Abstract
RNA-binding proteins (RBPs) have essential functions during germline and early embryo development. However, current methods are unable to identify the in vivo targets of a RBP in these low-abundance cells. Here, by coupling RBP-mediated reverse transcription termination with linear amplification of complementary DNA ends and sequencing, we present the LACE-seq method for identifying RBP-regulated RNA networks at or near the single-oocyte level. We determined the binding sites and regulatory mechanisms for several RBPs, including Argonaute 2 (Ago2), Mili, Ddx4 and Ptbp1, in mature mouse oocytes. Unexpectedly, transcriptomics and proteomics analysis of Ago2−/− oocytes revealed that Ago2 interacts with endogenous small interfering RNAs (endo-siRNAs) to repress mRNA translation globally. Furthermore, the Ago2 and endo-siRNA complexes fine-tune the transcriptome by slicing long terminal repeat retrotransposon-derived chimeric transcripts. The precise mapping of RBP-binding sites in low-input cells opens the door to studying the roles of RBPs in embryonic development and reproductive diseases.
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Data availability
All the sequencing data generated in this paper have been deposited in the Gene Expression Omnibus under accession number GSE137925. The MS data have been deposited in ProteomeXchange with the primary accession code PXD025846. Previously published CLIP-seq, iCLIP, irCLIP, eCLIP, sCLIP and tRIP-seq data that were re-analysed here are available under accession codes GSE42701, E-MTAB-3108, GSE78832, GSE92205, GSE92995 and DRA005743, respectively. RNA-seq for WT and DicerSOM/SOM oocytes were downloaded from the Gene Expression Omnibus database under accession number GSE132121. The small RNA-seq data were downloaded from the Sequence Read Archive database under accession number SRP045287. The UniProt mouse database was downloaded from https://www.uniprot.org/uniprot/?query=mouse&fil=reviewed%3Ayes&sort=score. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding authors upon reasonable request.
Code availability
The custom code for analysing LACE-seq data is available at GitHub at https://github.com/caochch/LACEseq.
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Acknowledgements
This work was supported by the Ministry of Science and Technology of China (2017YFA0504400), the Strategic Priority Program of CAS (XDB37000000) and the National Natural Science Foundation of China (32025008, 91740201, 91940306 and 81921003) to Y.X.; by the National R&D Program (2018YFA0107701) to Q.-Y.S.; by the Fundamental Research Funds for the Central Universities (BMU2017YJ003) and the Outstanding Technology Talent Program of Chinese Academy of Sciences (BMU2018XTZ002) to C.C.L.W.; by the Beijing Municipal Natural Science Foundation (5182024) grant to C.C.; and by the Young Scientists Fund of the National Natural Science Foundation of China to C.C. and L. Wang (31900465, 31701109).
Author information
Authors and Affiliations
Contributions
Y.X. conceived the project and designed the experiments. R.S. developed the LACE-seq method, performed the dual-luciferase assay and western blotting and prepared the small RNA-seq samples (with help from L. Wu). Z.C. performed the gel-shift experiments. L.-H.F., Y.-C.O., Y.W., W.-L.L. and Q.Z. collected the mouse oocytes, prepared the luciferase mRNA and completed the injection experiments (under the guidance of Q.-Y.S.). N.Z. performed the proteomics analysis of mouse oocytes (under the guidance of C.C.L.W.). X.Z. generated the Ago2loxP/loxP mice (under the supervision of Y.T.). C.C., L. Wang, H.Z. and Z.D. performed the bioinformatics analysis (under the guidance of S.H.). Y.X. wrote the manuscript with help from C.C., N.Z. and L.-H.F.
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Peer review information Nature Cell Biology thanks Haruhiko Siomi, Wayne Miles and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data
Extended Data Fig. 1 Validation of the LACE-seq method.
a, Bioanalyzer 2100 analysed the PTBP1 LACE-seq libraries generated from different numbers of HeLa cells. b, Scatter plots showing gradually reduced correlations between PTBP1 LACE-seq libraries generated from the sequentially decreased number of HeLa cells. Pearson’s R is indicated. Two independent LACE-seq experiments are shown here. c, PTBP1 LACE-seq reads were highly correlated with previously published bulk CLIP-seq data. d, PTBP1 peaks containing a higher percentage of cDNA end covering reads usually have a higher CU-rich motif density. The number of peaks is 1403, 2562, 3140, 4042, 5003, 4442, 4702, 4679, 4151, and 7461 from left to right. e, The CU-rich motif density was significantly higher in PTBP1 peaks containing more sequencing reads and termination sites. Peaks were classified into four equal categories (Q1 to Q4, average 10235 peaks for each quarter) based on the read density. P-values were calculated by two-tailed unpaired Student’s t-test. f-g, The sensitivity and precision of LACE-seq compared with targets generated from bulk CLIP-seq (GSE42701). The sensitivity decreased along with the reduced cell numbers, but the precision showed little change. h, UCSC genome browser views of LACE-seq reads on known PTBP1 targets GPRC5A and HMGA1. i, CDF plot showing the absolute value of delta percent-spliced-in (PSI) for cassette exons was significantly changed upon PTBP1 knockdown. Cassette exons and adjacent introns without PTBP1 binding were defined as the ‘Non-targets’ group. The PSI of each cassette exon was calculated based on the RNA-seq data (GSE42701) of wild-type (WT) and PTBP1 knockdown (KD) HeLa cells. P-values were calculated by one-tailed Kolmogorov-Smirnov test. j, Boxplots showing the abundance of PTBP1 target genes identified by CLIP-seq and LACE-seq. FPKM: fragments per kilobase of transcript per million mapped reads. The number of target genes is 7292, 4806, 1686, 1110, 208, and 307 from left to right. For box plots in d, e, and j, the centre line represents the median, the box borders represent the first (Q1) and third (Q3) quartiles, and the whiskers are the most extreme data points within 1.5× the interquartile range (from Q1 to Q3). Data in c-j represent results from two independent LACE-seq experiments.
Extended Data Fig. 2 Comparison between LACE-seq and CLIP-based methods.
a,b, LACE-seq tends to produce fewer PCR duplicates than other methods at high or low sequencing depths. c, Mapping rate comparison for LACE-seq, iCLIP, eCLIP, irCLIP, CLIP-seq, sCLIP, and tRIP-seq methods. All CLIP variant-generated datasets were mapped to the genome using the same pipeline. d, PTBP1 LACE-seq-specific peaks (red line) also showed accumulated CU-rich motifs compared with random controls (blue line). We picked 30 M of PTBP1 LACE-seq and irCLIP reads and generated an equal number of randomized peaks for such comparisons. e, Western blot showing the knockdown (KD) efficiency of PTBP1 in K562 cells. The experiment was independently repeated twice with similar results. f, Heatmap showing the LACE-seq signal around the peaks before and after PTBP1 knockdown in K562 cells. The scale stands for the number of LACE-seq reads per million. g, Metaprofile of the PTBP1 LACE-seq signal around the identified peaks. h, LACE-seq achieved a higher signal-to-noise ratio than eCLIP in K562 cells. The dashed line represents the cutoff of the two-fold signal-to-noise ratio. The P-value was calculated by two-tailed unpaired Student’s t-test. i, LACE-seq captured more bulk CLIP-seq-revealed target genes than irCLIP-seq with the same number of reads. j, The number of target genes identified by LACE-seq and irCLIP that could be confirmed by bulk CLIP-seq data. k, The sensitivity of LACE-seq is higher than that of irCLIP. The sensitivity was calculated by counting how many target genes revealed by bulk CLIP-seq could be captured by LACE-seq and irCLIP, along with different sequencing read inputs. l, The precision of LACE-seq is better than irCLIP. The precision was calculated by comparing the identified targets of LACE-seq and irCLIP to bulk CLIP-seq. Identical numbers of reads were randomly downsampled 10 times in i, j, k, and l (n = 10). Data are mean ± s.e.m., P-values in i, j, k, and l were calculated by two-tailed paired Student’s t-test. LACE-seq data in d and f-l represent results from two independent experiments.
Extended Data Fig. 3 LACE-seq methodology optimization and validation in oocytes using Ddx4 antibody.
a, A meta-analysis of Ddx4 LACE-seq libraries generated by cutting with a series of diluted micrococcal nuclease (from 1:120K to 1:15M). IgG served as a control. TSS: transcription start site; TES: transcription end site. b, The number of peaks detected in the Ddx4 libraries. Four independent LACE-seq experiments are shown in a and b. c, Heatmap showing the correlation of six Ddx4 LACE-seq datasets generated from different numbers of mouse oocytes. IgG samples were generated from ten oocytes. The colour intensity indicates the scale of Pearson’s correlation coefficient. d, Ddx4-RNA interacting sites were enriched around the start codons (left), stop codons (middle), and poly(A) sites (right). The IgG sample is shown as the blue line. e, Schematic diagram of the in vitro SELEX strategy to enrich Ddx4 preferentially bound RNA sequences. f, Ddx4-binding motifs deduced from the in vitro SELEX enriched reads. Top: in vitro SELEX-deduced consensus motif for Ddx4. Bottom: relative enrichment of U-rich or GC-rich sequences around the LACE-seq peaks of Ddx4. LACE-seq data in d and f represent results from six independent experiments.
Extended Data Fig. 4 Analysis of PTBP1, Ago2, and Mili binding features in mouse oocytes.
a, PTBP1- and Ago2-binding motifs identified by LACE-seq in mouse oocytes. b, Ago2 LACE-seq reads mapping to known endo-siRNA loci. c, Heatmap showing the LACE-seq signal around the identified peaks before and after Ago2 knockout in oocytes. The scale stands for the number of LACE-seq reads per million. d, Metaprofile of Ago2 LACE-seq signals from control (ctrl) or Ago2−/− mouse oocytes around the identified peaks. e, An example of abolished Ago2 binding at the transcript Tuba1a in Ago2-null oocytes. f, The number of reads and the identified peaks by the LACE-seq protocol with or without IVT steps from different cell inputs. LACE-seq data in c-f represent results from two independent experiments. g, Gel images and bar graphs showing that the yield of the LACE-seq library is strictly dependent on the IVT step if starting with 50 mouse oocytes. Data are mean ± s.e.m.; n = 3 or 4 biological replicates, two-tailed unpaired Student’s t-test. h, Saturation analysis of the identified Ago2 peaks in mouse oocytes. ‘Fraction’ indicates the percentage of randomly selected and inputted reads. i, Snapshot of Ago2 and Mili LACE-seq signals on mRNA specifically bound by Ago2 or by both Ago2 and Mili. Repeat elements are shown as black boxes at the bottom. j, GO analysis of Ago2-specific targets. LACE-seq data in a, b, and h–j represent results from three independent experiments.
Extended Data Fig. 5 The meiotic defects of oocytes in Ago2 conditional knockout mice.
a, Schematic of the Ago2 conditional knockout (denoted as cKO or Ago2−/−, Ago2loxP/loxP; Zp3−cre) strategy in mouse oocytes. Two loxP sites inserted at the adjacent intronic regions of exon 3 are shown as red triangles. The agarose gel shown in the bottom panel is the genotyping result with two primers (F+R) flanking both sides of one loxP site. The experiment was independently repeated three times with similar results. b, Over 80% of the Ago2 cKO oocytes showed spindle defects compared with oocytes from control littermates (Ago2loxP/+). Ctrl, control, n = 68; cKO, n = 75. c, The classification and percentage of abnormal phenotypes in Ago2−/− and Dicer−/− oocytes. The representative phenotypes are shown above the column. α-tubulin, green; DAPI, blue. d, Clustering analysis of single-cell RNA-seq data generated from control (Ctrl) and Ago2 conditional knockout (cKO) oocytes. e, Ago2 cKO samples are clustered together rather than to control oocytes by PCA. f, Percentage of Ago2 bound (red) and unbound (blue) transcripts revealed by LACE-seq. The upregulated, downregulated, and unchanged transcripts revealed by RNA-seq were further classified into Ago2-bound or Ago2-unbound groups. g, Scatter plot showing the abundance of Ago2 targets in wild-type (WT) and DicerO knockout (DicerSOM/SOM) oocytes. The percentage of unchanged targets is listed. h, Scatter plot showing that most Mili targets are not changed in Ago2−/−oocytes. Mili-specific targets are marked in red. i, Boxplot showing that the upregulated Mili targets have a higher ratio of Ago2 occupancy to Mili than other targets. The P-value was calculated by one-tailed unpaired Student’s t-test. The centre line represents the median, the box borders represent the first (Q1) and third (Q3) quartiles, and the whiskers are the most extreme data points within 1.5× the interquartile range (from Q1 to Q3). Data in d-f, h, and i represent results from five independent scRNA-seq experiments.
Extended Data Fig. 6 LTR-driven chimeric transcripts in mouse oocytes.
a, The number of upregulated (Up) and downregulated (Down) TEs revealed by RNA-seq in Ago2-null oocytes. Red: upregulated TEs; black: downregulated TEs. b, Boxplot showing that the median ratio of LTR-driven chimeric transcripts (n = 244) to endogenous transcripts is increased by 2.88-fold in Ago2−/− oocytes. The median value is shown in the boxes. c, Strategy for analysing naturally formed dsRNA transcripts. d, The number of LTR-driven transcripts and naturally occurring double-stranded RNAs accounted for the upregulated and downregulated genes, as revealed by RNA-seq. Red: upregulated genes; black: downregulated genes. e, Boxplot showing that the downregulated LTR-driven transcripts (n = 75) tend to have more Mili binding than Ago2 binding compared with upregulated chimeric transcripts (n = 244). f, Boxplot showing that the downregulated LTR-driven transcripts (n = 75) tend to have more Dicer binding than Ago2 binding compared with upregulated chimeric transcripts (n = 244). The Dicer-binding density at a specific transcript was quantified by normalizing the endo-siRNA amounts derived from small RNA-seq to the transcript length. g, Boxplot showing that the downregulated naturally formed dsRNA transcripts (n = 89) in Ago2-null oocytes also tend to be downregulated in DicerSOM/SOM oocytes. h, GO analysis of LTR-driven chimeric transcripts in MII oocytes. P-values in b, e, f, and g were calculated by two-tailed unpaired Student’s t-test. For the box plots in b, e, f, and g, the centre line represents the median, the box borders represent the first (Q1) and third (Q3) quartiles, and the whiskers are the most extreme data points within 1.5× the interquartile range (from Q1 to Q3). Data in a, b, and d-h represent results from five independent scRNA-seq experiments.
Extended Data Fig. 7 Endo-siRNA targeting rules in mouse oocytes.
a, Ago2-IP enriched small RNAs. b, Boxplots showing that endo-siRNAs (n = top 200) are preferably loaded into Ago2 than miRNAs (n = top 200) due to their relative abundance in oocytes. P-values were calculated by two-tailed Wilcoxon test. The centre line represents the median, the box borders represent the first (Q1) and third (Q3) quartiles, and the whiskers are the most extreme data points within 1.5× the interquartile range (from Q1 to Q3). c, Scatter plot showing the preference of endo-siRNA base-paired with class I or class II Ago2 clusters. Each point represents an endo-siRNA, and different colours indicate the source for endo-siRNA. The horizontal axis represents the difference in the average MFE of all the potential hybrids formed between each class of clusters and a given endo-siRNA. The vertical axis represents the difference in the proportion of clusters that could form hybrids with each endo-siRNA. As a positive control, miRNAs mostly pair with class II clusters. d, Endo-siRNAs paired with class II RNAs have a significantly lower MEF value than random controls. Two-tailed unpaired Student’s t-test was used to calculate the P-value. Ago2-IP enriched smRNA-seq data in a-d represent results from a single experiment. e, The single-nucleotide mutation at the seed region compromises the repression mediated by endo-siRNA-336 in oocytes. Data are mean ± s.e.m.; n = 3 biological replicates, two-tailed unpaired Student’s t-test.
Extended Data Fig. 8 Validation of functional endo-siRNA target sites in HEK293 cells.
a, The predicted endo-siRNA target sites in the 3′ UTRs of Birc5, Cdc42, Chk1, and Ska1 based on the Ago2 LACE-seq signal. Deduced base-pairing potentials and calculated MFEs are illustrated. Ago2 LACE-seq data represent results from three independent experiments. b-c, The relative Renilla luciferase reporter assay in HEK293 cells. Negative control (Ctrl) or endo-siRNA mimics were cotransfected with WT or seed mutants. d, The relative luciferase reporter assay of wild-type (WT) and mutant (MT) Chk1 in mouse oocytes. e-g, qPCR showing that the mRNA levels of Calm1, Bub3, and Nuf2 were not changed upon treatment with endo-siRNA-specific sponges in oocytes. Two-tailed unpaired Student’s t-test was used to calculate the P-values in b-g. Data are mean ± s.e.m., n = 3 biological replicates.
Extended Data Fig. 9 Proteome analysis of Ago2-null oocytes.
a, The mass spectrometry data were highly correlated in two biological replicates. b, The fold change and significance of the Calm1, Nuf2, Chk1, and Bub3 protein levels as revealed by mass spectrometry in Ago2-null oocytes. c, The predicted miRNA density (miRNA counts/mRNA length×1000) showing no correlation with the protein level changes upon Ago2 ablation in oocytes. P-value was calculated by two-tailed Kolmogorov-Smirnov test. d, The percentage of LTR-driven and upregulated chimeric proteins detected by mass spectrometry. e, The fold change in MS-detected (17.6%) and LTR-derived chimeric proteins in Ago2-null oocytes. MS data in b-e represent results from two independent experiments.
Supplementary information
Supplementary Tables
Supplementary Table 1: Comparison of LACE-seq with other RBP target mapping methods. Supplementary Table 2: The number of LACE-seq-revealed Ddx4 binding peaks. Supplementary Table 3: List of Mili targets. Supplementary Table 4: List of Ago2 targets. Supplementary Table 5: Differentially expressed transcripts in Ago2-deficient oocytes. The P values were calculated using DESeq2 (v.2.14.0). Supplementary Table 6: Ago2 bound and unbound LTR-driven transcripts in oocytes. Supplementary Table 7: Upregulated LTR-driven transcripts and naturally occurring dsRNAs after Ago2 deletion. Supplementary Table 8: The predicted endo-siRNA targets. Supplementary Table 9: Upregulated and downregulated proteins in Ago2-deficient oocytes. The P values were calculated using the OmicsBean webserver (v.2.0, http://www.omicsbean.cn). Supplementary Table 10: Primers used in this study.
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Su, R., Fan, LH., Cao, C. et al. Global profiling of RNA-binding protein target sites by LACE-seq. Nat Cell Biol 23, 664–675 (2021). https://doi.org/10.1038/s41556-021-00696-9
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DOI: https://doi.org/10.1038/s41556-021-00696-9
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