Loss of skeletal integrity during ageing and disease is associated with an imbalance in the opposing actions of osteoblasts and osteoclasts1. Here we show that intrinsic ageing of skeletal stem cells (SSCs)2 in mice alters signalling in the bone marrow niche and skews the differentiation of bone and blood lineages, leading to fragile bones that regenerate poorly. Functionally, aged SSCs have a decreased bone- and cartilage-forming potential but produce more stromal lineages that express high levels of pro-inflammatory and pro-resorptive cytokines. Single-cell RNA-sequencing studies link the functional loss to a diminished transcriptomic diversity of SSCs in aged mice, which thereby contributes to the transformation of the bone marrow niche. Exposure to a youthful circulation through heterochronic parabiosis or systemic reconstitution with young haematopoietic stem cells did not reverse the diminished osteochondrogenic activity of aged SSCs, or improve bone mass or skeletal healing parameters in aged mice. Conversely, the aged SSC lineage promoted osteoclastic activity and myeloid skewing by haematopoietic stem and progenitor cells, suggesting that the ageing of SSCs is a driver of haematopoietic ageing. Deficient bone regeneration in aged mice could only be returned to youthful levels by applying a combinatorial treatment of BMP2 and a CSF1 antagonist locally to fractures, which reactivated aged SSCs and simultaneously ablated the inflammatory, pro-osteoclastic milieu. Our findings provide mechanistic insights into the complex, multifactorial mechanisms that underlie skeletal ageing and offer prospects for rejuvenating the aged skeletal system.
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All sequencing data have been submitted to repositories and are available online. scRNA-seq data are available from the NCBI GEO under accession numbers GSE161946 and GSE172149. Bulk RNA-sequencing data have been deposited under GSE166441 and microarray data are publicly accessible as previously published under GSE34723 as well as in the GEXC database under https://gexc.riken.jp/models/2399 and https://gexc.riken.jp/models/2400. Source data are provided with this paper.
Boskey, A. L. & Coleman, R. Aging and bone. J. Dent. Res. 89, 1333–1348 (2010).
Chan, C. K. F. et al. Identification and specification of the mouse skeletal stem cell. Cell 160, 285–298 (2015).
Kenyon, C. J. The genetics of ageing. Nature 464, 504–512 (2010).
Rossi, D. J., Jamieson, C. H. M. & Weissman, I. L. Stems cells and the pathways to aging and cancer. Cell 132, 681–696 (2008).
Schmich, J. et al. Induction of reverse development in two marine hydrozoans. Int. J. Dev. Biol. 51, 45–56 (2007).
Ermolaeva, M., Neri, F., Ori, A. & Rudolph, K. L. Cellular and epigenetic drivers of stem cell ageing. Nat. Rev. Mol. Cell Biol. 19, 594–610 (2018).
de Haan, G. & Lazare, S. S. Aging of hematopoietic stem cells. Blood 131, 479–487 (2018).
Pang, W. W. et al. Human bone marrow hematopoietic stem cells are increased in frequency and myeloid-biased with age. Proc. Natl Acad. Sci. USA 108, 20012–20017 (2011).
Beerman, I. et al. Functionally distinct hematopoietic stem cells modulate hematopoietic lineage potential during aging by a mechanism of clonal expansion. Proc. Natl Acad. Sci. USA 107, 5465–5470 (2010).
Ambrosi, T. H., Longaker, M. T. & Chan, C. K. F. A revised perspective of skeletal stem cell biology. Front. Cell Dev. Biol. 7, 189 (2019).
Chan, C. K. F. et al. Identification of the human skeletal stem cell. Cell 175, 43–56 (2018).
Halloran, B. P. et al. Changes in bone structure and mass with advancing age in the male C57BL/6J mouse. J. Bone Miner. Res. 17, 1044–1050 (2002).
Ferguson, V. L., Ayers, R. A., Bateman, T. A. & Simske, S. J. Bone development and age-related bone loss in male C57BL/6J mice. Bone 33, 387–398 (2003).
Chan, C. K. F. et al. Clonal precursor of bone, cartilage, and hematopoietic niche stromal cells. Proc. Natl Acad. Sci. USA 110, 12643–12648 (2013).
Marecic, O. et al. Identification and characterization of an injury-induced skeletal progenitor. Proc. Natl Acad. Sci. USA 112, 9920–9925 (2015).
Ashapkin, V. V., Kutueva, L. I. & Vanyushin, B. F. in Reviews on New Drug Targets in Age-Related Disorders (ed. Guest, P. C.) 107–122 (Springer International Publishing, 2020).
Murphy, M. P. et al. Articular cartilage regeneration by activated skeletal stem cells. Nat. Med. 26, 1583–1592 (2020).
Baht, G. S. et al. Exposure to a youthful circulation rejuvenates bone repair through modulation of β-catenin. Nat. Commun. 6, 7131 (2015).
Pietras, E. M. Inflammation: a key regulator of hematopoietic stem cell fate in health and disease. Blood 130, 1693–1698 (2017).
Wright, D. E., Wagers, A. J., Gulati, A. P., Johnson, F. L. & Weissman, I. L. Physiological migration of hematopoietic stem and progenitor cells. Science 294, 1933–1936 (2001).
Suda, T. et al. Modulation of osteoclast differentiation and function by the new members of the tumor necrosis factor receptor and ligand families. Endocr. Rev. 20, 345–357 (1999).
Urist, M. R. Bone: formation by autoinduction. Science 150, 893–899 (1965).
Mizuhashi, K. et al. Resting zone of the growth plate houses a unique class of skeletal stem cells. Nature 563, 254–258 (2018).
Debnath, S. et al. Discovery of a periosteal stem cell mediating intramembranous bone formation. Nature 562, 133–139 (2018).
Jaiswal, S. et al. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl. J. Med. 371, 2488–2498 (2014).
Beerman, I. & Rossi, D. J. Epigenetic control of stem cell potential during homeostasis, aging, and disease. Cell Stem Cell 16, 613–625 (2015).
Tevlin, R. et al. Pharmacological rescue of diabetic skeletal stem cell niches. Sci. Transl. Med. 9, eaag2809 (2017).
Salazar, V. S. et al. Reactivation of a developmental Bmp2 signaling center is required for therapeutic control of the murine periosteal niche. eLife 8, e42386 (2019).
Ambrosi, T. H. et al. Adipocyte accumulation in the bone marrow during obesity and aging impairs stem cell-based hematopoietic and bone regeneration. Cell Stem Cell 20, 771–784 (2017).
Shen, B. et al. A mechanosensitive peri-arteriolar niche for osteogenesis and lymphopoiesis. Nature 591, 438–444 (2021).
Xie, M. et al. Schwann cell precursors contribute to skeletal formation during embryonic development in mice and zebrafish. Proc. Natl Acad. Sci. USA 116, 15068–15073 (2019).
Schurman, C. A., Verbruggen, S. W. & Alliston, T. Disrupted osteocyte connectivity and pericellular fluid flow in bone with aging and defective TGF-β signaling. Proc. Natl Acad. Sci. USA 118, e2023999118 (2021).
Sinha, P. et al. Loss of Gsα early in the osteoblast lineage favors adipogenic differentiation of mesenchymal progenitors and committed osteoblast precursors. J. Bone Miner. Res. 29, 2414–2426 (2014).
Yamazaki, S. et al. Nonmyelinating Schwann cells maintain hematopoietic stem cell hibernation in the bone marrow niche. Cell 147, 1146–1158 (2011).
Yue, R., Zhou, B. O., Shimada, I. S., Zhao, Z. & Morrison, S. J. Leptin receptor promotes adipogenesis and reduces osteogenesis by regulating mesenchymal stromal cells in adult bone marrow. Cell Stem Cell 18, 782–796 (2016).
Worthley, D. L. et al. Gremlin 1 identifies a skeletal stem cell with bone, cartilage, and reticular stromal potential. Cell 160, 269–284 (2015).
Newton, P. T. et al. A radical switch in clonality reveals a stem cell niche in the epiphyseal growth plate. Nature 567, 234–238 (2019).
Bianco, P. & Robey, P. G. Skeletal stem cells. Development 142, 1023–1027 (2015).
Gulati, G. S. et al. Isolation and functional assessment of mouse skeletal stem cell lineage. Nat. Protocols 13, 1294–1309 (2018).
Chan, C. K. F. et al. Endochondral ossification is required for haematopoietic stem-cell niche formation. Nature 457, 490–494 (2009).
Rossi, D. J. et al. Cell intrinsic alterations underlie hematopoietic stem cell aging. Proc. Natl Acad. Sci. USA 102, 9194–9199 (2005).
Wilkinson, A. C., Ishida, R., Nakauchi, H. & Yamazaki, S. Long-term ex vivo expansion of mouse hematopoietic stem cells. Nat. Protocols 15, 628–648 (2020).
Foster, D. S. et al. Elucidating the fundamental fibrotic processes driving abdominal adhesion formation. Nat. Commun. 11, 4061 (2020).
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).
Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 4, 1521 (2015).
Jiang, H., Lei, R., Ding, S.-W. & Zhu, S. Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinformatics 15, 182 (2014).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Nestorowa, S. et al. A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood 128, e20–e31 (2016).
Bergen, V. et al. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol 38, 1408-1414 (2020).
Gulati, G. S. et al. Single-cell transcriptional diversity is a hallmark of developmental potential. Science 367, 405–411 (2020).
Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).
O’Flanagan, C. H. et al. Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses. Genome Biol. 20, 210 (2019).
Denisenko, E. et al. Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows. Genome Biol. 21, 130 (2020).
We thank A. McCarthy and C. Wang for mouse colony management; L. Quinn, V. Ford, C. McQuarrie, T. Naik and L. Jerabek for laboratory management; P. Lovelace, S. Weber and C. Carswell-Crumpton for FACS support; M. R. Eckart and the Stanford Gene Expression Facility (PAN Facility) as well as the Stanford Human Immune Monitoring Center (HIMC) for technical support, assistance and/or advice on this project; and L. Penland, B. Yu and M. Tan from the Chan Zuckerberg BioHub for support with scRNA-seq. This work was supported by NIH–NIA K99 R00 AG049958-01A1, the Heritage Medical Foundation, the American Federation for Aging Research (AFAR)–Arthritis National Research Foundation (ANRF) and an endowment from the DiGenova Family to C.K.F.C.; the German Research Foundation (DFG-Fellowship) 399915929 and NIH–NIA 1K99AG066963 to T.H.A.; NIH (R56 DE025597, R01 DE026730, R01 DE021683, R21 DE024230, R01 DE027323, U01 HL099776, U24 DE026914 and R21 DE019274), CIRMTR1-01249, the Oak Foundation, the Hagey Laboratory, the Pitch Johnson Fund and the Gunn/Olivier Research Fund to M.T.L.; NIDDK SHINE Award R01 DK115600 to I.L.W; and NIH UG3TR003355, UG3TR002968, R01AI155696, R01GM138385 and R00CA151673 and UCOP-RGPO (R01RG3780, R00RG2628 & R00RG2642) to D.S. Additional support came from NIH S10 RR02933801 to the Stanford University Stem Cell FACS core, and NIH S10 1S10OD02349701 to the Stanford University Clark Imaging Center (Principal Investigator: T. Doyle).
The authors declare no competing interests.
Peer review information Nature thanks the anonymous reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Ageing alters bone physiology and fracture healing in mice.
a, Representative haematoxylin and eosin (H&E) staining of proximal femurs from 2-month-old, 12-month-old and 24-month-old mice (representative of sections from three independent mice per age group). b, Three-dimensional μCT reconstruction of femoral bone mass in 2-month-old, 12-month-old and 24-month-old mice. c, Quantification of bone parameters by μCT measurements in the three age groups (n = 3 per age group). d, Bone formation rate (BFR) assessment by calcein labelling in 2-month-old and 24-month-old mice (n = 3 per age group). MS, mineralizing surface; BS, bone surface; MAR: mineral apposition rate. e, Radiograph, μCT, and Movat’s pentachrome staining images of fracture calluses at day 10 and day 21 after injury. f, Callus index measurements at day 10 and day 21 after fracture in femurs from 2-month-old, 12-month-old and 24-month-old mice (day 10 12-mo, n = 5; all other groups, n = 3). g, Mechanical strength test of fracture calluses at day 21 after fracture (2-mo, n = 10; 24-mo, n = 8). Box-and-whisker plots with centre line as median, box extending from 25th to 75th percentile and minimum to maximum values for whiskers. h, μCT images of fracture calluses from 2-month-old, 12-month-old and 24-month-old mouse femurs at day 10 and day 21 after injury. i, Quantification of fracture callus parameters by μCT measurements in the three age groups (n = 3–6). All scatter plot data are mean + s.e.m. One-sided Student’s t-test for comparison of ageing groups to the 2-month-old group, adjusted for non-normality (Mann–Whitney test) or unequal variances (Welch’s test) where appropriate. For exact P values, see Source Data. Scale bars, 150 μm
Extended Data Fig. 2 Phenotypic SSCs are present in aged mice.
a, The mouse skeletal stem-cell lineage. A self-renewing SSC gives rise to a BCSP cell which is the precursor for committed cartilage, bone and stromal lineages. b, Schematic of experimental strategy to analyse intrinsic characteristics of highly purified SSC lineage cells from 2-month-old or 24-month-old mice. c, FACS gating strategy for the isolation of mouse SSC lineage cells. Representative FACS profiles for 2-month-old and 24-month-old mice are shown during the uninjured state and the day-10 fracture state. d, CD200 expression of SSC gated cells in 2-month-old (blue) and 24-month-old (red) mice. Isotype controls performed on SSCs from 2-month-old or 24-month-old mice are shown for gating of the CD200-positive fraction. e, Schematic representation of the experimental set-up investigating clonal activity in fractures of 2-month-old or 24-month-old Actin-CreERT Rainbow mice (dpi, days post-injury). f, Flow cytometric quantification of BCSPs per uninjured femur (2-mo, n = 15; 24-mo, n = 7). g, Prevalence of BCSPs at different days after fracture injury in 2-month-old and 24-month-old mice (2-mo, n = 5-11; 24-mo, n = 3). h, Flow cytometric analysis of CD49f+ phenotypic SSCs and BCSPs under uninjured (uninj.) and fractured (fx; day 10) conditions in 2-month-old and 24-month-old mice (n = 4 per state, age and population). i, Proliferative activity within SSCs and BCSPs at day 10 after fracture as measured by EdU incorporation (2-mo, n = 7; 24-mo, n = 6). j, Assessment of apoptotic activity within SSCs and BCSPs at day 10 after fracture as measured by Annexin V staining (2-mo, n = 4; 24-mo, n = 3). k, Flow cytometric quantification of THY1+ and 6C3+ downstream cell population frequency in 2-month-old and 24-month-old mice in response to fracture at day 10 after injury (n = 4 per age). l, Flow cytometric analysis of the lineage output of BCSPs freshly isolated from 2-month-old and 24-month-old mice and cultured for six days (n = 3 per age). Comparison of 2-month-old and 24-month-old age groups by two-sided Student’s t-test adjusted for non-normality (Mann–Whitney test) or unequal variances (Welch’s test) where appropriate. Data are mean + s.e.m. For exact P values, see Source Data
Extended Data Fig. 3 SSCs and BCSPs show reduced functionality in vitro and in vivo.
a, Fibroblast colony forming unit (CFU-F) ability of 2-month-old and 24-month-old SSC-derived cell populations of long bones (2-mo, n = 5-6; 24-mo, n = 9-10). Two-way ANOVA with Bonferroni’s post-hoc test. b, SSC- and BCSP-derived colony size of cells derived from uninjured and day-10-fractured bones (n = 7–120). Statistical testing between age groups by unpaired Student’s t-test or Mann–Whitney test for non-normality. c, Representative images of colonies stained by Crystal Violet (representative of CFU-F from three independent experiments). d, In vitro osteogenic capacity of SSCs and BCSPs from 2-month-old and 24-month-old mice as determined by Alizarin Red S staining. Representative staining (left) and quantification of osteogenesis (right) (n = 3 per age). e, In vitro chondrogenic capacity of SSCs and BCSPs from 2-month-old and 24-month-old mice as determined by Alican Blue staining. Representative staining (left) and quantification of chondrogenesis (right) (n = 3 per age). f, In vitro adipogenic capacity of SSCs and BCSPs from 2-month-old and 24-month-old mice as determined by Oil Red O staining. g, Renal capsule transplantation results of grafts excised 4 weeks after transplantation of GFP-labelled BCSPs derived from long bones of 2-month-old and 24-month-old mice. Representative gross images of kidneys and magnified graft as bright-field images and with GFP signal shown, for cells derived from 2-month-old (left) and 24-month-old (right) mice. Sectioned grafts stained by Movat’s pentachrome are displayed at the bottom. White and yellow arrows point at auto-fluorescent collagen sponge, which is not part of the graft (representative of 4 independent mice or experiments per age group). h, TRAP-staining images (top) and quantification (bottom) for osteoclast surfaces in sections derived from SSC-derived renal grafts (n = 4 per age group). Statistical testing by two-sided Student’s t-test adjusted for non-normality (Mann–Whitney test) or unequal variances (Welch’s test) where appropriate. Data are mean + s.e.m. For exact P values, see Source Data. Scale bars, 50 μm
Extended Data Fig. 4 Exposure to a young circulation does not rejuvenate the SSC lineage.
a, THY1+ and 6C3+ cell frequency as assessed by flow cytometry at four weeks of parabiosis (IY, n = 6; HY, n = 3; HA, n = 3; IA, n = 3). b, Callus index (highest width of callus divided by bone shaft width next to fracture) for parabiosed mice at day 10 (IY, n = 9; HY, n = 9; HA, n = 6; IA, n = 5) and day 21 (IY, n = 4; HY, n = 5; HA, n = 3; IA, n = 3) after fracture injury. Statistical testing by two-way ANOVA with Bonferroni post-hoc test. c, SSC lineage frequencies as assessed by flow cytometry at day 10 after fracture (Fx) in parabionts (IY, n = 6; HY, n = 3; HA, n = 3; IA, n = 3). Statistical testing by one-way ANOVA analyses with Tukey’s post-hoc test for all comparisons. d, Microarray-based inflammatory gene expression levels of purified SSCs from HA and HY mice. e, Blood serum concentration of RANKL in the circulation of four-week parabionts (n = 4 per group). f, Blood serum concentration of CTX1 in the circulation of four-week parabionts (n = 2 per group). g, Representative images of TRAP staining of fracture calluses of parabionts. h, Quantification of TRAP staining in fracture calluses of parabionts (IY, n = 4; HY, n = 4; HA, n = 3; IA, n = 4). i, Percentage of myeloid and lymphoid reconstitution from transplanted HSCs of parabionts into irradiated recipient mice (n = 4 per group). Statistical testing by one-way ANOVA analyses with Tukey’s post-hoc test for all comparisons. All data are mean + s.e.m. For exact P values, see Source Data. Scale bar, 100 μm
Extended Data Fig. 5 The bone marrow microenvironment influences HSC lineage output.
a, Schematic of experimental approach for transplanting freshly isolated HSCs from fetal liver or 24-month-old mice into either 2-month-old or 24-month-old lethally irradiated mice. b, BMD in 2-month-old and 24-month-old lethally irradiated mice transplanted with fetal liver (FL) HSCs or HSCs from 24-month-old mice 8 weeks after haematopoietic reconstitution (E15 FL into old mice, n = 6; n = 5, all other groups). BM, bone marrow. c, Callus index of recipient mice at day 14 after fracture induced at the 8-week time point after transplantation (E15 FL groups, n = 5; 24-mo BM groups, n = 4). d, Representative FACS-gating strategy for myeloid (GR1+) and lymphoid (B and T cells) cells in peripheral blood after haematopoietic reconstitution with GFP-donor HSCs (gated from TER119−, live cells). e, Representative bone marrow FACS-gating strategy of GFP+ donor-derived cells for haematopoietic lineage tree populations. f, Peripheral blood analysis for donor chimerism after haematopoietic reconstitution of 2-month-old and 24-month-old mice with young HSCs. g, BM analysis of donor-derived (GFP+) HSC lineage cell populations by flow cytometry. Two-way ANOVA with Bonferroni post-hoc test. h, Representative TRAP-staining and GFP-fluorescence images (same section) from day-10 fracture calluses of 2-month-old and 24-month-old mice reconstituted with GFP-labelled HSCs from 2-month-old mice. i, Quantification of the total area of TRAP+GFP+ regions in sections of fracture calluses of mice (n = 3 per age group). j, Flow cytometric analysis of lymphoid and myeloid cell types in 6-day co-cultures (no SSCs, n = 4; 2-mo, n = 5; 24-mo, n = 5). One-way ANOVA with Tukey’s posthoc test for comparison of more than two groups. k, Peripheral blood analysis for donor chimerism after haematopoietic reconstitution with co-cultured haematopoietic cells. Two-way ANOVA with Bonferroni post-hoc test. l, Bone marrow analysis of co-cultured donor-derived (GFP+) HSC lineage cell populations by flow cytometry (no SSCs, n = 3; 2-mo, n = 4; 24-mo, n = 3). One-way ANOVA with Tukey’s post-hoc test for comparison of more than two groups. Comparison of 2-month-old versus 24-month-old groups by two-sided Student’s t-test adjusted for non-normality (Mann–Whitney test) or unequal variances (Welch’s test) where appropriate. One-way ANOVA with Tukey’s post-hoc test. All data are mean + s.e.m. For exact P values, see Source Data. Scale bar, 100 μm
Extended Data Fig. 6 Distinct transcriptomic signatures in SSCs of different ages.
a, Heat map of the top 150 differentially expressed genes in each age group by Leiden clusters. b, Gene count per single cell as violin plots grouped by age (left) and in a UMAP plot. Statistical testing by Mann–Whitney test. c, Heat map showing the expression of apoptosis-related genes in single-cell data grouped by age. d, Heat map showing the expression of senescence-associated genes in single-cell data grouped by age. e, Electrophoresis gel showing telomerase expression in freshly purified SSCs from 2-month-old and 24-month-old mice. For gel source data, see Supplementary Data 1. f, Heat map showing the expression of tissue digest and stress-associated response genes in single-cell data grouped by age. g, Heat map showing the expression of tissue digest and stress-associated response genes in single-cell data grouped by Leiden cluster. h, Total read count per single cell in UMAP plot. i, Cell-cycle status of single cells illustrated in UMAP plot. j, Proportion of cell-cycle state per age group. k, CytoTrace scores of single SSCs grouped by Leiden cluster (Early-osteo, n = 48; Osteo-1, n = 19; Chondro, n = 48; Root, n = 51; Stromal-1, n = 19; Osteo-2, n = 56; Stromal-2, n = 33; GABRA2+, n = 28 single cells). Data are shown as box-and-whisker plots with centre line as median, box extending from 25th to 75th percentile and minimum to maximum values for whiskers. l, Single-cell data of selected age-associated genes related to enhanced bone loss and support of osteoclastogenesis, shown as violin plots grouped by age. Statistical testing between age groups by two-sided Student’s t-test adjusted for non-normality (Mann–Whitney test) or unequal variances (Welch’s test) where appropriate. m, EnrichR GO analysis of differentially expressed genes of SSCs from 24-month-old versus 0-month-old or 2-month-old SSCs and their relation to cell function as determined by GO Biological Processes
Extended Data Fig. 7 Skeletal-lineage-derived CSF1 promotes bone resorption with age.
a, Model of SSC-lineage-derived CSF1 actions as described in the literature for osteoclast function. b, Ligand (Csf2 or Csf3) and receptor (Csf2r or Csf3r) bulk microarray gene expression (%) in the 2-month-old and 24-month-old SSC lineage and in the haematopoietic lineage, respectively. c, Quantification of the number of in-vitro-cultured osteoclasts derived from the bone marrow of 2-month-old and 24-month-old mice (2-mo, n = 16; 24-mo, n = 18, number per field of view, from three mice per age group). d, Number of nuclei per derived osteoclast (n = 14 per age group). e, Representative bright-field images of in-vitro-derived osteoclasts. f, Quantification of in vitro resorption activity of bone-marrow-derived osteoclasts from the bone marrow of 2-month-old and 24-month-old mice (n = 5 wells with cells from two different mice per age). g, Representative bright-field images in the same experiment. h, Luminex protein data of eotaxin1 and TGFβ in the supernatant of SSC and BCSP cultures of 2-month-old and 24-month-old mice (n = 4 per age group). Statistical testing by two-sided Student’s t-test. i, Blood serum concentrations of selected inflammatory markers in 2-month-old and 24-month-old mouse blood (n = 4-5 per age). Statistical testing by two-sided Student’s t-test. j, Blood serum concentrations of CSF1, eotaxin1 and TGFβ in the circulation of 2-month-old and 24-month-old mice (n = 5 per age). Statistical testing by two-sided Student’s t-test. k, Gene expression of pro-haematopoietic or pro-osteoclastic and pro-osteogenic genes in bulk RNA-sequencing data of SSCs of day-10 fracture calluses from 2-month-old, 12-month-old and 24-month-old mice (n = 3 per age). One-sided Student’s t-test of ageing groups versus 2-month-old group. All data in scatter plots are mean + s.e.m., except c, d, f, which show box-and-whisker plots with centre line as median, box extending from 25th to 75th percentile and minimum to maximum values for whiskers. For exact P values, see Source Data
Extended Data Fig. 8 CSF1 levels control skeletal maintenance and repair.
a, Representative μCT images of day-10 fracture calluses at the time of surgery supplemented with hydrogel containing recombinant CSF1 (5 μg) or PBS as control. b, BMD of day-10 fracture calluses treated with or without rCSF1 (PBS, n = 5; rCSF1, n = 4). c, Total number of SSCs and BCSPs at day 10 assessed by FACS (PBS, n = 4; rCSF1, n = 3). d, Representative μCT reconstructions of femur bones from uninjured wild-type or haplo-insufficient Csf1KO (Csf1KO+/−) 15-month-old female and male mice. e, Trabecular BMD (top) and cortical total mineral density (TMD; bottom) of femur bones from female and male wild-type and Csf1KO mice (n = 4 per genotype and sex). f, Bone parameters quantified by μCT from uninjured 15-month-old wild-type and Csf1KO female and male mice (n = 4 per genotype and sex). g, Bone parameters quantified by μCT from 21-day fracture calluses of 15-month-old wild-type and Csf1KO female mice (WT, n = 4; Csf1KO, n = 7). All comparison of 2-month-old versus 24-month-old groups by two-sided Student’s t-test. Data are mean + s.e.m. For exact P values, see Source Data
Extended Data Fig. 9 Rejuvenating fracture healing in aged mice with defined factors.
a, Schematic representation of experimental set-up of rescue experiments with 24-month-old mice. b, Frequency of BCSPs, THY1+ and 6C3+ in 24-month-old mice at day 10 after fracture induction and application of factors (BMP2: 5 μg; CSF1low: 2 μg; CSF1high: 5 μg) (2-mo PBS, n = 6; PBS, n = 6; CSF1low, n = 5; CSF1high, n = 5; BMP2, n = 5; Combolow, n = 9; Combohigh, n = 5). c, μCT analysis of newly formed mineralized bone volume of treated fracture calluses at day 21 (2-mo PBS, n = 7; PBS, n = 9; CSF1low, n = 6; CSF1high, n = 7; BMP2, n = 12; Combolow, n = 12; Combohigh, n = 8). All two-sided Student’s t-tests between the 2-month-old group and each 24-month-old group adjusted for non-normality (Mann–Whitney test) or unequal variances (Welch’s test) where appropriate. d, CFU-F capacity of SSCs isolated from fracture calluses from the 2-mo-PBS, PBS and ‘Combolow treatment groups at day 10 (2-mo PBS, n = 6; PBS, n = 6; Combolow, n = 5). Two-sided Student’s t-test between the 2-month-old PBS-treated group and each 24-month-old group adjusted for non-normality (Mann–Whitney test) where appropriate (n.s., not significant). Data are mean + s.e.m. For exact P values, see Source Data
Extended Data Fig. 10 Compositional and transcriptomic changes in fracture calluses of aged mice after rescue treatment.
a, Leiden clustering of 10X scRNA-seq experiment of 17,230 fracture callus cells from 24-month-old mice treated with PBS and from 24-month-old mice treated with aCSF1low + BMP2 (Combolow). b, UMAP plot showing expression of selected marker genes for Leiden clusters. c, UMAP plot showing distribution of cells from each treatment group. Red, 24-mo PBS; grey, 24-mo Combolow. d, Percentual fraction of treatment group cells per Leiden cluster. e, Heat map showing positive and negative markers used to identify SSCs. f, Dot plot showing the absence of lymphoid gene expression in 10X datasets. g, UMAP plot with cells labelled by treatment group in 10X dataset subset for cells enriched for haematopoietic gene expression. h, Same UMAP plot showing expression of selected marker genes.
Extended Data Fig. 11 Graphical abstract of SSC-mediated skeletal ageing.
Loss of skeletal integrity with age owing to reduced bone formation and increased bone resorption is associated with reduced SSC frequency and activity. The 24-month-old skeleton is characterized by increased bone loss, impaired regeneration and lineage skewing of the SSC lineage towards osteoclast-supportive stroma. Skeletal regeneration can be rejuvenated by simultaneous application of recombinant BMP2 and a low dose of an antibody blocking the action of CSF1.
Supplementary Data 1
Raw data for Extended Figure Data 6e. The uncropped electrophoresis gel comparing telomerase activity between ‘2-mo’ and ‘24-mo’ SSCs.
Supplementary Table 1
Excel sheet with results of single cell RNA-sequencing analysis of SSCs from newborn, young adult, and aged mice. Differentially expressed genes between leiden clusters.
Supplementary Table 2
Excel sheet with results of single cell RNA-sequencing analysis of SSCs from newborn, young adult, and aged mice. Differentially expressed genes between age groups.
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Ambrosi, T.H., Marecic, O., McArdle, A. et al. Aged skeletal stem cells generate an inflammatory degenerative niche. Nature 597, 256–262 (2021). https://doi.org/10.1038/s41586-021-03795-7
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