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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. Access through your institution Buy or subscribe This is a preview of subscription content, access via your institution ACCESS OPTIONS Access

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support SIMILAR CONTENT BEING VIEWED BY OTHERS RNA BINDING PROTEIN SYNCRIP MAINTAINS PROTEOSTASIS AND SELF-RENEWAL OF HEMATOPOIETIC STEM AND PROGENITOR CELLS Article Open access 21 April

2023 TRNA M1A MODIFICATION REGULATE HSC MAINTENANCE AND SELF-RENEWAL VIA MTORC1 SIGNALING Article Open access 08 July 2024 YTHDC1-MEDIATED MICRORNA MATURATION IS ESSENTIAL FOR HEMATOPOIETIC

STEM CELLS MAINTENANCE Article Open access 16 October 2024 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

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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 * These

authors contributed equally: Hui Wang, Zhaoru Zhang, Chenxi Han, Penglei Jiang, Jiayue Xu, Yingli Han. AUTHORS AND AFFILIATIONS * 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 * 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 * 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 * Department of Hematology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical

College, Beijing, China Chenxi Han & Jian Li * 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 * The Key Laboratory of RNA and

Hematopoietic Regulation, Chinese Academy of Medical Sciences, Beijing, China Jiayue Xu, Jia Yu & Fang Wang * State Key Laboratory of Proteomics, Academy of Military Medical Sciences,

Academy of Military Sciences, Beijing, China Jie Zhou & Bing Liu * State Key Laboratory of Experimental Hematology, Institute of Hematology, Fifth Medical Center of Chinese PLA General

Hospital, Beijing, China Jie Zhou & Bing Liu * Stowers Institute for Medical Research, Kansas City, MO, USA Michael Durnin & Shiyuan Chen * Institute of Environmental Medicine, and

Cancer Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China Yaxin Liu & Jinghao Sheng * Collaborative Innovation Center for Diagnosis and

Treatment of Infectious Diseases, Zhejiang University, Hangzhou, China Yaxin Liu & Jinghao Sheng * MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of

Polymer Science and Engineering, Zhejiang University, Hangzhou, China Jie Cao & Jianzhao Liu * Institute of Blood Transfusion, Chinese Academy of Medical Sciences, Chengdu, China Jia Yu 

& Fang Wang * 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 Authors * Hui Wang View author publications You can also search for this

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Pengxu Qian View author publications You can also search for this author inPubMed Google Scholar 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. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. PEER REVIEW PEER REVIEW INFORMATION _Nature Cell Biology_ thanks the anonymous

reviewers for their contribution to the peer review of this work. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published

maps and institutional affiliations. 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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permissions ABOUT THIS ARTICLE CITE THIS ARTICLE 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 Download citation * Received: 20 July 2023 * Accepted: 11 December 2024 * Published: 31 January 2025

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