, enabling more accurate\nabsolute quantification.\nIn the original manuscript, we have already de-emphasized methodological\nnovelties: the main figures emphasize the quantification accuracy, while most technical\ndetails and optimizations are presented in the supplementary figures rather than\nhighlighted in the Result and Discussion. To further address the reviewer’s concern,\nwe have made the following revisions:\n1. Revise “Additionally, none of these sequencing approaches permit absolute\nsncRNA quantification” to “Additionally, few of these sequencing approaches has been\nadopted to determine estimated absolute quantification of sncRNAs”, as suggested by\nthe reviewer (lines 57-59).\n2. Add the sentence “Consequently, a comprehensive, bias-minimized, and\nquantitatively accurate reference for sncRNAs has yet to be established.” (lines 71-72)\nto clarify that our main objective is to establish an absolute quantitative sncRNA\ndatabase rather than introduce a novel unbiased sncRNA sequencing method.\n3. Revise “that minimizes bias in small RNA sequencing through innovations in\nadapter design and incorporation of exogenous RNA spike-ins.” (lines 74-75) to “that\nminimizes bias in small RNA sequencing through incorporation of exogenous\nratiometric RNA spike-ins.”, removing “innovations” to avoid overstating on method\nnovelty.\n4. Remove the sentence in line 431 that “It's well known that conventional ligation-\nbased methods introduce various biases, leading to the development of several bias-\nminimized sncRNA sequencing methods. However, none of these methods achieves\nthe absolute quantification of sncRNAs, a capability that is essential for generating\nreliable reference datasets and for advancing our understanding of the regulatory roles\nof sncRNAs.” to avoid redundancy and ensure the rigor of our statement.\n5. The 4NBoost workflow diagram (previously Figure 1A) has been relocated to\nSupplementary Figure 1G to further reduce emphasis on methodological development\nin the main figures.\nFinally, regarding the reviewer’s comment on UMI length, our analysis across\nmultiple sequencing samples shows that 8 random bases are insufficient to fully\ncapture the most abundant miRNAs, resulting in quantification bias. By contrast, 11\nrandom bases provide clear advantages (Response Fig. 1).\nResponse Fig. 1 Evaluation of UMI length on miR-21-5p deduplication efficiency\nUMIs of varying lengths were used for PCR duplicate removal during small RNA sequencing\nlibrary preparation.\nWe hope these revisions will help clarify that our central contribution lies in\nachieving accurate absolute quantification of sncRNAs and in establishing a reliable\nreference database, rather than on methodological novelty.\nLastly the authors’ main comparison is with TruSeq and not the more advanced\nmethods such as 4N-Xu and AQ-Seq that the authors mention in the introduction. To\nargue for improvement, the authors must compare with more advanced methods other\nthan TruSeq.\nRESPONSE: We thank the reviewer for this insightful comment. As mentioned above,\nour primary objective was to assess the method's quantification accuracy rather than\nits incremental improvements over other methods. The sncRNA datasets generated\nusing this approach across diverse cell types and tissues provide a more reliable\nreference compared with previous datasets, which were mainly generated using\nbiased methods such as TruSeq. By contrast, the adoption of newer methods such as\n4N-Xu/A/B/C/D and AQ-seq remains limited. For this reason, we selected TruSeq as\nthe primary comparison method rather than other approaches in the original\nmanuscript.\nNevertheless, as recommended, we compared the performance of 4NBoost with\nAQ-seq, which employs 30 equimolar artificial synthetic 3’-2’-OH RNAs as spike-ins\nfor evaluating quantification accuracy. To ensure a fair comparison, we divided our\nspike-ins into two groups: 3’-2’-OH RNAs and 3’-2’-Ome RNAs. The average CV\nvalues for 3’-2’-OH spike-ins were 0.51 for 4NBoost and 0.56 for AQ-seq, respectively,\nwhile for 3’-2’-Ome spike-ins, 4NBoost showed even better performance with a CV of\n0.48. These results suggest that 4NBoost performs slightly better than AQ-seq.\nIt is worth noting that AQ-seq requires two more rounds of gel purification—one to\nrecover small RNAs and another to recover the sncRNA-3’ adapter-ligated products.\nIn contrast, 4NBoost optimizes the overall library construction workflow, reducing these\nlabor-intensive gel purification steps, thereby simplifying the process and enabling\nhigher-throughput sample processing.\nIn summary, 4NBoost is easier to implement while delivering performance equal\nto or better than AQ-seq. These comparison results have been included in Figure 1A\nand Supplementary Figure 1H. Accordingly, we have revised the text at lines 121-123\nto read “Compared to TruSeq and AQ-seq, 4NBoost demonstrated the lowest average\nCV (1.73, 0.56, and 0.51, respectively, for TruSeq, AQ-seq, and 4NBoost)”.\nSupplementary Figure 1: Establishment of the 4NBoost method\n(H) Box plots of CVs for equimolar spike-ins distribution using total cellular RNA as background.\nCenter line represents the median; boxes indicate 25th–75th percentiles; whiskers show\nminimum and maximum values. The biological replicates for these experiments were n = 2 for\nTruSeq, n = 4 for 4NBoost and n = 10 for AQ-seq.\nThe fact that it is a single-tube sequencing protocol is interesting. It’d be interesting if\nthe author tested on low RNA input and its robustness in quantification.\nRESPONSE: We appreciate the reviewer’s positive comment and insightful\nsuggestion. In fact, we had already assessed the sensitivity of the method by\nperforming experiments with progressively lower RNA input in the original manuscript.\nWe performed sequencing using serial dilutions of input RNA, ranging from microgram\nto nanogram (ng) and picogram (pg) levels. As a benchmark, we compared miRNA\nexpression profiles generated from 1 µg of input RNA with those obtained from lower\ninput amounts (Supplementary Figure 1K–L). The number of detected miRNA species\nremained relatively stable down to 10 ng of input RNA. Although reproducibility in\nmiRNA detection declined moderately at 1 ng, Spearman correlation coefficients\nremained high (> 0.7). At inputs below 1 ng, performance declined more substantially,\nprimarily due to increased adapter self-ligation events competing with sncRNA ligation.\nCollectively, these results indicate that 4NBoost can faithfully profile sncRNA\nexpression from as little as 1 ng of total RNA without substantial loss of performance.\nTo present this information more clearly, we have revised the original manuscript\nby adding relevant details and presenting it as a separate paragraph (lines 135-144).\nIn addition, we recalculated the correlation using Spearman's rank correlation\ncoefficient instead of Pearson's, as we consider the rank order of miRNA abundances\nto be more relevant for this method.\nSupplementary Figure 1: Establishment of the 4NBoost method\n(K) Proportion of miRNA species detected from libraries at different dilutions, compared to the\nmiRNA species detected from 1 µg of input RNA.\n(L) Spearman correlation of miRNA expression profiles from libraries at different dilutions,\ncompared to the profile from 1 µg of input RNA.\nIt is worth mentioning that there is another method called QMAP-Seq that was\npublished in Nature Communications in 2020. Here’s the link to the manuscript for\nreference: https://www.nature.com/articles/s41467-020-19553-8\nRESPONSE: We sincerely appreciate the reviewer for bringing this important\ninformation to our attention. We acknowledge that our use of the name “qMap-seq”\ncould potentially cause confusion in the field, despite the fundamental differences\nbetween the two approaches. To avoid any nomenclature conflict and ensure clarity,\nwe have renamed our method as “4NBoost” in the revised manuscript.\nThe comprehensive profiling of multiple cell lines, mouse tissues, and macaque tissues\nis very impressive. The discovery of miRNA conservation between mouse and\nmacaque tissues is novel but not surprising as it is already known that microRNAs are\nconserved between mouse and human. Nevertheless, I do expect that this resource\nwill be valuable for the small RNA scientific community. The authors’ comment on 2’-\nO-methyl-modified sncRNAs was interesting. The authors mention that piRNAs and\nplant miRNAs harbor 3’ 2’-O-methyl modifications in line 56. Applying their method to\nplant tissues and germinal cells would help highlight the importance of using 2’-O-\nmethyl-modified RNA spike-ins.\nRESPONSE: We appreciate the reviewer’s positive comment on this resource and\ninsightful suggestion. Previous studies have shown that sncRNAs bearing 3’-2’-Ome\nexhibit reduced ligation efficiency. To further validate this conclusion, we performed an\nadditional analysis comparing the measured abundances of 3’-2’-Ome and 3’-2’-OH\nspike-ins that were added in equal amounts to 4NBoost and TruSeq libraries. Because\nthe input amounts of each modified and unmodified spike-in were identical, a\nmeasured ratio closer to 1 would indicate more comparable ligation efficiencies. Our\nresults showed that in 4NBoost libraries, the average ratio of unmodified to modified\nspike-ins was 1.8, whereas in TruSeq libraries it was 41.4. This demonstrates that\n4NBoost substantially improves the ligation efficiency of modified spike-ins, bringing it\nalmost in line with that of their unmodified counterparts. These additional results have\nbeen included in Supplementary Figure S1I of the revised manuscript. Accordingly, we\nhave revised the text at lines 128-132 to read “Notably, in 4NBoost libraries, spike-ins\nwith 3’ end methylation showed similar ligation efficiency to non-methylated spike-\nins (Supplementary Figure 1I), in sharp contrast to the TruSeq libraries, where spike-\nins with terminal methylation were detected at over 10-fold lower efficiency compared\nto their non-methylated counterparts.”.\nSupplementary Figure 1: Establishment of the 4NBoost method\n(I) Relative abundance of non-methylated spike-ins compared to their terminal methylated\ncounterparts. Seven spike-ins pairs from equimolar spike-in pools were spiked into 100 ng\nHEK293 total RNAs. Each pair consisted of one non-methylated and one terminally methylated\nRNA molecule with nearly identical sequences, differing by only two nucleotides. Higher relative\nabundance indicates more efficient ligation of the non-methylated spike-in compared to its\nmethylated counterpart. The biological replicates for these experiments were n = 2 for TruSeq\nand 4NBoost. The dashed line is the y-axis at 1.\nRegarding the reviewer’s suggestion, our original manuscript already included\nseveral germinal samples—mouse testes and ovary—both of which express abundant\npiRNAs, particularly the testes, where piRNAs represent the dominant small RNA\nspecies. Using both 4NBoost and TruSeq on mouse testes libraries, we observed that\n4NBoost yielded a markedly stronger 1U signature in piRNAs compared to TruSeq,\nconsistent with improved ligation efficiency for 2’-O-methyl-modified RNAs\n(Supplementary Figure 1J).\nIn addition, as suggested, we generated both 4NBoost and TruSeq datasets from\nfour Arabidopsis thaliana tissues (root, stem, leaf, and flower). 4NBoost markedly\nimproved the recovery of 24-nt species with a stronger 5’ adenine bias, and\nsignificantly enhanced miRNA detection. The analysis also showed pronounced\ndiscrepancies in overall miRNA abundance between the two methods, and TruSeq\nintroduced substantial inaccuracies in miRNA quantification. We anticipate that this\nplant dataset will provide a valuable reference for researchers working on small RNAs\nin plant biology.\nThese additional analyses have been included in Supplementary Figure 6 of the\nrevised manuscript. Accordingly, we have added a new paragraph titled “Re-evaluation\nof Plant sncRNA abundance” to describe the main findings (lines 374-393), and also\nupdated the Abstract (line 23).\nSupplementary Figure 6: Re-evaluation of sncRNAs abundance in Arabidopsis thaliana\ntissues\n(A) The distribution of the first nucleotide across sncRNA-seq reads of different lengths. Data\nare obtained from libraries of four Arabidopsis thaliana tissues prepared using either the\n4NBoost or TruSeq. Colors indicate the proportion of sncRNA-seq reads beginning with each\nnucleotide.\n(B) Proportions of various sncRNA types in four tissues libraries prepared using the 4NBoost\nand TruSeq.\n(C) Relative abundance of 24-nt sncRNAs starting with 5'-adenine in 4NBoost or TruSeq\nlibraries across all four tissues.\n(D) The number of distinct miRNA species detected in each tissue by the two methods.\n(E) Comparison of miRNA expression rankings between libraries prepared using the 4NBoost\nand TruSeq across all four tissues.\nAll panels are based on the data from three or four biological replicates per tissue. Data in (A),\n(B) and (E) are presented as mean values.\nIn line 24, the authors claim that they revealed substantial biases in miRNA abundance,\nstrand selection and tissue specific expression. However, this bias in TruSeq in\nparticular has been shown previously, especially in AQ-seq which is one of the previous\nmethods that the authors cite. In fact, in Figure 4C, they compare with AQ-seq data\nand find that the performance of both methods are comparable.\nRESPONSE: We thank the reviewers for their insightful comments. We agree that\nprevious studies, including AQ-seq, have reported inaccuracies in assessing miRNA\nabundance and strand selection with the TruSeq in limited sample sets, we also\nacknowledge that AQ-seq achieves comparable accuracy in determining miRNA\nstrand selection preferences. However, the datasets generated by these methods\nremain relatively limited and do not yet provide broad reference information. In contrast,\nour datasets provide more comprehensive and accurate resource on miRNA\nabundance, strand selection, and tissue-specific expression across a wider range of\ncell lines and tissues.\nRegarding their machine learning algorithm, the performance is interesting but the\nexact details of their approach and how it corrects bias in small RNA sequencing data\nis unclear. For example, in the result and method section, the authors do not describe\nhow they conducted feature engineering before applying XGBoost.\nRESPONSE: We appreciate reviewer’s valuable comments and apologized for the\nomission of this information. We have revised the manuscript to include detailed\ndescriptions of our machine learning approach in the Methods section (lines 670-703):\n“To enhance model compatibility, we integrated both in-house data generated\nusing NEB kits and an external raw dataset prepared with the same kits. After\npreprocessing, the datasets were merged. To minimize background noise, miRNAs\nwith expression levels below 5 amol in the 4NBoost data were excluded. The filtered\ndataset was split into three subsets: 72% for training, 20% for testing, and 8% as an\nindependent validation set not involved in model training. In addition to NEB-biased\nexpression levels as core input features, we extracted several parameters as auxiliary\nfeatures, including (i) Sequence features: RNA length, nucleotide composition\n(A/T/C/G ratio), and GC content; (ii) Thermodynamic parameters: minimum free\nenergy , free energy difference between 5’ and 3’ termini, and total duplex free\nenergy; (iii) Terminal motifs: the first 1, 3, 4, and 6 nucleotides at both 5’ and 3’\nends; (iv) miRNA structural features: paired ratio, bulge density, internal loop density,\nhairpin density, and pairing continuity. Feature correlation analysis showed that NEB-\nbiased expression contributed most significantly to the prediction performance\n(R=0.614). Terminal sequence features and some thermodynamic parameters\n(delta_G_sum and MFE) showed moderate correlation (R > 0.14), whereas structure-\nrelated features contributed minimally (R < 0.05). Therefore, the five miRNA-structure-\nrelated features were excluded from the final feature set.\nUsing default hyperparameters, we benchmarked four tree-based algorithms:\nRandom Forest (RF), Gradient Boosting Decision Tree (GBDT), LightGBM, and\nXGBoost. Comparative performance analysis identified XGBoost as the best-\nperforming framework for our dataset, a robust machine learning method known for its\nrobustness and high predictive accuracy. Model training was conducted on the\ndesignated training dataset, with hyperparameter optimization performed using the\nHyperopt package in Python.\nThe final model architecture incorporated the following optimized parameters: a\nlearning rate (eta) of 0.4, num_boost_round of 10, max_depth of 8, min_child_weight\nof 14, subsample of 0.75, colsample_bynode of 0.5, colsample_bytree of 1.0, and\nreg_lambda of 4.0. Model performance was evaluated on both a test set and an\nindependent external validation dataset. Two primary evaluation metrics were used:\nthe coefficient of determination (R²) to quantify the variance explained by the model in\npredicting absolute expression values, and Spearman's rank correlation coefficient to\nmeasure the concordance between predicted and actual expression rankings.”\nIn Figure 2c, the authors show a high abundance of other RNAs in cell lines compared\nto tissue samples. Also, the amount of rRNA is especially high in cell lines and mouse\nliver and pancreas tissues. The physiological significance of this is unclear. Could it be\nthat they are artifacts or noise from the sequencing method?\nRESPONSE: We appreciate the reviewer's insightful comments regarding the higher\nabundance of other RNAs in cell lines compared to tissues. To rule out the possibility\nthat this was caused by our method itself, we analyzed libraries generated not only by\n4NBoost but also by AQ-seq and TruSeq. The results showed that, compared with\nTruSeq, the proportion of other RNAs—mainly unannotated RNAs—was elevated in\nboth 4NBoost and AQ-seq libraries (Response Fig. 2), indicating that this phenomenon\nis common to random-adapter-based library preparation methods. Based on our\nexperience, random adapters tend to generate more by-products, especially at low\nRNA input amounts where more PCR amplification is required. Since both tissue and\ncell lines libraries were prepared from 100 ng of total RNA, this observation may reflect\nthat, at the same input amount, tissues contain a higher abundance of ligatable\nsncRNAs than cell lines. Indeed, our absolute quantification results showed that the\naverage miRNA abundance in tissues was approximately 4-fold higher than in cell lines\n(Fig. 3C-D), consistent with previous and recent studies (Gaur et al., 2007; Kraus et\nal., 2025; Lu et al., 2005).\nNevertheless, we cannot exclude the possibility that the lower abundance of\nmiRNAs in cell lines reduces their representation among total sncRNAs, thereby\nincreasing the relative proportion of other RNA species, including unannotated RNAs.\nSupporting this interpretation, we observed that nearly all other sncRNA categories\nwere proportionally higher in cell lines than in tissues (Fig. 2C). Taken together, our\nresults suggest that the elevated proportion of other RNAs in cell line libraries reflects\nthe lower abundance of miRNAs, which increases the relative ratio of by-products.\nResponse Fig. 2 Small RNA composition profiled by different library construction\nmethods in cell lines\nRegarding the high rRNA levels observed in cell lines, our results suggest that the\nlower abundance of miRNAs increases the relative proportion of other RNA species,\nincluding rRNAs. In contrast, the elevated rRNA levels detected in liver and pancreas\nsamples may reflect tissue-specific characteristics. Notably, tissues such as pancreas,\nliver, and thymus are known to contain relatively high levels of RNase, which could\ncontribute to increased rRNA fragmentation.\nIn Figure 3a, the authors compare the number of miRNA species across cell lines and\ntissues. However, the number of miRNA species will also depend on sequencing depth\nand the amount of artifacts and technical noise (e.g., rRNAs) from data generation.\nRESPONSE: We appreciate the reviewer for their insightful comments and fully agree\nthat the number of detected miRNA species can be influenced by sequencing depth\nand other factors. To mitigate the impact of variations in sequencing depth and\npotential artifacts, we randomly subsampled 1.5 million mapped molecules from each\nsample for analysis, as noted in line 192. However, this approach cannot completely\neliminate the impact of differences in the relative abundance of other RNA species or\nof highly overrepresented miRNAs. For instance, the abundance of miR-451 in\nerythrocytes and blood is extremely high, which can reduce the detection of other\nmiRNAs and resulting in a lower number of detected species. Therefore, to ensure the\nrigor of our results, we have added the following statement at lines 204-205: “However,\ndetected miRNA numbers may be influenced by factors such as sncRNA composition\nor incomplete annotation, and should be interpreted with caution.”\nAs for the web application, it’d be more applicable to the scientific community if it\nincluded the sequence alignments and sequence modifications.\nRESPONSE: We thank the reviewer for the valuable suggestion.\nOur web application is designed to provide an absolute quantification reference\nfor sncRNAs across multiple tissues and cell types, supporting searches based on both\nsequence and miRNA name. This allows users to quickly locate and retrieve\nquantitative information for specific sncRNAs. While we agree that sequence\nalignment is a useful tool, several well-established platforms already provide\ncomprehensive alignment analyses.\nIt is important to note that 4NBoost improves the ligation efficiency and detection\naccuracy for modified sncRNAs, but does not capture modification information.\nTherefore, our current database does not include sequence modification information.\nLastly, to claim absolute quantification, the authors must provide experimental\nconfirmation that is not based on sequencing.\nRESPONSE: We appreciate the reviewer's insightful suggestion, and we agree that\nthe absolute quantification accuracy of our method should be validated using non-\nsequencing approaches.\nIn original manuscript, we had performed absolute quantification of miR-221-3p\nby RT-qPCR across 10 different cell types (Fig. 1F), each with four biological replicates.\nThese cell types and miRNA were chosen because the miR-221-3p abundance\nspanned the entire linear range of our spike-in standard curve, evenly distributed to\nallow robust evaluation of quantification accuracy across concentrations. A synthetic\nmiR-221-3p oligonucleotide was serially diluted to generate a standard curve for RT-\nqPCR, which was then used to calculate the absolute abundance of miR-221-3p in\neach cell type. The 4NBoost-derived abundances strongly correlated with these RT-\nqPCR measurements (Pearson’s r = 0.93, p = 1.88 × 10-26), demonstrating the high\nquantitative accuracy of 4NBoost.\nAdditionally, as suggested by Reviewer 2, we validated the quantification accuracy\nof highly expressed miRNAs using miR-21-5p, the most abundantly expressed\nmiRNAs across most cell lines. Seven different cell lines were analyzed, each with\nthree or four biological replicates. A synthetic miR-21-5p oligonucleotide was used to\ngenerate a standard curve for absolute quantification, and comparison with 4NBoost-\nderived abundances showed excellent agreement (Pearson’s coefficient = 0.99, p =\n4.95×10-20; Supplementary Figure 1M). These results confirm the high quantitative\naccuracy of 4NBoost across a wide range of miRNA expression levels. This new\nvalidation data has been included as Supplementary Figure 1M, with a corresponding\ndescription at lines 153-156: “Similarly, miR-21-5p, the most abundantly expressed\nmiRNA across most cell lines, showed excellent agreement between RT-qPCR and\n4NBoost measurements (Pearson’s coefficient = 0.99, p = 4.95×10-20; Supplementary\nFigure 1M). Collectively, these results further support the high quantitative accuracy of\nour method”.\nSupplementary Figure 1: Establishment of the 4NBoost method\n(M) Scatter plot illustrating the correlation between the predicted miR-21-5p abundances\nmeasured by 4NBoost and RT-qPCR across multiple cell lines. Each cell line includes three to\nfour biological replicates. All p-values were calculated using two-tailed Student’s t-tests.\nReviewer #2 (Remarks to the Author):\nThis manuscript presents qMap-seq, an optimized protocol designed to minimize bias\nin small RNA-seq. By incorporating synthetic RNA spike-ins of known quantities, the\nmethod enables estimation of the absolute abundance of miRNAs in, e.g. 100 ng of\ntotal RNA. Leveraging this approach, the authors generate a comprehensive resource\ncharacterizing the miRNA transcriptome across a wide range of cell lines, tissues, and\nspecies, offering potentially a more reliable catalog of functional miRNAs. This\nmanuscript also introduces a machine learning-guided model for bias correction, which\nholds potential for retrospective correction of miRNA-seq datasets. Lastly, the authors\nprovide an interactive platform (SmRNAQuant) to facilitate access to the resource,\nwhich will be of value to the community.\nAlthough various methods have been developed to reduce bias in miRNA-seq, few\nhave been applied systematically across diverse biological contexts to generate a\nunified reference dataset. Furthermore, absolute quantification of miRNAs has not\nbeen extensively explored. This paper addresses both limitations.\nRESPONSE: We sincerely appreciate the reviewer’s supportive comments and\nrecognition of the significance of our study.\nHowever, I have several points for the authors to address:\nMajor comments:\n1. The authors use 11 random bases for UMIs to collapse PCR duplicates. The collision\nrate was estimated to be 1.4%, but this may be inaccurate if the authors assumed\nuniform ligation of all possible 11-nt UMI sequences. Given the known strong ligation\nbias in miRNA-seq, some UMI sequences are likely overrepresented. I strongly\nrecommend including a scatter plot comparing miRNA read counts before and after\nUMI deduplication. As PCR is known not to significantly distort the miRNA profile,\nsubstantial changes after UMI correction would suggest that 11-nt UMIs may be\ninsufficient for accurate deduplication.\nRESPONSE: We appreciate the reviewer’s insightful comments and agree that ligation\nbias can influence the total number of usable UMI sequences.\nAs recommended, we have included a scatter plot comparing the read counts\nbefore and after UMI deduplication for the top 100 most abundant miRNA in cell lines\nand tissues. The expression profiles were nearly identical (Spearman’s R = 0.997),\nindicating that UMI deduplication introduces minimal distortion, confirming that 11-nt\nUMIs are sufficient for accurate deduplication with a negligible impact from potential\nUMI collisions. This scatter plot has been added as Supplementary Figure 2E, with a\ncorresponding description at lines 172-175: “The high correlation of the read counts\nbefore and after UMI deduplication for the top 100 most abundant miRNAs further\nsupports that 11-nt UMIs are sufficient for accurate deduplication with negligible impact\nfrom potential UMI collisions.”.\nSupplementary Figure 2: The data quality of 4NBoost libraries from cell lines and tissues\n(E) Scatter plot comparing total read counts and UMI-deduplicated unique molecule counts for\nthe top 100 most abundant miRNA across cell lines and tissues.\n2. In Fig. 1F, the authors validate qMap-seq using miR-221-3p. In connection with point\n1, I suggest including an additional comparison between RT-qPCR and qMap-seq for\nthe most abundant miRNA in average across cell lines. This would help assess\nwhether qMap-seq performs well for highly expressed miRNAs.\nRESPONSE: We thank the reviewer for this valuable suggestion.\nFirst, we choose miR-221-3p because its abundance across the selected 10 cell\nlines spanned the entire linear range of our spike-in standard curve, with an even\ndistribution, allowing optimal evaluation of quantification accuracy of our method for\nmiRNAs at various concentrations.\nSecond, as recommended, we selected miR-21-5p, the most abundantly\nexpressed miRNAs across most cell lines, to demonstrate that 4NBoost can accurately\nquantify highly expressed miRNAs. Seven different cell lines were analyzed, each with\nthree or four biological replicates. A synthetic miR-21-5p oligonucleotide was used to\ngenerate a standard curve for absolute quantification, and comparison with 4NBoost-\nderived abundances showed excellent agreement (Pearson’s coefficient = 0.99, p\n=4.95×10-20; Supplementary Figure 1M). These results confirm the high quantitative\naccuracy of 4NBoost across a wide range of miRNA expression levels. This new\nvalidation data has been included as Supplementary Figure 1M, with a corresponding\ndescription at lines 153-156: “Similarly, miR-21-5p, the most abundantly expressed\nmiRNA across most cell lines, showed excellent agreement between RT-qPCR and\n4NBoost measurements (Pearson’s coefficient = 0.99, p =4.95×10-20; Supplementary\nFigure 1M). Collectively, these results further support the high quantitative accuracy of\nour method”.\nSupplementary Figure 1: Establishment of the 4NBoost method\n(M) Scatter plot illustrating the correlation between the predicted miR-21-5p abundances\nmeasured by 4NBoost and RT-qPCR across multiple cell lines. Each cell line includes three to\nfour biological replicates. All p-values were calculated using two-tailed Student’s t-tests.\n3. qMap-seq still exhibits bias and therefore does not guarantee fully accurate absolute\nquantification (Fig. 1C). I strongly suggest softening some of the more definitive claims\nthroughout the manuscript. For example, consider modifying the title from “absolute\nquantification” to “estimated absolute quantification,” and revising line 158 from “true”\nto “more reliable.”\nRESPONSE: We sincerely appreciate the reviewer’s comments and suggestions. We\nagree that the current method does not guarantee fully accurate absolute quantification,\nand we have accordingly softened the corresponding claims throughout the manuscript\nincluding in the Abstract and Results sections.\nAdditionally, we have added a statement in the Discussion section to explicitly\nacknowledge this limitation: “It should also be noted that although 4NBoost reduces\nligation bias and improves quantification accuracy, the method still exhibits some bias\nand therefore cannot guarantee fully precise absolute quantification.” (Lines 481-483).\nIn the field of RNA quantification, the term “absolute quantification” is commonly\nused to describe methods that estimate molecular numbers based on calibration\ncurves, even though some bias remains. For example, the labs of Thomas Tuschl and\nAndreas Bosio published a microarray-based study entitled “Absolute quantification of\nmicroRNAs by using a universal reference” (Bissels et al., 2009). More recently, Erik\nSontheimer and Ildar Gainetdinov reported a sequencing-based study entitled\n“Absolute quantification of mammalian microRNAs for therapeutic RNA cleavage and\ndetargeting” (Kraus et al., 2025). In line with this established nomenclature, we\npropose to retain the current title.\n4. Most importantly, estimating absolute miRNA quantities is a central contribution of\nthe paper. However, it remains unclear what specific biological insights are uniquely\nenabled by these absolute estimates, as opposed to relative quantification. For\nexample, do absolute abundance estimates more accurately reflect miRNA activity?\nRESPONSE: We thank the reviewer for raising this important point.\nSubstantial evidence indicating that intracellular miRNA concentration is closely\nrelated to functional activity. Early work by Brown et al. demonstrated that miRNA\nactivity depends on a threshold level of expression (Brown et al., 2007). In U937 cells,\nmiRNAs expressed at 11 or 50 copies/pg showed no target suppression, whereas\nthose expressed at over 100 copies effectively silenced target genes. Similarly,\nMullokandov et al. found that only the most abundant miRNAs can mediate target\nsuppression, with over 60% of detected miRNAs exhibiting no discernible activity\n(Mullokandov et al., 2012), highlighting that the functional “miRNAome” in a cell is\nconsiderably smaller than inferred from profiling studies.\nMore recent studies further support this conclusion. Oesinghaus et al.\nsystematically probed miRNA expression and activity across 10 cell lines and found\nthat mRNA stability begins decreasing at ~1,000 miRNA transcripts per million (TPM)\n(Oesinghaus et al., 2024). Kraus et al. determined the absolute miRNA abundance\nrequired for productive target cleavage, demonstrating that the IC50 was ~4,200 ± 100\nmiRNAs per 10 pg total RNA for highly expressed targets, whereas the IC50 was\n~1,700 ± 100 miRNAs for low-abundance targets (Kraus et al., 2025).\nIn vivo studies in mouse oocytes provide additional evidence for the link between\nmiRNA concentration and activity. Two independent studies (Ma et al., 2010; Suh et\nal., 2010) showed that miRNAs are largely inactive in oocytes. Kataruka et al. showed\nthat rapid growth and dilution of miRNAs during oocyte development reduces miRNA\nconcentration, limiting their activity (Kataruka et al., 2020). For example, in mouse\noocytes, 1.5 nM miRNA retained silencing activity, whereas 0.3 nM did not. Similar\nobservations were made in porcine and bovine oocytes, where highly expressed\nmiRNAs (e.g., ssc-miR-205 at 4.4 nM and bta-miR-10b at 0.5 nM) retained target-\nsilencing activity (Kataruka et al., 2022).\nTogether, these in vitro and in vivo data demonstrate that miRNA function is\nconcentration-dependent, though modulated by factors such as miRNA type, target\nabundance, and cellular context. Previous studies measured absolute expression for\nonly a subset of miRNAs in limited cell or tissue types, whereas our datasets provide\na more comprehensive and systematic reference. This resource can be used to predict\nwhich miRNAs are likely to be active and guide the selection of relevant candidates for\nfunctional studies.\nTo convey these points, we have added several sentences in the Introduction\n(lines 60-65) and a paragraph in the Discussion (lines 454-467) highlighting the\nrelationship between miRNA quantification and activity, and emphasizing the utility of\nour database for predicting miRNA activity and selecting appropriate miRNAs for\ndownstream studies.\n5. The authors highlight examples in which previous methods or databases\nmisestimate 1) strand bias, 2) family-level expression, or 3) tissue specificity. However,\nthese observations are not experimentally validated, although qMap-seq still has bias.\nI suggest validating at least one of these examples to substantiate the improved\nreliability of qMap-seq.\nRESPONSE: We sincerely appreciate this valuable suggestion from the reviewer.\nAs recommended, we performed miRNA RT-qPCR to determine the absolute\nabundance of the 3p and 5p strands of two representative miRNAs, pre-mir-423 and\npre-mir-362, and to assess their strand preference. The results showed that the strand\nratios estimated by RT-qPCR were consistent with our dataset, rather than with TruSeq\ndata or miRbase annotations. These new results have been added to Fig. 4F and 4G,\nwith the corresponding description in the text: “Among these, pre-mir-423 and pre-mir-\n362 were selected, and their strand preference were validated by RT-qPCR. The strand\nratios estimated by RT-qPCR were consistent with our dataset but differed from the\nTruSeq data or miRBase annotations.” (lines 285-288).\nFigure 4: Re-assessment of miRNA strand preference.\n(F-G) Log -transformed strand ratio of pre-mir-362 (F) and pre-mir-423 (G) obtained from RT-\n2\nqPCR-based absolute quantification across various cell lines.\nMinor comments:\n1. Lines 47-50: PCR has been shown not to be a major source of bias in miRNA-seq.\nNotably, the addition of 50 cycles did not substantially alter miRNA profiles (Hafner et\nal., RNA, 2011, Jayaprakash et al., Nucleic Acids Research, 2011). Thus, please revise\nthis section or provide a justification for diverging from these findings.\nRESPONSE: We sincerely appreciate the reviewer’s insightful comment and\napologize for the overstatement in our original text. We fully acknowledge that PCR is\nnot the primary source of bias in miRNA-seq. Accordingly, we have revised this section\nto more accurately describe the source of bias. The sentence now reads: “These\nbiases primarily arise from the structure-dependent mechanism of T4-mediated\nligation of adapters to sncRNAs, causing certain sncRNAs to be over- or under-\nrepresented, which can ultimately skew conclusions about their biological functions.”\n(lines 45-48).\n2. Please acknowledge the prior use of Lambda exonuclease in miRNA library\npreparation (Hagemann-Jensen et al.,Nat. Biotech., 2018).\nRESPONSE: We thank the reviewer for bringing this to our attention and apologize for\nthis unintentional oversight. The use of Lambda exonuclease in miRNA library\npreparation was first introduced by Rickard Sandberg’s group in a Nature\nBiotechnology paper (Faridani et al., 2016), with a detailed protocol subsequently\npublished in Nature Protocols in 2018 (Hagemann-Jensen et al., 2018). We have now\ncited both references in the revised manuscript (line 118).\n3. Please include strand information (e.g., -5p vs -3p) when referring to mature miRNAs\nthroughout the manuscript unless referring to families or precursor forms (pri- or pre-\nmiRNAs). For example, lines 209-211 currently lack this information.\nRESPONSE: We sincerely thank the reviewer for pointing out this oversight. In most\nparts of the manuscripts, we have clearly indicated miRNA strand information. The\nexception occurs in our comparison with the MirGeneDB dataset, where miRNA\nexpression profiles are provided at the precursor (pre-miRNA) level rather than mature\nstrand (-5p/-3p) level. To enable direct and meaningful comparison with this reference\ndatabase, we also conducted precursor-level analysis. To avoid potential confusion\nand ensure terminological accuracy, we have systematically revised all relevant\nsections to explicitly specify \"pre-miRNA\" when referring to precursor forms in the\nrevised manuscript.\n4. The machine learning-guided model could potentially be further improved by\nincorporating information on 5’ and 3’ termini of mapped reads. If the current model\naggregates all reads mapped to a given miRNA regardless of their precise ends,\nisoform-level resolution could be leveraged to refine the model.\nRESPONSE: We sincerely appreciate the reviewer's insightful suggestion and we fully\nagree that incorporating isomiR information could potentially improve our machine\nlearning model. However, the omission of isomiR analysis in this study is not due to a\nlack of awareness of its importance, but rather stems from concerns about potential\nartifacts that could compromise the accuracy of isoform-level quantification.\nTo assess this, we first examined the 5’ and 3’ termini of 30 synthetic spike-ins in\nour dataset. Surprisingly, ~10% of sequences displayed terminal variations despite\nbeing fixed sequences. The most common alterations were 5’ nucleotide deletion,\nfollowed by 3’ deletion, 5’ addition, and 3’ addition. To rule out method-specific artifacts,\nwe analyzed public datasets from AQ-seq, IsoSeek, NEXTFlex, and 4N-Xu/A/B/C/D,\nall of which included synthetic spike-ins. Consistent with previous findings (Gomez-\nMartin et al., 2023), all methods detected unexpected terminal variants in spike-in\ncontrols, with patterns similar to ours and even higher rates in some protocols (eg.,\n4N-A/B/C/D; Response Fig. 3). These differences likely arise from RNA degradation,\nlibrary construction artifacts (such as non-templated nucleotide additions), or synthesis\nerrors, as previously noted (Gomez-Martin et al., 2023; Hu et al., 2021).\nGiven these confounding factors, it remains challenging to distinguish true\nisomiRs from technical artifacts or to accurately determine their absolute abundance.\nFor this reason, we have not incorporated isomiR analysis in the present study,\nalthough we agree it represents an important avenue for future work once these\ntechnical limitations are resolved.\nResponse Fig. 3 Evaluation of isomiR artifacts using spike-in controls across different\nsmall RNA library construction methods\nIsomiR distribution was analyzed in the 963-miRNA Miltenyi miRXplore Universal Reference\npool (assessing methods 4N-A/B/C/D, 4N-Xu, ISoSeek, and 4N-NEXTflex) and a self-\nsynthesized spike-in pool (evaluating AQ-seq and 4NBoost). The following isomiR types are\nquantified: Iso_5p/3p: Sequence variants resulting from 5’ or 3’ end trimming or extension;\nIso_add5p/3p: Variants featuring non-templated nucleotide additions at the 5’ or 3’ end.\nReviewer #3 (Remarks to the Author):\nIn their manuscript \"An absolute quantification atlas of small non-coding RNAs across\ndiverse mammalian tissues and cell lines\" Xiao et al describe their improved method\nto clone small RNAs with absolute quantification (qMap-seq), and use this to generate\nan atlas of several dozen datasets of mammalian small RNAs, cell lines and tissues of\nseveral species. They also describe a machine learning method to correct small RNA\ncloning bias.\nThere are many small RNA cloning protocols, and many limitations of the early\nmethods have been recognized and improved. These include use of randomized\nadaptors on both ends, spike-ins, and UMI to minimize ligation bias and enable\nabsolute quantification of small RNAs. These have all been applied in other studies\n(eg from Narry Kim lab PMID 30605524 or Phil Zamore lab 30001700), and are\nimplemented in many lab's current workflows. In addition, a new paper from Ildar\nGainetdinov lab also includes small RNA cloning absolute quantification (PMID\n40436630). A difference with the submitted paper is that they use a series diluted spike-\nin RNAs with defined concentrations to the total RNA, so they can make a standard\ncurve and calculate the absolute miRNA levels according to the curve, similar to\nabsolute quantitive qPCR for individual genes. This submission is >1 month later than\nthe other recent one, so I would not make a big deal, but only to say the general cloning\nmethods are well-known, but they do a better job for absolute quantification. The\nauthors apply this method to a wide range of mammalian tissues and cell lines,\ngenerating a large dataset and providing a public resource. This dataset is useful, but\njust to note there are also extensive other data exist. This study also developed a\nmachine learning method to help correcting the previous small RNA seq datasets and\ninclude in their web database for users to adjust and do absolute quantification of these\nprevious small RNA seq data.\nI think the study is technically very competent. I don't really have substantial feedback\nthat would improve it or make it more confident. It provides some advances but the\nmethodology is not very novel, and the database is extensive but also other small RNA\ndatabases exist. I think its an editorial decision whether to publish in Nature\nCommunications. It could also be of interest to a journal with a more focused\nreadership in RNA (such as RNA or NAR), or perhaps genomics (Genome Research\nor Genome Biology).\nRESPONSE: We sincerely thank the reviewer for their thoughtful evaluation and\nconstructive comments. We are pleased that the reviewer recognizes the strengths of\nour work, including the improved quantification method, the extensive dataset, and the\nmachine learning approach, while also noting the limited novelty in methodology.\nAs the reviewer mentioned, a recent study (Kraus et al., 2025) from Ildar\nGainetdinov’lab reported related work with slightly different focus. However, that study\ndid not establishing a web-based repository or develop a machine learning model to\ncorrect biases in conventional datasets. This distinction further underscores that\naccurate miRNA quantification and the creation of a comprehensive quantitative\ndatabase remain important goals in the field. We believe our dataset provides a more\nprecise reference and will be valuable for future functional studies and applications of\nmiRNAs."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors’ revised manuscript now holds two main but moderate contributions to the RNA community: (1) a single-tube experimental workflow for small RNA-seq, and (2) small RNA resources including miRNA and piRNA in cell lines, mice, monkeys, and now plants. This is vastly different from what the authors have originally proposed in their manuscript.\nThe single-tube experiment is again interesting but unfortunately not applicable at 100 pg level or less. Achieving this pg level technique would have opened the possibility of single-cell small RNA-sequencing. That being said, this single-tube experiment offers new opportunities for scalable small RNA-sequencing. Regarding it as a small RNA resource, I do believe that it complements existing small RNA databases, especially in terms of quantification.", "reviewer_2": "The authors have adequately addressed my previous concerns and revised the manuscript accordingly.\nHowever, for Supplementary Figure 2, please repeat the same analysis separately within each cell line and each tissue, and present all corresponding scatter plots. To assess whether the 11-nt UMI is sufficient for deduplication, it is necessary to compare the abundance of the most highly expressed miRNAs before and after deduplication. If the 11-nt UMI is insufficient, the abundance of the most highly expressed—but not moderately or lowly expressed—miRNAs will be distorted after deduplication. Importantly, even if the UMI length is insufficient for the most abundant miRNA(s), integrating multiple datasets (as the authors have done) could underestimate or mask such distortion due to read counts from cell lines or tissues where the same miRNA is expressed at lower levels. Therefore, the absence of distortion should be confirmed independently for each cell line and each tissue."}, "author_response": "Reviewer #1 (Remarks to the Author):\nThe authors’ revised manuscript now holds two main but moderate contributions to the\nRNA community: (1) a single-tube experimental workflow for small RNA-seq, and (2)\nsmall RNA resources including miRNA and piRNA in cell lines, mice, monkeys, and\nnow plants. This is vastly different from what the authors have originally proposed in\ntheir manuscript.\nThe single-tube experiment is again interesting but unfortunately not applicable at 100\npg level or less. Achieving this pg level technique would have opened the possibility of\nsingle-cell small RNA-sequencing. That being said, this single-tube experiment offers\nnew opportunities for scalable small RNA-sequencing. Regarding it as a small RNA\nresource, I do believe that it complements existing small RNA databases, especially in\nterms of quantification.\nResponse: We sincerely appreciate the reviewer’s recognition of the contributions\nour work brings to the RNA community, and we thank the reviewer for the thoughtful\ncomments that helped us further refine and highlight the key advances of this study.\nWe would like to note that our initial goal was indeed to extend the method to ultra-\nlow-input or even single-cell applications. However, during method optimization, we\nfound that the use of randomized adapters inevitably generates substantial by-\nproducts, particularly when the input RNA amount is extremely low and extensive PCR\namplification is required. Although lowering the adapter concentration can partially\nmitigate this issue, it compromises quantitative accuracy. Future optimization efforts\nmay focus on enhancing 3’ adapter removal efficiency or exploring alternative ligation\nstrategies that could enable reliable picogram-level input processing.\nReviewer #2 (Remarks to the Author):\nThe authors have adequately addressed my previous concerns and revised the\nmanuscript accordingly.\nHowever, for Supplementary Figure 2, please repeat the same analysis separately\nwithin each cell line and each tissue, and present all corresponding scatter plots. To\nassess whether the 11-nt UMI is sufficient for deduplication, it is necessary to compare\nthe abundance of the most highly expressed miRNAs before and after deduplication.\nIf the 11-nt UMI is insufficient, the abundance of the most highly expressed—but not\nmoderately or lowly expressed—miRNAs will be distorted after deduplication.\nImportantly, even if the UMI length is insufficient for the most abundant miRNA(s),\nintegrating multiple datasets (as the authors have done) could underestimate or mask\nsuch distortion due to read counts from cell lines or tissues where the same miRNA is\nexpressed at lower levels. Therefore, the absence of distortion should be confirmed\nindependently for each cell line and each tissue.\nResponse: We sincerely appreciate the reviewer’s recognition of the improvements\nmade in the revised manuscript.\nWe apologize that the lack of clarity in our earlier description may have caused\nconfusion. The analysis shown in Supplementary Fig. 2E does not involve integrating\nmultiple datasets. Each point in the plot represents one miRNA within a single sample\nfrom a given cell line or tissue, rather than an aggregated value across different cell\nlines or tissues. To avoid further misunderstanding, we have now made this explicit in\nthe figure legend, which reads:\n“Each point represents one single miRNA within an individual sample of a cell\nline or tissue.” (line 1110-1111).\nIn addition, following the reviewer’s suggestion, we repeated the same analysis\nseparately for each individual cell line and each tissue. The results were fully consistent\nwith those in Supplementary Fig. 2E: read counts before and after UMI deduplication\nwere nearly identical, confirming that the 11-nt UMI is sufficient even for the most highly\nexpressed miRNAs. Because this analysis generates a large number of panels and\nSupplementary Fig. 2E essentially summarizes all these results, we include these\nresults as a response figure in the rebuttal rather than adding them to the\nsupplementary materials.\nResponse Fig.1 Comparison of total and UMI-deduplicated miRNA counts across\nsamples\n(A-C) Scatter plot comparing total read counts and UMI-deduplicated unique molecule counts\nfor the top 50 most abundant miRNA in each sample of various cell lines (A), monkey tissues\n(B), and mouse tissues (C). Each point represents one single miRNA within an individual\nsample."}]

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