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Stepwise cell fate decision pathways during osteoclastogenesis at single-cell resolution

Nature Metabolism volume 2, pages 1382–1390 (2020)Cite this article

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Abstract

Osteoclasts are the exclusive bone-resorbing cells, playing a central role in bone metabolism, as well as the bone damage that occurs under pathological conditions1,2. In postnatal life, haematopoietic stem-cell-derived precursors give rise to osteoclasts in response to stimulation with macrophage colony-stimulating factor and receptor activator of nuclear factor-κB ligand, both of which are produced by osteoclastogenesis-supporting cells such as osteoblasts and osteocytes1,2,3. However, the precise mechanisms underlying cell fate specification during osteoclast differentiation remain unclear. Here, we report the transcriptional profiling of 7,228 murine cells undergoing in vitro osteoclastogenesis, describing the stepwise events that take place during the osteoclast fate decision process. Based on our single-cell transcriptomic dataset, we find that osteoclast precursor cells transiently express CD11c, and deletion of receptor activator of nuclear factor-κB specifically in CD11c-expressing cells inhibited osteoclast formation in vivo and in vitro. Furthermore, we identify Cbp/p300-interacting transactivator with Glu/Asp-rich carboxy-terminal domain 2 (Cited2) as the molecular switch triggering terminal differentiation of osteoclasts, and deletion of Cited2 in osteoclast precursors in vivo resulted in a failure to commit to osteoclast fate. Together, the results of this study provide a detailed molecular road map of the osteoclast differentiation process, refining and expanding our understanding of the molecular mechanisms underlying osteoclastogenesis.

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Fig. 1: Determining cellular heterogeneity in the osteoclast culture system by scRNA-seq.
Fig. 2: The osteoclast differentiation trajectory.
Fig. 3: Stepwise biological processes during osteoclastogenesis.
Fig. 4: Identification of Cited2 as a molecular switch triggering terminal differentiation of osteoclasts.

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Data availability

The data that support the plots within this paper and other findings of this study are available from the corresponding author on reasonable request. The raw sequencing data are deposited in the Gene Expression Omnibus under accession GSE147174. Other databases and resources used in this study include: RIKEN (http://www2.clst.riken.jp/arg/methods.html and http://www2.clst.riken.jp/arg/Cassette/CassetteMap_12.html) and the NCBI protein dataset (NCBInr RefSeq Release 90; https://www.ncbi.nlm.nih.gov/refseq/). Source data are provided with this paper.

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Acknowledgements

We thank N. Mizushima, K. Kusubata, Y. Morishita, T. Tsubokawa, K. Nagumo, S. Yin, M. Tsutsumi, A. Suematsu, T. Asano and K. Kubo for thoughtful discussion and valuable technical assistance. This work was supported in part by Grants-in-Aid for Specially Promoted Research (15H05703), Scientific Research B (18H02919 and 19H03485), Challenging Research (18K19438), Young Scientists (19K18943), Research Fellowship for Young Scientists (18J00744) and an International Research Fellow (18F18095) from the Japan Society for the Promotion of Science; AMED under grant number JP20ek0410073; and AMED-CREST under grant number JP20gm1210008.

Author information

Authors and Affiliations

  1. Department of Immunology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Tokyo, Japan

    Masayuki Tsukasaki, Nam Cong-Nhat Huynh, Ryunosuke Muro, Noriko Komatsu, Warunee Pluemsakunthai, Takeshi Nitta & Hiroshi Takayanagi

  2. Department of Osteoimmunology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Tokyo, Japan

    Kazuo Okamoto & Asuka Terashima

  3. Department of Biochemistry and Molecular Biology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Yoshitaka Kurikawa

  4. Laboratory for Animal Resources and Genetic Engineering, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan

    Takaya Abe & Hiroshi Kiyonari

  5. Department of Laboratory Animal Medicine, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan

    Tadashi Okamura

  6. Department of Biochemistry and Molecular Biology, Nippon Medical School, Tokyo, Japan

    Mashito Sakai

  7. Department of Molecular Metabolic Regulation, Diabetes Research Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan

    Toshiya Matsukawa & Michihiro Matsumoto

  8. Division of Hard Tissue Research, Institute for Oral Science, Matsumoto Dental University, Shiojiri, Japan

    Yasuhiro Kobayashi

  9. IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Science, Vienna, Austria

    Josef M. Penninger

  10. Life Science Institute, The University of British Columbia, Vancouver, British Columbia, Canada

    Josef M. Penninger

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  1. Masayuki Tsukasaki
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Contributions

M.T. designed and performed most of the experiments, interpreted the results and wrote the manuscript. N.C.-N.H. performed most of the computational analyses and contributed to data interpretation and manuscript preparation. K.O., R.M., N.K. and T.N. provided advice on project planning and contributed to data interpretation and manuscript preparation. Y. Kurikawa performed proteome analysis and contributed to data interpretation and manuscript preparation. A.T. and W.P. conducted bone histomorphometric analyses and contributed to data interpretation. T.A., H.K., T.O., T.M., M.S., M.M., Y. Kobayashi and J.M.P. provided genetically modified mice and contributed to data interpretation and manuscript preparation. H.T. directed the project and wrote the manuscript.

Corresponding author

Correspondence to Hiroshi Takayanagi.

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Competing interests

The Department of Osteoimmunology is an endowment department, supported with an unrestricted grant from AYUMI Pharmaceutical, Chugai Pharmaceutical, MIKI HOUSE and Noevir.

Additional information

Peer review information Primary Handling Editors: Pooja Jha; George Caputa. Nature Metabolism thanks Luca Biasco and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 The expression of osteoclastic genes and marker genes for dendritic cells during osteoclast culture.

a, The mRNA expression levels of Acp5, Ctsk, Mmp9, Dcstamp, Ocstamp, Atp6v0d2, Irf8, Bcl6 and Mafb during osteoclastogenesis analyzed by bulk-RNA seq (n=3). Data are represented as mean ± SEM. b, The expression pattern of Itgax, Cd74, H2-Aa, H2-Eb1, H2-Ab1, Cd80 and CD86 in the UMAP visualization.

Extended Data Fig. 2 Clustering and trajectory analyses by Monocle 3 and FateID.

a, Expression of marker genes for monocytic precursors (Tnfrsf11a and Csf1r), DC-like precursor (Itgax) and mature osteoclast (Acp5, Ctsk and Mmp9) analyzed by Monocle 3. b, Pseudotime analysis by Monocle3. c, d, Clustering (c) and pseudotime analysis (d) by FateID. The cluster number is the same as that in the manuscript.

Extended Data Fig. 3 Effect of Cd11c-Cre mediated deletion of RANK on bone.

a, Bone formation parameters analyzed by bone histomorphometry using the tibiae of 11-week-old female Tnfrsf11aflox/Δ (n=4) and Tnfrsf11aflox/Δ CD11c-Cre (n=5) mice. Data are represented as mean ± SEM. P values were calculated using one-sided Student’s t-test. b, Representative data of the μCT analysis of the femur of 11-week-old male mice. Representative pictures of more than three independent experiments are shown. c, d, The μCT analysis of the femur of 4-week-old female Tnfrsf11aflox/flox (n=5) and Tnfrsf11aflox/flox CD11c-Cre (n=4) mice. Representative data (c) and quantification (d) are shown. Scale bars, 1mm. Data are represented as mean ± SEM. P values were calculated using one-sided Student’s t-test.

Extended Data Fig. 4 Marker genes for each cluster.

Heatmap of top 4 marker genes that were differentially expressed in each cluster.

Extended Data Fig. 5 Gating strategy for FACS analysis.

Representative flow cytometry profiles of gatig strategy used in Fig. 2j and Fig. 4e. Among single cells, B220−Ly6G−Vcam1− cells are subdivided into three fractions by using cell surface markers CSF1R and C5aR1.

Extended Data Fig. 6 Identification of Cited2 as a novel regulator of osteoclastogenesis.

a, The expression patterns of Cited2 during the trajectory of osteoclast differentiation. b, The expression pattern of Cited2 in the UMAP visualization. c, The expression pattern of Cited2 during osteoclastogenesis analyzed by bulk RNA-seq. Data are represented as mean ± SEM. d, The pictures of Cited2+/+ and Cited2–/ mice on embryonic day 16.5. The arrowhead indicates exencephaly. e, Osteoclast differentiation in Cited2+/+ and Cited2–/– cells. Representative data and quantification (n=6) are shown (right). Data are represented as mean ± SEM. P values were calculated using one-sided Student’s t-test. f, The expression pattern of genes enriched in Cited2+/+ (left) and Cited2−/− – cells (right) in the UMAP visualization. g, The differentiation trajectory of Cited2−/− – cells estimated by the cluster-based minimum spanning tree on a UMAP visualization. h, MA plot of significant genes that were differentially expressed in Cited2+/+ cells and Cited2−/− – cells (light blue dots). Cell cycle genes (Ccnd1, Ccna2, Nek6, E2f2, E2f1, Cdc6 and Dhfr) are denoted in red. i, Cell cycle analysis of G1, S and G2/M phases on the UMAP visualization.

Extended Data Fig. 7 Fetal liver transfer experiments using Cited2−/ – mice.

a, Schematic of the experimental setting for the fetal liver transfer into wild-type mice using Cited2+/+ or Cited2−/− − fetal liver cells. b, The μCT parameters of the femur of Cited2+/+ (n=3) and Cited2–/− – (n=6) bone marrow chimeric mice. Data are represented as mean ± SEM. P values were calculated using one-sided Student’s t-test. c, d, Bone histomorphometric analysis of the tibiae of Cited2+/+ (n=3) and Cited2−/− − (n=6) bone marrow chimeric mice. Scale bars, 200 μm. Quantification of osteoclastic bone resorption (c) and osteoblastic bone formation (d) were shown. Data are represented as mean ± SEM. P values were calculated using one-sided Student’s t-test.

Extended Data Fig. 8 Conditional gene-targeting of Cited2.

a, Scheme of the targeting strategy. Primer 1 (P1): 5′—CACACTTTCCCGGATCCTAGG—3′, Primer 2 (P2): 5′—GAGCACGTCTCAAGCTGCAGAC—3′. b, Genotyping PCR of tail genomic DNA using the primer pairs shown in (a). Representative pictures of more than three independent experiments are shown. c, The Cited2 mRNA expression levels during in vitro osteoclastogenesis in Cited2flox/flox and Cited2flox/flox Tnfrsf11a-Cre mice (n=4). Data are represented as mean ± SEM.

Source data

Extended Data Fig. 9 The expression of negative regulators of osteoclastogenesis.

The expression pattern of Irf8, Bcl6, Mafb, Rbpj, Stat5a, Zbtb7a, Cyld, Tank, Foxo3, Eif2ak2, Commd1, Gna13, Nfkb2, and Traf3 in the UMAP visualization.

Extended Data Fig. 10 The effect of osteoclastogenic cytokines on the pre-osteoclasts (cluster 2, 3 and 4) and failed osteoclasts (cluster 6 and 8).

a, Scheme of the in vitro culture system. b, TRAP staining on the cells induced by indicated conditions. Representative pictures of triplicated experiments are shown. Scale bars; 200 μm.

Supplementary information

Reporting Summary

Supplementary Table

A list of the marker genes for each cluster

Source data

Source Data Extended Data Fig. 8

Unprocessed gel image.

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Tsukasaki, M., Huynh, N.CN., Okamoto, K. et al. Stepwise cell fate decision pathways during osteoclastogenesis at single-cell resolution. Nat Metab 2, 1382–1390 (2020). https://doi.org/10.1038/s42255-020-00318-y

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