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SNORD113–114 cluster maintains haematopoietic stem cell self-renewal via orchestrating the translation machinery

Nature Cell Biology volume 27, pages 246–261 (2025)Cite this article

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Abstract

Haematopoietic stem cells (HSCs) self-renew and differentiate to replenish the pool of blood cells, which require a low but finely tuned protein synthesis rate. Nonetheless, the translatome landscape in HSCs and how the translation machinery orchestrates HSC self-renewal remain largely elusive. Here we perform ultra-low-input Ribo-seq in HSCs, progenitor and lineage cells, and reveal HSC-specific translated genes involved in rRNA processing. We systematically profile small nucleolar RNAs (snoRNAs) and uncover an indispensable role of the SNORD113–114 cluster in regulating HSC self-renewal. Maternal knockout (Mat-KO) of this cluster substantially impairs HSC self-renewal, whereas loss of the paternal allele shows no obvious phenotype. Mechanistically, Mat-KO results in dysregulation of translation machinery (rRNA 2′-O-Me modifications, pre-rRNA processing, 60S ribosome assembly and translation) and induces nucleolar stress in HSCs, which exempts p53 from Mdm2-mediated proteasomal degradation and leads to apoptosis. Collectively, our study provides a promising facet to our understanding of snoRNA-mediated regulation in HSC homeostasis.

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Fig. 1: Ultra-low-input Ribo-seq identifies specific translatome profile in HSCs.
Fig. 2: The SNORD113–114 cluster is specifically expressed in LT-HSCs and critical for HSC self-renewal.
Fig. 3: SNORD113–114 Mat-KO impairs HSC reconstitution capacity.
Fig. 4: Transcriptomic profiling reveals dysregulation of translation pathway in SNORD113–114 Mat-KO HSCs.
Fig. 5: Loss of SNORD113–114 cluster disrupts translation machinery.
Fig. 6: SNORD113–114 Mat-KO results in dysregulation of HSC-specific translatome.
Fig. 7: SNORD113–114 Mat-KO induces nucleolar stress and apoptosis in HSCs.
Fig. 8: SNORD113–114 Mat-KO mediated HSC deficiency is rescued by ectopic expression of snoRNAs or l-leucine treatment.

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Data availability

All sequencing data have been deposited in the Gene Expression Omnibus under the following accession codes: GSE166514, GSE166515, GSE166516, GSE166517, GSE166518, GSE267887, GSE269874 and GSE270936. The public mouse genome assembly GRCm38 was used for alignment. Previously published data by Spevak et al.3 that were re-analysed here (Fig. 1e) are available under accession code GSE113886. Data supporting the findings of this study are available from the corresponding authors on reasonable request. Source data are provided with this paper.

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Acknowledgements

This work was supported by grants from the National Key Research and Development Program of China (2022YFA1103500 and 2024YFA1107102 to P.Q., 2021YFA1102400 to F.W. and 2022YFA1106300 and 2024YFA1803500 to J.X.), the National Natural Science Foundation of China (82222003 and 92268117 to P.Q., 92268205 and 82330007 to J.Y., 82470123 to F.W. and 31900815 to H.W.), the Key R&D Program of Zhejiang (2024SSYS0024, 2024SSYS0023 and 2024SSYS0025 to P.Q.), the Zhejiang Provincial Natural Science Foundation of China (Z24H080001 to P.Q.), the Department of Science and Technology of Zhejiang Province (2023R01012 to P.Q.), the Fundamental Research Funds for the Central Universities (226-2024-00007 to P.Q.), Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (2021-I2M-1-040 to F.W.) and Zhejiang Province Postdoctoral Research Excellence Funding Project (ZJ2022098 to H.W.). Haihe Laboratory of Cell Ecosystem Innovation Fund (22HHXBSS00027 to J.Y.). P.Q. gratefully acknowledges the support of the K.C. Wong Education Foundation. Technical support was provided by the core facilities at the Zhejiang University School of Medicine (special thanks to C. Guo and X. Hong for assistance during experiments), as well as the core facilities at the Zhejiang University Medical Center and Liangzhu Laboratory. We thank State Key Laboratory of Common Mechanism Research of Major Diseases Platform for consultation and instrument availability that supported this work, and High-performance Computing Platform at the Center for Bioinformatics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences.

Author information

Author notes

  1. These authors contributed equally: Hui Wang, Zhaoru Zhang, Chenxi Han, Penglei Jiang, Jiayue Xu, Yingli Han.

Authors and Affiliations

  1. Bone Marrow Transplantation Center of the First Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, State Key Laboratory of Experimental Hematology, Hangzhou, China

    Hui Wang, Zhaoru Zhang, Penglei Jiang, Yingli Han, Deyu Huang & Pengxu Qian

  2. Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, China

    Hui Wang, Zhaoru Zhang, Penglei Jiang, Yingli Han, Deyu Huang & Pengxu Qian

  3. Institute of Hematology, Zhejiang University & Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China

    Hui Wang, Zhaoru Zhang, Penglei Jiang, Yingli Han, Deyu Huang & Pengxu Qian

  4. Department of Hematology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Chenxi Han & Jian Li

  5. State Key Laboratory of Common Mechanism Research for Major Disease, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China

    Jiayue Xu, Jia Yu & Fang Wang

  6. The Key Laboratory of RNA and Hematopoietic Regulation, Chinese Academy of Medical Sciences, Beijing, China

    Jiayue Xu, Jia Yu & Fang Wang

  7. State Key Laboratory of Proteomics, Academy of Military Medical Sciences, Academy of Military Sciences, Beijing, China

    Jie Zhou & Bing Liu

  8. State Key Laboratory of Experimental Hematology, Institute of Hematology, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China

    Jie Zhou & Bing Liu

  9. Stowers Institute for Medical Research, Kansas City, MO, USA

    Michael Durnin & Shiyuan Chen

  10. Institute of Environmental Medicine, and Cancer Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Yaxin Liu & Jinghao Sheng

  11. Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang University, Hangzhou, China

    Yaxin Liu & Jinghao Sheng

  12. MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, China

    Jie Cao & Jianzhao Liu

  13. Institute of Blood Transfusion, Chinese Academy of Medical Sciences, Chengdu, China

    Jia Yu & Fang Wang

  14. State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China

    Jia Yu

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  1. Hui Wang
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Contributions

H.W. and P.Q. conceived the project and designed the experiments. H.W. performed the experiments, analysed data and wrote the manuscript. C.H. and J. Li helped perform library construction work of Ribo-seq experiments. P.J., J.X. and S.C. analysed second-generation sequencing data. Z.Z., Y.H. and D.H. helped perform transplantation experiments and undertook mouse breeding work. Z.Z. and J.Z. performed the SMART-Seq2 experiments. Y.L. and J.S. helped perform the northern blot experiments. J.C. and J. Liu helped perform the UHPLC–MS experiments. M.D. helped make the SNORD113–114 cluster knockout mice. P.Q., B.L., J.Y. and F.W. supervised the overall project and co-wrote the manuscript. All authors contributed to reading and editing the manuscript.

Corresponding authors

Correspondence to Bing Liu, Jia Yu, Fang Wang or Pengxu Qian.

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The authors declare no competing interests.

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Nature Cell Biology thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Gating strategies used in flow cytometry.

a, Flow cytometry plots showing gating strategies used for detecting HSPCs, progenitors, mature cells. b, Flow cytometry plots showing gating strategies used for detecting human HSC. c, Flow cytometry plots showing gating strategies used for detecting donor repopulation rate.

Extended Data Fig. 2 Translatome profiling of haematopoietic cells.

a, Barplot showing representative reads distributions along the transcripts. Read position relative to different reading frames (0, 1, 2) are colour-coded. b, Barplot showing representative ratios of reads belonging to different reading frames. c, Reads length distribution frequencies of all cell types. d, Scatterplots showing Pearson’s correlation between two batches of data from Ribo-seq. e, Scatterplots showing Pearson’s correlation between RNA level and RPF level of genes in each cell type.

Source data

Extended Data Fig. 3 HSC-specific SNORD113–114 cluster is indispensable for HSC maintenance.

a, Heatmap of snoRNA expression (Z-score normalized) in progenitors and lineage mature cells. b, c, Raw read counts of different types of ncRNAs got from sncRNA-seq. d, Sequence alignments of snoRNAs belonging to SNORD113–114 cluster between Mus musculus and Homo sapiens. Only identical sequences of specific snoRNAs between human and mouse are presented. e, Electrophoresis showing PCR products patterns got from genotyping of WT and Mat-KO mice. f, Absolute numbers of total BM cells of WT and Mat-KO mice were studied by flow cytometry (n = 4 for WT, n = 3 for Mat-KO biological replicates). Error bars, SEM. g-j, Frequencies and absolute numbers of SLAM HSCs (g, h) and progenitors (i, j) from WT and Mat-KO mice detected by flow cytometry (n = 4 for WT, n = 3 for Mat = -KO biological replicates). Error bars, SEM. k, l, Frequency (l), absolute number (m) of lineage cells collected from WT and Mat-KO mice (n = 4 for WT, n = 3 for Mat-KO biological replicates). Error bars, SEM. All P values were determined by two-tailed unpaired Student’s t-test.

Source data

Extended Data Fig. 4 SNORD113–114 Mat-KO does not affect the spleen haematopoiesis.

a, b, Spleen weight (a) and cellularity (b) were examined and compared between WT and Mat-KO mice. N = 3 for WT, n = 4 for Mat-KO biological replicates in (a). N = 4 for WT, n = 3 for Mat-KO in (b). Error bars, SEM. c-h, Frequency and absolute number of spleen HSC (c, d), progenitors (e, f) and lineage cell (g, h) were analysed and compared between WT and Mat-KO mice (n = 4 for WT, n = 3 for Mat-KO biological replicates). Error bars, SEM. i, Whole blood counts of WT and Mat-KO mice (n = 7 biological replicates). Error bars, SEM. All P values were determined by two-tailed unpaired Student’s t-test.

Source data

Extended Data Fig. 5 SNORD113–114 Pat-KO does not lead to HSC deficiency.

a, BM cellularity of WT and Pat-KO mice (n = 3 biological replicates). Error bars, SEM. b, c, Frequency (b) and absolute number (c) of HSCs were analysed and compared between WT and Pat-KO mice (n = 3 biological replicates). Error bars, SEM. d, e, Apoptosis rate (d) and cell cycle distribution (e) were analysed and compared between WT and Pat-KO mice (n = 3 biological replicates). Different cell cycle phases are colour-coded and presented as a percentage of whole population (n = 6 biological replicates). Error bars, SEM. f, Schematic presentation of workflow of transplantation using sorted HSCs. g, h, Repopulation rate (g) and lineage distribution (h) of WT and Pat-KO mice HSC (n = 3 biological replicates). Error bars, SEM; All P values were determined by two-tailed unpaired Student’s t-test.

Source data

Extended Data Fig. 6 SnoRNAs in SNORD113–114 cluster are specifically important for HSC.

a-f, SNORD116 was not functionally important for HSCs. SNORD116 were knocked out using specific sgRNAs. After 7-day’s culture in 96-well plates, expressions of snoRNAs were examined by RT–qPCR (a), HSPC ratios and absolute numbers were examined by flow cytometry (b-d), and colony formation ability was examined (e, f). n = 3 biological replicates. Scale bar, 200 μm. Error bars, SEM. g, Expressions of specific snoRNAs were examined by RT–qPCR and compared between HSCs from male and female mice. n = 3 biological replicates. Error bars, SEM. h-k, SnoRNAs in SNORD113–114 cluster were also functionally important for human HSCs. CD34+ HSCs were collected from healthy donors, and 3 snoRNAs (SNORD113–6, SNORD113-9, SNORD114-1) were knocked out using specific sgRNAs. After 7-day’s culture in 96-well plates, expressions of snoRNAs were examined by RT–qPCR (h), HSPC ratios were examined by flow cytometry (i), and colony formation ability was examined (j, k). n = 3 biological replicates. Scale bar, 200 μm. Error bars, SEM. Cartoon illustrations were created by Figdraw. All P values were determined by two-tailed unpaired Student’s t-test.

Extended Data Fig. 7 High-throughput sequencing analysis of HSCs of WT/Mat-KO mice.

a, Raw read counts of different types of ncRNAs got from sncRNA-seq of WT and Mat-KO mice. b, Western blot result showing expressions of Dlk1 in WT and Mat-KO HSCs (n = 3 biological replicates). c, PCA analysis of sncRNA-seq of WT/Pat-KO mice LSK cells. d, Volcano-plot showing differentially expressed snoRNAs between WT/Pat-KO LSK cells. e, Raw read counts of different types of ncRNAs got from sncRNA-seq of WT and Pat-KO mice. f, Heatmap showing expression of snoRNAs belonging to SNORD113–114 cluster in WT/Pat-KO LSK cells. Only detected snoRNAs are shown. g, PCA analysis of RNA-seq results of WT/Pat-KO HSCs. h, Volcano-plot showing differentially expressed genes between WT/Pat-KO HSCs. i, Visualization of colour-coded clustering of HSCs via t-SNE based on experimental groups and gene signature, showing distribution of HSCs of WT and Mat-KO mice. j, Distribution of WT/Mat-KO HSCs among different cell populations. k, Violin-plot of different marker genes, showing expression patterns in all clusters. Violin box is colour-coded as for population clusters shown in Fig. 4m. l, GO term analysis of signature genes of different populations.

Source data

Extended Data Fig. 8 Loss of SNORD113–114 cluster dysregulates rRNA modification in HSCs.

a, Read length distributions of samples from RiboMethSeq. b, PCA analysis of RNA fragments got from RiboMethSeq of WT/Mat-KO LSK cells. c, Conservation of all differentially methylated sites detected by RiboMethSeq across E. coli, S. cerevisiae, M. musculus and H. sapiens. Different species are colour-coded, and sites that are not conserved were made vacant in the coloured circle representing according species. d, 2′-O-Me modification levels were examined by RTL-P. Upper panel shows schematic of primers used for reverse transcription and qPCR. Lower panel shows agarose electrophoresis images of semi-quantitative PCR results (n = 3 biological replicates). e, Targets of snoRNAs in SNORD113–114 cluster were predicted using online tool Snoscan. Predicted targets are listed under each snoRNA, and the nucleotide predicted to be 2′-O-methylated are labelled red. f, Relative methylation ratio (from data of RiboMethSeq) of specific sites that were predicted to be targeted by snoRNAs in SNORD113–114 cluster (n = 3 biological replicates). Error bars, SEM. All P values were determined by two-tailed unpaired Student’s t-test.

Source data

Extended Data Fig. 9 Loss of SNORD113–114 cluster impairs rRNA processing and translation in HSCs.

a, Schematic representation of primer designing for detecting various pre-rRNA isoforms generated during pre-rRNA processing. Red bars show PCR products of according primers. b–e, Relative expression levels of various pre-rRNA isoforms detected by RT–qPCR (n = 3 biological replicates). Error bars, SEM. f, PCA analysis of RPFs of WT and Mat-KO HSCs from Ribo-seq. g, Reads length distribution frequencies of RPFs from Ribo-seq. h-j, Expressions of representative genes in different sucrose gradients from polysome profiling assay were examined by RT–qPCR (n = 3 biological replicates). Error bars, SEM. k, Expressions of snoRNAs were examined by RT–qPCR after WT/Mat-KO HSCs were infected by according viruses (n = 3 biological replicates). Error bars, SEM. l, m, BM cells were infected by lentivirus carrying vectors expressing snoRNAs or according control empty vectors, and were analysed using flow cytometry after 7-day culture. Frequencies (l) and absolute numbers (m) were analysed in different groups. n = 3 biological replicates. Error bars, SEM. All P values were determined by two-tailed unpaired Student’s t-test.

Source data

Extended Data Fig. 10 SnoRNAs in SNORD113–114 cluster function through guiding 2′-O-Methylation of specific rRNA sites.

a-d, 2′-O-Me modifications of specific sites targeted by the ectopically expressed snoRNAs were also examined by RTL-P. e-g, Reconstitution ability analysis of HSCs from WT and Mat-KO mice after L-leucine treatment. BM cells of WT/Mat-KO mice were treated with L-leucine at dose of 5 μM for 48 h, and then were transplanted (2 e5/sample) into lethally irradiated recipient mice together with CD45.1 competitor cells (2 e5/sample). Repopulation rate (e), frequency of TNC (f) and absolute cell number (g) were analysed and compared between WT and Mat-KO mice at 16 weeks post-transplantation using flow cytometry (n = 7 biological replicates). Error bars, SEM. h-j, Statistical analysis of apoptosis ratio, cell cycle distributions and translation rate (OP-Puro assay) of HSCs after transplantation. N = 7 biological replicates. Error bars, SEM. k-p, 2′-O-Me modifications of specific sites were examined by RTL-P after L-leucine treatment (5 μM for 48 h). q, p53 expressions in WT/Mat-KO HSCs after L-leucine treatment (5 μM for 48 h). r, Expressions of HSC self-renewal related genes in WT/Mat-KO HSCs after L-leucine treatment (5 μM for 48 h). n = 3 biological replicates. Error bars, SEM. All P values were determined by two-tailed unpaired Student’s t-test.

Source data

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Table 1. RPFs of genes of all haematopoietic cell types by Ribo-seq. Supplementary Table 2. CPM of sncRNAs acquired from sncRNA-seq. Supplementary Table 3. FPKMs of all transcripts and DEGs in LSK cells of WT and Mat-KO mice by RNA-seq. Supplementary Table 4. GO analysis using biological process between Mat-KO/WT group. Supplementary Table 5. DEGs between WT and Mat-KO by single-cell RNA-seq. Supplementary Table 6. DMSs between WT and Mat-KO by RiboMethSeq. Supplementary Table 7. RPFs of genes of Mat-KO/WT HSC by Ribo-seq. Supplementary Table 8. Information of reagents and materials used in this study.

Source data

Source Data Figs. 2–5, 7 and 8, and Extended Data Figs. 2–5 and 7–9

Combined file for statistical source data for all figures.

Source Data Figs. 5g,k, 7d–g and 8k, and Extended Data Figs. 3e, 7b, 8d and 10a,c,k,m,q

Combined file for unprocessed western blots and gels for all figures.

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Wang, H., Zhang, Z., Han, C. et al. SNORD113–114 cluster maintains haematopoietic stem cell self-renewal via orchestrating the translation machinery. Nat Cell Biol 27, 246–261 (2025). https://doi.org/10.1038/s41556-024-01593-7

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