Uli-epic: profiling RNA modifications from ultra-low input samples

Weizhi He; Chu Xu; Wen Chen; Xiao Jiang; Yuhang Wang; Yong Peng; Shujuan Chang; Wei Zhu; Jiuhong Kang; Lulu Hu

doi:10.1186/s13059-025-03857-3

. 2025 Nov 11;26:384. doi: 10.1186/s13059-025-03857-3

Uli-epic: profiling RNA modifications from ultra-low input samples

Weizhi He ^1,^#, Chu Xu ^1,^#, Wen Chen ^3,^#, Xiao Jiang ¹, Yuhang Wang ³, Yong Peng ⁴, Shujuan Chang ³, Wei Zhu ³, Jiuhong Kang ^3,^✉, Lulu Hu ^1,^2,^✉

PMCID: PMC12606830 PMID: 41219823

Abstract

High-throughput sequencing with chemical labeling enables robust, transcriptome-wide detection of RNA modifications at single-nucleotide resolution. However, these methods typically require substantial RNA amounts due to harsh treatments. We introduce Uli-epic, an innovative library construction strategy that enables profiling epitranscriptomic modifications using 100 pg to 1 ng of RNA. Utilizing Uli-epic BID-seq, we investigate pseudouridine (Ψ) sites in neural stem cells and sperm RNA from wild-type and fetal growth restriction mice, using only 500 pg of rRNA-depleted RNA. Uli-epic GLORI quantifies m⁶A in sperm and neural stem cells from wild-type and fetal growth restriction mice, using 10 ng of rRNA-depleted RNA.

Graphical Abstract

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Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-025-03857-3.

Keywords: Uli-epic, M⁶A, Ψ, GLORI, BID-seq, Linear amplification, Ultra-low input, Fetal growth restriction

Background

Since the discovery of the first chemically modified RNA nucleotide, Ψ, in 1951, over 170 types of chemical modifications have been identified in RNA [1]. These modifications are crucial for various aspects of the RNA life cycle, including secondary structure [2], gene expression [3–7]; pre-mRNA splicing [3], nuclear export [8], RNA stability [9, 10], and translation efficiency [11, 12]. All four types of bases, as well as the ribose backbone, can undergo chemical modifications, resulting diverse types such as N⁶-methyladenosine (m⁶A), Ψ, m⁷G, N⁶,2′-O-dimethyladenosine (m⁶Am), N¹-methyladenosine (m¹A), inosine (I), 5-methylcytosine (m⁵C), 5-hydroxymethylcytosine (hm⁵C), 2′-O-methylation (Nm), and N⁴-acetylcytidine (ac⁴C). To explore the biological functions of these modifications, researchers have developed a range of methodologies to profile RNA modifications across the transcriptome, including antibody affinity-based, enzyme-assisted, and chemical-assisted sequencing techniques.

Utilizing anti-RNA modification antibodies to enrich the RNA fragments, producing maps with a resolution of 100 ~ 200 nt [13, 14], such as in m⁶A-seq or MeRIP-seq— has become a widely adopted approach in the epi-transcriptomic field due to its user-friendly nature. This approach has significantly advanced the study of RNA modifications. Researchers have further optimized the m⁶A-seq strategy to profile RNA m⁶A modifications at the single-cell level [15–17]. However, antibody-based techniques have notable limitations, including low resolution, and a lack of stoichiometric information [18]. To conquer this bottleneck, various enzyme-assisted and chemical-assisted techniques have been developed that allow for the quantification of RNA modification stoichiometries at single-nucleotide resolution across the transcriptome. Examples include m⁶A-SAC-seq [19], eTAM-seq [19], and GLORI for m⁶A detection [20, 21]; m¹A-MAP [22], m¹A-quant-seq [23]; and red-m¹A-seq [24] for m¹A detection; BID-seq [25], BACS [26] and PRAISE [27] for Ψ detection; UBS-seq [28] and m⁵C-TAC-seq [29] for m⁵C detection; AlkAniline-Seq [30] for m⁷G and N³-methylcytidine (m³C) profiling, and m⁷G-seq [31] for m⁷G detection; ac⁴C-seq [32] and RedaC:T-seq [33] for ac⁴C detection; and DAMM-seq, which simultaneously detects m¹A, m³C, N¹-methylguanosine (m¹G) and N²,N²-dimethylguanosine (m^2,2G) methylations [34].

Among these methods, enzyme-assisted and several relatively gentle chemical-assisted approaches achieve RNA modification quantification with nanogram-level RNA input. For instance, m⁶A-SAC-seq for m⁶A detection requires 2 ng of mRNA [25]; eTAM-seq [19] enables m⁶A profiling at single cell level; BID-seq [25] for Ψ detection requires 10 ng of mRNA; UBS-seq [28] for m⁵C detection requires 10 ng of mRNA; and m¹A-quant-seq for m¹A detection requires 100 ng of mRNA. While enzyme-assisted methods generally cause less damage to RNA samples, they often involve fragile enzymes, which pose stability risks. In contrast, chemical-based techniques are more stable and easier to operate but can lead to RNA degradation due to harsh treatment. For instance, GLORI [20, 21] allows for absolute quantification of m⁶A and is user-friendly and stable, but it requires 200 ng of mRNA, limiting its clinical applicability. In certain biological contexts, such as during embryogenesis or when working with clinical samples, only a few ng or even pg of rRNA depleted input may be available. Thus, there is a pressing need for stable, easy-to-use strategies that can detect RNA modifications in ultra-limited RNA samples using chemical-based techniques.

Here, we present Uli-epic, an advanced library construction strategy designed for ultra-limited epi-transcriptomic modification profiling. Uli-epic integrates poly(A) tailing, reverse transcription coupled with template switching, and T7 RNA polymerase-mediated in vitro transcription (IVT), to enable precise RNA modification profiling at single-nucleotide resolution. This technique is fully compatible with chemical-based sequencing technologies, allowing accurate quantification of RNA modifications. By combining Uli-epic with BID-seq [25] and GLORI [20, 21], we successfully profiled Ψ and m⁶A modifications using as little as 100 pg and 10 ng of mRNA extracted from HEK 293 T cells, respectively.

Fetal growth restriction (FGR), characterized by an estimated fetal weight below the 10th percentile for gestational age, affects approximately 5% of pregnancies globally [35, 36]. FGR represents the second leading cause of perinatal mortality and significantly elevates the offspring’s risk of developing adult-onset metabolic disorders (e.g., diabetes, hypertension) and neuropsychiatric conditions (including cognitive deficits, schizophrenia, and depression) [37–39]. Our previous research demonstrated that FGR impairs the proliferative capacity of hippocampal neural stem cells (NSCs) in offspring mice, resulting in cognitive dysfunction [40]. Notably, clinical studies have shown that disease phenotypes resulting from adverse intrauterine environments tend to be inherited across generations [41, 42]. Animal model research has further revealed that FGR offspring can transmit their pathological traits to the second generation through alterations in sperm epigenetic modifications (e.g., DNA methylation, histone modifications), facilitating intergenerational inheritance [43, 44]. However, FGR remains frequently underdiagnosed during pregnancy, and the molecular mechanisms underlying FGR-associated offspring disorders and intergenerational inheritance are not yet fully understood, hindering the development of effective clinical interventions. Importantly, the landscape of RNA modifications in NSCs and sperm in the context of FGR remains unexplored.

Leveraging Uli-epic BID-seq and Uli-epic GLORI, we successfully profiled Ψ and m⁶A modifications in RNA isolated from wild-type (WT) and FGR mouse sperm and NSCs, providing new insights into the molecular underpinnings of FGR.

Results

Uli-epic strategy

To detect RNA modifications in ultra-limited samples, we developed a technique termed "Uli-epic," an advanced library construction strategy designed for ultra-limited epi-transcriptomic modification profiling (Additional file 1: Fig. S1). The process begins with the fragmentation of low-input RNA, followed by specific chemical treatments to detect particular RNA modifications. For example, glyoxal and nitrite-mediated deamination of unmethylated adenosines (GLORI) is employed while preserving m⁶A modifications; bisulfite-induced deletion sequencing (BID-seq) is used to convert Ψ stoichiometrically into deletions upon reverse transcription. Subsequently, 3' end-repair is performed using T4 polynucleotide kinase (PNK), and a poly(A) tail is added to the 3’ end of RNA by E. coli poly(A) polymerase. Reverse transcription and template switching are then conducted using a reverse transcriptase enzyme mix, a T7-P7 oligo-dT primer, and a P5 template switch oligo (P5-TSO) (Additional file 2: Table S1). After degrading the original RNA template with E. coli RNase H, second-strand cDNA synthesis is performed using primer extension by DNA polymerase. The double-stranded cDNA template with the T7 promoter is then subjected to linear amplification via T7 RNA polymerase-mediated IVT. The amplified RNA product is finally reverse transcribed and prepared for library construction.

When uncoupled from chemical treatment, the Uli-epic strategy can also be applied to ultra-limited RNA sequencing (RNA-seq) (Additional file 1: Fig. S2a). To validate the efficacy of Uli-epic for ultra-limited RNA profiling, we conducted conventional RNA-seq with 20 ng of input mRNA and Uli-epic RNA-seq with 1 ng and 100 pg of input mRNA, comparing their performance side by side. The overall gene expression levels in the two RNA-seq replicates for Uli-epic RNA-seq were highly correlated, using both 1 ng and 100 pg of RNA input (Pearson correlation coefficient > 0.99; Additional file 1: Fig. S2b; Additional file 2: Table S2). Furthermore, the gene expression levels identified in the conventional RNA-seq strongly correlated with those from the Uli-epic RNA-seq using both 1 ng and 100 pg of RNA input (Pearson correlation coefficient > 0.92; Additional file 1: Fig. S2c). While read coverages across transcripts were similar in both conventional and Uli-epic RNA-seq libraries, Uli-epic exhibited a slight 3’-end bias (Additional file 1: Fig. S2d). The number of detected genes using 100 pg of input mRNA in Uli-epic RNA-seq was slightly lower than in conventional RNA-seq and Uli-epic RNA-seq using 1 ng of input mRNA. Nevertheless, the undetected genes were primarily low-expression genes (0 < TPM < 0.1), indicating that Uli-epic RNA-seq is robust in detecting genes with medium and high expression levels using as little as 100 pg of mRNA (Additional file 1: Fig. S2e). Two representative mRNAs, ACTB and ATF4, showed consistent read coverages in both conventional and Uli-epic RNA-seq (Additional file 1: Figs. S2f-g). Our results demonstrate that Uli-epic RNA-seq performs with accuracy comparable to conventional RNA-seq and is sensitive enough to profile ultra-limited input RNA materials.

Integrating Uli-epic with BID-seq

Pseudouridine (Ψ) is a prevalent RNA modification that influences various biological processes [45]. Recently, several technologies have been developed to quantify Ψ across the transcriptome at single-nucleotide resolution, including BID-seq, [25, 46] PRAISE [27], and BACS [26]. In the BID-seq strategy, Ψ reacts with bisulfite (BS) to form a Ψ-BS adduct, creating unique deletion signatures during reverse transcription. BID-seq typically requires approximately 10 ng of input poly(A)⁺ RNA. To achieve more sensitive detection, we integrated BID-seq with Uli-epic, resulting in Uli-epic BID-seq, which enables the quantification of Ψ with as little as 100 pg of input mRNA (Fig. 1a).

The Uli-epic BID-seq strategy (Fig. 1a) involves the following steps: First, low-input RNA is fragmented, 3’ end-repaired, and polyadenylated. The polyadenylated RNA is then divided into two groups: one remains untreated, termed the ‘input’ group, while the other undergoes BID treatment, termed the ‘experimental’ group. Both groups are subjected to reverse transcription (RT) followed by template switching. The Ψ-BS adduct is misrecognized by the reverse transcriptase, resulting in a deletion signal, whereas untreated Ψ is read as U. The linear amplification and library construction steps follow the Uli-epic strategy described above.

To validate the accuracy of Uli-epic BID-seq in detecting Ψ with low-input RNA, we performed BID-seq with 20 ng of mRNA and Uli-epic BID-seq with 1 ng and 100 pg of mRNA extracted from HEK 293 T cells. We incorporated spike-in Ψ control RNA (Additional file 2: Table S1) into all RNA samples. The spike-in control RNAs displayed consistent Ψ stoichiometries when detected by both BID-seq and Uli-epic BID-seq (Additional file 1: Fig. S3a), confirming the conversion efficiency of Uli-epic BID-seq. By calculating the overlapping sites between two biological replicates, we identified 2,345 Ψ sites using BID-seq with 20 ng of input RNA, 1,172 Ψ sites with Uli-epic BID-seq using 1 ng of input RNA, and 695 Ψ sites with Uli-epic BID-seq using 100 pg of input RNA (Additional file 1: Fig. S3b; Additional file 2: Tables S3-S8).

The Ψ stoichiometries calculated using overlapping Ψ sites between the two biological replicates demonstrated high consistency: BID-seq with 20 ng of input RNA (R = 0.9745), Uli-epic BID-seq with 1 ng of input RNA (R = 0.9697), and Uli-epic BID-seq with 100 pg of input mRNA (R = 0.9322) (Additional file 1: Fig. S3c). RNA transcripts containing Ψ-modified sites exhibited substantial overlap between BID-seq and Uli-epic BID-seq (Additional file 1: Fig. S3d). Furthermore, the Ψ stoichiometries identified by BID-seq using 20 ng of RNA input showed strong correlation with those identified by Uli-epic BID-seq using both 1 ng (R = 0.8897) and 100 pg (R = 0.6955) of RNA input (Additional file 1: Fig. S3e). Both strategies revealed that Ψ sites are primarily located in mRNA and ncRNA (excluding rRNA), with Ψ sites in mRNA predominantly distributed in the coding sequence (CDS) and 3′-UTR (Fig. 1b). The metagene profile indicated a slightly higher accumulation in the CDS regions and a lower accumulation in the 5’ UTR regions for Uli-epic BID-seq with low-input RNA compared to BID-seq with 20 ng of mRNA (Fig. 1c). Both methods identified the majority of mRNA Ψ sites with stoichiometries between 10–50% (Figs. 1d-e). Notably, the Uli-epic BID-seq strategy preferentially captures Ψ sites with high Ψ stoichiometries, while it tends to miss some Ψ sites with lower Ψ stoichiometries (Fig. 1e).

We further analyzed the frequency of motifs and the stoichiometries of Ψ sites detected by BID-seq and Uli-epic BID-seq. Our findings revealed that both strategies identified the most commonly reported motifs [25], including GCΨCT, TTΨCC, CTΨCC, GTΨCG, GTΨCA, GTΨCC, GTΨGG, TTΨGC, and TTΨGG (Fig. 1f). The Integrative Genomics Viewer (IGV) track of a representative highly modified Ψ site in AK2 mRNA is shown (Fig. 1g). Notably, the deletion rate at the Ψ site in the ‘input’ group is less than 1%, whereas both the BID-seq and Uli-epic BID-seq groups exhibit a deletion rate of over 80% (Fig. 1g). These results underscore the accuracy and sensitivity of Uli-epic BID-seq in quantifying Ψ using ultra-low RNA input.

Ψ landscapes in FGR mouse NSCs and sperm

To further validate the performance of Uli-epic BID-seq with ultra-limited RNA samples extracted from tissue samples and to uncover the potential biological functions of Ψ in FGR, we profiled Ψ using approximately 500 pg of rRNA-depleted total RNA isolated from NSCs and sperms of WT and FGR mice. NSC cells (nestin-positive) were sorted from embryonic day 17.5 Nestin-GFP mice using flow cytometry, followed by the application of Uli-epic BID-seq to identify Ψ sites in the rRNA-depleted NSC total RNA samples. The spike-in control RNA demonstrated robust deletion efficiency (Additional file 1: Fig. S4a). We identified 840 and 1,019 Ψ sites (each with a Ψ stoichiometry ≥ 10%) in NSCs of WT and FGR mice, respectively, calculated from overlapping sites between two biological replicates (Fig. 2a; Additional file 2: Tables S9-S12). Notably, most Ψ sites exhibited stoichiometries above 50% (Figs. 2a-b). The Ψ stoichiometries detected by Uli-epic BID-seq were comparable in NSCs of WT and FGR mice (Fig. 2b). The metagene profile indicated that Ψ sites in WT NSC mRNA tend to accumulate in the 3′ CDS near the 3′UTR, while Ψ sites in FGR NSC mRNA were more evenly distributed across the CDS (Fig. 2c). Ψ sites were enriched in noncoding RNA (ncRNA), CDS, and introns in NSCs of both WT and FGR mice (Fig. 2d).

Fig. 2 — Ψ landscapes of NSCs and sperm from WT and FGR mice. a Number of overlapping Ψ sites between two biological replicates in rRNA-depleted total RNA from FGR and WT NSCs. Ψ sites are categorized into three groups based on stoichiometry: > 50%, 20–50%, and 10–20%. b Curve graph (left panel) and box plot (right panel) showing the Ψ stoichiometries in rRNA-depleted total RNA from FGR and WT NSCs. Statistical significance was determined by one-sided Wilcoxon rank-sum tests. Boxes represent the 25th–75th percentiles (line at the median), with whiskers extending to 1.5 × IQR. c Metagene plots of Ψ sites (stoichiometry ≥ 10%) in WT and FGR NSC mRNA identified by Uli-epic BID-seq. d Pie charts depicting the genomic features of Ψ sites with stoichiometry ≥ 10% in WT (left panel) and FGR (right panel) NSCs. e Number of overlapping Ψ sites between two biological replicates in rRNA-depleted total RNA from FGR and WT sperm. Ψ sites are categorized into three groups based on stoichiometry: > 50%, 20–50%, and 10–20%. f Curve graph (left panel) and box plot (right panel) showing the Ψ stoichiometries in rRNA-depleted total RNA from FGR and WT sperm. Statistical significance was determined by one-sided Wilcoxon rank-sum tests. Boxes represent the 25th–75th percentiles (line at the median), with whiskers extending to 1.5 × IQR. g Metagene plots of Ψ sites (stoichiometry ≥ 10%) in WT and FGR sperm mRNA identified by Uli-epic BID-seq. h Pie charts depicting the genomic features of Ψ sites with stoichiometry ≥ 10% in WT (left panel) and FGR (right panel) sperm

Unlike normal cells, sperm contain a distinct suite of RNA populations [47], including thousands of mRNAs—albeit fewer than in somatic cells—and numerous noncoding RNAs (ncRNAs) [48]. Ψ guides germline small RNA transport and epigenetic inheritance in sperm [49]. Consequently, profiling Ψ in mRNAs and ncRNAs in WT and FGR sperm could provide valuable insights into their potential roles in the epigenetic inheritance of FGR. To investigate the characteristics of Ψ in FGR sperm, we applied Uli-epic BID-seq to profile Ψ in approximately 1 ng of rRNA-depleted total RNA samples from the sperm of WT and FGR mice. The spike-in control RNAs exhibited robust deletion at Ψ sites (Additional file 1: Fig. S4b), validating the performance of Uli-epic BID-seq. Using a cut-off of ≥ 10% Ψ stoichiometry, we identified 587 and 399 Ψ sites in the sperm of WT and FGR mice, respectively, based on overlapping sites between two biological replicates (Fig. 3e; Additional file 2: Tables S15-S18). Ψ sites with stoichiometries exceeding 50% predominated in both WT and FGR sperm (Figs. 3e-f). The Ψ stoichiometries detected by Uli-epic BID-seq exhibited a 'M-shaped’ distribution pattern, with stoichiometries of 10–20% and 70–90% being the most prevalent (Fig. 3f, left panel). Interestingly, the Ψ stoichiometries in FGR sperm were slightly lower than those in WT sperm (Fig. 3f, right panel). Metagene profiles revealed that Ψ sites in the mRNA of FGR sperm exhibited a slightly reduced Ψ level in the 3′ UTR and a higher accumulation in the 3′ CDS region compared to WT (Fig. 3g). Ψ sites in rRNA-depleted total RNA from sperm were enriched in introns and ncRNAs, characteristic of gene expression in sperm (Fig. 3h).

Fig. 3 — Dynamic Ψ induced by FGR conditions. a Volcano plot displaying differentially modified Ψ sites in rRNA-depleted total RNA from FGR versus WT NSCs. Red and blue dots represent FGR-upregulated and downregulated Ψ sites, respectively. The cutoff criteria are a p-value < 0.05 and an absolute Ψ stoichiometry change of ± 20%. b Metagene profiles showing the distributions of upregulated and downregulated Ψ sites in mRNA from FGR NSCs compared to WT NSCs. c Top 5 Gene Ontology (GO) enrichment analysis of biological processes (BP) for genes with upregulated (left panel) and downregulated (right panel) Ψ stoichiometry change in FGR NSCs compared to WT NSCs. P-values were adjusted using the Benjamini–Hochberg procedure. d IGV tracks illustrating a dynamic Ψ site detected by Uli-epic BID-seq in tRNA-Arg-CCT-4–1 from FGR and WT NSCs (top panel), along with the canonical structure of tRNA-Arg-CCT-4-1hilighting the Ψ27/Ψ28 site (bottom panel). e Volcano plot displaying differentially modified Ψ sites in rRNA-depleted total RNA of FGR versus WT sperm. Red and blue dots represent FGR-upregulated and downregulated Ψ sites, respectively. The cutoff criteria are a p-value < 0.05 and an absolute Ψ stoichiometry change of ± 20%. f IGV tracks illustrating a dynamic Ψ site detected by Uli-epic BID-seq in tRNA-Arg-CCT-4–1 from FGR and WT sperm (top panel). The structure of tRNA-Arg- CCT-4–1 with the dynamic Ψ54/Ψ55 site highlighted (bottom panel)

Linking pseudouridine modification to FGR

We further analyzed Ψ sites with differential stoichiometries in FGR NSCs compared to WT NSCs. Our analysis identified 232 upregulated and 101 downregulated Ψ sites in the FGR NSC transcriptome, using an absolute Ψ stoichiometry cutoff of ± 20% and a p-value of < 0.05 (Fig. 3a; Additional file 2: Table S13). The differential Ψ sites were predominantly enriched in CDS, ncRNA, and introns (Additional file 1: Fig. S5a). Notably, upregulated Ψ sites in FGR NSCs were enriched in the CDS near the 5′ UTR and 3′ UTR, whereas downregulated Ψ sites were enriched in both the CDS and 3′ UTR (Fig. 3b). A higher proportion of differential Ψ sites exhibited upregulation in FGR NSCs (Additional file 1: Figs. S5b).

Gene Ontology (GO) enrichment analysis indicated that transcripts with upregulated Ψ sites in FGR NSCs are involved in axon genesis and neuron projection guidance, while those with downregulated Ψ sites are associated with chromatin organization, spindle organization, and axon extension (Fig. 3c). One notable mRNA, Robo2, which plays a role in neuron development, showed an upregulated Ψ stoichiometry in FGR NSCs (Additional file 1: Fig. S5c). Ψ is one of the most fundamental and prevalent modifications in human tRNA which could control the efficiency of protein synthesis [50]. We found four Ψ sites with differential stoichiometries in tRNA of FGR NSCs compared to WT NSCs (Additional file 1: Fig. S5d). Specifically, the stoichiometries of Ψ27/Ψ28 in tRNA-Arg-CCT-4–1 and Ψ54/Ψ55 in tRNA-Arg-ACG-3–1 were significantly upregulated in FGR NSCs compared to WT NSCs (Figs. 3d and Additional file 1: S5e).

The Uli-epic BID-seq input group also serves as a Uli-epic RNA-seq. Our analysis of data from this group revealed 1,214 differentially expressed genes (DEGs) in FGR NSCs compared to WT NSCs (Additional file 1: Fig. S5f; Additional file 2: Table S14). Among these DEGs, 523 transcripts exhibited decreased expression, while 691 transcripts showed increased expression in FGR NSCs relative to WT NSCs (Additional file 1: Fig. S5f). Gene Ontology (GO) enrichment analysis of the upregulated genes revealed a significant enrichment of biological pathways related to the regulation of angiogenesis and vasculature development (Additional file 1: Fig. S5g). Given that NSCs play a neuroprotective role in treating various neurological diseases by secreting multiple factors that promote angiogenesis [51], it will be intriguing to explore the specific role of Ψ in these processes. The differential Ψ sites in FGR NSC transcripts identified by Uli-epic BID-seq may play a critical role in cognitive disorders associated with FGR, warranting further investigation.

Using a cutoff of ± 20% absolute Ψ stoichiometry change and a p-value of < 0.05, we identified 9 upregulated and 17 downregulated Ψ sites in FGR sperm RNA (Fig. 3e; Additional file 2: Table S19). We only found 2 sites with differential stoichiometries in tRNA of FGR sperm compared to WT sperm (Additional file 1: Fig. S6a). Notably, the Ψ stoichiometry at site 54/55 in a representative tRNA-Leu-TAG-3–1, was significantly reduced in FGR sperm compared to WT sperm (Additional file 1: Fig. S6b). Similarly, the Ψ stoichiometry at site 54/55 in tRNA-Arg-CCT-4–1 was significantly reduced in FGR sperm compared to WT sperm (Fig. 3f). It would be intriguing to explore whether these differential Ψ modifications in tRNA from FGR mice influence tRNA stability or translation efficiency. We also identified 693 upregulated and 689 downregulated differentially expressed genes (DEGs) in FGR compared to WT sperm (fold change > 1.5 or < 0.667, false discovery rate < 0.05) (Additional file 1: Fig. S6c; Additional file 2: Table S20). The upregulated genes are significantly enriched in pathways associated with DNA replication and MCM complex (Additional file 1: Fig. S6d). The MCM complex, serving as the core component of the replicative helicase, is pivotal for ensuring precise genomic DNA replication and preventing DNA damage [52, 53]. The observed upregulation of the MCM complex in FGR sperm may represent an adaptive mechanism to maintain genomic stability and ensure fertilization competence.

Integrating Uli-epic with GLORI

GLORI enables the absolute quantification of m⁶A in the transcriptome [20, 21]. In the GLORI strategy, glyoxal and nitrite are employed to efficiently deaminate adenosine (A), converting it to inosines, which is then read as guanine (G) during reverse transcription, leaving m⁶A unaffected [20]. However, the harsh chemical treatment in GLORI leads to RNA fragmentation and requires at least 200 ng of mRNA starting materials, limiting its biological application. To address this limitation, we integrated our Uli-epic strategy with GLORI, creating Uli-epic GLORI. This approach allows for the quantification of m⁶A at single-nucleotide resolution using as little as 10 ng of RNA. In the Uli-epic GLORI strategy (Fig. 4a), low-input RNA (10 ng) undergoes GLORI treatment without any fragmentation step, followed by 3’ end repair and polyadenylation. Subsequently, reverse transcription is performed with T7-P7 oligo-dT and template switching with P5 TSO (Additional file 2: Table S1). During this step, the reverse transcriptase recognizes inosine as G and m⁶A as A. The template switch, linear amplification, and library construction steps are outlined above. It is important to note that during the revision of our work, CAM-seq [54] and GLORI 2.0/3.0 [55] were reported as methods to identify RNA m⁶A sites with limited input materials. We believe that integrating these techniques with Uli-epic could further reduce the required input.

Fig. 4 — Validation of Uli-epic GLORI. a Schematic representation of the Uli-epic GLORI procedures. b Venn diagram illustrating the overlap of m⁶A sites in HEK 293 T mRNA at single-nucleotide resolution between two biological replicates identified by GLORI (200 ng input mRNA) and Uli-epic GLORI (10 ng input mRNA). c Line graph displaying the m⁶A stoichiometries in HEK 293 T mRNA identified by GLORI and Uli-epic GLORI. d Metagene profiles showing the genomic features of m⁶A sites across HEK 293 T mRNA transcriptome identified by GLORI and Uli-epic GLORI. Each segment is normalized to its average length according to RefSeq annotations. e Consensus motif analysis of m⁶A sites identified by GLORI (left panel) and Uli-epic GLORI (right panel). f Proportion of m⁶A sites within different motifs identified by GLORI (left panel) and Uli-epic GLORI (right panel). g m⁶A stoichiometries within different motifs for GLORI (left panel) and Uli-epic GLORI (right panel). Boxes represent the 25th–75th percentiles (line at the median), with whiskers extending to 1.5 × IQR. h Frequency distribution of transcripts containing multiple m⁶A sites identified by GLORI (left panel) and Uli-epic GLORI (right panel). i IGV tracks illustrating the stoichiometries of representative m⁶A sites within *DEGS1* mRNA detected by GLORI (upper panel) and Uli-epic GLORI (bottom panel)

We profiled m⁶A in HEK 293 T cells using GLORI with 200 ng of mRNA and Uli-epic GLORI with 10 ng of mRNA. The spike-in control m⁶A RNAs for both strategies demonstrated efficient A-to-G conversion while leaving m⁶A sites unaffected (Additional file 1: Fig. S7a), thereby validating the accuracy of Uli-epic GLORI. At the same time, Uli-epic GLORI detect m⁶A 1832, m^6,6A 1850 and m^6,6A 1851 in 18S of HEK 293 T mRNA. Uli-epic GLORI also detect m⁶A 4220 in 28S of HEK 293 T mRNA. The well-characterized m⁶A sites of rRNA detected by Uli-epic GLORI further validate sensitivity and specificity of the Uli-epic (Additional file 1: Fig. S7b).

Both GLORI and Uli-epic GLORI exhibited an approximate A-to-G conversion rate of 98% (Additional file 1: Fig. S7c). Using GLORI, we identified 132,989 and 144,171 m⁶A sites in two biological replicates, with 102,365 overlapping m⁶A sites (~ 71%) (Fig. 4b). In parallel, Uli-epic GLORI detected 110,176 and 109,169 m⁶A sites between two biological replicates, with 74,182 overlapping m⁶A sites (~ 68%), indicating the reproducibility of Uli-epic GLORI (Fig. 4b; Additional file 2: Tables S21-S22). Correlation analysis showed consistent m⁶A stoichiometries between two biological replicates for both GLORI (R = 0.96) and Uli-epic GLORI (R = 0.95) (Additional file 1: Fig. S7d). RNA transcripts harboring m⁶A sites identified by Uli-epic GLORI show a strong overlap with those identified by GLORI (Additional file 1: Fig. S7e).

The majority of m⁶A sites exhibit stoichiometries ranging between 10 and 20% (Fig. 4c). The metagene profiles show similar enrichment of m⁶A sites in the 3’ UTR near the stop codon for both strategies (Fig. 4d). However, the methylation stoichiometries and distribution patterns differ slightly between GLORI and Uli-epic GLORI (Fig. 4c, Additional file 1: Fig. S7f). m⁶A sites in less abundant transcripts and those with low stoichiometries are often excluded by Uli-epic GLORI (Additional file 1: Fig. S7g), which is consistent with the observed differences between GLORI and GLORI 2.0/3.0 [55]. These differences may account for the distinct patterns observed. The m⁶A sites detected by both strategies display the canonical DRACH motif (D = G/A/T, R = A/G, H = A/C/T; Fig. 4e). The top fourteen consensus motifs for m⁶A sites detected by GLORI and Uli-epic GLORI are similar (Fig. 4f), with GGACT being the most common consensus motif identified by both strategies (Fig. 4g). Most transcripts harbor a single m⁶A site across the entire transcript, though some transcripts contain multiple m⁶A sites, consistent between GLORI and Uli-epic GLORI (Fig. 4h). IGV tracks for two representative transcripts, DEGS1 and SPRTN, show comparable m⁶A stoichiometries detected by both GLORI and Uli-epic GLORI (Figs. 4i and Additional file 1: S7h). These findings demonstrate that the accuracy of the Uli-epic GLORI strategy is on par with that of GLORI, while significantly reducing the required RNA input from 200 to 10 ng.

m⁶A landscapes in FGR mouse NSC and sperm

m⁶A modification has emerged as a core epigenetic mechanism regulating cognitive function by dynamically controlling NSC proliferation and differentiation, as well as synaptic plasticity [56–58]. m⁶A is also reported to be crucial in regulating gene expression and protein translation during spermatogenesis [59, 60]. Knockout mice lacking m⁶A writer, eraser, or reader genes exhibit disrupted spermatogenesis and male infertility [60–62]. To validate the performance of Uli-epic GLORI in RNA extracted from tissue samples and to elucidate the potential role of m⁶A in FGR-related cognitive disorders, we applied Uli-epic GLORI to profile m⁶A in rRNA-depleted total RNA from NSCs and sperm of WT and FGR mice. Spike-in RNA controls demonstrated efficient A-to-G conversion while maintaining unaffected m⁶A levels for Uli-epic GLORI strategy (Additional file 1: Fig. S8).

The m⁶A stoichiometries in FGR NSCs were higher than those in WT NSCs (Fig. 5a; Additional file 2: Tables S23-S26). As expected, we observed enrichment of m⁶A sites in the CDS and 3′-UTR (Figs. 5b-c). Metagene profiles revealed that m⁶A sites in FGR NSCs were more enriched in the 3′ UTR and reduced in the CDS compared to WT (Fig. 5b-c). Furthermore, m⁶A stoichiometries were higher across all genomic regions in FGR NSCs compared to WT (Fig. 5d).

To further investigate the role of m⁶A in FGR related sperm abnormalities, we utilized Uli-epic GLORI to profile m⁶A in the sperm RNA of WT and FGR mice. Using 5–10 ng of ribosomal RNA-depleted total sperm RNA, we conducted Uli-epic GLORI treatment. Spike-in control RNAs demonstrated efficient A-to-G conversion while maintaining unaffected m⁶A levels for Uli-epic GLORI strategy (Additional file 1: Fig. S8b). The m⁶A stoichiometries in FGR sperm RNA are lower than those in WT (Fig. 5e; Additional file 2: Tables S28-31). In FGR sperm RNA, m⁶A sites are less enriched in the 3’ UTR near the stop codon and more enriched in introns compared to WT (Figs. 5f-g). Most genomic regions of sperm RNA, including the 5’ UTR, 3' UTR, CDS, and ncRNA, display considerable m⁶A distribution while m⁶A stoichiometries in FGR sperm was higher compared to WT (Figs. 5e, h).

Dynamic m⁶A sites linked to FGR

Using a cutoff of ± 15% m⁶A stoichiometry change and a p-value < 0.05, we analyzed differential m⁶A sites in WT and FGR NSCs (Fig. 6a; Additional file 2: Table S27). In the FGR group, 5,734 sites exhibited upregulated m⁶A stoichiometries, while 1,848 sites displayed downregulated m⁶A stoichiometries (Fig. 6a). The heatmap clearly indicated that m⁶A stoichiometries are upregulated at more sites in FGR NSCs compared to WT NSCs (Additional file 1: Fig. S9a). The differential m⁶A sites in FGR NSCs were predominantly enriched in the coding sequence (CDS) and the 3’ UTR, near the stop codon (Figs. 6b-c). There was substantial redistribution of m⁶A sites among different RNA regions in FGR NSCs compared to WT (Fig. 6d). We identified transcripts encoding TFs critical for NSC regulation [63, 64] exhibited m⁶A stoichiometry changes along with expression level changes in the FGR NSCs (Fig. 6e). IGV tracks of an m⁶A site within Prox1 mRNA—a critical intrinsic regulator of proliferation versus differentiation decisions in NSCs [65, 66]—revealed significantly upregulated stoichiometry in FGR NSCs (Additional file 1: Fig. S9b).

We found that transcripts exhibiting upregulated m⁶A stoichiometry in FGR NSCs compared to WT NSCs are involved in pathways related to mRNA processing, synapse organization, RNA splicing, and axonogenesis (Additional file 1: Fig. S9c). Furthermore, transcripts displaying downregulated m⁶A stoichiometry are involved in pathways related to chromation organization, regulation of neurogenesis, and histon modification (Additional file 1: Fig. S9d). These findings suggest that differential m⁶A sites may play crucial roles in FGR-related cognitive disorders, warranting further investigation.

Using a cutoff of ± 15% m⁶A stoichiometry change and a p-value < 0.05, we analyzed differential m⁶A sites in WT and FGR sperm (Fig. 6f; Additional file 2: Table S32). In the FGR group, 16,311 sites exhibited upregulated m⁶A stoichiometries, while 1,363 sites displayed downregulated m⁶A stoichiometries (Fig. 6f). The heatmap clearly indicated that m⁶A stoichiometries of more transcripts were upregulated in FGR sperm compared to WT sperm (Additional file 1: Fig. S10a). The upregulated m⁶A sites were predominantly located in introns, CDS, and 3’ UTR, whereas the downregulated m⁶A sites were enriched in the CDS and 3’ UTR near the stop codon (Figs. 6g, h). There is substantial redistribution of m⁶A sites among different RNA regions in FGR sperm compared to WT sperm (Fig. 6i). IGV tracks of an m⁶A site within a representative Setx mRNA, which is involved in spermatogenesis, showed significantly upregulated stoichiometry in FGR sperm (Additional file 1: Fig. S10b).

Our results revealed that transcripts with upregulated m⁶A stoichiometry in FGR sperm, compared to WT sperm, are involved in pathways associated with chromatin organization, mRNA processing, and autophagy (Additional file 1: Fig. S10c). Conversely, transcripts with downregulated m⁶A stoichiometry are involved in pathways associated with chromatin organization, mRNA processing, and dendrite development (Additional file 1: Fig. S10d). Our findings suggest that these differential m⁶A sites may play important biological roles in FGR-related sperm abnormalities.

We also analyzed the shared differential m⁶A sites in both FGR NSCs and sperm, compared to WT NSCs and sperm, identifying a total of 41 shared differential m⁶A sites (Additional file 2: Table S33). Among these, Crcp exhibited upregulated m⁶A stoichiometris in both NSCs and sperm of FGR mice (Fig. 6j). We further analyzed the transcripts showing shared differential m⁶A stoichiometry in both NSCs and sperm. GO enrichment analysis revealed that these transcripts are involved in pathways related to DNA binding, regulation of transcription by RNA polymerase II, DNA-templated transcription regulation, and postsynaptic processes (Fig. 6k).

Discussion

In this study, we developed a novel library-construction strategy, Uli-epic, which, when coupled with chemical-based RNA modification detection technologies, enables the detection of epitranscriptomic modifications using ultra-limited RNA materials (Figs. 1a, 4a). Uli-epic BID-seq, which integrates Uli-epic with BID-seq, demonstrates comparable accuracy to BID-seq in quantifying Ψ at single-nucleotide resolution while reducing the detection limit from 10 ng of mRNA to 100 pg (Figs. 1c-g). Similarly, Uli-epic GLORI, which combines Uli-epic with GLORI, shows comparable accuracy to GLORI in quantifying m⁶A at single-nucleotide resolution while reducing the detection limit from 200 ng of mRNA to 10 ng (Fig. 4). Using Uli-epic BID-seq and Uli-epic GLORI, we successfully profiled Ψ and m⁶A sites in the HEK 293 T cell line, NSCs, and sperm of WT and FGR mice with low-input RNA.

Although several library construction strategies are suitable for ultra-limited RNA samples, they are not well-suited for the detection of RNA modifications. In Uli-epic, the RNA nick followed by primer extension in VASA-seq [67] is replaced with a template-switch strategy, thereby omitting the cDNA truncation and the subsequent inefficient adapter ligation steps used in VASA-seq.The key advantage of Uli-epic over CATS [68] is the incorporation of a linear amplification step, which helps to reduce bias introduced by exponential PCR amplification. This approach enables more sensitive detection of RNA modifications in less abundant transcripts. Linear amplification has been shown to significantly improve sensitivity, accuracy, and library complexity in various applications, such as detecting mutations, copy number variations, and DNA methylation in ultra-limited DNA samples [69–71]. Importantly, we are the first to integrate linear amplification with RNA modification detection. In contrast, LAST-seq [72] amplifies poly-A-containing transcripts using ssRNA as a template with T7 RNA polymerase, a method that often misses RNA modification information. Similarly, the SMARTer single-cell total RNA sequencing method [73] uses random primers to capture full-length transcript fragments, leading to the omission of key RNA modification information following chemical treatment. In contrast, the Uli-epic strategy, where reverse transcription is initiated from the poly(A) tail at the 3’ end of the fragmented transcript, allows for more accurate and comprehensive capture of RNA modification information.

Our study identified a set of mRNAs and tRNAs exhibiting differential Ψ stoichiometries in FGR NSCs and sperm compared to WT. As Ψ modification in mRNA has been shown to influence tRNA selection by the ribosome and increase amino acid substitution levels in human cells [74], it would be intriguing to further investigate translation efficiency and amino acid sequence variability in NSCs and sperm of FGR mice. Specifically, the stoichiometries of Ψ27/Ψ28 in tRNA-Arg-CCT-4–1 and Ψ54/Ψ55 in tRNA-Arg-ACG-3–1 are upregulated in FGR NSCs, while the stoichiometries at Ψ54/Ψ55 in tRNA-Leu-TAG-3–1 and tRNA-Arg-CCT-4–1 are significantly reduced in FGR sperm compared to WT sperm. Intriguingly, Ψ54/Ψ55 in tRNA-Arg-CCT-4–1 is upregulated in FGR NSCs but downregulated in FGR sperm. Human PUS10 has been reported to function as a pseudouridylate synthase, catalyzing the synthesis of Ψ54 and Ψ55 in the TΨC arm of tRNAs [75–77]. Additionally, mouse pseudouridine synthase 1 (Pus1p) has been shown to catalyze Ψ formation at positions 27, 28, 34, and 36 in in vitro-produced tRNAs [78]. Consequently, investigating the expression differences and functional roles of PUS10 and Pus1p in NSCs and germ cells of FGR versus WT mice holds significant value. Furthermore, the mechanisms regulating the molecular interactions between the TΨC arm of tRNA and the ribosome, influenced by variable Ψ54/Ψ55 stoichiometry [79], as well as the impact of increased Ψ27/Ψ28 stoichiometry on coverclip folding, warrant thorough investigation [80]. In contrast to the relatively limited research on Ψ modifications, extensive research has demonstrated that m⁶A modification plays crucial roles in both the reproductive and nervous systems [56–58], and serves as a key regulator in transgenerational inheritance [81–83]. We identified 41 shared differential m⁶A sites in both FGR NSCs and sperm, compared to WT NSCs and sperm. Functional enrichment analysis revealed that the corresponding genes are significantly associated with transcriptional regulation and synaptic function, suggesting their potential involvement in FGR-associated cognitive impairment and intergenerational transmission mechanisms. Collectively, these findings suggest that targeted intervention against key molecules—such as pseudouridine synthases (e.g., PUS10, Pus1p) and genes/pathways associated with the common m⁶A modifications in NSCs and sperm—may offer potential therapeutic strategies for mitigating cognitive deficits and interrupting the intergenerational transmission in FGR offspring.

We acknowledge that the libraries generated by Uli-epic exhibit a slight 3’-end bias (Additional file 1: Fig. S2d), likely due to the preferential capture of the 3’ end of poly (A) RNA fragments containing inherent A tails. Additionally, the RNA fragments used for modification profiling lack a 5’ cap, reducing the template switch efficiency of reverse transcriptase in Uli-epic. Nevertheless, Uli-epic BID-seq and Uli-epic GLORI demonstrate comparable accuracy to BID-seq and GLORI, respectively, while significantly lowering the detection limit to ultra-low input RNA, proving to be practical library construction strategies for RNA modification profiling. The detection limit of Uli-epic depends on the severity of the chemical treatment. For milder chemical treatments such as BID-seq, Uli-epic BID-seq requires as little as 100 pg of RNA, whereas Uli-epic GLORI, which involves a harsher A-to-G conversion step, requires 10 ng of input RNA. Notably, two techniques, CAM-seq [54] and GLORI 2.0/3.0 [55], optimize the chemical treatment step to achieve mild deamination of adenosine which significantly reduce the amount of RNA input. In the future, we plan to combine these approaches with the Uli-epic library construction strategy to enable the use of rarer RNA input, thereby enhancing data quality.

In our previous work, we developed LAMP-MS, a method for visually quantifying RNA m⁶A modifications via mass array analysis [71]. By integrating LAMP-MS with the Uli-epic strategy introduced in this study, we can achieve visual quantification of specific m⁶A and Ψ sites from ultra-low input RNA samples. This combination significantly broadens the applicability of liquid biopsies by enabling epi-transcriptomic profiling of cfRNA, thereby expanding the utility of RNA modifications in clinical diagnostics.

Conclusions

In summary, our work introduces a valuable library construction strategy for ultra-limited RNA epitranscriptome profiling, enriching the toolkit of epigeneticists. The quantitative and single-nucleotide resolution maps of Ψ and m⁶A we generated serve as a comprehensive resource, paving the way for investigating the biological functions of these modifications in fetal growth restriction.

Methods

Cell culture

HEK 293 T cells were obtained from the Cell Bank/Stem Cell Bank of the Chinese Academy of Sciences. All cell lines were screened for mycoplasma contamination using the MycAway™ Plus-Color One-Step Mycoplasma Detection Kit (Yeasen), and only mycoplasma-free cells were utilized for subsequent experiments. The cells were cultured in high glucose DMEM (GIBCO), supplemented with 10% heat-inactivated fetal bovine serum (FBS), 100 U/mL penicillin, and 100 μg/mL streptomycin. Cultures were maintained at 37 °C in a humidified atmosphere with 5% CO₂.

Experimental animals

C57BL/6 J mice utilized in this study were procured from SLAC Laboratory Animal Company, Shanghai, China. Nestin-GFP mice, on a C57BL/6 background, were kindly provided by the Cai-Wen Duan Lab at the Shanghai Jiao Tong University School of Medicine. Genomic DNA was extracted from the transgenic mice and analyzed via PCR using the following primers: Forward: 5'-TGT ACA AGT AAA GCG GCC AG-3'; Reverse: 5'-AAG AAG CCG AGG ACA GTC AG-3', yielding a 377 bp product. All animals were housed in a pathogen-free environment with controlled temperature, humidity, and a 12-h light/dark cycle. Virgin C57BL/6 J female mice were mated with males, and pregnancy was confirmed by the detection of a vaginal plug the following morning, designated as 0.5 days post coitum (dpc).

In the fetal growth restriction (FGR) model, pregnant mice at 0.5 dpc were individually housed and fed either a standard normal chow diet (NC; 20% protein) or a protein-restricted diet (PR; 8% protein) until parturition. Newborn pups remained with their mothers until weaning. All animal care and experimental procedures were conducted in accordance with the guidelines approved by the Institutional Animal Care and Use Committee of Tongji University.

Sperm sample collection

Sperm were harvested from the cauda epididymis of 10–12-week-old mice and released into 2 mL of PBS, maintained at 37 °C for 20 min. Post-incubation, the suspension was filtered through a 40 µm cell strainer to remove tissue debris. The sperm were then treated with 10 mL of somatic cell lysis buffer (0.1% SDS, 0.5% Triton X-100) on ice for 40 min to eliminate any somatic cell contamination. The purity of the sperm sample was verified through microscopic examination. The sperm pellet underwent two washes with 5 mL of PBS, followed by centrifugation at 1000 g for 5 min. The final sperm pellet was resuspended in TRIzol reagent (Invitrogen), homogenized, and processed for RNA extraction. The experiment included both WT and FGR groups, with each group comprising two biological replicates. Each replicate sperm sample was pooled from three adult mice.

Purification of dentate neural progenitors

Hippocampi were isolated from embryonic day 17.5 Nestin-GFP mice and dissociated using the Neural Tissue Dissociation Kit (Miltenyi Biotec, 130–092–628). Following enzymatic digestion, the solution was diluted with 9 mL of Hibernate™-A medium (Invitrogen, A1247501) and centrifuged at 250 × g for 8 min. The resulting cell pellets were resuspended in 0.5 mL of 1 × red blood cell lysis buffer (Biogems, 64,010–00–100) and incubated for 1 min. Subsequently, 5 mL of Hibernate™-A medium was added, and the suspension was centrifuged again at 250 × g for 6 min. The cells were then resuspended in FACS sorting medium (0.5% BSA in Hibernate™-A), incubated with DAPI (Solarbio, C0060) at a 1:1,000 dilution for 5 min to label dead cells, and passed through a cell strainer. Nestin-GFP positive cells were sorted using a BD FACS Aria II cell sorter. Total RNA was extracted from the sorted cells using TRIzol reagent (Invitrogen) combined with MaXtract™ High Density (Qiagen). The experiment included WT and FGR groups, each consisting of two biological replicates. Each Nestin-GFP-positive cell sample was pooled from hippocampal tissue derived from three fetal mice.

RNA isolation

Total RNA was extracted from HEK 293 T cells using TRIzol reagent (Invitrogen, 15,596,018) following the manufacturer’s protocol. mRNA was subsequently purified using VAHTS mRNA capture beads (Vazyme, N401). Ribosomal RNA was depleted with the Ribo-off rRNA Depletion Kit (Vazyme, N406).

Conventional RNA-seq

For conventional RNA-seq library construction, 20 ng of mRNA was processed as follows. Briefly, RNA was fragmented using 0.1 M NaHCO₃ (pH 9.2) and incubated at 95 °C for 5 min, followed by neutralization with 3 M NaOAc (pH 5.2). The RNA was 3′ end-repaired using PNK (NEB, M0201S) to generate free 3′-OH termini. The RNA was then purified using silane beads and ligated to a 3′ adapter (sequence:/5rApp/AGATCGGAAGAGCGTCGTG/3Bio/) with T4 RNA ligase 2, truncated KQ (NEB, M0373S). Excess adapter was removed by treatment with 5′ deadenylase (NEB, M0331S) and RecJf (NEB, M0247S). Following another round of purification with silane beads, the RNA was reverse transcribed using SuperScript™ III (Thermo Fisher Scientific, 18,080,051) and an RT primer (sequence: 5′-ACACGACGCTCTTCCGATCT-3′).

The resulting cDNA was purified with silane beads and ligated to a cDNA adapter (sequence:/5Phos/NNNNNNAGATCGGAAGAGCACACGTCTG/3SpC3/) using T4 RNA Ligase 1 (ssRNA Ligase), high concentration (NEB, M0437M). Libraries were constructed with single index primers (NEB, E7335S). All libraries were purified using a 3.5% low melting point agarose gel and sequenced on the MGI T7 sequencing platform using paired-end 100 bp mode at GenePlus, Shenzhen, China, achieving a sequencing depth of approximately 10 G per library.

It should be noted that the conventional RNA-seq protocol is identical to the BID-seq input protocol. In this study, data from HEK 293 T BID-seq input were used as conventional RNA-seq data.

Uli-epic RNA-seq.

For Uli-epic RNA-seq library preparation, 100 pg of mRNA was fragmented using 0.1 M NaHCO₃ (pH 9.2) and incubated at 95 °C for 5 min, followed by neutralization with 3 M NaOAc (pH 5.2). The fragmented RNA was 3′ end-repaired using T4 polynucleotide kinase (PNK) (NEB, M0201), and a poly(A) tail was added to the 3′ end using E. coli Poly(A) Polymerase (NEB, M0276). The reaction was terminated with an EDTA solution, and 10 ng of carrier RNA (sequence: 5′-GGGUUUCCCCCUUUCGCCUUGGCUGGCGUUUUCCUUUCCCCCUUUCGCCUUGGCUGGCGUUUUCC-3′) was added, followed by purification using the Oligo Clean & Concentrator kit (Zymo Research, D4061).

Reverse transcription and template switching were performed using SuperScript™ IV Template Switching RT Master Mix (Thermo Fisher, A65423), a T7-P7 oligo-dT primer (sequence: 5′-GCCGCGAAATTAATACGACTCACTATAGGGATAATGAGCAGACGTGTGCTCTTCCGATCTTTTTTTTTTTTTTTTTTTTTVN-3′), and a template switch oligo (TSO: 5′-Biotin-GCTAATCATTGCACACGACGCTCTTCCGATCTrGrGrG-3′) at 50 °C for 60 min. The RNA template was then degraded using E. coli RNase H (NEB, M0297L), followed by second strand cDNA synthesis using Q5 Hot Start High-Fidelity Master Mix (NEB, M0494). Excess T7 primer was removed with exonuclease I (NEB, M0293S), and the double-stranded cDNA was purified using the Oligo Clean & Concentrator kit (Zymo Research, D4061).

In vitro transcription was performed on the double-stranded cDNA using the T7 High Yield RNA Transcription Kit (Vazyme). Post-transcription, DNase I was added to degrade the double-stranded cDNA, and the RNA was purified using 1.8 × SPRIselect beads (Beckman, B23318). The RNA was then reverse transcribed using an RT primer (sequence: 5′-ACACGACGCTCTTCCGATCT-3′) and SuperScript™ III (Thermo Fisher Scientific, 18,080,051). The resulting cDNA was purified with 1.8 × SPRIselect beads (Beckman, B23318).

Libraries were constructed using single index primers (NEB, E7335S). All libraries were purified using a 3.5% low melting point agarose gel and sequenced on the MGI T7 sequencing platform using paired-end 100 bp mode at GenePlus, Shenzhen, China, achieving a sequencing depth of approximately 30 G per library.

It should be noted that the Uli-epic RNA-seq protocol is identical to the Uli-epic BID-seq input protocol. In this study, data from HEK 293 T Uli-epic BID-seq input (1 ng and 100 pg of input RNA) were used as Uli-epic RNA-seq data.

GLORI

For GLORI treatment, 300 ng of mRNA from HEK 293 T cells were processed following the protocol described previously [20, 84]. The resulting dataset has been deposited in the Gene Expression Omnibus (GEO) under accession number GSE242760 in previous study [84].

Uli-epic GLORI.

The Uli-epic GLORI procedures integrate Uli-epic and the previously reported GLORI [20] technique. Briefly, 10 ng of mRNA from HEK 293 T cells, mouse sperm ribosomal RNA-depleted total RNA, or mouse NSC ribosomal RNA-depleted total RNA was added to a 40 µL reaction buffer containing 6 µL of glyoxal solution (Sigma-Aldrich, 50,649), 20 µL of DMSO, and nuclease-free water. The mixture was incubated at 50 °C for 30 min. Subsequently, 10 µL of saturated H₃BO₃ (Sigma-Aldrich, B0394) solution was added, and the incubation continued at 50 °C for an additional 30 min.

The 50 µL reaction mixture was added to a premixed deamination buffer and gently mixed. The tubes were incubated at 16 °C for 8 h. The RNA was purified using the Oligo Clean & Concentrator kit (Zymo Research, D4061) and then dissolved in 50 µL of deprotection buffer, followed by incubation at 95 °C for 10 min. After purification using the Oligo Clean & Concentrator kit, the RNA was end-repaired using T4 PNK (NEB, M0201S) to expose the 3′ OH. Poly(A) tail was added using E. coli Poly(A) Polymerase (NEB, M0276L), followed by purification with the Oligo Clean & Concentrator kit.

Reverse transcription and template switch were performed with a template switching RT enzyme mix (NEB, M0466L), a T7-P7 oligo-dT primer (sequence: 5′-GCCGCGAAATTAATACGACTCACTATAGGGATAATGAGCAGACGTGTGCTCTTCCGATCTTTTTTTTTTTTTTTTTTTTTVN-3′), and a P5 Template switch oligo (P5-TSO: 5′-Biotin-GCTAATCATTGCACACGACGCTCTTCCGATCTrGrGrG-3′) at 42 °C for 90 min. The RNA template was degraded using E. coli RNase H (NEB, M0297L), and the second strand cDNA was synthesized using Q5 Hot Start High Fidelity Master Mix (NEB, M0494). Excess T7 primer was digested with exonuclease I (NEB, M0293S), and the double-stranded cDNA was purified using the Oligo Clean & Concentrator kit.

Linear amplification was performed with the double-stranded cDNA using the T7 High Yield RNA Transcription Kit (Vazyme). Following transcription, DNase I was added to digest the double-stranded cDNA, and the amplified RNA was purified using 1.8 × SPRIselect beads (Beckman, B23318). Reverse transcription of the RNA was then carried out using an RT primer (sequence: 5′-ACACGACGCTCTTCCGATCT-3′) and SuperScript™ III (Thermo Fisher Scientific, 18,080,051). The resulting cDNA was purified with 1.8 × SPRIselect beads, and libraries were constructed using single index primers (NEB, E7335S).

Sequencing was performed on the MGI T7 sequencing platform using paired-end 100 bp mode at GenePlus, Shenzhen, China, with a sequencing depth of approximately 160 G per library.

BID-seq

A total of 20 ng of HEK 293 T mRNA was subjected to BID-seq treatment according to the protocol described in previous literature [25, 46]. Sequencing was performed on the MGI T7 sequencing platform using paired-end 100 bp mode at GenePlus. The sequencing depth for BID-seq was approximately 20 G per library.

Uli-epic BID-seq

The Uli-epic BID-seq protocol combines procedures from both the Uli-epic and BID-seq methodologies. Specifically, 1 ng or 100 pg of HEK 293 T mRNA was subjected to fragmentation using 0.1 M NaHCO₃ (pH 9.2) and incubated at 95 °C for 5 min, and then neutralized with 3 M NaOAc (pH 5.2). The RNA was subjected to 3′ end-repair using T4 polynucleotide kinase (PNK) (NEB, M0201) and poly(A) tailing using E. coli Poly(A) Polymerase (NEB, M0276L). The polyadenylated RNA, along with carrier DNA, was purified using the Oligo Clean & Concentrator kit (Zymo Research, D4061), and eluted with 7 µl of RNase-free water.

A 1.5 µL aliquot of the purified RNA was reserved as the ‘input’ for library construction, while the remaining 5 µL was designated for BID-seq treatment as the ‘experimental’ group. The 5 µL RNA sample was mixed with 45 µL of freshly prepared BID-seq bisulfite reagent (2.4 M Na₂SO₃ and 0.36 M NaHSO₃, prepared by dissolving 270 mg Na₂SO₃ and 34 mg NaHSO₃ in 850 µL RNase-free water) and incubated at 70 °C for 3 h. Following incubation, the reaction mixture was combined with 100 µL of RNase-free H₂O, 250 µL of RNA binding buffer (RNA Clean & Concentrator), and 400 µL of ethanol. The mixture was thoroughly mixed and loaded onto an RNA Clean & Concentrator-5 column. After centrifugation and washing with 200 µL of RNA wash buffer, 200 µL of RNA Desulphonation Buffer (Zymo Research, R5001-3–40) was added to the column, which was then incubated at room temperature (25 °C) for 1 h. This was followed by two washes with 700 µL of RNA wash buffer, and the RNA was eluted with 10.5 µL of RNase-free water.

Reverse transcription and template switching were performed using a template switching reverse transcriptase enzyme mix (NEB, M0466L), a T7-P7 oligo-dT primer (5′-GCCGCGAAATTAATACGACTCACTATAGGGATAATGAGCAGACGTGTGCTCTTCCGATCTTTTTTTTTTTTTTTTTTTTTVN-3′), and a P5 template switch oligo (P5-TSO: 5′-Biotin-GCTAATCATTGCACACGACGCTCTTCCGATCTrGrGrG-3′) at 42 °C for 90 min. Subsequently, the RNA template was degraded using E. coli RNase H (NEB, M0297L), and the second strand cDNA was synthesized using Q5 Hot Start High Fidelity Master Mix (NEB, M0494). Excess T7 primer was digested with exonuclease I (NEB, M0293S), and the double-stranded cDNA was purified using the Oligo Clean & Concentrator kit.

Linear amplification of the double-stranded cDNA was performed using the T7 High Yield RNA Transcription Kit (Vazyme). Following transcription, DNase I was added to degrade the double-stranded cDNA, and the resulting RNA was purified using 1.8 × SPRIselect beads (Beckman, B23318). The RNA was then reverse transcribed using an RT primer (5′-ACACGACGCTCTTCCGATCT-3′) and SuperScript™ III (Thermo Fisher Scientific, 18,080,051). The cDNA was purified with 1.8 × SPRIselect beads (Beckman, B23318), and libraries were constructed using single index primers (NEB, E7335S). All libraries were purified using a 3.5% low melting point agarose gel, and DNA bands between 200 and 500 bp were excised and recovered.

Sequencing was conducted on the MGI T7 platform using paired-end 100 bp mode at GenePlus, China. The sequencing depth was approximately 50 G per library for Uli-epic BID-seq with 1 ng of input RNA and 100 G per library for Uli-epic BID-seq with 100 pg of input RNA.

Data processing and analysis for BID-seq/GLORI

The strand orientation of the original RNA was preserved during the library construction process, resulting in reads from R2 being sense-oriented to the original RNA for BID-seq/GLORI data. Therefore, only R2 reads were utilized for BID-seq/GLORI analysis in this study. Sequencing reads were initially processed using Trim Galore (v.0.6.10) for adapter removal and quality trimming with Cutadapt (v.4.2). The command used was: trim_galore -q 20 –stringency –fastqc –length 20. Following trimming, Clumpify was employed to deduplicate PCR artifacts. Post-deduplication, Cutadapt was used to remove six bases from the 5’ end of the reads with the command: cutadapt -u 6. m⁶A stoichiometries were detected and quantified using GLORI-tools (https://github.com/liucongcas/GLORI-tools), while Ψ levels were detected and quantified using BID-seq-tools (https://github.com/y9c/pseudoU-BIDseq).

Data processing and analysis for Uli-epic BID-seq/GLORI

The strand orientation of the original RNA was preserved during the library construction process, with R1 reads yielding sequences that are sense-oriented to the original RNA for Uli-epic BID-seq/GLORI data. Consequently, only R1 reads were used for Uli-epic BID-seq/GLORI in this study. Sequencing reads were first processed with Trim Galore (v.0.6.10) for adapter removal and quality trimming using Cutadapt (v.4.2). The command used was: trim_galore -q 20 –stringency –fastqc –length 20. Following this, Clumpify was used to deduplicate PCR artifacts. After deduplication, Cutadapt was employed to remove poly (A) tails from the 3’ end with the command: cutadapt -a AAAAA -m 30. Additionally, Cutadapt was used to trim three bases from the 5’ end with the command: cutadapt -u 3. For the paired-end sequencing data from BGI, read names in the FASTQ files were modified to be compatible with GLORI tools. Ψ stoichiometries were detected and quantified using GLORI-tools (https://github.com/liucongcas/GLORI-tools), while Ψ levels were detected and quantified using BID-seq-tools (https://github.com/y9c/pseudoU-BIDseq).

Differential m⁶A and Ψ stoichiometry analysis

Differential m⁶A and Ψ stoichiometry sites between WT and FGR samples were identified by ttest_ind which calculate the T-test for the means of two independent samples of scores. The original GLORI and BID-seq pipelines generated m⁶A and Ψ sites with stoichiometries ≥ 0.1. To comprehensively identify all differentially modified sites, we take the union of all modification sites identified across the four samples (two biological replicates of WT and FGR group). We obtained the sequencing depth information for all sites using the “samtools depth” command. Modification sites showing undetectable mutation or deletion signals and with read coverage depth ≥ 15 were assigned a stoichiometry of 0; sites with read coverage depth < 15 were filtered out as undetectable. Differential m⁶A sites were identified based on a minimum absolute difference in stoichiometry of 0.15 and a p-value < 0.05. Similarly, differential Ψ sites were identified based on a minimum absolute difference in stoichiometry of 0.2 and a p-value < 0.05.

Motif analysis of m⁶A or Ψ sites

For motif analysis, we extracted the flanking sequences of identified m⁶A or Ψ sites from the reference genome. The sequence context motifs were determined using the MEME motif discovery tool.

Quantification and statistical analysis

All of the statistical details of experiments can be found in the figure legends. Error bars represent the mean ± SD.

Supplementary Information

13059_2025_3857_MOESM1_ESM.pdf^{(2.1MB, pdf)}

Additional file 1. Supplementary notes and supplementary figures S1-S10.

13059_2025_3857_MOESM2_ESM.xlsx^{(97.8MB, xlsx)}

Additional file 2. Supplementary tables S1-S33.

Acknowledgements

This work was supported by National Key R&D Program of China (2024YFA1307800, 2021YFA1100400), General Program of National Natural Science Foundation of China (32471340), Natural science foundation of Science and Technology Commission of Shanghai Municipality (STCSM, 21ZR1480300), and Fudan University Start-up funding to L.H. J. K. was supported by National Key R&D Program of China (2021YFA1100400), and the National Natural Science Foundation of China (82230054). W.C. was General Program of National Natural Science Foundation of China (82271723, 82071728). We thank the Core Facility of Shanghai Medical College, Fudan University for providing the instruments used in this work. We thank the Core Instrument Sharing Platform of Shanghai Sycamore Research Institute of Life Sciences for providing the instruments used in this work.

Peer review information

Kin Fai Au and Wenjing She were the primary editors of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. The peer-review history is available in the online version of this article.

Authors’ contributions

L.H. conceived the project. W.H. and L.H. designed the methodological procedures. W.H. conducted all library preparations. C.X., and Y.P. performed all bioinformatic analyses. W.C., Y.W., S.C., and W.Z. developed the FGR mouse model and collected sperm and NSC samples under the guidance of J.K. X.J. helped W.H. in figure preparation. W.H. and L.H. prepared the manuscript with contributions from all authors. All authors read and approved the final manuscript.

Funding

Data availability

The sequencing data generated in this study have been deposited in the NCBI Gene Expression Omnibus (GEO) under accession codes GSE277358 [85] and GSE277359 [86]. Public GLORI datasets for HEK 293 T cells were retrieved from the GEO database under accession code GSE242760 [87]. Reference genomes GRCh38 and mm10 were downloaded from the UCSC Genome Browser (https://hgdownload.soe.ucsc.edu/downloads.html). Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. No other scripts and software were used other than those mentioned in this study.

Declarations

Ethics approval and consent to participate

All animal care and experimental procedures were conducted in accordance with the guidelines approved by the Institutional Animal Care and Use Committee of Tongji University.

Consent for publication

Not applicable.

Competing interests

A patent has been filed with the Fudan University Shanghai Cancer Center. The authors declare that they have no competing interests. The results and the method described in this study can be reproduced or utilized by other researchers without any limitations, provided that the use is for non-commercial purposes.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Weizhi He, Chu Xu and Wen Chen contributed equally to this work.

Contributor Information

Jiuhong Kang, Email: jhkang@tongji.edu.cn.

Lulu Hu, Email: luluhu@fudan.edu.cn.

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Associated Data

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

Supplementary Materials

13059_2025_3857_MOESM1_ESM.pdf^{(2.1MB, pdf)}

Additional file 1. Supplementary notes and supplementary figures S1-S10.

13059_2025_3857_MOESM2_ESM.xlsx^{(97.8MB, xlsx)}

Additional file 2. Supplementary tables S1-S33.

Data Availability Statement

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PERMALINK

Uli-epic: profiling RNA modifications from ultra-low input samples

Weizhi He

Chu Xu

Wen Chen

Xiao Jiang

Yuhang Wang

Yong Peng

Shujuan Chang

Wei Zhu

Jiuhong Kang

Lulu Hu

Abstract

Graphical Abstract

Supplementary Information

Background

Results

Uli-epic strategy

Integrating Uli-epic with BID-seq

Fig. 1.

Ψ landscapes in FGR mouse NSCs and sperm

Fig. 2.

Fig. 3.

Linking pseudouridine modification to FGR

Integrating Uli-epic with GLORI

Fig. 4.

m6A landscapes in FGR mouse NSC and sperm

Fig. 5.

Dynamic m6A sites linked to FGR

Fig. 6.

Discussion

Conclusions

Methods

Cell culture

Experimental animals

Sperm sample collection

Purification of dentate neural progenitors

RNA isolation

Conventional RNA-seq

GLORI

BID-seq

Uli-epic BID-seq

Data processing and analysis for BID-seq/GLORI

Data processing and analysis for Uli-epic BID-seq/GLORI

Differential m6A and Ψ stoichiometry analysis

Motif analysis of m6A or Ψ sites

Quantification and statistical analysis

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m⁶A landscapes in FGR mouse NSC and sperm

Dynamic m⁶A sites linked to FGR

Differential m⁶A and Ψ stoichiometry analysis

Motif analysis of m⁶A or Ψ sites